[
    "use \"map elections\" approach szufa et al. (aama 2020) analyz sever well-known vote distributions. them, give explicit formula effici algorithm comput frequenc matrix, captur probabl given candid appear given posit sampl vote. use matric draw \"skeleton map\" distributions, evalu robustness, analyz properties. use identifi natur sever real-world elections.",
    "introduc novel contrast represent learn object train scheme clinic time series. specifically, project high dimension e.h.r. data close unit ball low dimension, encod geometr prior origin repres ideal perfect health state euclidean norm associ patient' mortal risk. moreover, use septic patient example, show could learn associ angl two vector differ organ system failures, thereby, learn compact represent indic mortal risk specif organ failure. show learn embed use onlin patient monitoring, supplement clinician improv perform downstream machin learn tasks. work partial motiv desir need introduc systemat way defin intermedi reward reinforc learn critic care medicine. hence, also show design term learn embed result qualit differ polici valu distributions, compar use termin rewards.",
    "clinician frontlin need assess quickli whether patient symptom inde covid-19 not. difficulti task exacerb low resourc set may access biotechnolog tests. furthermore, tuberculosi (tb) remain major health problem sever low- middle-incom countri common symptom includ fever, cough tiredness, similarli covid-19. order help detect covid-19, propos extract deep featur (df) chest x-ray images, technolog avail hospitals, subsequ classif use machin learn method requir larg comput resources. compil five-class dataset x-ray chest imag includ balanc number covid-19, viral pneumonia, bacteri pneumonia, tb, healthi cases. compar perform pipelin combin 14 individu state-of-the-art pre-train deep network df extract tradit machin learn classifiers. pipelin consist resnet-50 df comput ensembl subspac discrimin classifi best perform classif five classes, achiev detect accuraci 91.6+ 2.6% (accuraci + 95% confid interval). furthermore, pipelin achiev accuraci 98.6+1.4% 99.9+0.5% simpler three-class two-class classif problem focus distinguish covid-19, tb healthi cases; covid-19 healthi images, respectively. pipelin comput effici requir 0.19 second extract df per x-ray imag 2 minut train tradit classifi 2000 imag cpu machine.",
    "result suggest potenti benefit use pipelin detect covid-19, particularli resource-limit set run limit comput resources.",
    "feder learn (fl) challeng set optim due heterogen data across differ client give rise client drift phenomenon. fact, obtain algorithm fl uniformli better simpl central train major open problem thu far. work, propos gener algorithm framework, mime, i) mitig client drift ii) adapt arbitrari central optim algorithm momentum adam cross-devic feder learn setting. mime use combin control-vari server-level statist (e.g. momentum) everi client-upd step ensur local updat mimic central method run iid data. prove reduct result show mime translat converg gener algorithm central set converg feder setting. further, show combin momentum base varianc reduction, mime provabl faster central method--th first result. also perform thorough experiment explor mime' perform real world datasets.",
    "deep gener model (dgms) effect learn multilay represent complex data perform infer input data explor gener ability. however, rel insuffici empow discrimin abil dgm make accur predictions. paper present max-margin deep gener model (mmdgms) class-condit variant (mmdcgms), explor strongli discrimin principl max-margin learn improv predict perform dgm supervis semi-supervis learning, retain gener capability. semi-supervis learning, use predict max-margin classifi miss label instead perform full posterior infer efficiency; also introduc addit max-margin label-bal regular term unlabel data effectiveness. develop effici doubli stochast subgradi algorithm piecewis linear object differ settings. empir result variou dataset demonstr that: (1) max-margin learn significantli improv predict perform dgm meanwhil retain gener ability; (2) supervis learning, mmdgm competit best fulli discrimin network employ convolut neural network gener recognit models; (3) semi-supervis learning, mmdcgm perform effici infer achiev state-of-the-art classif result sever benchmarks.",
    "due wider avail modern electron health records, patient care data often store form time-series. cluster time-seri data crucial patient phenotyping, anticip patients' prognos identifi \"similar\" patients, design treatment guidelin tailor homogen patient subgroups. paper, develop deep learn approach cluster time-seri data, cluster compris patient share similar futur outcom interest (e.g., advers events, onset comorbidities). encourag cluster homogen futur outcomes, cluster carri learn discret represent best describ futur outcom distribut base novel loss functions. experi two real-world dataset show model achiev superior cluster perform state-of-the-art benchmark identifi meaning cluster translat action inform clinic decision-making.",
    "acceler 4-bit product quantiz (pq) arm architecture. notably, drastic perform convent 4-bit pq strongli reli x64-specif simd register, avx2; hence, cannot yet achiev good perform arm. fill gap, first bundl two 128-bit regist one 256-bit component. appli shuffl oper use arm-specif neon instruction. make simpl critic modification, achiev dramat speedup 4-bit pq arm architecture. experi show propos method consist achiev 10x improv naiv pq accuracy.",
    "entiti match problem identifi record refer real-world entity. activ research decades, varieti differ approach developed. even today, remain challeng problem, still gener room improvement. recent year seen new method base upon deep learn techniqu natur languag process emerge. survey, present neural network use entiti matching. specifically, identifi step entiti match process exist work target use neural networks, provid overview differ techniqu use step. also discuss contribut deep learn entiti match compar tradit methods, propos taxonomi deep neural network entiti matching.",
    "electron healthcar record import sourc inform use patient stratif discov novel diseas phenotypes. however, challeng work data often spars irregularli sampled. one approach solv limit learn dens embed repres individu patient trajectori use recurr neural network autoencod (rnn-ae). process suscept unwant data biases. show patient embed cluster use previous propos rnn-ae model might impact trajectori bias, mean result domin amount data contain patient trajectory, instead clinic relev details. investig bia 2 dataset (from differ hospitals) 2 diseas area well use differ part patient trajectory. result use 2 previous publish baselin method indic particularli strong bia case event-to-end trajectory. present method overcom issu use adversari train scheme top rnn-ae. result show approach reduc trajectori bia cases.",
    "develop intellig tutor system greatli influenc way student learn practice, increas learn efficiency. intellig tutor system must model learners' masteri knowledg provid feedback advic learners, one class algorithm call \"knowledg tracing\" sure important. paper propos deep self-attent knowledg trace (dsakt) base data pta, onlin assess system use student mani univers china, help student learn efficiently. experiment data pta show dsakt outperform model knowledg trace improv auc 2.1% average, model also good perform assist dataset.",
    "acoust echo cancel (aec) play key role voic interaction. due explicit mathemat principl intellig natur accommod conditions, adapt filter differ type implement alway use aec, give consider performance. however, would kind residu echo results, includ linear residu introduc mismatch estim realiti non-linear residu mostli caus non-linear compon audio devices. linear residu reduc elabor structur methods, leav non-linear residu intract suppression. though, non-linear process method alreadi raised, complic ineffici suppression, would bring damag speech audio. paper, fusion scheme combin adapt filter neural network propos aec. echo could reduc larg scale adapt filtering, result littl residu echo. though much smaller speech audio, could also perceiv human ear would make commun annoy. neural network elabor design train suppress residu echo. experi compar prevail method conducted, valid effect superior propos combin scheme.",
    "reinforc learn symbol plan use build intellig autonom agents. reinforc learn reli learn interact real world, often requir unfeas larg amount experience. symbol plan reli manual craft symbol knowledge, may robust domain uncertainti changes. paper present unifi framework {\\em peorl} integr symbol plan hierarch reinforc learn (hrl) cope decision-mak dynam environ uncertainties. symbol plan use guid agent' task execut learning, learn experi fed back symbol knowledg improv planning. method lead rapid polici search robust symbol plan complex domains. framework test benchmark domain hrl.",
    "automat speech recognition, gmm-hmm wide use acoust modelling. current advanc deep learning, gaussian mixtur model (gmm) acoust model replac deep neural network, name dnn-hmm acoust models. gmm model wide use creat align train data hybrid deep neural network model, thu make import task creat accur alignments. mani factor train dataset size, train data augmentation, model hyperparameters, etc., affect model learning. tradit machin learning, larger dataset tend better performance, smaller dataset tend trigger over-fitting. collect speech data accur transcript signific challeng vari differ languages, cases, might limit big organizations. moreover, case avail larg datasets, train model use data requir addit time comput resources, may available. data accuraci state-of-the-art asr model open-sourc dataset published, studi impact size dataset acoust model readili available. work aim investig impact dataset size variat perform variou gmm-hmm acoust model respect comput costs.",
    "demonstr model train simul use solv manipul problem unpreced complex real robot. made possibl two key components: novel algorithm, call automat domain random (adr) robot platform built machin learning. adr automat gener distribut random environ ever-increas difficulty. control polici vision state estim train adr exhibit vastli improv sim2real transfer. control policies, memory-aug model train adr-gener distribut environ show clear sign emerg meta-learn test time. combin adr custom robot platform allow us solv rubik' cube humanoid robot hand, involv control state estim problems. video summar result available: https://openai.com/blog/solving-rubiks-cube/",
    "robot work alongsid human perform unstructur environments, must learn new motion skill adapt unseen situat fly. demand learn model captur relev motion patterns, offer enough flexibl adapt encod skill new requirements, dynam obstacl avoidance. introduc riemannian manifold perspect problem, propos learn riemannian manifold human demonstr geodes natur motion skills. realiz variat autoencod (vae) space posit orient robot end-effector. geodes motion skill let robot plan movement arbitrari point data manifold. also provid straightforward method avoid obstacl redefin ambient metric onlin fashion. moreover, geodes natur exploit manifold result multiple--mod task design motion explicitli demonstr previously. test learn framework use 7-dof robot manipulator, robot satisfactorili learn reproduc realist skill featur elabor motion patterns, avoid previous unseen obstacles, gener novel movement multiple-mod settings.",
    "collabor train improv accuraci model user trade model' bia (introduc use data user potenti different) varianc (due limit amount data singl user). work, formal person collabor learn problem stochast optim task $0$ given access $n$ relat differ task $1,\\dots, n$. give converg guarante two algorithm set -- popular collabor method known \\emph{weight gradient averaging}, novel \\emph{bia correction} method -- explor condit achiev linear speedup w.r.t. number auxiliari task $n$. further, also empir studi perform confirm theoret insights.",
    "autom generate-and-valid (g&v) program repair techniqu typic reli hard-cod rules, fix bug follow specif patterns, hard adapt differ program languages. propos encore, new g&v technique, use ensembl learn convolut neural machin translat (nmt) model automat fix bug multipl program languages. take advantag random hyper-paramet tune build multipl model fix differ bug combin use ensembl learning. new convolut nmt approach outperform standard long short-term memori (lstm) approach use previou work, better captur local long-dist connect tokens. evalu two popular benchmarks, defects4j quixbugs, show encor fix 42 bugs, includ 16 fix exist techniques. addition, encor first g&v repair techniqu appli four popular program languag (java, c++, python, javascript), fix total 67 bug across five benchmarks.",
    "articl present unsupervis low-frequ method aim detect disaggreg power use cumul water heater (cwh) residenti homes. model circumv inher difficulti unsupervis signal disaggreg use shape power spike time occurr identifi contribut cwh reliably. indeed, mani chw franc configur turn automat off-peak hour only, abl use domain knowledg aid peak identif despit low sampl frequency. order test model, equip home sensor record ground-truth consumpt water heater. appli model larger dataset energi consumpt hello watt user consist one month consumpt data 5k home 30-minut resolution. dataset success identifi cwh major case consum declar use them. remain part like due possibl misconfigur cwhs, sinc trigger off-peak hour requir specif wire electr panel house. model, despit simplicity, offer promis applications: detect mis-configur cwh off-peak contract slow perform degradation.",
    "gradual type becom increasingli popular languag like python typescript, grow need infer type annot automatically. type annot help task like code complet static error catching, annot cannot fulli determin compil tediou annot hand. paper propos probabilist type infer scheme typescript base graph neural network. approach first use lightweight sourc code analysi gener program abstract call type depend graph, link type variabl logic constraint well name usag information. given program abstraction, use graph neural network propag inform relat type variabl eventu make type predictions. neural architectur predict standard types, like number string, well user-defin type encount training. experiment result show approach outperform prior work space $14\\%$ (absolute) librari types, abil make type predict scope exist techniques.",
    "articl offer 3-paramet model testing, 1) differ abil level examine item difficulty; 2) examine discrimin 3) item discrimin model parameters.",
    "reap benefit internet thing (iot), imper secur system cyber attack order enabl mission critic real-tim applications. end, intrus detect system (idss) wide use detect anomali caus cyber attack iot systems. however, due large-scal natur iot, id must oper distribut manner minimum depend central controller. moreover, mani scenario health financi applications, dataset privat iotd may intend share data. end, paper, distribut gener adversari network (gan) propos provid fulli distribut id iot detect anomal behavior without relianc central controller. architecture, everi iotd monitor data well neighbor iotd detect intern extern attacks. addition, propos distribut id requir share dataset iotds, thus, implement iot preserv privaci user data health monitor system financi applications. shown analyt propos distribut gan higher accuraci detect intrus compar standalon id access singl iotd dataset. simul result show that, propos distribut gan-bas id 20% higher accuracy, 25% higher precision, 60% lower fals posit rate compar standalon gan-bas ids.",
    "linear dimension reduct method commonli use extract low-dimension structur high-dimension data. however, popular method disregard tempor structure, render prone extract nois rather meaning dynam appli time seri data. time, mani success unsupervis learn method temporal, sequenti spatial data extract featur predict surround context. combin approaches, introduc dynam compon analysi (dca), linear dimension reduct method discov subspac high-dimension time seri data maxim predict information, defin mutual inform past future. test dca synthet exampl demonstr superior abil extract dynam structur compar commonli use linear methods. also appli dca sever real-world datasets, show dimens extract dca use extract method predict futur state decod auxiliari variables. overall, dca robustli extract dynam structur noisy, high-dimension data retain comput effici geometr interpret linear dimension reduct methods.",
    "machin learn practition often access spectrum data: label data target task (which often limited), unlabel data, auxiliari data, mani avail label dataset tasks. describ taglets, system built studi techniqu automat exploit three type data creat high-quality, servabl classifiers. key compon taglet are: (1) auxiliari data organ accord knowledg graph, (2) modul encapsul differ method exploit auxiliari unlabel data, (3) distil stage ensembl modul combin servabl model. compar taglet state-of-the-art transfer learn semi-supervis learn method four imag classif tasks. studi cover rang settings, vari amount label data semant related auxiliari data target task. find intellig incorpor auxiliari unlabel data multipl learn techniqu enabl taglet match-and often significantli surpass-thes alternatives. taglet avail open-sourc system github.com/batsresearch/taglets.",
    "machin learn method tend outperform tradit statist model prediction. predict academ achievement, ml model shown substanti improv logist regression. far, result almost entir focus colleg achievement, due avail administr datasets, contain rel small sampl size ml standards. articl appli popular machin learn model larg dataset ($n=1.2$ million) contain primari middl school perform standard test given annual australian students. show machin learn model outperform logist regress detect student perform `below standard' band achiev upon sit next test, even large-$n$ setting.",
    "estim score, i.e., gradient log densiti function, set sampl gener unknown distribut fundament task infer learn probabilist model involv flexibl yet intract densities. kernel estim base stein' method score match shown promise, howev theoret properti relationship fully-understood. provid unifi view estim framework regular nonparametr regression. allow us analys exist estim construct new one desir properti choos differ hypothesi space regularizers. unifi converg analysi provid estimators. finally, propos score estim base iter regular enjoy comput benefit curl-fre kernel fast convergence.",
    "keyphras extract receiv consider attent recent years, rel studi exist extract keyphras social media platform twitter, even fewer extract disaster-rel keyphras sources. disaster, keyphras extrem use filter relev tweet enhanc situat awareness. previously, joint train two differ layer stack recurr neural network keyword discoveri keyphras extract shown effect extract keyphras gener twitter data. improv model' perform gener twitter data disaster-rel twitter data incorpor contextu word embeddings, pos-tags, phonetics, phonolog features. moreover, discuss shortcom often use f1-measur evalu qualiti predict keyphras respect ground truth annotations. instead f1-measure, propos use embedding-bas metric better captur correct predict keyphrases. addition, also present novel extens embedding-bas metric. extens allow one better control penalti differ number ground-truth predict keyphras",
    "use surrog object maximum likelihood estim latent variabl models, evid lower bound (elbo) produc state-of-the-art results. inspir this, consid extens elbo famili lower bound defin particl filter' estim margin likelihood, filter variat object (fivos). fivo take argument elbo, exploit model' sequenti structur form tighter bounds. present result relat tight fivo' bound varianc particl filter' estim consid gener case bound defin log-transform likelihood estimators. experimentally, show train fivo result substanti improv train model architectur elbo sequenti data.",
    "large-scal pretrain languag model shown thrill gener capabilities, especi gener consist long text thousand word ease. however, user model control prefix sentenc certain global aspect gener text. challeng simultan achiev fine-grain control preserv state-of-the-art uncondit text gener capability. paper, first propos new task name \"outlin story\" (o2s) test bed fine-grain control gener long text, gener multi-paragraph stori cascad events, i.e. sequenc outlin event guid subsequ paragraph generation. creat dedic dataset futur benchmarks, built state-of-the-art keyword extract techniques. finally, propos extrem simpl yet strong baselin method o2 task, fine tune pre-train languag model augment sequenc outline-stori pair simpl languag model objective. method introduc new paramet perform architectur modification, except sever special token delimit build augment sequences. extens experi variou dataset demonstr state-of-the-art condit stori gener perform model, achiev better fine-grain control user flexibility. paper among first one knowledg propos model creat dataset task \"outlin story\". work also instanti research interest fine-grain control gener open-domain long text, control input repres short text.",
    "large-scal label train dataset enabl deep neural network excel across wide rang benchmark vision tasks. however, mani applications, prohibit expens time-consum obtain larg quantiti label data. cope limit label train data, mani attempt directli appli model train large-scal label sourc domain anoth spars label unlabel target domain. unfortunately, direct transfer across domain often perform poorli due presenc domain shift dataset bias. domain adapt machin learn paradigm aim learn model sourc domain perform well differ (but related) target domain. paper, review latest single-sourc deep unsupervis domain adapt method focus visual task discuss new perspect futur research. begin definit differ domain adapt strategi descript exist benchmark datasets. summar compar differ categori single-sourc unsupervis domain adapt methods, includ discrepancy-bas methods, adversari discrimin methods, adversari gener methods, self-supervision-bas methods. finally, discuss futur research direct challeng possibl solutions.",
    "onlin class-increment continu learn (cl) studi problem learn new class continu onlin non-stationari data stream, intend adapt new data mitig catastroph forgetting. memori replay shown promis results, recenc bia onlin learn caus commonli use softmax classifi remain unsolv challenge. although nearest-class-mean (ncm) classifi significantli undervalu cl community, demonstr simpl yet effect substitut softmax classifier. address recenc bia avoid structur chang fully-connect layer new classes. moreover, observ consider consist perform gain replac softmax classifi ncm classifi sever state-of-the-art replay methods. leverag ncm classifi effectively, data embed belong class cluster well-separ differ class label. end, contribut supervis contrast replay (scr), explicitli encourag sampl class cluster tightli embed space push differ class apart replay-bas training. overall, observ propos scr substanti reduc catastroph forget outperform state-of-the-art cl method signific margin varieti datasets.",
    "algorithm perform supervis learn combin memorization, generalization, luck. estim much inform algorithm memor dataset, set lower bound amount perform due factor gener luck. goal mind, introduc label distribut matrix (ldm) tool estim capac learn algorithms. method attempt character divers possibl output algorithm differ train datasets, use measur algorithm flexibl respons data. test method sever supervis learn algorithms, find result conclusive, ldm allow us gain potenti valuabl insight predict behavior algorithms. also introduc label record addit tool estim algorithm capacity, promis initi results.",
    "deep learn (dl) techniqu gain signific popular among softwar engin (se) research recent years. often solv mani se challeng without enorm manual featur engin effort complex domain knowledge. although mani dl studi report substanti advantag state-of-the-art model effectiveness, often ignor two factors: (1) replic - whether report experiment result approxim reproduc high probabl dl model data; (2) reproduc - whether one report experiment find reproduc new experi experiment protocol dl model, differ sampl real-world data. unlik tradit machin learn (ml) models, dl studi commonli overlook two factor declar minor threat leav futur work. mainli due high model complex mani manual set paramet time-consum optim process. study, conduct literatur review 93 dl studi recent publish twenti se journal conferences. statist show urgenc investig two factor se. moreover, re-ran four repres dl model se. experiment result show import replic reproducibility, report perform dl model could replic unstabl optim process. reproduc could substanti compromis model train convergent, perform sensit size vocabulari test data.",
    "therefor urgent se commun provid long-last link replic package, enhanc dl-base solut stabil convergence, avoid perform sensit differ sampl data.",
    "gan provid framework train gener model mimic data distribution. however, mani case wish train gener model optim auxiliari object function within data generates, make aesthet pleas images. cases, object function difficult evaluate, e.g. may requir human interaction. here, develop system effici improv gan target object involv human interaction, specif gener imag increas rate posit user interactions. improv gener model, build model human behavior target domain rel small set interactions, use behavior model auxiliari loss function improv gener model. show system success improv posit interact rates, least simul data, character factor affect performance.",
    "report aim survey multi-ag q-learn algorithms, analyz differ game theori framework used, address framework' applications, report challeng futur directions. target applic studi resourc manag wireless sensor network. first section, author provid introduct regard applic wireless sensor networks. that, author present summari q-learn algorithm, well-known classic solut model-fre reinforc learn problems. third section, author extend q-learn algorithm multi-ag scenario discuss challenges. fourth section, author survey set game-theoret framework research use address problem resourc alloc task schedul wireless sensor networks. lastly, author mention interest open challeng domain.",
    "bayesian network (bns) becom increasingli popular last decad tool reason uncertainti field divers medicine, biology, epidemiology, econom social sciences. especi true real-world area seek answer complex question base hypothet evid determin action intervention. however, determin graphic structur bn remain major challenge, especi model problem causal assumptions. solut problem includ autom discoveri bn graph data, construct base expert knowledge, combin two. paper provid comprehens review combinator algorithm propos learn bn structur data, describ 61 algorithm includ prototypical, well-establish state-of-the-art approaches. basic approach algorithm describ consist terms, similar differ highlighted. method evalu algorithm compar perform discuss includ consist claim made literature. approach deal data nois real-world dataset incorpor expert knowledg learn process also covered.",
    "spike neural network (snn), spike emiss spars irregularli distribut time network architecture. sinc current featur snn low averag activity, effici implement snn usual base event-driven simul (eds). hand, simul larg scale neural network take advantag distribut neuron set processor (either workstat cluster parallel computer). articl present damned, larg scale snn simul framework abl gather benefit ed parallel computing. two level parallel combined: distribut map neural topology, network level, local multithread alloc resourc simultan process events, neuron level. base causal events, distribut solut propos solv complex problem schedul without synchron barrier.",
    "decentr train deep learn model key element enabl data privaci on-devic learn networks. realist learn scenarios, presenc heterogen across differ clients' local dataset pose optim challeng may sever deterior gener performance. paper, investig identifi limit sever decentr optim algorithm differ degre data heterogeneity. propos novel momentum-bas method mitig decentr train difficulty. show extens empir experi variou cv/nlp dataset (cifar-10, imagenet, ag news) sever network topolog (ring social network) method much robust heterogen clients' data exist methods, signific improv test perform ($1\\% \\!-\\! 20\\%$). code publicli available.",
    "partial observ markov decis process wide use provid model real-world decis make problems. paper, provid method slightli differ version call mix observ markov decis process, momdp, go join problem. basically, aim offer behaviour model interact intellig agent music pitch environ show momdp shed light build decis make model music pitch conveniently.",
    "paper introduc new effici nonlinear one-class classifi formul rayleigh quotient criterion optimisation. method, oper reproduc kernel hilbert space, minimis scatter target distribut along optim project direct time keep project posit observ distant mean neg class. provid graph embed view problem solv effici use spectral regress approach. sense, unlik previou similar method often requir costli eigen-comput dens matrices, propos approach cast problem consider regress framework comput efficient. particular, shown domin complex propos method complex comput kernel matrix. addit appeal characterist propos one-class classifi are: 1-the abil train increment fashion (allow applic stream data scenario also reduc comput complex non-stream oper mode); 2-be unsupervised, provid option refin solut use neg train examples, available; last least, 3-the use kernel trick facilit nonlinear map data high-dimension featur space seek better solutions.",
    "today, domin paradigm train neural network involv minim task loss larg dataset. use world knowledg inform model, yet retain abil perform end-to-end train remain open question. paper, present novel framework introduc declar knowledg neural network architectur order guid train prediction. framework systemat compil logic statement comput graph augment neural network without extra learnabl paramet manual redesign. evalu model strategi three tasks: machin comprehension, natur languag inference, text chunking. experi show knowledge-aug network strongli improv baselines, especi low-data regimes.",
    "machin learn technolog increasingli develop use healthcare. research commun focus creat state-of-the-art models, less focu real world implement associ challeng accuracy, fairness, accountability, transpar come actual, situat use. seriou question remain examin regard ethic build models, interpret explain model output, recogn account biases, minim disrupt profession expertis work cultures. address gap literatur provid detail case studi cover development, implementation, evalu sepsi watch, machin learning-driven tool assist hospit clinician earli diagnosi treatment sepsis. we, team develop evalu tool, discuss conceptu tool model deploy world instead socio-techn system requir integr exist social profession contexts. rather focus model interpret ensur fair account machin learning, point toward four key valu practic consid develop machin learn support clinic decision-making: rigor defin problem context, build relationship stakeholders, respect profession discretion, creat ongo feedback loop stakeholders. work signific implic futur research regard mechan institut account consider design machin learn systems. work underscor limit model interpret solut ensur transparency, accuracy, account practice.",
    "instead, work demonstr mean goal achiev fatml valu design practice.",
    "propos polici search approach learn control specif given signal tempor logic (stl) formulae. system model, unknown assum affin control system, learn togeth control policy. model implement two feedforward neural network (fnns) - one drift, one control directions. captur histori depend stl specifications, use recurr neural network (rnn) implement control policy. contrast preval model-fre methods, learn approach propos take advantag learn model efficient. use control barrier function (cbfs) learn model improv safeti system. valid algorithm via simul experiments. result show approach satisfi given specif within system runs, use on-lin control.",
    "consid stochast adversari set continuum arm bandit arm index [0,1]^d. reward function r:[0,1]^d -> r assum intrins depend k coordin variabl impli r(x_1,..,x_d) = g(x_{i_1},..,x_{i_k}) distinct unknown i_1,..,i_k {1,..,d} local holder continu g:[0,1]^k -> r expon 0 < alpha <= 1. firstly, assum (i_1,..,i_k) fix across time, propos simpl modif cab1 algorithm construct discret set sampl point obtain bound o(n^((alpha+k)/(2*alpha+k)) (log n)^((alpha)/(2*alpha+k)) c(k,d)) regret, c(k,d) depend polynomi k sub-logarithm d. construct base creat partit {1,..,d} k disjoint subset probabilistic, henc result hold high probability. secondli extend result also handl gener case (i_1,...,i_k) chang time deriv regret bound same.",
    "consid problem power alloc time-vari channel unknown distribut energi harvest commun systems. problem, transmitt choos transmit power base amount store energi batteri goal maxim averag rate obtain time. model problem markov decis process (mdp) transmitt agent, batteri statu state, transmit power action rate obtain reward. averag reward maxim problem mdp solv linear program (lp) use transit probabl state-act pair reward valu choos power alloc policy. sinc reward associ state-act pair unknown, propos two onlin learn algorithms: uclp epoch-uclp learn reward adapt polici along way. uclp algorithm solv lp step decid current polici use upper confid bound rewards, epoch-uclp algorithm divid time epochs, solv lp begin epoch follow obtain polici epoch. prove reward loss regret incur algorithm upper bound constants. epoch-uclp incur higher regret compar uclp, reduc comput requir substantially. also show present algorithm work onlin learn cost minim problem like packet schedul power-delay tradeoff minor changes.",
    "introduc gener method improv converg rate gradient-bas optim easi implement work well practice. demonstr effect method rang optim problem appli stochast gradient descent, stochast gradient descent nesterov momentum, adam, show significantli reduc need manual tune initi learn rate commonli use algorithms. method work dynam updat learn rate optim use gradient respect learn rate updat rule itself. comput \"hypergradient\" need littl addit computation, requir one extra copi origin gradient store memory, reli upon noth provid reverse-mod automat differentiation.",
    "differ type malici activ flag multipl permissionless blockchain bitcoin, ethereum etc. malici activ exploit vulner infrastructur blockchain, target user social engin techniques. address problems, aim automat flag blockchain account origin malici exploit account participants. end, identifi robust supervis machin learn (ml) algorithm resist bia induc represent certain malici activ avail dataset, well robust adversari attacks. find malici activ report thu far, example, ethereum blockchain ecosystem, behav statist similar. further, previous use ml algorithm identifi malici account show bia toward particular malici activ over-represented. sequel, identifi neural network (nn) hold best face bia induc dataset time robust certain adversari attacks.",
    "mani method develop approxim cloud vector embed high-dimension space simpler objects: start princip point linear manifold self-organ maps, neural gas, elast maps, variou type princip curv princip trees, on. type approxim measur approxim complex develop too. measur necessari find balanc accuraci complex defin optim approxim given type. propos measur complex (geometr complexity) applic approxim sever type allow compar data approxim differ types.",
    "stochast shortest path (ssp) well-known problem plan control, agent reach goal state minimum total expect cost. paper present adversari ssp model also account adversari chang cost time, underli transit function remain unchanged. formally, agent interact ssp environ $k$ episodes, cost function chang arbitrarili episodes, transit unknown agent. develop first algorithm adversari ssp prove high probabl regret bound $\\widetild (\\sqrt{k})$ assum cost strictli positive, $\\widetild (k^{3/4})$ gener case. first consid natur set adversari ssp obtain sub-linear regret it.",
    "dopamin (da) organ chemic influenc sever part behaviour physic functions. fast-scan cyclic voltammetri (fscv) techniqu use vivo phasic dopamin releas measurements. analysi measurements, though, requir notabl effort. paper, present use convolut neural network (cnns) identif phasic dopamin releases.",
    "larg number neural network model associ memori propos literature. includ classic hopfield network (hns), spars distribut memori (sdms), recent modern continu hopfield network (mchns), possess close link self-attent machin learning. paper, propos gener framework understand oper memori network sequenc three operations: similarity, separation, projection. deriv memori model instanc gener framework differ similar separ functions. extend mathemat framework krotov et al (2020) express gener associ memori model use neural network dynam second-ord interact neurons, deriv gener energi function lyapunov function dynamics. finally, use framework, empir investig capac use differ similar function associ memori models, beyond dot product similar measure, demonstr empir euclidean manhattan distanc similar metric perform substanti better practic mani tasks, enabl robust retriev higher memori capac exist models.",
    "recently, discuss ill-pos natur super-resolut multipl possibl reconstruct exist given low-resolut image. use normal flows, srflow[23] achiev state-of-the-art perceptu qualiti learn distribut output instead determinist output one estimate. paper, adapt concept srflow improv gan-bas super-resolut properli implement one-to-mani property. modifi gener estim distribut map random noise. improv content loss hamper perceptu train objectives. also propos addit train techniqu enhanc perceptu qualiti gener images. use propos methods, abl improv perform esrgan[1] x4 perceptu sr achiev state-of-the-art lpip score x16 perceptu extrem sr appli method rfb-esrgan[21].",
    "paper, propos analyz sparsity-awar sign subband adapt filter individu weight factor (s-iwf-ssaf) algorithm, consid applic acoust echo cancel (aec). furthermore, design joint optim scheme step-siz sparsiti penalti paramet enhanc s-iwf-ssaf perform term converg rate steady-st error. theoret analysi show s-iwf-ssaf algorithm outperform previou sign subband adapt filter individu weight factor (iwf-ssaf) algorithm spars scenarios. particular, compar exist analysi iwf-ssaf algorithm, propos analysi requir assumpt larg number subbands, long adapt filter, paraunitari analysi filter bank, match well simul results. simul system identif aec situat demonstr theoret analysi effect propos algorithms.",
    "low level classif extract featur elements, i.e. physic use train model later classification. high level classif use high level features, exist patterns, relationship data combin low high level featur classification. high level featur got complex network creat data. local global featur use describ structur complex network, i.e. averag neighbor degree, averag clustering. present work propos novel featur describ architectur network follow ant coloni system approach. experi show advantag use featur sensibl data differ classes.",
    "goal build classif model use combin free-text structur data. this, repres structur data text sentences, datawords, similar data item map sentence. permit model mixtur text structur data use text-model algorithms. sever exampl illustr possibl improv text classif perform first run extract tool (name entiti recognition), convert output datawords, ad dataword origin text -- model build classification. approach also allow us produc explan infer term free text structur data.",
    "present ladder, first deep reinforc learn agent success learn control polici large-scal real-world problem directli raw input compos high-level semant information. agent base asynchron stochast variant dqn (deep q network) name dasqn. input agent plain-text descript state game incomplet information, i.e. real-tim larg scale onlin auctions, reward auction profit larg scale. appli agent essenti portion jd' onlin rtb (real-tim bidding) advertis busi find easili beat former state-of-the-art bid polici care engin calibr human experts: jd.com' june 18th anniversari sale, agent increas company' ad revenu portion 50%, advertisers' roi (return investment) also improv significantly.",
    "natur languag text exhibit hierarch structur varieti respects. ideally, could incorpor prior knowledg hierarch structur unsupervis learn algorithm work text data. recent work nickel & kiela (2017) propos use hyperbol instead euclidean embed space repres hierarch data demonstr encourag result embed graphs. work, extend method re-parameter techniqu allow us learn hyperbol embed arbitrarili parameter objects. appli framework learn word sentenc embed hyperbol space unsupervis manner text corpora. result embed seem encod certain intuit notion hierarchy, word-context frequenc phrase constituency. however, implicit continu hierarchi learn hyperbol space make interrog model' learn hierarchi difficult model learn explicit edg items. learn hyperbol embed show improv euclidean embed -- -- downstream tasks, suggest hierarch organ use task others.",
    "self-supervis learn comput vision aim pre-train imag encod use larg amount unlabel imag (image, text) pairs. pre-train imag encod use featur extractor build downstream classifi mani downstream task small amount label train data. work, propos badencoder, first backdoor attack self-supervis learning. particular, badencod inject backdoor pre-train imag encod downstream classifi built base backdoor imag encod differ downstream task simultan inherit backdoor behavior. formul badencod optim problem propos gradient descent base method solv it, produc backdoor imag encod clean one. extens empir evalu result multipl dataset show badencod achiev high attack success rate preserv accuraci downstream classifiers. also show effect badencod use two publicli available, real-world imag encoders, i.e., google' imag encod pre-train imagenet openai' contrast language-imag pre-train (clip) imag encod pre-train 400 million (image, text) pair collect internet. moreover, consid defens includ neural cleans mntd (empir defenses) well patchguard (a provabl defense). result show defens insuffici defend badencoder, highlight need new defens badencoder. code publicli avail at: https://github.com/jjy1994/badencoder.",
    "annot cancer region whole-slid imag (wsis) patholog sampl play critic role clinic diagnosis, biomed research, machin learn algorithm development. however, gener exhaust accur annot labor-intensive, challenging, costly. draw coars approxim annot much easier task, less costly, allevi pathologists' workload. paper, studi problem refin approxim annot digit patholog obtain accur ones. previou work explor obtain machin learn model inaccur annotations, tackl refin problem mislabel region explicitli identifi corrected, requir -- often larg -- number train samples. present method, name label clean multipl instanc learn (lc-mil), refin coars annot singl wsi without need extern train data. patch crop wsi inaccur label process jointli within multipl instanc learn framework, mitig impact predict model refin segmentation. experi heterogen wsi set breast cancer lymph node metastasis, liver cancer, colorect cancer sampl show lc-mil significantli refin coars annotations, outperform state-of-the-art alternatives, even learn singl slide. moreover, demonstr real annot drawn pathologist effici refin improv propos approach. result demonstr lc-mil promising, light-weight tool provid fine-grain annot coars annot patholog sets.",
    "estim heterogen treatment effect import problem across mani domains. order accur estim treatment effects, one typic reli data observ studi random experiments. currently, exist work reli exclus observ data, often confound and, hence, yield bias estimates. observ data confounded, random data unconfounded, sampl size usual small learn heterogen treatment effects. paper, propos estim heterogen treatment effect combin larg amount observ data small amount random data via represent learning. particular, introduc two-step framework: first, use observ data learn share structur (in form representation); then, use random data learn data-specif structures. analyz finit sampl properti framework compar sever natur baselines. such, deriv condit combin observ random data beneficial, not. base this, introduc sample-effici algorithm, call cornet. use extens simul studi verifi theoret properti cornet multipl real-world dataset demonstr method' superior compar exist methods.",
    "address problem predict diseas develop, i.e., medic event time (met), patient' electron health record (ehr). met non-communic diseas like diabet highli correl cumul health conditions, specifically, much time patient spent specif health condit past. common time-seri represent indirect extract inform ehr focus detail depend valu success observations, cumul information. propos novel data represent ehr call cumul stay-tim represent (ctr), directli model cumul health conditions. deriv trainabl construct ctr base neural network flexibl fit target data scalabl handl high-dimension ehr. numer experi use synthet real-world dataset demonstr ctr alon achiev high predict performance, enhanc perform exist model combin them.",
    "propos use machin learn model direct synthesi on-chip electromagnet (em) passiv structur enabl rapid even autom design optim rf/mm-wave circuits. proof concept, demonstr direct synthesi 1:1 transform 45nm soi process use propos neural network model. use pre-exist transform s-paramet file geometr design train samples, model predict target geometr designs.",
    "consid problem learn high-dimension low-rank matrix large-scal dataset distribut sever machines, low-rank enforc convex trace norm constraint. propos dfw-trace, distribut frank-wolf algorithm leverag low-rank structur updat achiev effici time, memori commun usage. step heart dfw-trace solv approxim use distribut version power method. provid theoret analysi converg dfw-trace, show ensur sublinear converg expect optim solut power iter per epoch. implement dfw-trace apach spark distribut program framework valid use approach synthet real data, includ imagenet dataset high-dimension featur extract deep neural network.",
    "domain adapt semant segment recent activ studi increas gener capabl deep learn models. vast major domain adapt method tackl single-sourc case, model train singl sourc domain adapt target domain. however, method limit practic real world applications, sinc usual one multipl sourc domain differ data distributions. work, deal multi-sourc domain adapt problem. method, name standardgan, standard sourc target domain data similar data distributions. use standard sourc domain train classifi segment standard target domain. conduct extens experi two remot sens data sets, first one consist multipl citi singl country, one contain multipl citi differ countries. experiment result show standard data gener standardgan allow classifi gener significantli better segmentation.",
    "reinforc learn (rl) capabl sophist motion plan control robot uncertain environments. however, state-of-the-art deep rl approach typic lack safeti guarantees, especi robot environ model unknown. justifi widespread deployment, robot must respect safeti constraint without sacrif performance. thus, propos black-box reachability-bas safeti layer (brsl) three main components: (1) data-driven reachabl analysi black-box robot model, (2) trajectori rollout planner predict futur action observ use ensembl neural network train online, (3) differenti polytop collis check reachabl set obstacl enabl correct unsaf actions. simulation, brsl outperform state-of-the-art safe rl method turtlebot 3, quadrotor, trajectory-track point mass unsaf set adjac area highest reward.",
    "recent years, increas popular deep learn model intellig condit monitor diagnosi well prognost use mechan system structur observed. previou studies, however, major assumpt accept default, train test data take featur distribution. unfortunately, assumpt mostli invalid real application, result certain lack applic tradit diagnosi approaches. inspir idea transfer learn leverag knowledg learnt rich label data sourc domain facilit diagnos new similar target task, new intellig fault diagnosi framework, i.e., deep transfer network (dtn), gener deep learn model domain adapt scenario, propos paper. extend margin distribut adapt (mda) joint distribut adapt (jda), propos framework exploit discrimin structur associ label data sourc domain adapt condit distribut unlabel target data, thu guarante accur distribut matching. extens empir evalu three fault dataset valid applic practic dtn, achiev mani state-of-the-art transfer result term divers oper conditions, fault sever fault types.",
    "non-intrus load monitor (nilm) help disaggreg household' main electr consumpt energi usag individu appliances, thu greatli cut cost fine-grain household load monitoring. address arisen privaci concern nilm applications, feder learn (fl) could leverag nilm model train sharing. appli fl paradigm real-world nilm applications, however, face challeng edg resourc restriction, edg model person edg train data scarcity. paper present fednilm, practic fl paradigm nilm applic edg client. specifically, fednilm design deliv privacy-preserv person nilm servic large-scal edg clients, leverag i) secur data aggreg feder learning, ii) effici cloud model compress via filter prune multi-task learning, iii) person edg model build unsupervis transfer learning. experi real-world energi data show that, fednilm abl achiev person energi disaggreg state-of-the-art accuracy, ensur privaci preserv edg client.",
    "large-amplitud chatter vibrat one import phenomena machin processes. often detriment cut oper caus poor surfac finish decreas tool life. therefore, chatter detect use machin learn activ research area last decade. three challeng identifi appli machin learn chatter detect larg industry: insuffici understand univers chatter featur across differ processes, need autom featur extraction, exist limit data specif workpiece-machin tool combination. three challeng group umbrella transfer learning. paper studi autom chatter detect evalu transfer learn promin well novel chatter detect methods. investig chatter classif accuraci use varieti featur extract turn mill experi differ cut configurations. studi method includ fast fourier transform (fft), power spectral densiti (psd), auto-correl function (acf), wavelet packet transform (wpt), ensembl empir mode decomposit (eemd). also examin recent approach base topolog data analysi (tda) similar measur time seri base discret time warp (dtw). evalu transfer learn potenti approach train test within across turn mill data sets. result show care chosen time-frequ featur lead high classif accuraci albeit cost requir manual pre-process tag expert user.",
    "hand, found tda dtw approach provid accuraci f1 score par time-frequ method without need manual preprocessing.",
    "introduc stochast variat infer procedur train scalabl gaussian process (gp) model whose per-iter complex independ number train points, $n$, number basi function use kernel approximation, $m$. central contribut includ unbias stochast estim evid lower bound (elbo) gaussian likelihood, well stochast estim lower bound elbo sever likelihood laplac logistic. independ stochast optim updat complex $n$ $m$ enabl infer huge dataset use larg capac gp models. demonstr accur infer larg classif regress dataset use gp relev vector machin $m = 10^7$ basi functions.",
    "rapid escal appli machin learn (ml) variou domain led pay attent qualiti ml components. growth techniqu tool aim improv qualiti ml compon integr ml-base system safely. although tool use bugs' lifecycle, standard benchmark bug assess performance, compar discuss advantag weaknesses. study, firstli investig reproduc verifi bug ml-base system show import factor one. then, explor challeng gener benchmark bug ml-base softwar system provid bug benchmark name defect4ml satisfi criteria standard benchmark, i.e. relevance, reproducibility, fairness, verifiability, usability. faultload benchmark contain 113 bug report ml develop github stack overflow, use two popular ml frameworks: tensorflow keras. defect4ml also address import challeng softwar reliabl engin ml-base softwar systems, like: 1) fast chang frameworks, provid variou bug differ version frameworks, 2) code portability, deliv similar bug differ ml frameworks, 3) bug reproducibility, provid fulli reproduc bug complet inform requir depend data, 4) lack detail inform bugs, present link bugs' origins. defect4ml interest ml-base system practition research assess test tool techniques.",
    "glioma grade surgeri critic prognosi predict treatment plan making. present novel wavelet scattering-bas radiom method predict noninvas accur glioma grades. method consist wavelet scatter featur extraction, dimension reduction, glioma grade prediction. dimension reduct achiev use partial least squar (pls) regress glioma grade predict use support vector machin (svm), logist regress (lr) random forest (rf). predict obtain multimod magnet reson imag 285 patient well-label intratumor peritumor region show area receiv oper characterist curv (auc) glioma grade predict increas 0.99 consid intratumor peritumor featur multimod images, repres increas 13% compar tradit radiomics. addition, featur extract peritumor region increas accuraci glioma grading.",
    "paper, introduc novel hybrid model predict compress strength concret use ultrason puls veloc (upv) rebound number (rn). first, 516 data 8 studi upv rebound hammer (rh) test collected. then, high correl variabl creator machin (hvcm) use creat new variabl better correl output improv predict models. three singl models, includ step-by-step regress (sbsr), gene express program (gep) adapt neuro-fuzzi infer system (anfis) well three hybrid models, i.e. hcvcm-sbsr, hcvcm-gep hcvcm-anfis, employ predict compress strength concrete. statist paramet error term coeffici determination, root mean squar error (rmse), normal mean squar error (nmse), fraction bias, maximum posit neg errors, mean absolut percentag error (mape), comput evalu compar models. result show hcvcm-anfi predict compress strength concret better models. hcvcm improv accuraci anfi 5% coeffici determination, 10% rmse, 3% nmse, 20% mape, 7% maximum neg error.",
    "heterogen inform networks(hins) becom popular recent year strong capabl model object abund inform use explicit network structure. network embed prove effect method convert inform network lower-dimension space, wherea core inform well preserved. however, tradit network embed algorithm sub-optim captur rich potenti incompat semant provid hins. address issue, novel meta-path-bas hin represent learn framework name mshine design simultan learn multipl node represent differ meta-paths. specifically, one represent learn modul inspir rnn structur develop multipl node represent learn simultaneously, represent associ one respect meta-path. measur relev node design object function, learn modul appli downstream link predict tasks. set criteria select initi meta-path propos modul mshine import reduc optim meta-path select cost prior knowledg suitabl meta-path available. corrobor effect mshine, extens experiment studi includ node classif link predict conduct five real-world datasets. result demonstr mshine outperform state-of-the-art hin embed methods.",
    "trajectori predict alway challeng problem autonom driving, sinc need infer latent intent behavior interact traffic participants. problem intrins hard, particip may behav differ differ environ interactions. key effect model interlac influenc spatial context tempor context. exist work usual encod two type context separately, would lead inferior model scenarios. paper, first propos unifi approach treat time space dimens equal model spatio-tempor context. propos modul simpl easi implement within sever line codes. contrast exist method heavili reli recurr neural network tempor context hand-craft structur spatial context, method could automat partit spatio-tempor space adapt data. lastly, test propos framework two recent propos trajectori predict dataset apolloscap argoverse. show propos method substanti outperform previou state-of-the-art method maintain simplicity. encourag result valid superior approach.",
    "machin learn tool build model accur repres input train data. undesir bias concern demograph group train data, well-train model reflect biases. present framework mitig bias includ variabl group interest simultan learn predictor adversary. input network x, text censu data, produc predict y, analog complet incom bracket, adversari tri model protect variabl z, gender zip code. object maxim predictor' abil predict minim adversary' abil predict z. appli analog completion, method result accur predict exhibit less evid stereotyp z. appli classif task use uci adult (census) dataset, result predict model lose much accuraci achiev close equal odd (hardt, et al., 2016). method flexibl applic multipl definit fair well wide rang gradient-bas learn models, includ regress classif tasks.",
    "largest theoret contribut neural network come vc dimens character sampl complex classif model probabilist view wide use studi gener error. far literatur vc dimens use approxim gener error bound differ neural network architectures. vc dimens yet implicitli explicitli state fix network size import wrong configur could lead high comput effort train lead fitting. need bound unit task comput suffici number parameters. binari classif task shallow network use univers approxim properti enough size hidden layer width networks. paper bring theoret justif requir attribut size correspond hidden layer dimens given sampl set give optim binari classif result minimum train complex singl layer feed forward network framework. paper also establish proof exist bound width hidden layer rang subject certain conditions. find paper experiment analyz three differ dataset use mathlab 2018 (b) software.",
    "practic machin learn applic involv time seri data, firewal log analysi proactiv detect anomal behavior, concern real time analysi stream data. consequently, need updat ml model statist characterist data may shift frequent time. one altern explor literatur retrain model updat data whenev model accuraci observ degrade. however, method reli near real time avail ground truth, rare fulfilled. further, applic season data, tempor concept drift confound season variation. work, propos approach call unsupervis tempor drift detector utdd flexibl account season variation, effici detect tempor concept drift time seri data absenc ground truth, subsequ adapt ml model concept drift better generalization.",
    "demonstr iftt-pin, self-calibr version pin-entri method introduc roth et al. (2004) [1]. [1], digit split two set assign color respectively. commun digit, user press button color assign digit, identifi elimin iterations. iftt-pin use principl pre-assign color button. instead, user free choos button use color. iftt-pin infer user' pin prefer button-to-color map time, process call self-calibration. differ version iftt-pin test https://jgrizou.github.io/iftt-pin/ video introduct https://youtu.be/5i1ibpjdlhm.",
    "\"mind-controlling\" capabl alway mankind' fantasy. recent advanc electroencephalograph (eeg) techniques, brain-comput interfac (bci) research explor variou solut allow individu perform variou task use minds. however, commerci off-the-shelf devic run accur egg signal collect usual expens compar cheaper devic present coars results, prevent practic applic devic domest services. tackl challenge, propos develop end-to-end solut enabl fine brain-robot interact (bri) embed learn coars eeg signal low-cost devices, name deepbrain, peopl difficulti move, elderly, mildli command control robot perform basic household tasks. contribut two folds: 1) present stack long short term memori (stack lstm) structur specif pre-process techniqu handl time-depend eeg signal classification. 2) propos person design captur multipl featur achiev accur recognit individu eeg signal enhanc signal interpret stack lstm attent mechanism. real-world experi demonstr propos end-to-end solut low cost achiev satisfactori run-tim speed, accuraci energy-efficiency.",
    "paper, util result convex analysi monoton oper theori deriv addit properti softmax function yet cover exist literature. particular, show softmax function monoton gradient map log-sum-exp function. exploit connection, show invers temperatur paramet determin lipschitz co-coerc properti softmax function. demonstr use properti applic game-theoret reinforc learning.",
    "paper use relationship graph conduct spectral cluster studi (i) failur spectral cluster (ii) benefit regularization. explan simple. spars stochast graph creat lot small tree connect core graph one edge. graph conduct sensit noisi `dangl sets'. spectral cluster inherit sensitivity. second part paper start previous propos form regular spectral cluster show relat graph conduct `regular graph'. call conduct regular graph corecut. base upon previou argument relat graph conduct spectral cluster (e.g. cheeger inequality), minim corecut relax regular spectral clustering. simpl inspect corecut reveal less sensit small cut graph. together, result show unbalanc partit spectral cluster understood overfit nois peripheri spars stochast graph. regular fix overfitting. addit statist benefit, result also demonstr regular improv comput speed spectral clustering. provid simul data exampl illustr results.",
    "present class model that, via simpl construction, enabl exact, incremental, non-parametric, polynomial-time, bayesian infer condit measures. approach reli upon creat sequenc cover condit variabl maintain differ model set within cover. infer remain tractabl specifi probabilist model term random walk within sequenc covers. demonstr approach problem condit densiti estimation, which, knowledg first closed-form, non-parametr bayesian approach problem.",
    "breast cancer health problem affect mainli femal population. earli detect increas chanc effect treatment, improv prognosi disease. regard, comput tool propos assist specialist interpret breast digit imag exam, provid featur detect diagnos tumor cancer cells. nonetheless, detect tumor high sensit rate reduc fals posit rate still challenging. textur descriptor quit popular medic imag analysis, particularli histopatholog imag (hi), due variabl textur found imag tissu appear due irregular stain process. variabl may exist depend differ stain protocol fixation, inconsist stain condition, reagents, either laboratori laboratory. textur featur extract quantifi hi inform discrimin way challeng given distribut intrins properti imag form non-determinist complex system. paper propos method character textur across consider success rate. employ ecolog divers measur discret wavelet transform, possibl quantifi intrins properti imag promis accuraci two hi dataset compar state-of-the-art methods.",
    "motif repetitive/frequ pattern time-series. discoveri motif crucial practition order understand interpret phenomena occur sequenti data. currently, motif search among seri sub-sequences, aim select frequent occur ones. search-bas methods, tri seri sub-sequ motif candidates, current believ best method find frequent patterns. however, paper propos entir new perspect find motifs. demonstr search non-optim sinc domain motif restricted, instead propos principl optim approach abl find optim motifs. treat occurr frequenc function time-seri motif parameters, therefor \\textit{learn} optim motif maxim frequenc function. contrast searching, method abl discov repetit pattern (henc optimal), even case explicitli occur sub-sequences. experi sever real-lif time-seri dataset show motif found method highli frequent one found searching, exactli distanc threshold.",
    "mani field study, observ lower bound true respons valu experiments. fit regress model predict distribut outcomes, cannot simpli drop right-censor observations, need properli model them. work, focu concept censor data light model-bas optim prematur termin evalu (and thu gener right-censor data) key factor efficiency, e.g., search algorithm configur minim runtim algorithm hand. neural network (nns) demonstr work well core model-bas optim procedur extend handl censor observations. propos (i)~a loss function base tobit model incorpor censor sampl train (ii) use ensembl network model posterior distribution. nevertheless effici term optimization-overhead, propos use thompson sampl s.t. need train singl nn iteration. experi show train regress model achiev better predict qualiti sever baselin approach achiev new state-of-the-art perform model-bas optim two optim problems: minim solut time sat solver time-to-accuraci neural networks.",
    "competit perform neural machin translat (nmt) critic reli larg amount train data. however, acquir high-qual translat pair requir expert knowledg costly. therefore, best util given dataset sampl divers qualiti characterist becom import yet understudi question nmt. curriculum learn method introduc nmt optim model' perform prescrib data input order, base heurist assess nois difficulti levels. however, exist method requir train scratch, practic nmt model pre-train big data already. moreover, heuristics, gener well. paper, aim learn curriculum improv pre-train nmt model re-select influenti data sampl origin train set formul task reinforc learn problem. specifically, propos data select framework base determinist actor-critic, critic network predict expect chang model perform due certain sample, actor network learn select best sampl random batch sampl present it. experi sever translat dataset show method improv perform nmt origin batch train reach ceiling, without use addit new train data, significantli outperform sever strong baselin methods.",
    "given sequenc sets, set contain arbitrari number elements, problem tempor set predict aim predict element subsequ set. practice, tempor set predict much complex predict model tempor event time series, still open problem. mani possibl exist methods, adapt problem tempor set prediction, usual follow two-step strategi first project tempor set latent represent learn predict model latent representations. two-step approach often lead inform loss unsatisfactori predict performance. paper, propos integr solut base deep neural network tempor set prediction. uniqu perspect approach learn element relationship construct set-level co-occurr graph perform graph convolut dynam relationship graphs. moreover, design attention-bas modul adapt learn tempor depend element sets. finally, provid gate updat mechan find hidden share pattern differ sequenc fuse static dynam inform improv predict performance. experi real-world data set demonstr approach achiev competit perform even portion train data outperform exist method signific margin.",
    "patient diabet self-monitor decid right meal much insulin take. standard bolu advisor exists, never actual proven optim sense. challeng rule appli reinforc learn techniqu data simul t1dm, fda-approv simul develop kovatchev et al. model gluco-insulin interaction. result show optim bolu rule fairli differ standard bolu advisor, follow actual avoid hypoglycemia episodes.",
    "increas market penetr electr vehicl (evs) may pose signific electr demand power systems. electr demand affect inher uncertainti evs' travel behavior make forecast daili charg demand (cd) challenging. project, use nation hous hold survey (nhts) data form sequenc trips, develop machin learn model predict paramet next trip drivers, includ trip start time, end time, distance. paramet later use model tempor charg behavior evs. simul result show propos model effect estim daili cd pattern base travel behavior evs, simpl machin learn techniqu forecast travel paramet accept accuracy.",
    "propos first method adapt modifi durat given speech signal. approach use bayesian framework defin latent attent map link frame input target utterances. train mask convolut encoder-decod network produc attent map via stochast version mean absolut error loss function; model also predict length target speech signal use encod embeddings. predict length determin number step decod operation. inference, gener attent map proxi similar matrix given input speech unknown target speech signal. use similar matrix, comput warp path align two signals. experi demonstr adapt framework produc similar result dynam time warping, reli known target signal, voic convers emot convers tasks. also show techniqu result high qualiti gener speech par state-of-the-art vocoders.",
    "key challeng autonom drive safe trajectori plan cluttered, urban environ dynam obstacles, pedestrians, bicyclists, vehicles. reliabl predict futur environment, includ behavior dynam agents, would allow plan algorithm proactiv gener trajectori respons rapidli chang environment. present novel framework predict futur occup state local environ surround autonom agent learn motion model occup grid data use neural network. take advantag tempor structur grid data util convolut long-short term memori network form prednet architecture. method valid kitti dataset demonstr higher accuraci better predict power baselin methods.",
    "self-driv vehicl expand dramat last years. udac releas dataset containing, among data, set imag steer angl captur driving. udac challeng aim predict steer angl base provid images. explor two differ model perform high qualiti predict steer angl base imag use differ deep learn techniqu includ transfer learning, 3d cnn, lstm resnet. udac challeng still ongoing, model would place top ten entries.",
    "present hero, novel framework large-scal video+languag omni-represent learning. hero encod multimod input hierarch structure, local context video frame captur cross-mod transform via multimod fusion, global video context captur tempor transformer. addit standard mask languag model (mlm) mask frame model (mfm) objectives, design two new pre-train tasks: (i) video-subtitl match (vsm), model predict global local tempor alignment; (ii) frame order model (fom), model predict right order shuffl video frames. hero jointli train howto100m large-scal tv dataset gain deep understand complex social dynam multi-charact interactions. comprehens experi demonstr hero achiev new state art multipl benchmark text-bas video/video-mo retrieval, video question answer (qa), video-and-languag infer video caption task across differ domains. also introduc two new challeng benchmark how2qa how2r video qa retrieval, collect divers video content multimodalities.",
    "propos deep hierarch machin (dhm), model inspir divide-and-conqu strategi emphas represent learn abil flexibility. stochast rout framework use recent deep neural decision/regress forest incorporated, remov need evalu unnecessari comput path util differ topolog introduc probabilist prune technique. also show specifi version dhm (dshm) efficiency, inherit spars featur extract process tradit decis tree pixel-differ feature. achiev spars featur extraction, propos util spars convolut oper dshm show one possibl introduc spars convolut kernel use local binari convolut layer. dhm appli classif regress problems, valid standard imag classif face align task show advantag past architectures.",
    "present new kernel-bas algorithm model evenli distribut multidimension dataset reli input space sparsification. present method reorgan typic single-lay kernel-bas model deep hierarch structure, weight kernel model dimens model adjac dimension. show model weight suggest structur lead signific comput speedup improv model accuracy.",
    "learner abil predict futur structur time-vari signal must maintain memori recent past. signal characterist timescal relev futur prediction, memori simpl shift register---a move window extend past, requir storag resourc linearli grow timescal represented. however, independ gener purpos learner cannot priori know characterist prediction-relev timescal signal. moreover, mani natur occur signal show scale-fre long rang correl impli natur prediction-relev timescal essenti unbounded. henc learner maintain inform longest possibl timescal allow resourc availability. construct fuzzi memori system optim sacrific tempor accuraci inform scale-fre fashion order repres prediction-relev inform exponenti long timescales. use sever illustr examples, demonstr advantag fuzzi memori system shift regist time seri forecast natur signals. avail storag resourc limited, suggest gener purpos learner would better commit fuzzi memori system.",
    "recent work suggest auto-encod variant good job captur local manifold structur unknown data gener density. paper contribut mathemat understand phenomenon help defin better justifi sampl algorithm deep learn base auto-encod variants. consid mcmc step sampl gaussian whose mean covari matrix depend previou state, defin asymptot distribut target density. first, show good choic (in sens consistency) mean covari function local expect valu local covari target density. show auto-encod contract penalti captur estim local moment reconstruct function jacobian. contribut work thu novel altern maximum-likelihood densiti estimation, call local moment matching. also justifi recent propos sampl algorithm contract auto-encod extend denois auto-encoder.",
    "classif may reliabl sever reasons: nois data, insuffici input information, overlap distribut sharp definit classes. face sever possibl neural network may case still use instead classif elimin improb class done. elimin may construct use classifi assign new case pool sever class instead one win class. elimin may done help sever classifi use modifi error functions. real life medic applic neural network present illustr use elimination.",
    "accur load forecast critic electr market oper real-tim decision-mak task power systems. paper consid short-term load forecast (stlf) problem residenti custom within community. exist stlf work mainli focus forecast aggreg load either feeder system singl customer, effort made forecast load individu applianc level. work, present stlf algorithm effici predict power consumpt individu electr appliances. propos method build upon power recurr neural network (rnn) architectur deep learning, term long short-term memori (lstm). applianc uniqu repetit consumpt patterns, pattern predict error track past predict error use improv final predict performance. numer test real-world load dataset demonstr improv propos method exist lstm-base method benchmark approaches.",
    "compar sampl complex privat learn [kasiviswanathan et al. 2008] sanitization~[blum et al. 2008] pure $\\epsilon$-differenti privaci [dwork et al. tcc 2006] approxim $(\\epsilon,\\delta)$-differenti privaci [dwork et al. eurocrypt 2006]. show sampl complex task approxim differenti privaci significantli lower pure differenti privacy. defin famili optim problems, call quasi-concav promis problems, gener consid tasks. observ quasi-concav promis problem privat approxim use solut smaller instanc quasi-concav promis problem. allow us construct effici recurs algorithm solv problem privately. specifically, construct privat learner point functions, threshold functions, axis-align rectangl high dimension. similarly, construct sanit point function threshold functions. also examin sampl complex label-priv learners, relax privat learn learner requir protect privaci label sample. show vc dimens complet character sampl complex learners, is, sampl complex learn label privaci equal (up constants) learn without privacy.",
    "interest develop automat visual recognit emerg task possibl assign object label images, yet still feasibl collect annot reflect human judgement them. machin learning-bas predictor task reli supervis train model behavior annotators, i.e., would averag person' judgement image? key open question type work, especi applic inconsist human behavior lead ethic lapses, evalu epistem uncertainti train predictors, i.e., uncertainti come predictor' model. propos bayesian framework evalu black box predictor regime, agnost predictor' intern structure. framework specifi estim epistem uncertainti come predictor respect human label approxim condit distribut produc credibl interv predict measur performance. framework success appli four imag classif task use subject human judgements: facial beauti assessment, social attribut assignment, appar age estimation, ambigu scene labeling.",
    "surpris describ rang phenomena unexpect event behavior responses. propos measur surpris use surprise-driven learning. surpris measur take account data likelihood well degre commit belief via entropi belief distribution. find surprise-minim learn dynam adjust balanc new old inform without need knowledg tempor statist environment. appli framework dynam decision-mak task maze explor task. surpris minim framework suitabl learn complex environments, even environ undergo gradual sudden chang could eventu provid framework studi behavior human anim encount surpris events.",
    "purpose: coronaviru 2019 (covid-19), emerg wuhan, china affect whole world, cost live thousand people. manual diagnosi ineffici due rapid spread virus. reason, automat covid-19 detect studi carri support artifici intellig algorithms. methods: study, deep learn model detect covid-19 case high perform presented. propos method defin convolut support vector machin (csvm) automat classifi comput tomographi (ct) images. unlik pre-train convolut neural network (cnn) train transfer learn method, csvm model train scratch. evalu perform csvm method, dataset divid two part train (%75) test (%25). csvm model consist block contain three differ number svm kernels. results: perform pre-train cnn network csvm model assessed, csvm (7x7, 3x3, 1x1) model show highest perform 94.03% acc, 96.09% sen, 92.01% spe, 92.19% pre, 94.10% f1-score, 88.15% mcc 88.07% kappa metric values. conclusion: propos method effect methods. proven experi perform inspir combat covid futur studies.",
    "paper introduc evalu distal explan model model-fre reinforc learn agent gener explan `why' `whi not' questions. start point observ causal model gener opportun chain take form `a enabl b b caus c'. use insight analysi 240 explan gener human-ag experiment, defin distal explan model analys counterfactu opportun chain use decis tree causal models. recurr neural network employ learn opportun chains, decis tree use improv accuraci task predict gener counterfactuals. comput evalu model 6 reinforc learn benchmark use differ reinforc learn algorithms. studi 90 human participants, show distal explan model result improv outcom three scenario compar two baselin explan models.",
    "miss data crucial issu appli machin learn algorithm real-world datasets. start simpl assumpt two batch extract randomli dataset share distribution, leverag optim transport distanc quantifi criterion turn loss function imput miss data values. propos practic method minim loss use end-to-end learning, exploit parametr assumpt underli distribut values. evalu method dataset uci repository, mcar, mar mnar settings. experi show ot-bas method match out-perform state-of-the-art imput methods, even high percentag miss values.",
    "anomali detect time seri wide research import practic applications. recent years, anomali detect algorithm mostli base deep-learn gener model use reconstruct error detect anomalies. tri captur distribut normal data reconstruct normal data train phase, calcul reconstruct error test data anomali detection. however, use normal data train phase ensur reconstruct process anomali data. so, anomali data also well reconstruct sometim get low reconstruct error, lead omiss anomalies. what' more, neighbor inform data point time seri data fulli util algorithms. paper, propos ran base idea reconstruct anomali normal appli unsupervis time seri anomali detection. minim reconstruct error normal data maxim anomali data, ensur normal data reconstruct well, also tri make reconstruct anomali data consist distribut normal data, anomali get higher reconstruct errors. implement idea introduc \"imit anomali data\" combin special design latent vector-constrain autoencod discrimin construct adversari network. extens experi time-seri dataset differ scene ecg diagnosi also show ran detect meaning anomalies, outperform algorithm term auc-roc.",
    "research paper, studi capabl artifici neural network model emul storm surg base storm track/size/intens history, leverag databas synthet storm simulations. traditionally, comput fluid dynam solver employ numer solv storm surg govern equat partial differenti equat gener costli simulate. studi present neural network model predict storm surge, inform databas synthet storm simulations. model serv fast afford emul expens cfd solvers. neural network model train storm track paramet use drive cfd solvers, output model time-seri evolut predict storm surg across multipl node within spatial domain interest. model trained, deploy predict base new storm track inputs. develop neural network model time-seri model, long short-term memory, variat recurr neural network, enrich convolut neural networks. convolut neural network employ captur correl data spatially. therefore, tempor spatial correl data captur combin mention models, convlstm model. problem sequenc sequenc time-seri problem, encoder-decod convlstm model designed. techniqu process model train also employ enrich model performance.",
    "result show propos convolut recurr neural network outperform gaussian process implement examin synthet storm database.",
    "paper, continu previou work dirichlet mixtur model (dmm)-base vq deriv perform bound lsf vq. lsf paramet transform $\\delta$lsf domain underli distribut $\\delta$lsf paramet model dmm finit number mixtur components. quantiz distortion, term mean squar error (mse), calcul high rate theory. map relat perceptu motiv log spectral distort (lsd) mse empir approxim polynomial. map function, minimum requir bit rate transpar code lsf estimated.",
    "machin learn (ml) system get increasingli popular, drive applic servic daili life. led grow concern user privacy, sinc human interact data typic need transmit cloud order train improv systems. feder learn (fl) recent emerg method train ml model edg devic use sensit user data seen way mitig concern data privacy. however, sinc ml model commonli train label supervision, need way extract label edg make fl viable. work, propos strategi train fl model use posit neg user feedback. also design novel framework studi differ nois pattern user feedback, explor well standard noise-robust object help mitig nois train model feder setting. evalu propos train setup detail experi two text classif dataset analyz effect vari level user reliabl feedback nois model performance. show method improv substanti self-train baseline, achiev perform closer model train full supervision.",
    "formul asymmetr (or non-commutative) distanc task base fisher inform matrices, call fisher task distance. distanc repres complex transfer knowledg one task another. provid proof consist distanc theorem experi variou classif task mnist, cifar-10, cifar-100, imagenet, taskonomi datasets. next, construct onlin neural architectur search framework use fisher task distance, access past learn tasks. use fisher task distance, identifi closest learn task target task, util knowledg learn relat task target task. here, show propos distanc target task set learn task use reduc neural architectur search space target task. complex reduct search space task-specif architectur achiev build optim architectur similar task instead full search without use side information. experiment result task mnist, cifar-10, cifar-100, imagenet dataset demonstr efficaci propos approach improvements, term perform number parameters, gradient-bas search methods, enas, darts, pc-darts.",
    "attempt produc ml model less reliant spuriou pattern nlp datasets, research recent propos curat counterfactu augment data (cad) via human-in-the-loop process given document (initial) labels, human must revis text make counterfactu label applicable. importantly, edit necessari flip applic label prohibited. model train augment data appear, empirically, reli less semant irrelev word gener better domain. work draw loos causal thinking, underli causal model (even abstract level) principl underli observ out-of-domain improv remain unclear. paper, introduc toy analog base linear gaussian models, observ interest relationship causal models, measur noise, out-of-domain generalization, relianc spuriou signals. analysi provid insight help explain efficaci cad. moreover, develop hypothesi ad nois causal featur degrad in-domain out-of-domain performance, ad nois non-caus featur lead rel improv out-of-domain performance. idea inspir specul test determin whether featur attribut techniqu identifi causal spans. ad nois (e.g., random word flips) highlight span degrad in-domain out-of-domain perform batteri challeng datasets, ad nois complement give improv out-of-domain, suggest identifi causal spans.",
    "present large-scal empir studi compar span edit creat cad select attent salienc maps. across numer domain models, find hypothes phenomenon pronounc cad.",
    "non-degrad plastic wast stay decad land water, jeopard environment; yet modern lifestyl current technolog imposs sustain without plastics. bio-synthes biodegrad altern polym famili polyhydroxyalkano (phas) potenti replac larg portion world' plastic suppli cradle-to-cradl materials, chemic complex divers limit tradit resource-intens experimentation. work, develop multitask deep neural network properti predictor use avail experiment data divers set nearli 23000 homo- copolym chemistries. use predictors, identifi 14 pha-bas bioplast search space almost 1.4 million candid could serv potenti replac seven petroleum-bas commod plastic account 75% world' yearli plastic production. discuss possibl synthesi rout identifi promis materials. develop multitask polym properti predictor made avail part polym genom project https://polymergenome.org.",
    "combinatori optim one fundament research field extens studi theoret comput scienc oper research. develop algorithm combinatori optimization, commonli assum paramet edg weight exactli known inputs. however, assumpt may fulfil sinc input paramet often uncertain initi unknown mani applic recommend systems, crowdsourcing, commun networks, onlin advertisement. resolv uncertainty, problem combinatori pure explor multi-arm bandit (cpe) variant reciev increas attention. earlier work cpe studi semi-bandit feedback assum outcom individu edg alway access rounds. however, due practic constraint budget ceil privaci concern, strong feedback alway avail recent applications. article, review recent propos techniqu combinatori pure explor problem limit feedback.",
    "focu commonli use synchron gradient descent paradigm large-scal distribut learning, grow interest develop effici robust gradient aggreg strategi overcom two key system bottlenecks: commun bandwidth stragglers' delays. particular, ring-allreduc (rar) design propos avoid bandwidth bottleneck particular node allow worker commun neighbor arrang logic ring. hand, gradient code (gc) recent propos mitig straggler master-work topolog allow care design redund alloc data set workers. propos joint commun topolog design data set alloc strategy, name codedreduc (cr), combin best rar gc. is, parallel commun tree topolog lead effici bandwidth utilization, care design redund data set alloc code strategi node make propos gradient aggreg scheme robust stragglers. particular, quantifi commun parallel gain resili propos cr scheme, prove optim commun topolog regular tree. moreover, character expect run-tim cr show order-wis speedup compar benchmark schemes. finally, empir evalu perform propos cr design amazon ec2 demonstr achiev speedup 27.2x 7.0x, respect benchmark gc rar.",
    "optim transport (ot) problem rapidli find way machin learning. favor use metric properties. mani problem admit solut guarante object embed metric spaces, use non-metr complic solv them. multi-margin ot (mmot) gener ot simultan transport multipl distributions. captur import relat miss transport involv two distributions. research mmot, however, focus existence, uniqueness, practic algorithms, choic cost functions. lack discuss metric properti mmot, limit theoret practic use. here, prove new gener metric properti new famili mmots. first explain difficulti prove via two neg results. afterward, prove mmots' metric properties. finally, show gener triangl inequ famili mmot cannot improved. illustr superior mmot gener metrics, non-metr synthet real tasks.",
    "nowaday grow research interest possibl enrich small fli robot autonom sens onlin navig capabilities. enabl larg number applic span remot surveil logistics, smarter citi emerg aid hazard environments. context, emerg problem track unauthor small unman aerial vehicl (uavs) hide behind build conceal larg uav networks. contrast current solut mainli base static on-ground radars, paper propos idea dynam radar network uav real-tim high-accuraci track malici targets. end, describ solut real-tim navig uav track dynam target use heterogen sens information. inform share uav neighbor via multi-hops, allow track target local bayesian estim run agent. sinc path equal term inform gather point-of-view, uav plan trajectori minim posterior covari matrix target state uav kinemat anti-collis constraints. result show dynam network radar attain better local result compar fix configur on-board sensor technolog impact accuraci track target differ radar cross sections, especi non line-of-sight (nlos) situations.",
    "recent surg interest develop new class deep learn (dl) architectur integr explicit time dimens fundament build block learn represent mechanisms. turn, mani recent result show topolog descriptor observ data, encod inform shape dataset topolog space differ scales, is, persist homolog data, may contain import complementari information, improv perform robust dl. converg two emerg ideas, propos enhanc dl architectur salient time-condit topolog inform data introduc concept zigzag persist time-awar graph convolut network (gcns). zigzag persist provid systemat mathemat rigor framework track import topolog featur observ data tend manifest time. integr extract time-condit topolog descriptor dl, develop new topolog summary, zigzag persist image, deriv theoret stabil guarantees. valid new gcn time-awar zigzag topolog layer (z-gcnets), applic traffic forecast ethereum blockchain price prediction. result indic z-gcnet outperform 13 state-of-the-art method 4 time seri datasets.",
    "paper, present new idea transfer learn (tl) base gibb sampling. gibb sampl algorithm instanc like transfer new state higher possibl respect probabl distribution. find algorithm employ transfer instanc domains. restrict boltzmann machin (rbm) energi base model feasibl train repres data distribut also perform gibb sampling. use rbm captur data distribut sourc domain use order cast target instanc new data distribut similar distribut sourc data. use dataset commonli use evalu tl methods, show method success enhanc target classif consider ratio. additionally, propos method advantag common da method need target data process train models.",
    "comput perman non-neg matrix core problem practic applic rang target track statist thermodynamics. however, problem also #p-complete, leav littl hope find exact solut comput efficiently. problem admit fulli polynomi random approxim scheme, method seen littl use ineffici practic difficult implement. present adapart, simpl effici method draw exact sampl unnorm distribution. use adapart, show construct tight bound perman hold high probability, guarante polynomi runtim dens matrices. find adapart provid empir speedup exceed 25x prior sampl method matric challeng variat base approaches. finally, context multi-target tracking, exact sampl distribut defin matrix perman allow us use optim propos distribut particl filtering. use adapart, show lead improv track perform use order magnitud fewer samples.",
    "fast develop variou posit techniqu global posit system (gps), mobil devic remot sensing, spatio-tempor data becom increasingli avail nowadays. mine valuabl knowledg spatio-tempor data critic import mani real world applic includ human mobil understanding, smart transportation, urban planning, public safety, health care environment management. number, volum resolut spatio-tempor dataset increas rapidly, tradit data mine methods, especi statist base method deal data becom overwhelmed. recently, advanc deep learn techniques, deep lean model convolut neural network (cnn) recurr neural network (rnn) enjoy consider success variou machin learn task due power hierarch featur learn abil spatial tempor domains, wide appli variou spatio-tempor data mine (stdm) task predict learning, represent learning, anomali detect classification. paper, provid comprehens survey recent progress appli deep learn techniqu stdm. first categor type spatio-tempor data briefli introduc popular deep learn model use stdm. framework introduc show gener pipelin util deep learn model stdm. next classifi exist literatur base type st data, data mine tasks, deep learn models, follow applic deep learn stdm differ domain includ transportation, climat science, human mobility, locat base social network, crime analysis, neuroscience.",
    "finally, conclud limit current research point futur research directions.",
    "precondit gradient method among gener power tool optimization. however, precondit requir store manipul prohibit larg matrices. describ analyz new structure-awar precondit algorithm, call shampoo, stochast optim tensor spaces. shampoo maintain set precondit matrices, oper singl dimension, contract remain dimensions. establish converg guarante stochast convex setting, proof build upon matrix trace inequalities. experi state-of-the-art deep learn model show shampoo capabl converg consider faster commonli use optimizers. although involv complex updat rule, shampoo' runtim per step compar simpl gradient method sgd, adagrad, adam.",
    "authent identif method base human fingerprint ubiquit sever system rang govern organ consum products. perform reliabl system directli reli volum data verified. unfortunately, larg volum fingerprint databas publicli avail due mani privaci secur concerns. paper, introduc new approach automat gener high-fidel synthet fingerprint scale. approach reli (i) gener adversari network estim probabl distribut human fingerprint (ii) super-resolut method synthes fine-grain textures. rigor test system show methodolog first gener fingerprint comput indistinguish real ones, task prior art could accomplish.",
    "frequenc domain processing, particular use modifi discret cosin transform (mdct), widespread approach audio coding. however, low bitrates, audio quality, especi speech, degrad drastic due lack avail bit directli code transform coefficients. traditionally, post-filt use mitig artefact code speech exploit a-priori inform sourc extra transmit parameters. recently, data-driven post-filt shown better results, cost signific addit complex delay. work, propos mask-bas post-filt oper directli mdct domain codec, induc extra delay. real-valu mask appli quantiz mdct coeffici estim rel lightweight convolut encoder-decod network. solut test recent standard low-delay, low-complex codec (lc3) lowest possibl bitrat 16 kbps. object subject assess clearli show advantag approach convent post-filter, averag improv 10 mushra point lc3 code speech.",
    "layer normal (layernorm) success appli variou deep neural network help stabil train boost model converg capabl handl re-cent re-scal input weight matrix. however, comput overhead introduc layernorm make improv expens significantli slow underli network, e.g. rnn particular. paper, hypothes re-cent invari layernorm dispens propos root mean squar layer normalization, rmsnorm. rmsnorm regular sum input neuron one layer accord root mean squar (rms), give model re-scal invari properti implicit learn rate adapt ability. rmsnorm comput simpler thu effici layernorm. also present partial rmsnorm, prmsnorm rm estim p% sum input without break properties. extens experi sever task use divers network architectur show rmsnorm achiev compar perform layernorm reduc run time 7%~64% differ models. sourc code avail https://github.com/bzhanggo/rmsnorm.",
    "studi implicit bia adagrad separ linear classif problems. show adagrad converg direct character solut quadrat optim problem feasibl set hard svm problem. also give discuss differ choic hyperparamet adagrad might impact direction. provid deeper understand adapt method seem gener abil good gradient descent practice.",
    "neural approach program synthesi understand prolifer wide last years; time graph base neural network becom promis new tool. work aim first empir studi compar effect natur languag model static analysi graph base model repres program deep learn systems. compar graph convolut network use differ graph represent task program embedding. show sparsiti control flow graph implicit aggreg graph convolut network caus model perform wors naiv models. therefor conclud simpli augment pure linguist statist model formal inform perform well due nuanc natur formal properti introduc nois structur graph convolut networks.",
    "princip compon analysi (pca) wide use dimension reduct featur extraction. robust pca (rpca), differ robust distanc metrics, l1-norm l2, p-norm, deal nois outlier extent. however, real-world data may display structur fulli captur simpl functions. addition, exist method treat complex simpl sampl equally. contrast, learn pattern typic adopt human be learn simpl complex less more. base principle, propos novel method call self-pac pca (spca) reduc effect nois outliers. notably, complex sampl calcul begin iter order integr sampl simpl complex training. base altern optimization, spca find optim project matrix filter outlier iteratively. theoret analysi present show ration spca. extens experi popular data set demonstr propos method improv state of-the-art result considerably.",
    "paper propos novel environment sound classif approach incorpor unsupervis featur learn codebook via spheric $k$-means++ algorithm new architectur high-level data augmentation. audio signal transform 2d represent use discret wavelet transform (dwt). dwt spectrogram augment novel architectur cycle-consist gener adversari network. high-level augment bootstrap gener spectrogram intra inter class manner translat structur featur sampl sample. codebook built code dwt spectrogram speeded-up robust featur detector (surf) k-means++ algorithm. random forest final learn algorithm learn environment sound classif task cluster codeword codebook. experiment result four benchmark environment sound dataset (esc-10, esc-50, urbansound8k, dcase-2017) shown propos classif approach outperform state-of-the-art classifi scope, includ advanc dens convolut neural network alexnet googlenet, improv classif rate 3.51% 14.34%, depend dataset.",
    "feder learn (fl) intens investig term commun efficiency, privacy, fairness. however, effici annotation, pain point real-world fl applications, less studied. project, propos appli activ learn (al) sampl strategi fl framework reduc annot workload. expect al fl improv perform complementarily. propos feder activ learn (f-al) method, client collabor implement al obtain instanc consid inform fl distribut optim manner. compar test accuraci global fl model use convent random sampl strategy, client-level separ al (s-al), propos f-al. empir demonstr f-al outperform baselin method imag classif tasks.",
    "use hybrid machin learn epidemiolog approaches, propos novel data-driven approach predict us covid-19 death counti level. model give complet descript daili death distribution, output quantile-estim instead mean deaths, model' object minim pinbal loss death report new york time coronaviru counti dataset. result quantil estim accur forecast death individual-counti level variable-length forecast period, approach gener well across differ forecast period lengths. caltech-run model competit 50+ teams, aggreg competit best covid-19 model system (on root mean squar error).",
    "empirically, neural network attempt learn program data exhibit poor generalizability. moreover, tradit difficult reason behavior model beyond certain level input complexity. order address issues, propos augment neural architectur key abstraction: recursion. application, implement recurs neural programmer-interpret framework four tasks: grade-school addition, bubbl sort, topolog sort, quicksort. demonstr superior generaliz interpret small amount train data. recurs divid problem smaller piec drastic reduc domain neural network component, make tractabl prove guarante overal system' behavior. experi suggest order neural architectur robustli learn program semantics, necessari incorpor concept like recursion.",
    "real-world network incomplet observed. algorithm accur predict link miss dramat speedup collect network data improv valid network models. mani algorithm exist predict miss links, given partial observ network, remain unknown whether singl best predictor exists, link predict vari across method network differ domains, close optim current method are. answer question systemat evalu 203 individu link predictor algorithms, repres three popular famili methods, appli larg corpu 548 structur divers network six scientif domains. first show individu algorithm exhibit broad divers predict errors, one predictor famili best, worst, across realist inputs. exploit divers via meta-learn construct seri \"stacked\" model combin predictor singl algorithm. appli broad rang synthet networks, may analyt calcul optim performance, stack model achiev optim nearli optim level accuracy. appli real-world networks, stack model also superior, accuraci vari strongli domain, suggest link predict may fundament easier social network biolog technolog networks. result indic state-of-the-art link predict come combin individu algorithms, achiev nearli optim predictions. close brief discuss limit opportun improv results.",
    "introduc approach select object neural volumetr 3d representations, multi-plan imag (mpi) neural radianc field (nerf). approach take set foreground background 2d user scribbl one view automat estim 3d segment desir object, render novel views. achiev result, propos novel voxel featur embed incorpor neural volumetr 3d represent multi-view imag featur input views. evalu approach, introduc new dataset human-provid segment mask depict object real-world multi-view scene captures. show approach out-perform strong baselines, includ 2d segment 3d segment approach adapt task.",
    "wasserstein-gan introduc address defici gener adversari network (gans) regard problem vanish gradient mode collaps training, lead improv converg behaviour improv imag quality. however, wasserstein-gan requir discrimin lipschitz continuous. current state-of-the-art wasserstein-gan constraint enforc via gradient norm regularization. paper, demonstr regular encourag broad distribut spectral-valu discrimin weights, henc result less fidel learn distribution. therefor investig possibl substitut lipschitz constraint orthogon constraint weight matrices. compar three differ weight orthogon techniqu regard converg properties, abil ensur lipschitz condit achiev qualiti learn distribution. addition, provid comparison wasserstein-gan train current state-of-the-art methods, demonstr potenti sole use orthogonality-bas regularization. context, propos improv train procedur wasserstein-gan util orthogon increas gener capability. finally, provid novel metric evalu gener capabl discrimin differ wasserstein-gans.",
    "segment model altern frame-bas model sequenc prediction, hypothes path weight base entir segment score rather singl frame time. neural segment model segment model use neural network-bas weight functions. neural segment model achiev competit result speech recognition, end-to-end train explor sever studies. work, review neural segment models, view consist neural network-bas acoust encod finite-st transduc decoder. studi end-to-end segment model differ weight functions, includ one base frame-level neural classifi segment recurr neural networks. studi reduc search space size impact perform differ weight functions. also compar sever loss function end-to-end training. finally, explor train approaches, includ multi-stag vs. end-to-end train multitask train combin segment frame-level losses.",
    "effect model hidden structur network practic theoret challenging. exist relat model involv limit information, name binari direct link data, embed network learn hidden network structures. rich meaning inform (e.g., variou attribut entiti granular inform binari element \"like\" \"dislike\") missed, play critic role form understand relat network. work, propos inform relat model (infrm) framework adequ involv rich inform granular network, includ metadata inform entiti variou form link data. firstly, effect metadata inform incorpor method employ prior inform relat model mmsb lfrm. encourag entiti similar metadata inform similar hidden structures. secondly, propos variou solut cater altern form link data. substanti effort made toward model appropri efficiency, example, use conjug priors. evalu framework infer algorithm differ datasets, show gener effect model captur implicit structur networks.",
    "tradit supervis learn aim train classifi closed-set world, train test sampl share label space. paper, target challeng realist setting: open-set learn (osl), exist test sampl class unseen training. although research design mani method algorithm perspectives, method provid gener guarante abil achiev consist perform differ train sampl drawn distribution. motiv transfer learn probabl approxim correct (pac) theory, make bold attempt studi osl prove gener error-given train sampl size n, estim error get close order o_p(1/\\sqrt{n}). first studi provid gener bound osl, theoret investig risk target classifi unknown classes. accord theory, novel algorithm, call auxiliari open-set risk (aosr) propos address osl problem. experi verifi efficaci aosr. code avail github.com/anjin-liu/openset_learning_aosr.",
    "digit harm widespread mobil ecosystem. devic gain ever promin daili lives, increas potenti malici attack individuals. last line defens rang digit harm - includ digit distraction, polit polaris hate speech, children expos damag materi - user interface. work introduc greasetermin enabl research develop, deploy, test intervent harm end-users. demonstr eas intervent develop deployment, well broad rang harm potenti cover greasetermin five in-depth case studies.",
    "leaf imag recognit techniqu activ research plant speci identification. howev remain unclear whether leaf pattern provid suffici inform cultivar recognition. paper report first attempt soybean cultivar recognit plant leav challeng research problem also import soybean cultivar evaluation, select product agriculture. paper, propos novel multiscal slide chord match (mscm) approach extract leaf pattern distinct soybean cultivar identification. chord defin slide along contour measur synchronis pattern exterior shape interior appear soybean leaf images. multiscal slide chord strategi develop extract featur coarse-to-fin hierarch order. joint descript integr leaf descriptor differ part soybean plant propos enhanc discrimin power cultivar description. built cultivar leaf imag database, soycultivar, consist 1200 sampl leaf imag 200 soybean cultivar perform evaluation. encourag experiment result propos method comparison state-of-the-art leaf speci recognit method demonstr avail cultivar inform soybean leav effect propos mscm soybean cultivar identification, may advanc research leaf recognit speci cultivar.",
    "mani complex deep learn model use differ variat variou prognost tasks. higher learn paramet necessarili ensur great accuracy. solv consid chang deep model mani regular base techniques. paper train deep neural network use mani regular layer residu concaten process best fit polycyst ovari syndrom diagnosi prognostication. network built improv everi step failur meet need data achiev accuraci 99.3% seamlessly.",
    "adversari train among effect techniqu improv robust model adversari perturbations. however, full effect approach model well understood. example, adversari train reduc adversari risk (predict error adversary), sometim increas standard risk (gener error adversary). even more, behavior impact variou element learn problem, includ size qualiti train data, specif form adversari perturb input, model overparameterization, adversary' power, among others. paper, focu \\emph{distribut perturbing} adversari framework wherein adversari chang test distribut within neighborhood train data distribution. neighborhood defin via wasserstein distanc distribut radiu neighborhood measur adversary' manipul power. studi tradeoff standard risk adversari risk deriv pareto-optim tradeoff, achiev specif class models, infinit data limit featur dimens kept fixed. consid three learn settings: 1) regress class linear models; 2) binari classif gaussian mixtur data model, class linear classifiers; 3) regress class random featur model (which equival repres two-lay neural network random first-lay weights). show tradeoff standard adversari risk manifest three settings.",
    "character pareto-optim tradeoff curv discuss varieti factors, featur correlation, adversary' power width two-lay neural network would affect tradeoff.",
    "mani recent breakthrough deep learn achiev train increasingli larger model massiv datasets. however, train model prohibit expensive. instance, cluster use train gpt-3 cost \\$250 million. result, research cannot afford train state art model contribut development. hypothetically, research could crowdsourc train larg neural network thousand regular pc provid volunteers. raw comput power hundr thousand \\$2500 desktop dwarf \\$250m server pod, one cannot util power effici convent distribut train methods. work, propos learning@home: novel neural network train paradigm design handl larg amount poorli connect participants. analyz performance, reliability, architectur constraint paradigm compar exist distribut train techniques.",
    "show stochast acceler achiev perturb iter framework (mania et al., 2017) asynchron lock-fre optimization, lead optim increment gradient complex finite-sum objectives. prove new acceler method requir linear speed-up condit exist non-acceler methods. core algorithm discoveri new acceler svrg variant spars updates. empir result present verifi theoret findings.",
    "much remark progress comput vision focus around fulli supervis learn mechan reli highli curat dataset varieti tasks. contrast, human often learn world littl extern supervision. take inspir infant learn environ play interaction, present comput framework discov object learn physic properti along paradigm learn interaction. agent, place within near photo-realist physics-en ai2-thor environment, interact world learn objects, geometr extent rel masses, without extern guidance. experi reveal agent learn effici effectively; object interact before, also novel instanc seen categori well novel object categories.",
    "natur gradient descent proven effect mitig effect patholog curvatur neural network optimization, littl known theoret converg properties, especi \\emph{nonlinear} networks. work, analyz first time speed converg natur gradient descent nonlinear neural network squared-error loss. identifi two condit guarante effici converg random initializations: (1) jacobian matrix (of network' output train case respect parameters) full row rank, (2) jacobian matrix stabl small perturb around initialization. two-lay relu neural networks, prove two condit fact hold throughout training, assumpt nondegener input overparameterization. extend analysi gener loss functions. lastly, show k-fac, approxim natur gradient descent method, also converg global minima assumptions, give bound rate convergence.",
    "graph neural network (gnns) achiev great success variou graph mine tasks.however, drastic perform degrad alway observ gnn stack mani layers. result, gnn shallow architectures, limit express power exploit deep neighborhoods.most recent studi attribut perform degrad deep gnn \\textit{over-smoothing} issue. paper, disentangl convent graph convolut oper two independ operations: \\textit{propagation} (\\textbf{p}) \\textit{transformation} (\\textbf{t}).follow this, depth gnn split propag depth ($d_p$) transform depth ($d_t$). extens experiments, find major caus perform degrad deep gnn \\textit{model degradation} issu caus larg $d_t$ rather \\textit{over-smoothing} issu mainli caus larg $d_p$. further, present \\textit{adapt initi residual} (air), plug-and-play modul compat kind gnn architectures, allevi \\textit{model degradation} issu \\textit{over-smoothing} issu simultaneously. experiment result six real-world dataset demonstr gnn equip air outperform gnn shallow architectur owe benefit larg $d_p$ $d_t$, time cost associ air ignored.",
    "finit mixtur regress (fmr) refer mixtur model scheme learn multipl regress model train data set. charg subset. fmr effect scheme handl sampl heterogeneity, singl regress model enough captur complex condit distribut observ sampl given features. paper, propos fmr model 1) find sampl cluster jointli model multipl incomplet mixed-typ target simultaneously, 2) achiev share featur select among task cluster components, 3) detect anomali task cluster structur among tasks, accommod outlier samples. provid non-asymptot oracl perform bound model high-dimension learn framework. propos model evalu synthet real-world data sets. result show model achiev state-of-the-art performance.",
    "recent year seen surg interest meta-learn techniqu tackl few-shot learn (fsl) problem. however, meta-learn prone overfit sinc avail samples, identifi sampl nois clean dataset. moreover, handl data noisi labels, meta-learn could extrem sensit label nois corrupt dataset. address two challenges, present eigen-reptil (er) updat meta-paramet main direct histor task-specif paramet allevi sampl label noise. specifically, main direct comput fast way, scale calcul matrix relat number gradient step instead number parameters. furthermore, obtain accur main direct eigen-reptil presenc mani noisi labels, propos introspect self-pac learn (ispl). theoret experiment demonstr sound effect propos eigen-reptil ispl. particularly, experi differ task show propos method abl outperform achiev highli competit perform compar gradient-bas method without noisi labels. code data propos method provid research purpos https://github.com/anfeather/eigen-reptile.",
    "gener featur match network (gfmn) approach train implicit gener model imag perform moment match featur pre-train neural networks. paper, present new gfmn formul effect sequenti data. experiment result show effect propos method, seqgfmn, three distinct gener task english: uncondit text generation, class-condit text generation, unsupervis text style transfer. seqgfmn stabl train outperform variou adversari approach text gener text style transfer.",
    "messag pass graph neural network (gnns) provid power model framework relat data. however, express power exist gnn upper-bound 1-weisfeiler-lehman (1-wl) graph isomorph test, mean gnn abl predict node cluster coeffici shortest path distances, cannot differenti differ d-regular graphs. develop class messag pass gnns, name identity-awar graph neural network (id-gnns), greater express power 1-wl test. id-gnn offer minim power solut limit exist gnns. id-gnn extend exist gnn architectur induct consid nodes' ident messag passing. emb given node, id-gnn first extract ego network center node, conduct round heterogen messag passing, differ set paramet appli center node surround node ego network. propos simplifi faster version id-gnn inject node ident inform augment node features. altogether, version id-gnn repres gener extens messag pass gnns, experi show transform exist gnn id-gnn yield averag 40% accuraci improv challeng node, edge, graph properti predict tasks; 3% accuraci improv node graph classif benchmarks; 15% roc auc improv real-world link predict tasks. additionally, id-gnn demonstr improv compar perform task-specif graph networks.",
    "studi gener multi-arm bandit problem multipl play cost associ pull arm agent budget time dictat much expect spend. deriv asymptot regret lower bound uniformli effici algorithm setting. studi variant thompson sampl bernoulli reward variant kl-ucb single-paramet exponenti famili bounded, finit support rewards. show algorithm asymptot optimal, rateand lead problem-depend constants, includ thick margin set multipl arm fall decis boundary.",
    "studi gener perform onlin learn algorithm train sampl come depend sourc data. show gener error stabl onlin algorithm concentr around regret--an easili comput statist onlin perform algorithm--when underli ergod process $\\beta$- $\\phi$-mixing. show high probabl error bound assum loss function convex, also establish sharp converg rate deviat bound strongli convex loss sever linear predict problem linear logist regression, least-squar svm, boost depend data. addition, result straightforward applic stochast optim depend data, analysi requir martingal converg arguments; need reli power statist tool empir process theory.",
    "paper present end-to-end radar odometri system deliv robust, real-tim pose estim base learn embed space free sens artefact distractor objects. system deploy fulli differentiable, correlation-bas radar match approach. provid level interpret establish scan-match method allow principl deriv uncertainti estimates. system train (self-)supervis way use previous obtain pose inform train signal. use 280km urban drive data, demonstr approach outperform previou state-of-the-art radar odometri reduc error 68% whilst run order magnitud faster.",
    "graph fundament abstract model relat data. however, graph discret combinatori nature, learn represent suitabl machin learn task pose statist comput challenges. work, propos graphite, algorithm framework unsupervis learn represent node larg graph use deep latent variabl gener models. model parameter variat autoencod (vae) graph neural networks, use novel iter graph refin strategi inspir low-rank approxim decoding. wide varieti synthet benchmark datasets, graphit outperform compet approach task densiti estimation, link prediction, node classification. finally, deriv theoret connect messag pass graph neural network mean-field variat inference.",
    "use deep learn base approach predict whether select element mobil ui screenshot perceiv user tappable, base pixel instead view hierarchi requir previou work. help design better understand model predict provid action design feedback predict alone, addit use ml interpret techniqu help explain output model. use xrai highlight area input screenshot strongli influenc tappabl predict select region, use k-nearest neighbor present similar mobil ui dataset oppos influenc tappabl perception.",
    "glaucoma seriou ocular disord screen diagnosi carri examin optic nerv head (onh). color fundu imag (cfi) common modal use ocular screening. cfi, central r",
    "manifold learn occupi vital role field nonlinear dimension reduct idea also serv relev methods. graph-bas method graph convolut network (gcn) show idea common manifold learning, although belong differ fields. inspir gcn, introduc neighbor propag lle propos local neighbor propag embed (lnpe). linear comput complex increas compar lle, lnpe enhanc local connect interact neighborhood extend $1$-hop neighbor $n$-hop neighbors. experiment result show lnpe could obtain faith robust embed better topolog geometr properties.",
    "suggest development psycholog literatur commun affect mother infant correl socioemot cognit develop infants. study, obtain day-long audio record 10 mother-inf pair order studi affect commun speech focu mother' speech. order build model speech emot detection, use ryerson audio-visu databas emot speech song (ravdess) train convolut neural net model abl classifi 6 differ emot 70% accuracy. appli model mother' speech found domin emot angri sad, true. base observations, conclud emot speech databas made help actor cannot gener well real-lif settings, suggest activ learn unsupervis approach future.",
    "onlin advertis industry, process design ad creativ (i.e., ad text image) requir manual labor. typically, advertis launch multipl creativ via onlin a/b test infer effect creativ target audience, refin iter fashion. due manual natur process, time-consum learn, refine, deploy modifi creatives. sinc major ad platform typic run a/b test multipl advertis parallel, explor possibl collabor learn ad creativ refin via a/b test multipl advertisers. particular, given input ad creative, studi approach refin given ad text imag by: (i) gener new ad text, (ii) recommend keyphras new ad text, (iii) recommend imag tag (object image) select new ad image. base a/b test conduct multipl advertisers, form pairwis exampl inferior superior ad creatives, use pair train model tasks. gener new ad text, demonstr efficaci encoder-decod architectur copi mechanism, allow word (inferior) input text copi output incorpor new word associ higher click-through-rate. keyphras imag tag recommend task, demonstr efficaci deep relev match model, well rel robust rank approach compar ad text gener cold-start scenario unseen advertisers.",
    "also share broadli applic insight experi use data yahoo gemini ad platform.",
    "revisit fundament problem predict expert advice, set environ benign gener loss stochastically, feedback observ learner subject moder adversari corruption. prove variant classic multipl weight algorithm decreas step size achiev constant regret set perform optim wide rang environments, regardless magnitud inject corruption. result reveal surpris dispar often compar follow regular leader (ftrl) onlin mirror descent (omd) frameworks: show expert corrupt stochast regime, regret perform omd fact strictli inferior ftrl.",
    "say algorithm batch size-invari chang batch size larg compens chang hyperparameters. stochast gradient descent well-known properti small batch sizes, via learn rate. however, polici optim algorithm (such ppo) property, control size polici updates. work show make algorithm batch size-invariant. key insight decoupl proxim polici (use control polici updates) behavior polici (use off-polici corrections). experi help explain algorithm work, addit show make effici use stale data.",
    "standard l-bfg method reli gradient approxim domin noise, search direct descent directions, line search reliable, quasi-newton updat yield use quadrat model object function. appear call full batch approach, sinc small batch size give rise faster algorithm better gener properties, l-bfg current consid algorithm choic large-scal machin learn applications. one need not, however, choos two extrem repres full batch highli stochast regimes, may instead follow progress batch approach sampl size increas cours optimization. paper, present new version l-bfg algorithm combin three basic compon - progress batching, stochast line search, stabl quasi-newton updat - perform well train logist regress deep neural networks. provid support converg theori method.",
    "learn spatio-tempor data numer applic human-behavior analysis, object tracking, video compression, physic simulation.however, exist method still perform poorli challeng video task long-term forecasting. kind challeng task requir learn long-term spatio-tempor correl video sequence. paper, propos higher-ord convolut lstm model effici learn correlations, along succinct represent history. accomplish novel tensor train modul perform predict combin convolut featur across time. make feasibl term comput memori requirements, propos novel convolut tensor-train decomposit higher-ord model. decomposit reduc model complex jointli approxim sequenc convolut kernel asa low-rank tensor-train factorization. result, model outperform exist approaches, use fraction parameters, includ baselin models.our result achiev state-of-the-art perform wide rang applic datasets, includ multi-step video predict moving-mnist-2and kth action dataset well earli activ recognit something-someth v2 dataset.",
    "machin learn (ml) security, attack like evasion, model steal membership infer gener studi individually. previou work also shown relationship attack decis function curvatur target model. consequently, studi ml model allow direct control decis surfac curvature: gaussian process classifi (gpcs). evasion, find chang gpc' curvatur robust one attack algorithm boil enabl differ norm attack algorithm succeed. back formal analysi show static secur guarante oppos learning. concern intellectu property, show formal lazi learn necessarili leak inform applied. practice, often seemingli secur curvatur found. example, abl secur gpc empir membership infer proper configuration. configuration, however, gpc' hyper-paramet leaked, e.g. model revers engin succeeds. conclud attack classif studi isolation, relat other.",
    "emerg industri cyber-phys system (icpss), joint design commun control sub-system essential, sub-system interconnected. paper, studi joint design problem event-trigg control energy-effici resourc alloc fifth gener (5g) wireless network. formal state problem multi-object optim one, aim minim number updat actuators' input power consumpt downlink transmission. address problem, propos model-fre hierarch reinforc learn approach \\textcolor{blue}{with uniformli ultim bounded stabil guarantee} learn four polici simultaneously. polici contain updat time polici actuators' input, control policy, energy-effici sub-carri power alloc policies. simul result show propos approach properli control simul icp significantli decreas number updat actuators' input well downlink power consumption.",
    "static analysi tool wide use vulner detect understand program complex behavior million line code. despit popularity, static analysi tool known gener excess fals positives. recent abil machin learn model understand program languag open new possibl appli static analysis. however, exist dataset train model vulner identif suffer multipl limit limit bug context, limit size, synthet unrealist sourc code. propos d2a, differenti analysi base approach label issu report static analysi tools. d2a dataset built analyz version pair multipl open sourc projects. project, select bug fix commit run static analysi version commits. issu detect before-commit version disappear correspond after-commit version, like real bug got fix commit. use d2a gener larg label dataset train model vulner identification. show dataset use build classifi identifi possibl fals alarm among issu report static analysis, henc help develop priorit investig potenti true posit first.",
    "lipschitz constant neural network explor variou context deep learning, provabl adversari robustness, estim wasserstein distance, stabilis train gans, formul invert neural networks. work focus bound lipschitz constant fulli connect convolut networks, compos linear map pointwis non-linearities. paper, investig lipschitz constant self-attention, non-linear neural network modul wide use sequenc modelling. prove standard dot-product self-attent lipschitz unbound input domain, propos altern l2 self-attent lipschitz. deriv upper bound lipschitz constant l2 self-attent provid empir evid asymptot tightness. demonstr practic relev theoret work, formul invert self-attent use transformer-bas architectur character-level languag model task.",
    "tempor link prediction, one crucial work tempor graphs, attract lot attent research area. wsdm cup 2022 seek solut predict exist probabl edg within time span tempor graph. paper introduc solut antgraph, win 1st place competition. first analysi theoret upper-bound perform remov tempor information, impli structur attribut inform graph could achiev great performance. base hypothesis, introduc sever well-design features. finally, experi conduct competit dataset show superior proposal, achiev auc score 0.666 dataset 0.902 dataset b, ablat studi also prove effici feature. code publicli avail https://github.com/im0qianqian/wsdm2022tgp-antgraph.",
    "studi problem differenti privat stochast convex optim (dp-sco) heavy-tail data. specifically, focu $\\ell_1$-norm linear regress $\\epsilon$-dp model. previou work focus case loss function lipschitz, need assum variat bound moments. firstly, studi case $\\ell_2$ norm data bound second order moment. propos algorithm base exponenti mechan show possibl achiev upper bound $\\tilde{o}(\\sqrt{\\frac{d}{n\\epsilon}})$ (with high probability). next, relax assumpt bound $\\theta$-th order moment $\\theta\\in (1, 2)$ show possibl achiev upper bound $\\tilde{o}(({\\frac{d}{n\\epsilon}})^\\frac{\\theta-1}{\\theta})$. algorithm also extend relax case coordin data bound moments, get upper bound $\\tilde{o}({\\frac{d}{\\sqrt{n\\epsilon}}})$ $\\tilde{o}({\\frac{d}{({n\\epsilon})^\\frac{\\theta-1}{\\theta}}})$ second $\\theta$-th moment case respectively.",
    "present infomotif, new semi-supervised, motif-regularized, learn framework graphs. overcom two key limit messag pass popular graph neural network (gnns): local (a k-layer gnn cannot util featur outsid k-hop neighborhood label train nodes) over-smooth (structur indistinguishable) representations. propos concept attribut structur role node base occurr differ network motifs, independ network proximity. two node share attribut structur role particip topolog similar motif instanc co-vari set attributes. further, infomotif achiev architectur independ regular node represent arbitrari gnn via mutual inform maximization. train curriculum dynam priorit multipl motif learn process without reli distribut assumpt underli graph learn task. integr three state-of-the-art gnn framework, show signific gain (3-10% accuracy) across six diverse, real-world datasets. see stronger gain node spars train label divers attribut local neighborhood structures.",
    "remain puzzl deep neural network (dnns), paramet samples, often gener well. attempt understand puzzl discov implicit bias underli train process dnns, frequenc principl (f-principle), i.e., dnn often fit target function low high frequencies. inspir f-principle, propos effect model linear f-principl (lfp) dynam accur predict learn result two-lay relu neural network (nns) larg widths. lfp dynam ration linear mean field residu dynam nns. importantly, long-tim limit solut lfp dynam equival solut constrain optim problem explicitli minim fp-norm, higher frequenc feasibl solut heavili penalized. use optim formulation, priori estim gener error bound provided, reveal higher fp-norm target function increas gener error. overall, explicit implicit bia f-principl explicit penalti two-lay nns, work make step toward quantit understand learn gener gener dnns.",
    "given increasingli seriou air pollut problem, monitor air qualiti index (aqi) urban area drawn consider attention. paper present imgsensingnet, vision guid aerial-ground sens system, fine-grain air qualiti monitor forecast use fusion haze imag taken unmanned-aerial-vehicl (uav) aqi data collect on-ground three-dimension (3d) wireless sensor network (wsn). specifically, imgsensingnet first leverag comput vision techniqu tell aqi scale differ region taken haze images, haze-relev featur deep convolut neural network (cnn) design direct learn haze imag correspond aqi scale. base learnt aqi scale, imgsensingnet determin whether wake on-ground wireless sensor small-scal aqi monitor inference, greatli reduc energi consumpt system. entropy-bas model employ accur real-tim aqi infer unmeasur locat futur air qualiti distribut forecasting. implement evalu imgsensingnet two univers campus sinc feb. 2018, collect 17,630 photo 2.6 million aqi data samples. experiment result confirm imgsensingnet achiev higher infer accuraci greatli reduc energi consumption, compar state-of-the-art aqi monitor approaches.",
    "studi propos multivari kernel densiti estim stagewis minim algorithm base $u$-diverg simpl dictionary. dictionari consist appropri scalar bandwidth matrix part origin data. result estim bring us data-adapt weight paramet bandwidth matrices, realiz spars represent kernel densiti estimation. develop non-asymptot error bound estim obtain via propos stagewis minim algorithm. confirm simul studi propos estim perform competit sometim better well-known densiti estimators.",
    "inform theory, optim problem result convex optim problem strictli convex function probabl densities. note, studi problem show condit minim uniqu minim exist minimizer.",
    "sever method predict uncertainti deep network recent proposed, readili translat larg complex datasets. paper util simplifi form mixtur densiti network (mdns) produc one-shot approach quantifi uncertainti regress problems. show uncertainti bound on-par better report exist methods. appli standard regress benchmark datasets, show improv predict log-likelihood root-mean-square-error compar exist state-of-the-art methods. also demonstr method' efficaci stochastic, highli volatil time-seri data stock price predict next time interval. result uncertainti graph summar signific anomali stock price chart. furthermore, appli method task age estim challeng imdb-wiki dataset half million face images. success predict uncertainti associ predict empir analyz underli caus uncertainties. uncertainti quantif use pre-process low qualiti dataset enabl learning.",
    "comput simul often involv qualit numer inputs. exist gaussian process (gp) method handl mainli assum differ respons surfac combin level qualit factor relat via multirespons cross-covari matrix. introduc substanti differ approach map qualit factor underli numer latent variabl (lv), map valu level estim similarli correl parameters. provid parsimoni gp parameter treat qualit factor numer variabl view effect respons via similar physic mechanisms. strong physic justification, effect qualit factor physics-bas simul model must alway due underli numer variables. even underli variabl many, suffici dimens reduct argument impli effect repres low-dimension lv. conjectur support superior predict perform observ across varieti examples. moreover, map lv provid substanti insight natur effect qualit factors.",
    "multi-subject fmri data analysi interest challeng problem human brain decod studies. inher anatom function variabl across subject make necessari anatom function align classif analysis. besides, come big data, time complex becom problem cannot ignored. paper propos gradient hyperalign (gradient-ha) gradient-bas function align method suitabl multi-subject fmri dataset larg amount sampl voxels. advantag gradient-ha solv independ high dimens problem use independ compon analysi (ica) stochast gradient ascent (sga). valid use multi-classif task big data demonstr gradient-ha method less time complex better compar perform compar state-of-the-art function align methods.",
    "paper, propos method obtain sentence-level embeddings. problem secur word-level embed well studied, propos novel method obtain sentence-level embeddings. obtain simpl method context solv paraphras gener task. use sequenti encoder-decod model gener paraphrase, would like gener paraphras semant close origin sentence. one way ensur ad constraint true paraphras embed close unrel paraphras candid sentenc embed far. ensur use sequenti pair-wis discrimin share weight encod train suitabl loss function. loss function penal paraphras sentenc embed distanc large. loss use combin sequenti encoder-decod network. also valid method evalu obtain embed sentiment analysi task. propos method result semant embed outperform state-of-the-art paraphras gener sentiment analysi task standard datasets. result also shown statist significant.",
    "machin learning-bas program analys recent shown promis integr formal probabilist reason toward aid softwar development. however, absenc larg annot corpora, train analys challenging. toward address this, present buglab, approach self-supervis learn bug detect repair. buglab co-train two models: (1) detector model learn detect repair bug code, (2) selector model learn creat buggi code detector use train data. python implement buglab improv 30% upon baselin method test dataset 2374 real-lif bug find 19 previous unknown bug open-sourc software.",
    "relat represent learn late receiv increas interest due flexibl model varieti system like interact particles, materi industri project for, e.g., design spacecraft. promin method deal relat data knowledg graph embed algorithms, entiti relat knowledg graph map low-dimension vector space preserv semant structure. recently, graph embed method propos map graph element tempor domain spike neural networks. however, reli encod graph element popul neuron spike once. here, present model allow us learn spike train-bas embed knowledg graphs, requir one neuron per graph element fulli util tempor domain spike patterns. code scheme implement arbitrari spike neuron model long gradient respect spike time calculated, demonstr integrate-and-fir neuron model. general, present result show relat knowledg integr spike-bas systems, open possibl merg event-bas comput relat data build power energi effici artifici intellig applic reason systems.",
    "work propos low-pow high-accuraci embed hand-gestur recognit algorithm target battery-oper wearabl devic use low power short-rang radar sensors. 2d convolut neural network (cnn) use rang frequenc doppler featur combin tempor convolut neural network (tcn) time sequenc prediction. final algorithm model size 46 thousand parameters, yield memori footprint 92 kb. two dataset contain 11 challeng hand gestur perform 26 differ peopl record contain total 20,210 gestur instances. 11 hand gestur dataset, accuraci 86.6% (26 users) 92.4% (singl user) achieved, compar state-of-the-art, achiev 87% (10 users) 94% (singl user), use tcn-base network 7500x smaller state-of-the-art. furthermore, gestur recognit classifi implement parallel ultra-low power processor, demonstr real-tim predict feasibl 21 mw power consumpt full tcn sequenc predict network, system-level power consumpt less 100 mw achieved. provid open-sourc access code data collect use work tinyradar.ethz.ch.",
    "much work design convolut network last five year revolv around empir investig import depth, filter sizes, number featur channels, recent studi shown branching, i.e., split comput along parallel distinct thread aggreg outputs, repres new promis dimens signific improv performance. combat complex design choic multi-branch architectures, prior work adopt simpl strategies, fix branch factor, input fed parallel branches, addit combin output produc branch aggreg points. work remov predefin choic propos algorithm learn connect branch network. instead chosen priori human designer, multi-branch connect learn simultan weight network optim singl loss function defin respect end task. demonstr approach problem multi-class imag classif use three differ dataset yield consist higher accuraci compar state-of-the-art \"resnext\" multi-branch network given learn capacity.",
    "anomali outlier detect long-stand problem machin learning. cases, anomali detect easy, data drawn well-character distribut gaussian. however, data occupi high-dimension spaces, anomali detect becom difficult. present clam (cluster learn approxim manifolds), manifold map techniqu metric space. clam begin fast hierarch cluster techniqu induc graph cluster tree, base overlap cluster select use sever geometr topolog features. use graphs, implement chaoda (cluster hierarch anomali outlier detect algorithms), explor variou properti graph constitu cluster find outliers. chaoda employ form transfer learn base train set datasets, appli knowledg separ test set dataset differ cardinalities, dimensionalities, domains. 24 publicli avail datasets, compar chaoda (bi measur roc auc) varieti state-of-the-art unsupervis anomaly-detect algorithms. six dataset use training. chaoda outperform approach 16 remain 18 datasets. clam chaoda scale large, high-dimension \"big data\" anomaly-detect problems, gener across dataset distanc functions. sourc code clam chaoda freeli avail github https://github.com/uri-abd/clam.",
    "usag unman aerial vehicl (uavs) civil militari applic continu increas due numer advantag provid convent approaches. despit abund advantages, imper investig perform uav util consid design limitations. paper investig deploy uav swarm uav carri machin learn classif task. avoid data exchang ground-bas process nodes, feder learn approach adopt uav leader swarm member improv local learn model avoid excess air-to-ground ground-to-air communications. moreover, propos deploy framework consid stringent energi constraint uav problem class imbalance, show consid design paramet significantli improv perform uav swarm term classif accuracy, energi consumpt avail uav compar sever baselin algorithms.",
    "machin learn techniqu combin in-hom monitor technolog provid uniqu opportun autom diagnosi earli detect advers health condit long-term condit dementia. however, access suffici label train sampl integr high-quality, routin collect data heterogen in-hom monitor technolog main obstacl hinder utilis technolog real-world medicine. work present semi-supervis model continu learn routin collect in-hom observ measur data. show model process highli imbalanc dynam data make robust predict analys risk urinari tract infect (utis) dementia. uti common older adult constitut one main caus avoid hospit admiss peopl dementia (pwd). health-rel conditions, uti, lower preval individuals, classifi sporad case (i.e. rare scattered, yet import events). limit access suffici train data, without supervis learn model risk becom overfit biased. introduc probabilist semi-supervis learn framework address issues. propos method produc risk analysi score uti use routin collect data in-hom sens technologies.",
    "common belief grow medium livestream valu lie \"live\" component. paper, leverag data larg livestream platform examin belief. abl platform also allow viewer purchas record version livestream. summar valu livestream content estim demand respond price before, day of, livestream. propos gener orthogon random forest framework. framework allow us estim heterogen treatment effect presenc high-dimension confound whose relationship treatment polici (i.e., price) complex partial known. find signific dynam price elast demand tempor distanc schedul livestream day after. specifically, demand gradual becom less price sensit time livestream day inelast livestream day. post-livestream period, demand still sensit price, much less pre-livestream period. indic vlaue livestream persist beyond live component. finally, provid suggest evid like mechan drive results. qualiti uncertainti reduct pattern pre- post-livestream potenti real-tim interact creator day livestream.",
    "consid problem recov invert $n \\time n$ matrix $a$ spars $n \\time p$ random matrix $x$ base observ $y = ax$ (up scale permut column $a$ row $x$). use elementari tool theori empir process show version er-spud algorithm spielman, wang wright high probabl recov $a$ $x$ exactly, provid $p \\ge cn\\log n$, optim constant $c$.",
    "effici deep neural network (dnn) infer mobil embed devic typic involv quantiz network paramet activations. particular, mix precis network achiev better perform network homogen bitwidth size constraint. sinc choos optim bitwidth straight forward, train methods, learn them, desirable. differenti quantiz straight-through gradient allow learn quantizer' paramet use gradient methods. show suit parametr quantiz key achiev stabl train good final performance. specifically, propos parametr quantiz step size dynam range. bitwidth infer them. parametrizations, explicitli use bitwidth, consist perform worse. confirm find experi cifar-10 imagenet obtain mix precis dnn learn quantiz parameters, achiev state-of-the-art performance.",
    "maximum entropi (maxent) method larg number applic theoret appli machin learning, sinc provid conveni non-parametr tool estim unknown probabilities. method major contribut statist physic probabilist inference. however, systemat approach toward valid limit current missing. studi maxent bayesian decis theori set-up, i.e. assum exist well-defin prior dirichlet densiti unknown probabilities, averag kullback-leibl (kl) distanc employ decid qualiti applic variou estimators. allow evalu relev variou maxent constraints, check gener applicability, compar maxent estim variou degre depend prior, viz. regular maximum likelihood (ml) bayesian estimators. show maxent appli spars data regimes, need specif type prior information. particular, maxent outperform optim regular ml provid prior rank correl estim random quantiti probabilities.",
    "multi-task learn (mtl) neural network leverag common task improv performance, often suffer task interfer reduc benefit transfer. address issu introduc rout network paradigm, novel neural network train algorithm. rout network kind self-organ neural network consist two components: router set one function blocks. function block may neural network - exampl fully-connect convolut layer. given input router make rout decision, choos function block appli pass output back router recursively, termin fix recurs depth reached. way rout network dynam compos differ function block input. employ collabor multi-ag reinforc learn (marl) approach jointli train router function blocks. evalu model cross-stitch network shared-lay baselin multi-task set mnist, mini-imagenet, cifar-100 datasets. experi demonstr signific improv accuracy, sharper convergence. addition, rout network nearli constant per-task train cost cross-stitch network scale linearli number tasks. cifar-100 (20 tasks) obtain cross-stitch perform level 85% reduct train time.",
    "paper, studi gener perform regular multi-task learn (rmtl) vector-valu framework, mtl consid learn process vector-valu functions. mainli concern two theoret questions: 1) condit rmtl perform better smaller task sampl size stl? 2) condit rmtl generaliz guarante consist task simultan learning? particular, investig two type task-group relatedness: observ discrepancy-depend measur (oddm) empir discrepancy-depend measur (eddm), detect depend two group multipl relat task (mrts). introduc cartesian product-bas uniform entropi number (cpuen) measur complex vector-valu function classes. appli specif deviat symmetr inequ vector-valu framework, obtain gener bound rmtl, upper bound joint probabl event least one task larg empir discrep expect empir risks. finally, present suffici condit guarante consist task simultan learn process, discuss task related affect gener perform rmtl. theoret find answer aforement two questions.",
    "nowaday consum loan play import role promot econom growth, credit card popular consum loan. one essenti part credit card credit limit management. traditionally, credit limit adjust base limit heurist strategies, develop experienc professionals. paper, present data-driven approach manag credit limit intelligently. firstly, condit independ test conduct acquir data build models. base test data, respons model built measur heterogen treatment effect increas credit limit (i.e. treatments) differ customers, depict sever control variabl (i.e. features). order incorpor diminish margin effect, care select log transform introduc treatment variable. moreover, model' capabl enhanc appli non-linear transform featur via gbdt encoding. finally, well-design metric propos properli measur perform compar methods. experiment result demonstr effect propos approach.",
    "uncertainti quantif (uq) import compon molecular properti prediction, particularli drug discoveri applic model predict direct experiment design unanticip imprecis wast valuabl time resources. need uq especi acut neural models, becom increasingli standard yet challeng interpret. sever approach uq propos literature, clear consensu compar perform models. paper, studi question context regress tasks. systemat evalu sever method five benchmark dataset use multipl complementari perform metrics. experi show none method test unequivoc superior others, none produc particularli reliabl rank error across multipl datasets. believ result show exist uq method suffici common use-cas demonstr benefit research, conclud practic recommend exist techniqu seem perform well rel others.",
    "extract robust gener 3d local featur key downstream task point cloud registr reconstruction. exist learning-bas local descriptor either sensit rotat transformations, reli classic handcraft featur neither gener representative. paper, introduc new, yet conceptu simple, neural architecture, term spinnet, extract local featur rotat invari whilst suffici inform enabl accur registration. spatial point transform first introduc map input local surfac care design cylindr space, enabl end-to-end optim so(2) equivari representation. neural featur extractor leverag power point-bas 3d cylindr convolut neural layer util deriv compact repres descriptor matching. extens experi indoor outdoor dataset demonstr spinnet outperform exist state-of-the-art techniqu larg margin. critically, best gener abil across unseen scenario differ sensor modalities. code avail https://github.com/qingyonghu/spinnet.",
    "previou approach lyrics-to-audio align use pre-develop automat speech recognit (asr) system innat suffer sever difficulti adapt speech model individu singers. signific aspect miss previou work self-learn repetit vowel pattern sing voice, vowel part use consist conson part. base this, system first learn discrimin subspac vowel sequences, base weight symmetr non-neg matrix factor (ws-nmf), take self-similar standard acoust featur input. then, make use canon time warp (ctw), deriv recent comput vision technique, find optim spatiotempor transform text acoust sequences. experi korean english data set show deploy method pre-developed, unsupervised, sing sourc separ achiev promis result state-of-the-art unsupervis approach exist asr-bas system.",
    "defin studi problem modular concept learning, is, learn concept cross product compon concepts. element' membership concept depend sole membership components, learn concept whole reduc learn components. analyz problem respect differ type oracl interfaces, defin differ set queries. given oracl interfac cannot answer question components, learn difficult, even compon easi learn type oracl queries. learn superset queri easy, learn membership, equivalence, subset queri harder. however, show problem becom tractabl oracl given posit exampl allow ask membership queries.",
    "multilay perceptron (mlp) class network compos multipl layer perceptrons, essenti mathemat function. base mlp, develop new numer method find extrema functionals. demonstrations, present solut three physic scenes. ideally, method applic case object curve/surfac fit second-ord differenti functions. method also extend case finit number non-differenti (but continuous) points/surfaces.",
    "latent featur model attract imag modeling, sinc imag gener contain multipl objects. however, mani latent featur model ignor object appear differ locat requir pre-segment images. transform indian buffet process (tibp) provid method model transformation-invari featur unseg binari images, current form inappropri real imag comput cost model assumptions. combin tibp likelihood appropri real imag develop effici inference, use cross-correl imag features, theoret empir faster exist infer techniques. method discov reason compon achiev effect imag reconstruct natur images.",
    "studi reinforc learn linear function approxim transit probabl reward function linear respect featur map $\\boldsymbol{\\phi}(s,a)$. specifically, consid episod inhomogen linear markov decis process (mdp), propos novel computation-effici algorithm, lsvi-ucb$^+$, achiev $\\widetilde{o}(hd\\sqrt{t})$ regret bound $h$ episod length, $d$ featur dimension, $t$ number steps. lsvi-ucb$^+$ build weight ridg regress upper confid valu iter bernstein-typ explor bonus. statist result obtain novel analyt tools, includ new bernstein self-norm bound conservat ellipt potentials, refin analysi correct term. best knowledge, first minimax optim algorithm linear mdp logarithm factors, close $\\sqrt{hd}$ gap best known upper bound $\\widetilde{o}(\\sqrt{h^3d^3t})$ \\cite{jin2020provably} lower bound $\\omega(hd\\sqrt{t})$ linear mdps.",
    "loss surfac overparameter neural network (nn) possess mani global minima zero train error. explain common variant standard nn train procedur chang minim obtained. first, make explicit size initi strongli overparameter nn affect minim deterior final test performance. propos strategi limit effect. then, demonstr adapt optim adagrad, obtain minim gener differ gradient descent (gd) minimizer. adapt minim chang stochast mini-batch training, even though non-adapt case gd stochast gd result essenti minimizer. lastly, explain effect remain relev less overparameter nns. overparameter benefits, work highlight induc sourc error absent underparameter models, challeng control.",
    "haptic guidanc share steer assist system drawn signific attent intellig vehicl fields, owe mutual commun abil vehicl control. exert continu torqu steer wheel, driver support system share later control vehicle. however, current haptic guidanc steer system demonstr defici assist lane changing. studi explor new steer interact method, includ design evalu intention-bas haptic share steer system. intention-bas method support lane keep lane chang assistance, detect driver lane chang intention. use deep learning-bas method model driver decis time regard lane crossing, adapt gain control method propos realiz steer control system. intent consist method propos detect whether driver system act toward target trajectori accur captur driver intention. drive simul experi conduct test system performance. particip requir perform six trial assist method one trial without assistance. result demonstr support system decreas lane departur risk lane keep task could support fast stabl lane chang maneuver.",
    "classif dataset two distinct class import machin learn task. mani method abl classifi binari classif task high accuraci test data, cannot provid easili interpret explan user deeper understand reason split data two classes. paper, highlight evalu recent propos nonlinear decis tree approach number commonli use classif method number dataset involv larg number features. studi reveal key issu effect classif method' paramet values, complex classifi versu achiev accuracy, interpret result classifiers.",
    "mine explor databas provid user knowledg new insights. tile data strive unveil true underli structur distinguish valuabl inform variou kind noise. propos novel boolean matrix factor algorithm solv tile problem, base recent result optim theory. contrast exist work, new algorithm minim descript length result factorization. approach well known model select data compression, find suitabl factor via numer optimization. demonstr superior robust new approach presenc sever kind nois type underli structure. moreover, gener framework work cost measur suitabl real-valu relaxation. thereby, convex assumpt met. experiment result synthet data imag data show new method identifi interpret pattern explain data almost alway better compet algorithms.",
    "consequenti decision-mak incentiv individu strateg adapt behavior specif decis rule. long line work view strateg adapt game attempt mitig effects, recent work instead sought design classifi incentiv individu improv desir quality. key account cost function dictat adapt ration undertake. work, develop causal framework strateg adaptation. causal perspect clearli distinguish game improv reveal import obstacl incent design. prove procedur design classifi incentiv improv must inevit solv non-trivi causal infer problem. moreover, show similar result hold design cost function satisfi requir previou work. benefit hindsight, result show much prior work strateg classif causal model disguise.",
    "autonom vehicl oper highli dynam environ necessit accur assess aspect scene move move to. popular approach 3d motion estimation, term scene flow, employ 3d point cloud data consecut lidar scans, although approach limit small size real-world, annot lidar data. work, introduc new large-scal dataset scene flow estim deriv correspond track 3d objects, $\\sim$1,000$\\times$ larger previou real-world dataset term number annot frames. demonstr previou work bound base amount real lidar data available, suggest larger dataset requir achiev state-of-the-art predict performance. furthermore, show previou heurist oper point cloud down-sampl heavili degrad performance, motiv new class model tractabl full point cloud. address issue, introduc fastflow3d architectur provid real time infer full point cloud. additionally, design human-interpret metric better captur real world aspect account ego-mot provid breakdown per object type. hope dataset may provid new opportun develop real world scene flow systems.",
    "improv statist learn model order increas effici solv classif regress problem still goal pursu scientif community. way, support vector machin model one success power algorithm tasks. however, perform depend directli choic kernel function hyperparameters. tradit choic them, actually, comput expens kernel choic tune processes. article, propos novel framework deal kernel function select call random machines. result improv accuraci reduc comput time. data studi perform simul data 27 real benchmark datasets.",
    "graph augment multi-lay perceptron (ga-mlp) model attract altern graph neural network (gnns). resist over-smooth problem, deeper ga-mlp model yield better performance. ga-mlp model tradit optim stochast gradient descent (sgd). however, sgd suffer layer depend problem, prevent gradient differ layer ga-mlp model calcul parallel. paper, propos parallel deep learn altern direct method multipli (pdadmm) framework achiev model parallelism: paramet layer ga-mlp model updat parallel. extend pdadmm-q algorithm reduc commun cost util quantiz technique. theoret converg critic point pdadmm algorithm pdadmm-q algorithm provid sublinear converg rate $o(1/k)$. extens experi six benchmark dataset demonstr pdadmm lead high speedup, outperform exist state-of-the-art comparison methods.",
    "human motion captur data wide use data-driven charact animation. order gener realistic, natural-look motions, data-driven approach requir consider effort pre-processing, includ motion segment annotation. exist (semi-) automat solut either requir hand-craft featur motion segment produc semant annot requir motion synthesi build large-scal motion databases. addition, human label annot data suffer inter- intra-label inconsist design. propos semi-automat framework semant segment motion captur data base supervis machin learn techniques. first transform motion captur sequenc ``motion image'' appli convolut neural network imag segmentation. dilat tempor convolut enabl extract tempor inform larg recept field. model outperform two state-of-the-art model action segmentation, well popular network sequenc modeling. all, method robust noisi inaccur train label thu handl human error label process.",
    "state-of-the-art deep learn system often requir larg amount data computation. reason, leverag known unknown structur data paramount. convolut neural network (cnns) success exampl principle, defin characterist shift-equivariance. slide filter input, input shifts, respons shift amount, exploit structur natur imag semant content independ absolut pixel positions. properti essenti success cnn audio, imag video recognit tasks. thesis, extend equivari kind transformations, rotat scaling. propos equivari model differ transform defin group symmetries. main contribut (i) polar transform networks, achiev equivari group similar plane, (ii) equivari multi-view networks, achiev equivari group symmetri icosahedron, (iii) spheric cnns, achiev equivari continu 3d rotat group, (iv) cross-domain imag embeddings, achiev equivari 3d rotat 2d inputs, (v) spin-weight spheric cnns, gener spheric cnn achiev equivari 3d rotat spheric vector fields. applic includ imag classification, 3d shape classif retrieval, panoram imag classif segmentation, shape align pose estimation. model common leverag symmetri data reduc sampl model complex improv gener performance.",
    "advantag signific (but limit to) challeng task data limit input perturb arbitrari rotat present.",
    "work studi numer construct optim clinic diagnost test detect sporad creutzfeldt-jakob diseas (scjd). cerebrospin fluid sampl (csf) suspect scjd patient subject process initi aggreg protein present case scjd. aggreg indirectli observ real-tim regular intervals, longitudin set data construct analys evid aggregation. best exist test base sole final valu set data, compar threshold conclud whether aggregation, thu scjd, present. test criterion decid upon analys data total 108 scjd non-scjd samples, done subject support mathemat analysi declar criterion exploit avail data optimally. paper address deficiency, seek valid improv test primarili via support vector machin (svm) classification. besid this, address number addit issu i) earli stop measur process, ii) possibl detect particular type scjd iii) incorpor addit patient data age, sex, diseas durat time csf sampl construct test.",
    "transfer learn (tl) promis way improv sampl effici reinforc learning. however, effici transfer knowledg across task differ state-act space investig earli stage. previou studi address inconsist across differ state space learn common featur space, without consid similar action differ action space relat task share similar semantics. paper, propos method learn action embed leverag idea, framework learn state embed action embed transfer polici across task differ state action spaces. experiment result variou task show propos method learn inform action embed acceler polici learning.",
    "sever variant stochast gradient descent (sgd) propos improv learn effect effici train deep neural networks, among recent influenti attempt would like adapt control parameter-wis learn rate (e.g., adam rmsprop). although show larg improv converg speed, adapt learn rate method suffer compromis gener compar sgd. paper, propos adapt gradient method resili momentum (adarem), motiv observ oscil network paramet slow training, give theoret proof convergence. parameter, adarem adjust parameter-wis learn rate accord whether direct one paramet chang past align direct current gradient, thu encourag long-term consist paramet updat much fewer oscillations. comprehens experi conduct verifi effect adarem train variou model large-scal imag recognit dataset, e.g., imagenet, also demonstr method outperform previou adapt learn rate-bas algorithm term train speed test error, respectively.",
    "recent hyperbol geometri proven effect build embed encod hierarch entail information. make particularli suit model complex asymmetr relationship chines charact words. paper first train larg scale hyperboloid skip-gram model chines corpus, appli charact embed downstream hyperbol transform model deriv principl gyrovector space poincar disk model. experi character-bas transform outperform word-bas euclidean equivalent. best knowledge, first time chines nlp character-bas model outperform word-bas counterpart, allow circumvent challeng domain-depend task chines word segment (cws).",
    "studi problem train person deep learn model decentr peer-to-p setting, focus set data distribut differ client differ client differ local learn tasks. studi covari label shift, contribut algorithm client find benefici collabor base similar estim local task. method reli hyperparamet hard estimate, number client clusters, rather continu adapt network topolog use soft cluster assign base novel adapt gossip algorithm. test propos method variou set data independ ident distribut among clients. experiment evalu show propos method perform better previou state-of-the-art algorithm problem setting, handl situat well previou method fail.",
    "one-shot method evolv one popular method neural architectur search (nas) due weight share singl train supernet. however, exist method gener suffer two issues: predetermin number channel layer suboptimal; model averag effect poor rank correl caus weight coupl continu expand search space. explicitli address issues, paper, broadening-and-shrink one-shot na (bs-nas) framework proposed, `broadening' refer broaden search space spring block enabl search number channel train supernet; `shrinking' refer novel shrink strategi gradual turn underperform operations. innov broaden search space wider represent shrink gradual remov underperform operations, follow evolutionari algorithm effici search optim architecture. extens experi imagenet illustr effect propos bs-na well state-of-the-art performance.",
    "propos self-supervis gaussian attent network imag cluster (gatcluster). rather extract intermedi featur first perform tradit cluster algorithm, gatclust directli output semant cluster label without post-processing. theoretically, give label featur theorem guarante learn featur one-hot encod vectors, trivial solut avoided. train gatclust complet unsupervis manner, design four self-learn task constraint transform invariance, separ maximization, entropi analysis, attent mapping. specifically, transform invari separ maxim task learn relationship sampl pairs. entropi analysi task aim avoid trivial solutions. captur object-ori semantics, design self-supervis attent mechan includ parameter attent modul soft-attent loss. guid signal cluster self-gener train process. moreover, develop two-step learn algorithm memory-effici cluster large-s images. extens experi demonstr superior propos method comparison state-of-the-art imag cluster benchmarks. code made publicli avail https://github.com/niuchuangnn/gatcluster.",
    "end-to-end deep reinforc learn enabl agent learn littl preprocess humans. however, still difficult learn stabli effici learn method usual use nonlinear function approximation. neural episod control (nec), propos order improv sampl efficiency, abl learn stabli estim action valu use non-parametr method. paper, propos architectur incorpor random project nec train stability. addition, verifi effect architectur atari' five games. main idea reduc number paramet learn replac neural network random project order reduc dimens keep learn end-to-end.",
    "machin learn model increasingli deploy high-stak domain legal financi decision-making, grow interest post-hoc method gener counterfactu explanations. explan provid individu advers impact predict outcom (e.g., applic deni loan) recours -- i.e., descript chang featur obtain posit outcome. propos novel algorithm leverag adversari train pac confid set learn model theoret guarante recours affect individu high probabl without sacrif accuracy. demonstr efficaci approach via extens experi real data.",
    "recent literatur onlin learn focus develop adapt algorithm take advantag regular sequenc observations, yet retain worst-cas perform guarantees. complementari direct develop predict method perform well complex benchmarks. paper, address two direct together. present fulli adapt method compet dynam benchmark regret guarante scale regular sequenc cost function comparators. notably, regret bound adapt smaller complex measur problem environment. finally, appli result drift zero-sum, two-play game player achiev regret guarante best sequenc action hindsight.",
    "larg part current success deep learn lie effect data -- precisely: label data. yet, label dataset human annot continu carri high costs, especi videos. imag domain, recent method allow gener meaning (pseudo-) label unlabel dataset without supervision, develop miss video domain learn featur represent current focus. work, a) show unsupervis label video dataset come free strong featur encod b) propos novel cluster method allow pseudo-label video dataset without human annotations, leverag natur correspond audio visual modalities. extens analysi show result cluster high semant overlap ground truth human labels. introduc first benchmark result unsupervis label common video dataset kinetics, kinetics-sound, vgg-sound ave.",
    "feder learn enabl global machin learn model train collabor distributed, mutual non-trust learn agent desir maintain privaci train data hardware. global model distribut clients, perform training, submit newly-train model aggreg superior model. however, feder learn system vulner interfer malici learn agent may desir prevent train induc target misclassif result global model. class byzantine-toler aggreg algorithm emerged, offer vari degre robust attacks, often caveat number attack bound quantiti known prior training. paper present simeon: novel approach aggreg appli reputation-bas iter filter techniqu achiev robust even presenc attack exhibit arbitrari behaviour. compar simeon state-of-the-art aggreg techniqu find simeon achiev compar superior robust varieti attacks. notably, show simeon toler sybil attacks, algorithm not, present key advantag approach.",
    "develop effici altern framework learn gener version factor machin (gfm) steam data provabl guarantees. instanc sampl $d$ dimension random gaussian vector target second order coeffici matrix gfm rank $k$, algorithm converg linearly, achiev $o(\\epsilon)$ recoveri error retriev $o(k^{3}d\\log(1/\\epsilon))$ train instances, consum $o(kd)$ memori one-pass dataset requir matrix-vector product oper iteration. key ingredi framework construct estim sequenc endow so-cal condit independ rip condit (ci-rip). special case gfm, framework appli symmetr asymmetr rank-on matrix sens problems, induct matrix complet phase retrieval.",
    "exist determinist variat infer approach diffus process use simpl propos target margin densiti posterior. construct variat process control version prior process approxim posterior set moment functions. combin moment closure, smooth problem reduc determinist optim control problem. exploit path-wis fisher information, propos optim procedur correspond natur gradient descent variat parameters. approach allow richer variat approxim extend state-depend diffus terms. classic gaussian process approxim recov special case.",
    "graph neural network (gnns) attract much attent due abil learn represent graph-structur data. despit success applic gnn mani domains, optim gnn less well studied, perform node classif heavili suffer long-tail node degre distribution. paper focus improv perform gnn via normalization. detail, studi long-tail distribut node degre graph, propos novel normal method gnns, term resnorm (\\textbf{res}hap long-tail distribut normal-lik distribut via \\textbf{norm}alization). $scale$ oper resnorm reshap node-wis standard deviat (nstd) distribut improv accuraci tail node (\\textit{i}.\\textit{e}., low-degre nodes). provid theoret interpret empir evid understand mechan $scale$. addit long-tail distribut issue, over-smooth also fundament issu plagu community. end, analyz behavior standard shift prove standard shift serv precondition weight matrix, increas risk over-smoothing. over-smooth issu mind, design $shift$ oper resnorm simul degree-specif paramet strategi low-cost manner. extens experi valid effect resnorm sever node classif benchmark datasets.",
    "deep neural network shown great success low dose ct denoising. however, deep neural network sever hundr thousand trainabl parameters. this, combin inher non-linear neural network, make deep neural network diffcult understand low accountability. studi introduc jbfnet, neural network low dose ct denoising. architectur jbfnet implement iter bilater filtering. filter function joint bilater filter (jbf) learn via shallow convolut networks. guidanc imag estim deep neural network. jbfnet split four filter blocks, perform joint bilater filtering. jbf block consist 112 trainabl parameters, make nois remov process comprehendable. nois map (nm) ad filter preserv high level features. train jbfnet data bodi scan 10 patients, test aapm low dose ct grand challeng dataset. compar jbfnet state-of-the-art deep learn networks. jbfnet outperform cpce3d, gan deep gfnet test dataset term nois remov preserv structures. conduct sever ablat studi test perform network architectur train method. current setup achiev best performance, still maintain behaviour accountability.",
    "deep neural network achiev outstand perform variou tasks, critic issue: over-confid predict even complet unknown samples. mani studi propos success filter unknown samples, consid narrow specif tasks, refer misclassif detection, open-set recognition, out-of-distribut detection. work, argu task treat fundament ident problem ideal model possess detect capabl tasks. therefore, introduc unknown detect task, integr previou individu tasks, rigor examin detect capabl deep neural network wide spectrum unknown samples. end, unifi benchmark dataset differ scale construct unknown detect capabl exist popular method subject comparison. found deep ensembl consist outperform approach detect unknowns; however, method success specif type unknown. reproduc code benchmark dataset avail https://github.com/daintlab/unknown-detection-benchmark .",
    "decision-mak increasingli reli machin learn (ml) (big) data, issu fair data-driven artifici intellig (ai) system receiv increas attent research industry. larg varieti fairness-awar machin learn solut propos involv fairness-rel intervent data, learn algorithm and/or model outputs. however, vital part propos new approach evalu empir benchmark dataset repres realist divers settings. therefore, paper, overview real-world dataset use fairness-awar machin learning. focu tabular data common data represent fairness-awar machin learning. start analysi identifi relationship differ attributes, particularli w.r.t. protect attribut class attribute, use bayesian network. deeper understand bia datasets, investig interest relationship use exploratori analysis.",
    "robust bodi reinforc learn techniqu develop solv complex sequenti decis make problems. however, method assum train evalu task come similarli ident distribut environments. assumpt hold real life small novel chang environ make previous learn polici fail introduc simpler solut might never found. end explor concept {\\em novelty}, defin work sudden chang mechan properti environment. provid ontolog novelti relev sequenti decis making, distinguish novelti affect object versu actions, unari properti versu non-unari relations, distribut solut task. introduc novgrid, novelti gener framework built minigrid, act toolkit rapidli develop evalu novelty-adaptation-en reinforc learn techniques. along core novgrid provid exemplar novelti align ontolog instanti novelti templat appli mani minigrid-compli environments. finally, present set metric built framework evalu novelty-adaptation-en machine-learn techniques, show characterist baselin rl model use metrics.",
    "standard techniqu onlin learn combinatori object perform multipl updat follow project convex hull objects. however, methodolog expens convex hull contain mani facets. example, convex hull $n$-symbol huffman tree known exponenti mani facet (maurra et al., 2010). get around difficulti exploit extend formul (kaibel, 2011), encod polytop combinatori object higher dimension \"extended\" space polynomi mani facets. develop gener framework convert extend formul effici onlin algorithm good rel loss bounds. present applic framework onlin learn huffman tree permutations. regret bound result algorithm within factor $o(\\sqrt{\\log(n)})$ state-of-the-art special algorithm permutations, depend loss regimes, improv match state-of-the-art huffman trees. method gener appli combinatori objects.",
    "one barrier widespread adopt differenti privat neural network entail accuraci loss. address issue, relationship neural network architectur model accuraci differenti privaci constraint need better understood. first step, test whether extant knowledg architectur design also hold differenti privat setting. find show not; architectur perform well without differenti privacy, necessarili differenti privacy. consequently, extant knowledg neural network architectur design cannot seamlessli translat differenti privaci context. futur research requir better understand relationship neural network architectur model accuraci enabl better architectur design choic differenti privaci constraints.",
    "smooth classifi probabl densiti function gaussian kernel appear unrelated, work, unifi problem robust classification. key build block approxim $\\textit{energi function}$ random variabl $y=x+n(0,\\sigma^2 i_d)$ neural network use formul problem robust classif term $\\widehat{x}(y)$, $\\textit{bay estimator}$ $x$ given noisi measur $y$. introduc $\\textit{empir bay smooth classifiers}$ within framework $\\textit{random smoothing}$ studi theoret two-class linear classifier, show one improv robust $\\textit{th margin}$. test theori mnist show learn smooth energi function linear classifi achiev provabl $\\ell_2$ robust accuraci competit empir defenses. setup significantli improv $\\textit{learning}$ empir bay smooth classifi adversari train mnist show achiev provabl robust accuraci higher state-of-the-art empir defens rang radii. discuss fundament challeng random smooth base geometr interpret due concentr gaussian high dimensions, finish paper propos use walk-jump sampling, base learn smooth densities, robust classification.",
    "take new look paramet estim gaussian mixtur model (gmms). particular, propos use \\emph{riemannian manifold optimization} power counterpart expect maxim (em). out-of-the-box invoc manifold optimization, however, fail spectacularly: converg solut vastli slower. driven intuit manifold convexity, propos reparamer remark empir consequences. make manifold optim match em---a highli encourag result given poor record nonlinear program method em far---but also outperform em mani practic settings, display much less variabl run times. highlight strength manifold optim develop somewhat tune manifold lbfg method prove even competit reliabl exist manifold optim tools. hope result encourag wider consider manifold optim paramet estim problems.",
    "adversari exampl input machin learn model design attack caus model make mistakes. paper, demonstr adversari exampl also util good improv perform imbalanc learning. provid new perspect deal imbalanc data: adjust bias decis boundari train guid adversari exampl (gaes). method effect increas accuraci minor class sacrif littl accuraci major classes. empir show, sever benchmark datasets, propos method compar state-of-the-art method. best knowledge, first deal imbalanc learn adversari examples.",
    "convolut neural network (cnns) dilat filter wavenet tempor convolut network (tcn) shown good result varieti sequenc model tasks. however, effici model long-term depend sequenc still challenging. although recept field model grow exponenti number layers, comput convolut long sequenc featur layer time memory-intensive, prohibit use longer recept field practice. increas efficiency, make use \"slow feature\" hypothesi state mani featur interest slowli vari time. this, use u-net architectur comput featur multipl time-scal adapt auto-regress scenario make convolut causal. appli model (\"seq-u-net\") varieti task includ languag audio generation. comparison tcn wavenet, network consist save memori comput time, speed-up train infer 4x audio gener experi particular, achiev compar perform tasks.",
    "invers reinforc learn address problem infer expert' reward function demonstrations. however, mani applications, access expert' near-optim behavior, also observ part learn process. paper, propos new algorithm setting, goal recov reward function optim agent, given sequenc polici produc learning. approach base assumpt observ agent updat polici paramet along gradient direction. extend method deal realist scenario access dataset learn trajectories. settings, provid theoret insight algorithms' performance. finally, evalu approach simul gridworld environ mujoco environments, compar state-of-the-art baseline.",
    "work, develop distribut least squar approxim (dlsa) method abl solv larg famili regress problem (e.g., linear regression, logist regression, cox' model) distribut system. approxim local object function use local quadrat form, abl obtain combin estim take weight averag local estimators. result estim prove statist effici global estimator. moreover, requir one round communication. conduct shrinkag estim base dlsa estim use adapt lasso approach. solut easili obtain use lar algorithm master node. theoret shown result estim possess oracl properti select consist use newli design distribut bayesian inform criterion (dbic). finit sampl perform comput effici illustr extens numer studi airlin dataset. airlin dataset 52 gb size. entir methodolog implement python {\\it de-facto} standard spark system. propos dlsa algorithm spark system take 26 minut obtain logist regress estimator, effici memori friendli convent methods.",
    "heart diseas becom one seriou diseas signific impact human life. emerg one lead caus mortal among peopl across globe last decade. order prevent patient damage, accur diagnosi heart diseas time essenti factor. recent seen usag non-invas medic procedures, artifici intelligence-bas techniqu field medical. special machin learn employ sever algorithm techniqu wide use highli use accur diagnos heart diseas less amount time. however, predict heart diseas easi task. increas size medic dataset made complic task practition understand complex featur relat make diseas predictions. accordingly, aim research identifi import risk-factor highli dimension dataset help accur classif heart diseas less complications. broader analysis, use two heart diseas dataset variou medic features. classif result benchmark model prove high impact relev featur classif accuracy. even reduc number features, perform classif model improv significantli reduc train time compar model train full featur set.",
    "embed system demand on-devic process data use neural network (nns) conform memory, power comput constraints, lead effici accuraci tradeoff. bring nn edg devices, sever optim model compress pruning, quantization, off-the-shelf architectur effici design extens adopted. algorithm deploy real world sensit applications, requir resist infer attack protect privaci user train data. however, resist infer attack account design nn model iot. work, analys three-dimension privacy-accuracy-effici tradeoff nn iot devic propos gecko train methodolog explicitli add resist privat infer design objective. optim inference-tim memory, computation, power constraint embed devic criterion design nn architectur also preserv privacy. choos quantiz design choic highli effici privat models. choic driven observ compress model leak inform compar baselin model off-the-shelf effici architectur indic poor effici privaci tradeoff. show model train use gecko methodolog compar prior defenc black-box membership attack term accuraci privaci provid efficiency.",
    "adversari imit learn (ail) class popular state-of-the-art imit learn algorithm artifici adversary' misclassif use reward signal optim standard reinforc learn (rl) algorithm. unlik rl settings, reward ail differenti model-fre rl algorithm make use properti train policy. contrast, leverag differenti properti ail reward function formul class actor residu critic (arc) rl algorithm draw parallel standard actor-crit (ac) algorithm rl literatur use residu critic, c function (instead standard q function) approxim discount futur return (exclud immedi reward). arc algorithm similar converg properti standard ac algorithm addit advantag gradient immedi reward exact. discret (tabular) case finit states, actions, known dynamics, prove polici iter $c$ function converg optim policy. continu case function approxim unknown dynamics, experiment show arc aid ail outperform standard ail simul continuous-control real robot manipul tasks. arc algorithm simpl implement incorpor exist ail implement ac algorithm.",
    "dna-encod librari (del) screen quantit structure-act relationship (qsar) model two techniqu use drug discoveri find small molecul bind protein target. appli qsar model del data facilit select compound off-dna synthesi evaluation. combin approach shown recent train binari classifi learn del enrich aggreg \"disynthons\" accommod spars noisi natur del data. however, binari classifi cannot distinguish differ level enrichment, inform potenti lost disynthon aggregation. here, demonstr regress approach learn del enrich individu molecul use custom neg log-likelihood loss function effect denois del data introduc opportun visual learn structure-act relationship (sar). approach explicitli model poisson statist sequenc process use del experiment workflow frequentist view. illustr approach dataset 108k compound screen caix, dataset 5.7m compound screen seh sirt2. due treatment uncertainti data neg log-likelihood loss function, model ignor low-confid outliers. approach demonstr benefit extrapol novel structures, expect denois visual pipelin use identifi sar trend enrich pharmacophor del data.",
    "further, approach uncertainty-awar regress applic spars noisi dataset natur stochast known modeled; particular, poisson enrich ratio metric use appli set compar sequenc count data two experiment conditions.",
    "paper visualenv, new tool creat visual environ reinforc learn introduced. product integr open-sourc model render software, blender, python modul use gener environ model simulation, openai gym. visualenv allow user creat custom environ photorealist render capabl full integr python. framework describ test seri exampl problem showcas featur train reinforc learn agents.",
    "bia data unintend consequ propag design, development, deploy machin learn models. financi servic sector, result discrimin certain financi instrument services. time, data privaci paramount importance, recent data breach seen reput damag larg institutions. present paper trust model-lifecycl manag platform attempt ensur consum data protection, anonymization, fairness. specifically, examin dataset reproduc use deep learn techniqu effect retain import statist featur dataset whilst simultan protect data privaci enabl safe secur share sensit person inform beyond current state-of-practice.",
    "study, take departur explor explainability-driven strategi data auditing, action insight data hand discov eye quantit explain behaviour dummi model prototyp expos data. demonstr strategi audit two popular medic benchmark datasets, discov hidden data qualiti issu lead deep learn model make predict wrong reasons. action insight gain explain driven data audit strategi leverag address discov issu enabl creation high-perform deep learn model appropri predict behaviour. hope explainability-driven strategi complimentari data-driven strategi facilit respons develop machin learn algorithm comput vision applications.",
    "variat autoencod (vaes), well gener models, shown effici accur captur latent structur vast amount complex high-dimension data. however, exist vae still directli handl data heterogen (mix continu discrete) incomplet (with miss data random), inde common real-world applications. paper, propos gener framework design vae suitabl fit incomplet heterogen data. propos hi-va includ likelihood model real-valued, posit real valued, interval, categorical, ordin count data, allow accur estim (and potenti imputation) miss data. furthermore, hi-va present competit predict perform supervis tasks, outperform supervis model train incomplet data.",
    "reinforc learn algorithm highli sensit choic hyperparameters, typic requir signific manual effort identifi hyperparamet perform well new domain. paper, take step toward address issu use metagradi automat adapt hyperparamet onlin meta-gradi descent (xu et al., 2018). appli algorithm, self-tun actor-crit (stac), self-tun differenti hyperparamet actor-crit loss function, discov auxiliari tasks, improv off-polici learn use novel leaki v-trace operator. stac simpl use, sampl effici requir signific increas compute. abl studi show overal perform stac improv adapt hyperparameters. appli arcad learn environ (bellemar et al. 2012), stac improv median human normal score 200m step 243% 364%. appli dm control suit (tassa et al., 2018), stac improv mean score 30m step 217 389 learn features, 108 202 learn pixels, 195 295 real-world reinforc learn challeng (dulac-arnold et al., 2020).",
    "simplex-valu data appear throughout statist machin learning, exampl context transfer learn compress deep networks. exist model class data reli dirichlet distribut relat loss functions; show standard choic suffer systemat number limitations, includ bia numer issu frustrat use flexibl network model upstream distributions. resolv limit introduc novel exponenti famili distribut model simplex-valu data - continu categorical, aris nontrivi multivari gener recent discov continu bernoulli. unlik dirichlet typic choices, continu categor result well-behav probabilist loss function produc unbias estimators, preserv mathemat simplic dirichlet. well explor theoret properties, introduc sampl method distribut amen reparameter trick, evalu performance. lastly, demonstr continu categor outperform standard choic empirically, across simul study, appli exampl multi-parti elections, neural network compress task.",
    "multimod analysi use numer time seri textual corpora input data sourc becom promis approach, especi financi industry. however, main focu analysi achiev high predict accuraci littl effort spent import task understand associ two data modalities. perform time seri henc receiv littl explan though human-understand textual inform available. work, address problem given numer time series, gener corpu textual stori collect period time series, task time discov succinct set textual stori associ time series. toward goal, propos novel multi-mod neural model call msin jointli learn numer time seri categor text articl order unearth associ them. multipl step data interrel two data modalities, msin learn focu small subset text articl best align perform time series. succinct set time discov present recommend documents, act autom inform filtering, given time series. empir evalu perform model discov relev news articl two stock time seri appl googl companies, along daili news articl collect thomson reuter period seven consecut years.",
    "experiment result demonstr msin achiev 84.9% 87.2% recal ground truth articl respect two examin time series, far superior state-of-the-art algorithm reli convent attent mechan deep learning.",
    "consid problem predict next observ given sequenc past observations, consid extent accur predict requir complex algorithm explicitli leverag long-rang dependencies. perhap surprisingly, posit result show broad class sequences, algorithm predict well average, base predict recent observ togeth set simpl summari statist past observations. specifically, show distribut observations, mutual inform past observ futur observ upper bound $i$, simpl markov model recent $i/\\epsilon$ observ obtain expect kl error $\\epsilon$---and henc $\\ell_1$ error $\\sqrt{\\epsilon}$---with respect optim predictor access entir past know data gener distribution. hidden markov model $n$ hidden states, $i$ bound $\\log n$, quantiti depend mix time, show trivial predict algorithm base empir frequenc length $o(\\log n/\\epsilon)$ window observ achiev error, provid length sequenc $d^{\\omega(\\log n/\\epsilon)}$, $d$ size observ alphabet. also establish result cannot improv upon, even class hmms, follow two senses: first, hmm $n$ hidden states, window length $\\log n/\\epsilon$ information-theoret necessari achiev expect $\\ell_1$ error $\\sqrt{\\epsilon}$.",
    "second, $d^{\\theta(\\log n/\\epsilon)}$ sampl requir estim markov model observ alphabet size $d$ necessari comput tractabl learn algorithm, assum hard strongli refut certain class csps.",
    "graph ubiquit form structur data represent use machin learning. model, however, pairwis relat node design encod higher-ord relat found mani real-world datasets. model complex relations, hypergraph proven natur representation. learn node represent hypergraph complex graph involv inform propag two levels: within everi hyperedg across hyperedges. current approach first transform hypergraph structur graph use exist geometr deep learn algorithms. transform lead inform loss, sub-optim exploit hypergraph' express power. present hypersage, novel hypergraph learn framework use two-level neural messag pass strategi accur effici propag inform hypergraphs. flexibl design hypersag facilit differ way aggreg neighborhood information. unlik major relat work transductive, approach, inspir popular graphsag method, inductive. thus, also use previous unseen nodes, facilit deploy problem evolv partial observ hypergraphs. extens experimentation, show hypersag outperform state-of-the-art hypergraph learn method repres benchmark datasets. also demonstr higher express power hypersag make stabl learn node represent compar alternatives.",
    "introduc use rectifi linear unit (relu) classif function deep neural network (dnn). conventionally, relu use activ function dnns, softmax function classif function. however, sever studi use classif function softmax, studi addit those. accomplish take activ penultim layer $h_{n - 1}$ neural network, multipli weight paramet $\\theta$ get raw score $o_{i}$. afterwards, threshold raw score $o_{i}$ $0$, i.e. $f(o) = \\max(0, o_{i})$, $f(o)$ relu function. provid class predict $\\hat{y}$ argmax function, i.e. argmax $f(x)$.",
    "relat extract model suffer limit qualifi train data. use human annot label sentenc expens scale well especi deal larg datasets. paper, use auxiliari classifi gener adversari network (ac-gans) gener high-qual relat sentenc improv perform relat classifi end-to-end models. ac-gan, discrimin give probabl distribut real source, also probabl distribut relat labels. help gener meaning relat sentences. experiment result show propos data augment method significantli improv perform relat extract compar state-of-the-art method",
    "graph neural network trigger resurg graph-bas text classif methods, defin today' state art. show wide multi-lay perceptron (mlp) use bag-of-word (bow) outperform recent graph-bas model textgcn hetegcn induct text classif set compar hypergat. moreover, fine-tun sequence-bas bert lightweight distilbert model, outperform state-of-the-art models. result question import synthet graph use modern text classifiers. term efficiency, distilbert still twice larg bow-bas wide mlp, graph-bas model like textgcn requir set $\\mathcal{o}(n^2)$ graph, $n$ vocabulari plu corpu size. finally, sinc transform need comput $\\mathcal{o}(l^2)$ attent weight sequenc length $l$, mlp model show higher train infer speed dataset long sequences.",
    "consider recent activ appli deep convolut neural net (cnns) data particl physic experiments. current approach atlas/cm larg focuss subset calorimeter, identifi object particular particl types. explor approach use entir calorimeter, combin track information, directli conduct physic analyses: i.e. classifi event known-phys background new-phys signals. use exist rpv-supersymmetri analysi case studi explor cnn multi-channel, high-resolut spars images: appli gpu multi-nod cpu architectur (includ knight land (knl) xeon phi nodes) cori supercomput nersc.",
    "paper, propos use in-train matrix factor reduc model size neural machin translation. use in-train matrix factorization, paramet matric may decompos product smaller matrices, compress larg machin translat architectur vastli reduc number learnabl parameters. appli in-train matrix factor differ layer standard neural architectur show in-train factor capabl reduc nearli 50% learnabl paramet without associ loss bleu score. further, find in-train matrix factor especi power embed layers, provid simpl effect method curtail number paramet minim impact model performance, and, times, increas performance.",
    "imag super-resolut (sr) one vital imag process method improv resolut imag field comput vision. last two decades, signific progress made field super-resolution, especi util deep learn methods. survey effort provid detail survey recent progress single-imag super-resolut perspect deep learn also inform initi classic method use imag super-resolution. survey classifi imag sr method four categories, i.e., classic methods, supervis learning-bas methods, unsupervis learning-bas methods, domain-specif sr methods. also introduc problem sr provid intuit imag qualiti metrics, avail refer datasets, sr challenges. deep learning-bas approach sr evalu use refer dataset. review state-of-the-art imag sr method includ enhanc deep sr network (edsr), cycle-in-cycl gan (cincgan), multiscal residu network (msrn), meta residu dens network (meta-rdn), recurr back-project network (rbpn), second-ord attent network (san), sr feedback network (srfbn) wavelet-bas residu attent network (wran). finally, survey conclud futur direct trend sr open problem sr address researchers.",
    "investig learn collect languag text induct infer machin access current datum bound memori form states. bound memori state (bms) learner consid success case eventu settl correct hypothesi exploit finit mani differ states. give complet map pairwis relat establish collect criteria successful learning. prominently, show non-u-shaped restrictive, conserv (strong) monoton are. result carri iter learn gener lemma show that, wealth restrict (the semant restrictions), iter bound memori state learn equivalent. also give exampl non-semant restrict (strongli non-u-shapedness) two set differ.",
    "gpt-2 bert demonstr effect use pre-train languag model (lms) variou natur languag process tasks. however, lm fine-tun often suffer catastroph forget appli resource-rich tasks. work, introduc concert train framework (\\method) key integr pre-train lm neural machin translat (nmt). propos cnmt consist three techniques: a) asymptot distil ensur nmt model retain previou pre-train knowledge; b) dynam switch gate avoid catastroph forget pre-train knowledge; c) strategi adjust learn pace accord schedul policy. experi machin translat show \\method gain 3 bleu score wmt14 english-german languag pair even surpass previou state-of-the-art pre-train aid nmt 1.4 bleu score. larg wmt14 english-french task 40 million sentence-pairs, base model still significantli improv upon state-of-the-art transform big model 1 bleu score.",
    "gener adversari network (gans) shown power flexibl prior solv invers problems. one challeng use overcom represent error, fundament limit network repres particular signal. recently, multipl propos invers algorithm reduc represent error optim intermedi layer representations. method typic appli gener model train agnost downstream invers algorithm. work, introduc principl gener model intend invers use algorithm base optim intermedi layers, train way regular intermedi layers. instanti principl two notabl recent invers algorithms: intermedi layer optim multi-cod gan prior. invers algorithms, introduc new regular gan train algorithm demonstr learn gener model result lower reconstruct error across wide rang sampl ratio solv compress sensing, inpainting, super-resolut problems.",
    "hand hygien crucial prevent virus infections. due pervas outbreak covid-19, wear mask hand hygien appear effect way public curb spread viruses. world health organ (who) recommend guidelin alcohol-bas hand rub eight step ensur surfac hand entir clean. step involv complex gestures, human assess lack enough accuracy. however, deep neural network (dnn) machin vision made possibl accur evalu hand rub qualiti purpos train feedback. paper, autom deep learn base hand rub assess system real-tim feedback presented. system evalu complianc 8-step guidelin use dnn architectur train dataset video collect volunt variou skin tone hand characterist follow hand rub guideline. variou dnn architectur tested, inception-resnet model led best result 97% test accuracy. propos system, nvidia jetson agx xavier embed board run software. efficaci system evalu concret situat use variou users, challeng step identified. experiment, averag time taken hand rub step among volunt 27.2 seconds, conform guidelines.",
    "autom audio caption (aac) task automat gener textual descript gener audio signals. caption system identifi variou inform input signal express natur language. exist work mainli focu investig new method tri improv perform measur exist datasets. attract attent recently, work aac studi perform exist pre-train audio natur languag process resources. paper, evalu perform off-the-shelf model transformer-bas caption approach. util freeli avail clotho dataset compar four differ pre-train machin listen models, four word embed models, combin mani differ settings. evalu suggest yamnet combin bert embed produc best captions. moreover, general, fine-tun pre-train word embed lead better performance. finally, show sequenc audio embed process use transform encod produc higher-qu captions.",
    "annot qualiti quantiti posit affect perform sequenc labeling, vital task natur languag processing. hire domain expert annot corpu set costli term money time. crowdsourc platforms, amazon mechan turk (amt), deploy assist purpose. however, platform prone human error due lack expertise; hence, one worker' annot cannot directli use train model. exist literatur annot aggreg focus binari multi-choic problems. recent years, handl sequenti label aggreg task imbalanc dataset complex depend token challenging. conquer challenge, propos optimization-bas method infer best set aggreg annot use label provid workers. propos aggreg method sequenti label crowd ($aggslc$) jointli consid characterist sequenti label tasks, workers' reliabilities, advanc machin learn techniques. evalu $aggslc$ differ crowdsourc data name entiti recognit (ner), inform extract task biomed (pico), simul dataset. result show propos method outperform state-of-the-art aggreg methods. achiev insight framework, studi $aggslc$ components' effect ablat studi evalu model absenc predict modul inconsist loss function. theoret analysi algorithm' converg point propos $aggslc$ halt finit number iterations.",
    "paper analyz done, onlin optim algorithm iter minim unknown function base costli noisi measurements. algorithm maintain surrog unknown function form random fourier expans (rfe). surrog updat whenev new measur available, use determin next measur point. algorithm compar bayesian optim algorithms, comput complex per iter depend number measurements. deriv sever theoret result provid insight hyper-paramet algorithm chosen. algorithm compar bayesian optim algorithm benchmark problem three applications, namely, optic coher tomography, optic beam-form network tuning, robot arm control. found done algorithm significantli faster bayesian optim discuss problems, achiev similar better performance.",
    "causal decis make (cdm) base machin learn becom routin part business. busi algorithm target offers, incentives, recommend affect consum behavior. recently, seen acceler research relat cdm causal effect estim (cee) use machine-learn models. articl highlight import perspective: cdm cee, counterintuitively, accur cee necessari accur cdm. experi well understood practition researchers. technically, estimand interest different, import implic model use statist model cdm. draw prior research highlight three implications. (1) consid care object function causal machin learning, possible, optim accur treatment assign rather accur effect-s estimation. (2) confound effect cdm cee. upshot support cdm may good even better learn confound data unconfound data. finally, (3) causal statist model may necessari support cdm proxi target statist model might well better. third observ help explain least one broad common cdm practic seem wrong first blush: widespread use non-caus model target interventions.",
    "last two implic particularli import practice, acquir (unconfounded) data counterfactu costli often impracticable. observ open substanti research ground. hope facilit research area point relat articl multipl contribut fields, includ two dozen articl publish last three four years.",
    "indoor local fundament problem location-bas applications. current approach problem typic reli radio frequenc technology, requir support infrastructur human effort measur calibr signal. moreover, data collect locat indispens exist methods, turn hinder large-scal deployment. paper, propos novel neural network base architectur graph locat network (gln) perform infrastructure-free, multi-view imag base indoor localization. gln make locat predict base robust locat represent extract imag message-pass networks. furthermore, introduc novel zero-shot indoor local set tackl extend propos gln dedic zero-shot version, exploit novel mechan map2vec train location-awar embed make predict novel unseen locations. extens experi show propos approach outperform state-of-the-art method standard setting, achiev promis accuraci even zero-shot set data half locat available. sourc code dataset publicli avail https://github.com/coldmanck/zero-shot-indoor-localization-release.",
    "learn algorithm produc softwar model realis critic classif tasks. decis tree model simpler model neural network use variou critic domain medic aeronautics. low unknown learn abil algorithm permit us trust produc softwar models, lead costli test activ valid model wast learn time case model like faulti due learn inability. method evalu decis tree learn ability, well models, need especi sinc test learn model still hot topic. propos novel oracle-cent approach evalu (the learn abil of) learn algorithm decis trees. consist gener data refer tree play role oracles, produc learn tree exist learn algorithms, determin degre correct (doe) learn tree compar oracles. averag doe use estim qualiti learn algorithm. assess five decis tree learn algorithm base propos approach.",
    "collect behavior, swarm format particular, studi sever perspect within larg varieti fields, rang biolog physics. work, appli project simul model individu artifici learn agent interact neighbor surround order make decis learn them. within reinforc learn framework, discuss one-dimension learn scenario agent need get food resourc rewarded. observ differ type collect motion emerg depend distanc agent need travel reach resources. instance, strongli align swarm emerg food sourc place far away region agent situat initially. addition, studi properti individu trajectori occur within differ type emerg collect dynamics. agent train find distant resourc exhibit individu trajectori l\\'evy-lik characterist consequ collect motion, wherea agent train reach nearbi resourc present brownian-lik trajectories.",
    "decis theori formal solv problem ration agent uncertain world true environment probabl distribut known. solomonoff' theori univers induct formal solv problem sequenc predict unknown distribution. unifi theori give strong argument result univers aixi model behav optim comput environment. major drawback aixi model uncomputable. overcom problem, construct modifi algorithm aixi^tl, still superior time space l bound agent. comput time aixi^tl order x 2^l.",
    "work propos novel method supervised, keyshot base video summar appli conceptu simpl comput effici soft, self-attent mechanism. current state art method leverag bi-direct recurr network bilstm combin attention. network complex implement comput demand compar fulli connect networks. end propos simple, self-attent base network video summar perform entir sequenc sequenc transform singl feed forward pass singl backward pass training. method set new state art result two benchmark tvsum summe, commonli use domain.",
    "agent system optim object function environment. together, goal environ induc secondari objectives, incentives. model agent-environ interact use causal influenc diagrams, answer two fundament question agent' incent directli graph: (1) node agent incentiv observe, (2) node agent incentiv control? answer tell us inform influenc point need extra protection. example, may want classifi job applic use ethnic candidate, reinforc learn agent take direct control reward mechanism. differ algorithm train paradigm lead differ causal influenc diagrams, method use identifi algorithm problemat incent help design algorithm better incentives.",
    "matrix factor success practic recommend applic e-commerce. due data shortag stringent regulations, hard collect suffici data build perform recommend system singl company. feder learn provid possibl bridg data silo build machin learn model without compromis privaci security. particip share common user item collabor build model data participants. work explor applic feder learn recommend system privaci issu collabor filter systems. however, privaci threat feder matrix factor studied. paper, categor feder matrix factor three type base partit featur space analyz privaci threat type feder matrix factor model. also discuss privacy-preserv approaches. far aware, first studi privaci threat matrix factor method feder learn framework.",
    "industri 4.0 becom possibl converg oper inform technologies. requir realiz converg integr fog platform. fog platform introduc cloud server edg devic unpreced gener data caus burden cloud server, lead inelig latency. new paradigm, divid comput task push edg devices. furthermore, local comput (at edg side) may improv privaci trust. address problems, present new method, decompos data aggreg processing, divid edg devic fog node intelligently. appli activ learn edg devices; feder learn fog node significantli reduc data sampl train model well commun cost. show effect propos method, implement evalu perform imag classif task. addition, consid two settings: massiv distribut non-mass distribut offer correspond solutions.",
    "princip compon analysi (pca) one import unsupervis method handl high-dimension data. however, due high comput complex eigen decomposit solution, hard appli pca large-scal data high dimensionality. meanwhile, squar l2-norm base object make sensit data outliers. recent research, l1-norm maxim base pca method propos effici comput robust outliers. however, work use greedi strategi solv eigen vectors. moreover, l1-norm maxim base object may correct robust pca formulation, lose theoret connect minim data reconstruct error, one import intuit goal pca. paper, propos maxim l21-norm base robust pca objective, theoret connect minim reconstruct error. importantly, propos effici non-greedi optim algorithm solv object gener l21-norm maxim problem theoret guarante convergence. experiment result real world data set show effect propos method princip compon analysis.",
    "success deep learn spark interest improv relat tabl tasks, like data prepar search, tabl represent model train larg tabl corpora. exist tabl corpora primarili contain tabl extract html pages, limit capabl repres offlin databas tables. train evalu high-capac model applic beyond web, need resourc tabl resembl relat databas tables. introduc gittables, corpu 1m relat tabl extract github. continu curat aim grow corpu least 10m tables. analys gittabl show structure, content, topic coverag differ significantli exist tabl corpora. annot tabl column gittabl semant types, hierarch relat descript schema.org dbpedia. evalu annot pipelin t2dv2 benchmark illustr approach provid result par human annotations. present three applic gittables, demonstr valu learn semant type detect models, schema complet methods, benchmark table-to-kg matching, data search, preparation. make corpu code avail https://gittables.github.io.",
    "rate-control essenti ensur effici video delivery. typic rate-control algorithm reli bit alloc strategies, appropri distribut bit among frames. refer frame essenti exploit tempor redundancies, intra frame usual assign larger portion avail bits. paper, accur method estim number bit qualiti intra frame proposed, use bit alloc rate-control scheme. algorithm base deep learning, network train use origin frame inputs, distort size compress frame encod use ground truths. two approach propos either local global distort predicted.",
    "introduc studi problem onlin continu compression, one attempt simultan learn compress store repres dataset non i.i.d data stream, observ sampl once. naiv applic auto-encod set encount major challenge: represent deriv earlier encod state must usabl later decod states. show use discret auto-encod effect address challeng introduc adapt quantiz modul (aqm) control variat compress abil modul given stage learning. enabl select appropri compress incom samples, take account overal memori constraint current progress learn compression. unlik previou methods, approach requir pretraining, even challeng datasets. show use aqm replac standard episod memori continu learn set lead signific gain continu learn benchmarks. furthermor demonstr approach larger images, lidar, reinforc learn environments.",
    "rapid growth number devic internet, malwar pose threat affect devic also abil use said devic launch attack internet ecosystem. rapid malwar classif import tool combat threat. one success approach classif base malwar imag deep learning. mani deep learn architectur accur usual take long time train. work perform experi multipl well known, pre-trained, deep network architectur context transfer learning. show almost classifi malwar accur short train period.",
    "report extens kera model, call ctcmodel, perform connectionist tempor classif (ctc) transpar way. combin recurr neural networks, connectionist tempor classif refer method deal unseg input sequences, i.e. data coupl observ label sequenc label relat subset observ frames. ctcmodel make use ctc implement tensorflow backend train predict sequenc label use keras. consist three branch made kera models: one training, comput ctc loss function; one predicting, provid sequenc labels; one evalu return standard metric analyz sequenc predictions.",
    "regular mitig gener gap train infer introduc induct bias. exist work alreadi propos variou induct bias divers perspectives. however, best knowledge, none explor induct bia perspect class-depend respons distribut individu neurons. paper, conduct substanti analysi characterist distribution. base analysi results, articul neuron steadi hypothesis: neuron similar respons instanc class lead better generalization. accordingly, propos new regular method call neuron steadi regular reduc neuron intra-class respons variance. conduct extens experi multilay perceptron, convolut neural network, graph neural network popular benchmark dataset divers domains, show neuron steadi regular consist outperform vanilla version model signific gain low addit overhead.",
    "exist approach graph neural network commonli suffer oversmooth issue, regardless neighborhood aggregated. method also focu transduct scenario fix graphs, lead poor gener unseen graphs. address issues, propos new graph neural network consid edge-bas neighborhood relationship node-bas entiti features, i.e. graph entiti step mixtur via random walk (gesm). gesm employ mixtur variou step random walk allevi oversmooth problem, attent dynam reflect interrel depend node information, structure-bas regular enhanc embed representation. intens experiments, show propos gesm achiev state-of-the-art compar perform eight benchmark graph dataset compris transduct induct learn tasks. furthermore, empir demonstr signific consid global information.",
    "studi problem optim expens blackbox function combinatori space (e.g., sets, sequences, trees, graphs). boc (baptista poloczek, 2018) state-of-the-art bayesian optim method tractabl statist models, perform semi-definit program base acquisit function optim (afo) select next structur evaluation. unfortunately, boc scale poorli larg number binari and/or categor variables. base recent advanc submodular relax (ito fujimaki, 2016) solv binari quadrat programs, studi approach refer parametr submodular relax (psr) toward goal improv scalabl accuraci solv afo problem boc model. psr approach reli two key ideas. first, reformul afo problem submodular relax unknown parameters, solv effici use minimum graph cut algorithms. second, construct optim problem estim unknown paramet close approxim true objective. experi divers benchmark problem show signific improv psr boc model. sourc code avail https://github.com/aryandeshwal/submodular_relaxation_boc .",
    "base recent advanc natur languag model text gener capabilities, propos novel data augment method text classif tasks. use power pre-train neural network model artifici synthes new label data supervis learning. mainli focu case scarc label data. method, refer language-model-bas data augment (lambada), involv fine-tun state-of-the-art languag gener specif task initi train phase exist (usual small) label data. use fine-tun model given class label, new sentenc class generated. process filter new sentenc use classifi train origin data. seri experiments, show lambada improv classifiers' perform varieti datasets. moreover, lambada significantli improv upon state-of-the-art techniqu data augmentation, specif applic text classif task littl data.",
    "introduc half centuri ago, granger causal becom popular tool analyz time seri data mani applic domains, econom financ genom neuroscience. despit popularity, valid notion infer causal relationship among time seri remain topic continu debate. moreover, origin definit general, limit comput tool primarili limit applic granger causal simpl bivari vector auto-regress process pairwis relationship among set variables. start review earli develop debates, paper discuss recent advanc address variou shortcom earlier approaches, model high-dimension time seri recent develop account nonlinear non-gaussian observ allow sub-sampl mix frequenc time series.",
    "metric $k$-center cluster fundament unsupervis learn primitive. although wide used, primit heavili affect nois data, sensibl variant seek best solut disregard given number $z$ point dataset, call outliers. provid effici algorithm import variant stream model slide window setting, where, time step, dataset cluster window $w$ recent data items. algorithm achiev $o(1)$ approxim and, remarkably, requir work memori linear $k+z$ logarithm $|w|$. by-product, show estim effect diamet window $w$, measur spread window points, disregard given fraction noisi distances. also provid experiment evid practic viabil theoret results.",
    "consid nonparametr contextu multi-arm bandit problem arm $a \\in [k]$ associ nonparametr reward function $f_a: [0,1] \\to \\mathbb{r}$ map context expect reward. suppos larg set arms, yet simpl unknown structur amongst arm reward functions, e.g. finit type smooth respect unknown metric space. present novel algorithm learn data-driven similar amongst arms, order implement adapt partit context-arm space effici learning. provid regret bound along simul highlight algorithm' depend local geometri reward functions.",
    "context supervis statist learning, typic assum train set come distribut draw test samples. case, behavior learn model unpredict becom depend upon degre similar distribut train set distribut test set. one research topic investig scenario refer domain adaptation. deep neural network brought dramat advanc pattern recognit mani attempt provid good domain adapt algorithm models. take differ avenu approach problem increment point view, model adapt new domain iteratively. make use exist unsupervis domain-adapt algorithm identifi target sampl greater confid true label. output model analyz differ way determin candid samples. select set ad sourc train set consid label provid network ground truth, process repeat target sampl labelled. result report clear improv respect non-increment case sever datasets, also outperform state-of-the-art domain adapt algorithms.",
    "nois contrast estim (nce) power paramet estim method log-linear models, avoid calcul partit function deriv train step, comput demand step mani cases. close relat neg sampl methods, wide use nlp. paper consid nce-bas estim condit models. condit model frequent encount practice; howev rigor theoret analysi nce setting, argu subtl import question gener nce condit case. particular, analyz two variant nce condit models: one base classif objective, base rank objective. show ranking-bas variant nce give consist paramet estim weaker assumpt classification-bas method; analyz statist effici ranking-bas classification-bas variant nce; final describ experi synthet data languag model show effect trade-off methods.",
    "nowadays, automobil manufactur make effort develop way make car fulli safe. monitor driver' action comput vision techniqu detect drive mistak real-tim plan autonom drive avoid vehicl collis one import issu investig machin vision intellig transport system (its). main goal studi prevent accid caus fatigue, drowsiness, driver distraction. avoid incidents, paper propos integr safeti system continu monitor driver' attent vehicl surroundings, final decid whether actual steer control statu safe not. purpose, equip ordinari car call faraz vision system consist four mount camera along univers car tool commun surround factory-instal sensor car systems, send command actuators. propos system leverag scene understand pipelin use deep convolut encoder-decod network driver state detect pipeline. identifi assess domest capabl develop technolog specif ordinari vehicl order manufactur smart car eke provid intellig system increas safeti assist driver variou conditions/situations.",
    "translat rotat input imag affect result mani comput vision tasks. convolut neural network (cnns) alreadi translat equivariant: input imag translat produc proportion featur map translations. case rotations. global rotat equivari typic sought data augmentation, patch-wis equivari difficult. present harmon network h-nets, cnn exhibit equivari patch-wis translat 360-rotation. achiev replac regular cnn filter circular harmonics, return maxim respons orient everi recept field patch. h-net use rich, parameter-effici low comput complex representation, show deep featur map within network encod complic rotat invariants. demonstr layer gener enough use conjunct latest architectur techniques, deep supervis batch normalization. also achiev state-of-the-art classif rotated-mnist, competit result benchmark challenges.",
    "propos spatially-adapt normalization, simpl effect layer synthes photorealist imag given input semant layout. previou method directli feed semant layout input deep network, process stack convolution, normalization, nonlinear layers. show suboptim normal layer tend ``wash away'' semant information. address issue, propos use input layout modul activ normal layer spatially-adaptive, learn transformation. experi sever challeng dataset demonstr advantag propos method exist approaches, regard visual fidel align input layouts. finally, model allow user control semant style. code avail https://github.com/nvlabs/spad .",
    "advanc deep neural network (dnn) greatli bolster real-tim detect anomal iot data. however, iot devic bare afford complex dnn model due limit comput power energi supply. one offload anomali detect task cloud, incur long delay requir larg bandwidth thousand iot devic stream data cloud concurrently. paper, propos adapt anomali detect approach hierarch edg comput (hec) system solv problem. specifically, first construct three anomali detect dnn model increas complexity, associ three layer hec bottom top, i.e., iot devices, edg servers, cloud. then, design adapt scheme select one model base contextu inform extract input data, perform anomali detection. select formul contextu bandit problem character single-step markov decis process, object achiev high detect accuraci low detect delay simultaneously. evalu propos approach use real iot dataset, demonstr reduc detect delay 84% maintain almost accuraci compar offload detect task cloud. addition, evalu also show outperform baselin schemes.",
    "paper propos new neural network base spd manifold learn skeleton-bas hand gestur recognition. given stream hand' joint positions, approach combin two aggreg process respect spatial tempor domains. pipelin network architectur consist three main stages. first stage base convolut layer increas discrimin power learn features. second stage reli differ architectur spatial tempor gaussian aggreg joint features. third stage learn final spd matrix skelet data. new type layer propos third stage, base variant stochast gradient descent stiefel manifolds. propos network valid two challeng dataset show state-of-the-art accuraci datasets.",
    "design architect materi connect mechan behavior across scales, comput model critic tool solid mechanics. recently, grow interest use machin learn reduc comput cost physics-bas simulations. notably, machin learn approach reli graph neural network (gnns) shown success learn mechanics, perform gnn yet investig myriad solid mechan problems. work, examin abil gnn predict fundament aspect mechan driven emerg behavior: connect column' geometr structur direct buckles. accomplish this, introduc asymmetr buckl column (abc) dataset, dataset compris three sub-dataset asymmetr heterogen column geometri goal classifi direct symmetri break (left right) compress onset instability. complex local geometry, \"image-like\" data represent requir implement standard convolut neural network base metamodel ideal, thu motiv use gnns. addit investig gnn model architecture, studi effect differ input data represent approaches, data augmentation, combin multipl model ensemble. abl obtain good results, also show predict solid mechan base emerg behavior non-trivial.",
    "model implement dataset distribut open-sourc licenses, hope futur research build work creat enhanc mechanics-specif machin learn pipelin captur behavior complex geometr structures.",
    "grow interest large-scal machin learn optim decentr networks, e.g. context multi-ag learn feder learning. due immin need allevi commun burden, investig communication-effici distribut optim algorithm - particularli empir risk minim - flourish recent years. larg fraction algorithm develop master/slav setting, reli central paramet server commun agents. paper focus distribut optim networks, decentr optimization, agent allow aggreg inform neighbors. properli adjust global gradient estim via local averag conjunct proper correction, develop communication-effici approxim newton-typ method network-dane, gener dane decentr scenarios. key idea appli systemat manner obtain decentr version master/slav distribut algorithms. notabl develop network-svrg/sarah, employ varianc reduct acceler local computation. establish linear converg network-dan network-svrg strongli convex losses, network-sarah quadrat losses, shed light impact data homogeneity, network connectivity, local averag upon rate convergence. extend network-dan composit optim allow nonsmooth penalti term. numer evid provid demonstr appeal perform algorithm competit baselines, term commun comput efficiency. work suggest perform certain amount local commun comput per iter substanti improv overal efficiency.",
    "major archetyp artifici intellig develop algorithm facilit tempor effici accuraci boost gener performance. even latest develop machin learning, key limit ineffici featur extract initi data, essenti perform optimization. here, introduc featur extract method inspir sensori cortic network brain. dub bioinspir cortex, algorithm provid converg orthogon featur stream signal superior comput effici process data compress form. demonstr perform new algorithm use artifici creat complex data compar commonli use tradit cluster algorithms, birch, gmm, k-means. data process time significantli reduced, second versu hours, encod distort remain essenti new algorithm provid basi better generalization. although show herein superior perform cortex model cluster vector quantization, also provid potent implement opportun machin learn fundament components, reasoning, anomali detect classif larg scope applications, e.g., finance, cybersecurity, healthcare.",
    "problem domain adapt convent consid set sourc domain plenti label data, target domain (with differ data distribution) plenti unlabel data none limit label data. paper, address set target domain limit label data distribut expect chang frequently. first propos fast light-weight method adapt gaussian mixtur densiti network (mdn) use small set target domain samples. method well-suit set distribut target data chang rapidli (e.g., wireless channel), make challeng collect larg number sampl retrain. appli propos mdn adapt method problem end-of-end learn wireless commun autoencoder. commun autoencod model encoder, decoder, channel use neural networks, learn jointli minim overal decod error rate. however, error rate autoencod train particular (source) channel distribut degrad channel distribut chang frequently, allow enough time data collect retrain autoencod target channel distribution. propos method adapt autoencod without modifi encod decod neural networks, adapt mdn model channel. method util featur transform decod compens chang channel distribution, effect present decod sampl close sourc distribution.",
    "experiment evalu simul dataset real mmwave wireless channel demonstr propos method quickli adapt mdn model, improv maintain error rate autoencod chang channel conditions.",
    "paper address sequenti changepoint detect problem, assum durat chang may finit unknown. problem import mani applications, e.g., signal imag process signal appear disappear unknown point time space. contrast convent optim criterion quickest chang detect requir minim expect delay detect given averag run length fals alarm, focu reliabl maximin chang detect criterion maxim minim probabl detect given time (or space) window given local maxim probabl fals alarm prescrib window. show optim detect procedur modifi cusum procedure. compar oper characterist optim procedur popular engin finit move averag (fma) detect algorithm ordinari cusum procedur use mont carlo simulations, show typic later algorithm almost perform optim one. time, fma procedur substanti advantag -- independ intens signal, usual unknown. finally, fma algorithm appli detect faint streak satellit optic images.",
    "transform improv state-of-the-art across numer task sequenc modeling. besid quadrat comput memori complex w.r.t sequenc length, self-attent mechan process inform scale, i.e., attent head resolution, result limit power transformer. remedi this, propos novel effici structur name adapt multi-resolut attent (adamra short), scale linearli sequenc length term time space. specifically, leverag multi-resolut multi-head attent mechanism, enabl attent head captur long-rang contextu inform coarse-to-fin fashion. moreover, captur potenti relat queri represent clue differ attent granularities, leav decis resolut attent use query, improv model' capac compar vanilla transformer. effort reduc complexity, adopt kernel attent without degrad performance. extens experi sever benchmark demonstr effect effici model achiev state-of-the-art performance-efficiency-memori trade-off. facilit adamra util scientif community, code implement made publicli available.",
    "interest artifici intellig (ai) applic seen unpreced growth last years. success partli attribut advanc made sub-field ai machin learning, comput vision, natur languag processing. much growth field made possibl deep learning, sub-area machin learn use artifici neural networks. creat signific interest integr vision language. survey, focu ten promin task integr languag vision discuss problem formulation, methods, exist datasets, evalu measures, compar result obtain correspond state-of-the-art methods. effort go beyond earlier survey either task-specif concentr one type visual content, i.e., imag video. furthermore, also provid potenti futur direct field research anticip survey stimul innov thought idea address exist challeng build new applications.",
    "mathemat formal neurolog mechan olfactori circuit fruit-fli local sensit hash (flyhash) bloom filter (fbf) recent propos \"reprogrammed\" variou machin learn task similar search, outlier detect text embeddings. propos novel reprogram hash bloom filter emul canon nearest neighbor classifi (nnc) challeng feder learn (fl) setup train test data spread across parti data leav respect parties. specifically, util flyhash fbf creat flynn classifier, theoret establish condit flynn match nnc. show flynn train exactli fl setup low commun overhead produc flynnfl, differenti private. empirically, demonstr (i) flynn match nnc accuraci across 70 openml datasets, (ii) flynnfl train highli scalabl low commun overhead, provid $8\\times$ speedup $16$ parties.",
    "human comput interact facilit intellig commun human computers, gestur recognit play promin role. paper propos machin learn system identifi dynam gestur use tri-axi acceler data acquir two public datasets. datasets, uwav sony, acquir use acceleromet embed wii remot smartwatches, respectively. dynam gestur sign user character gener set featur extract across time frequenc domains. system analyz end-us perspect model oper three modes. mode oper determin subset data use train test system. initi set seven classifiers, three chosen evalu dataset across mode render system toward mode-neutr dataset-independence. propos system abl classifi gestur perform vari speed minimum preprocessing, make comput efficient. moreover, system found run low-cost embed platform - raspberri pi zero (usd 5), make econom viable.",
    "studi class realist comput vision set wherein one influenc design object recognized. develop framework leverag capabl significantli improv vision models' perform robustness. framework exploit sensit modern machin learn algorithm input perturb order design \"robust objects,\" i.e., object explicitli optim confid detect classified. demonstr efficaci framework wide varieti vision-bas task rang standard benchmarks, (in-simulation) robotics, real-world experiments. code found https://git.io/unadversari .",
    "grow field robot artifici intellig (ai) research human-robot collaboration, whose target enabl effect teamwork human robots. however, mani situat human team still superior human-robot teams, primarili human team easili agre common goal language, individu member observ effectively, leverag share motor repertoir sensorimotor resources. paper show cognit robot possible, inde fruitful, combin knowledg acquir interact element environ (afford exploration) probabilist observ anoth agent' actions. propos model unit (i) learn robot afford word descript (ii) statist recognit human gestur vision sensors. discuss theoret motivations, possibl implementations, show initi result highlight that, acquir knowledg surround environment, humanoid robot gener knowledg case observ anoth agent (human partner) perform motor action previous execut training.",
    "commun privaci two critic concern distribut learning. mani exist work treat concern separately. work, argu natur connect exist method commun reduct privaci preserv context distribut machin learning. particular, prove count sketch, simpl method data stream summarization, inher differenti privaci properties. use deriv privaci guarantees, propos novel sketch-bas framework (diffsketch) distribut learning, compress transmit messag via sketch simultan achiev commun effici provabl privaci benefits. evalu demonstr diffsketch provid strong differenti privaci guarante (e.g., $\\varepsilon$= 1) reduc commun 20-50x margin decreas accuracy. compar baselin treat privaci commun separately, diffsketch improv absolut test accuraci 5%-50% offer privaci guarante commun compression.",
    "present novel nonneg tensor decomposit method, call legendr decomposition, factor input tensor multipl combin parameters. thank well-develop theori inform geometry, reconstruct tensor uniqu alway minim kl diverg input tensor. empir show legendr decomposit accur reconstruct tensor nonneg tensor decomposit methods.",
    "recent advanc neural variat infer spawn renaiss deep latent variabl models. paper introduc gener variat infer framework gener condit model text. tradit variat method deriv analyt approxim intract distribut latent variables, construct infer network condit discret text input provid variat distribution. valid framework two differ text model applications, gener document model supervis question answering. neural variat document model combin continu stochast document represent bag-of-word gener model achiev lowest report perplex two standard test corpora. neural answer select model employ stochast represent layer within attent mechan extract semant question answer pair. two question answer benchmark model exce previou publish benchmarks.",
    "studi investig waveform represent audio signal classification. recently, mani studi audio waveform classif acoust event detect music genr classif published. studi audio waveform classif propos use deep learn (neural network) framework. generally, frequenc analysi method fourier transform appli extract frequenc spectral inform input audio waveform input raw audio waveform neural network. contrast previou studies, paper, propos novel waveform represent method, audio waveform repres bit sequence, audio classification. experiment, compar propos bit represent waveform, directli given neural network, represent audio waveform raw audio waveform power spectrum two classif tasks: one acoust event classif task sound/mus classif task. experiment result show bit represent waveform achiev best classif perform tasks.",
    "monograph aim provid introduct key concepts, algorithms, theoret result machin learning. treatment concentr probabilist model supervis unsupervis learn problems. introduc fundament concept algorithm build first principles, also expos reader advanc topic extens pointer literature, within unifi notat mathemat framework. materi organ accord clearli defin categories, discrimin gener models, frequentist bayesian approaches, exact approxim inference, well direct undirect models. monograph meant entri point research background probabl linear algebra.",
    "processor design valid debug difficult complex task, consum lion' share design process. design bug affect processor perform rather function especi difficult catch, particularli new microarchitectures. because, unlik function bugs, correct processor perform new microarchitectur complex, long-run benchmark typic determinist known. thus, perform benchmark new microarchitectures, perform team may assum design correct perform new microarchitectur exce previou generation, despit signific perform regress exist design. work, present two-stage, machin learning-bas methodolog abl detect exist perform bug microprocessors. result show best techniqu detect 91.5% microprocessor core perform bug whose averag ipc impact across studi applic greater 1% versu bug-fre design zero fals positives. evalu memori system bugs, techniqu achiev 100% detect zero fals positives. moreover, detect automatic, requir littl perform engin time.",
    "graph convolut network (gcns) emerg state-of-the-art graph learn model. however, notori challeng infer gcn larg graph datasets, limit applic larg real-world graph hinder explor deeper sophist gcn graphs. real-world graph extrem larg sparse. furthermore, node degre gcn tend follow power-law distribut therefor highli irregular adjac matrices, result prohibit ineffici data process movement thu substanti limit achiev gcn acceler efficiency. end, paper propos gcn algorithm acceler co-design framework dub gcod larg allevi aforement gcn irregular boost gcns' infer efficiency. specifically, algorithm level, gcod integr split conquer gcn train strategi polar graph either denser sparser local neighborhood without compromis model accuracy, result graph adjac matric (mostly) mere two level workload enjoy larg enhanc regular thu eas acceleration. hardwar level, develop dedic two-prong acceler separ engin process aforement denser sparser workloads, boost overal util acceler efficiency. extens experi ablat studi valid gcod consist reduc number off-chip accesses, lead speedup 15286x, 294x, 7.8x, 2.5x compar cpus, gpus, prior-art gcn acceler includ hygcn awb-gcn, respectively, maintain even improv task accuracy.",
    "code avail https://github.com/rice-eic/gcod.",
    "comput vision research, process autom architectur engineering, neural architectur search (nas), gain substanti interest. past, na hardli access research without access large-scal comput systems, due long comput time recurr search evalu new candid architectures. nas-bench-101 dataset facilit paradigm chang toward classic method supervis learn evalu neural architectures. paper, propos graph encod built upon graph neural network (gnn). demonstr effect propos encod na perform predict seen architectur type well unseen one (i.e., zero shot prediction). also provid new variational-sequenti graph autoencod (vs-gae) base propos graph encoder. vs-gae special encod decod graph vari length util gnns. experi differ sampl method show embed space learn vs-gae increas stabil accuraci predict task.",
    "number user equip (ues) variou data rate latenc requir increas wireless networks, resourc alloc problem orthogon frequency-divis multipl access (ofdma) becom challenging. particular, vari requir lead non-convex optim problem maxim system data rate preserv fair ues. paper, solv non-convex optim problem use deep reinforc learn (drl). outline, train evalu drl agent, perform task media access control schedul downlink ofdma scenario. kickstart train agent, introduc mimick learning. improv schedul performance, full buffer state inform base station (e.g. packet age, packet size) taken account. techniqu like input featur compression, packet shuffl age cap improv perform agent. train evalu agent use nokia' wireless suit evalu differ benchmark agents. show agent clearli outperform benchmark agents.",
    "studi fast algorithm comput fundament properti posit semidefinit kernel matrix $k \\in \\mathbb{r}^{n \\time n}$ correspond $n$ point $x_1,\\ldots,x_n \\in \\mathbb{r}^d$. particular, consid estim sum kernel matrix entries, along top eigenvalu eigenvector. show sum matrix entri estim $1+\\epsilon$ rel error time $sublinear$ $n$ linear $d$ mani popular kernels, includ gaussian, exponential, ration quadrat kernels. kernels, also show top eigenvalu (and approxim eigenvector) approxim $1+\\epsilon$ rel error time $subquadratic$ $n$ linear $d$. algorithm repres signific advanc best known runtim problems. leverag posit definit kernel matrix, along recent line work effici kernel densiti estimation.",
    "solv deep neural network (dnn)' huge train dataset high comput issue, so-cal teacher-stud (t-s) dnn transfer knowledg t-dnn s-dnn proposed. however, exist t-s-dnn limit rang use, knowledg t-dnn insuffici transfer s-dnn. improv qualiti transfer knowledg t-dnn, propos new knowledg distil use singular valu decomposit (svd). addition, defin knowledg transfer self-supervis task suggest way continu receiv inform t-dnn. simul result show s-dnn comput cost 1/5 t-dnn 1.1\\% better t-dnn term classif accuracy. also assum comput cost, s-dnn outperform s-dnn driven state-of-the-art distil perform advantag 1.79\\%. code avail https://github.com/sseung0703/sskd\\_svd.",
    "optic coher tomographi (oct) non-invas imag modal wide use clinic ophthalmology. oct imag capabl visual deep retin layer crucial earli diagnosi retin diseases. paper, describ comprehens open-access databas contain 500 highresolut imag categor differ patholog conditions. imag class includ normal (no), macular hole (mh), age-rel macular degener (amd), central serou retinopathi (csr), diabet retinopathi (dr). imag obtain raster scan protocol 2mm scan length 512x1024 pixel resolution. also includ 25 normal oct imag correspond ground truth delin use accur evalu oct imag segmentation. addition, provid user-friendli gui use clinician manual (and semi-automated) segmentation.",
    "consid privat share person privaci loss incur object perturbation, use per-inst differenti privaci (pdp). standard differenti privaci (dp) give us worst-cas bound might order magnitud larger privaci loss particular individu rel fix dataset. pdp framework provid fine-grain analysi privaci guarante target individual, per-inst privaci loss might function sensit data. paper, analyz per-inst privaci loss releas privat empir risk minim learn via object perturbation, propos group method privat accur publish pdp loss littl addit privaci cost.",
    "paper consid problem simultan learn sens matrix sparsifi dictionari (smsd) larg train dataset. address formul joint learn problem, propos onlin algorithm consist closed-form solut optim sens matrix fix sparsifi dictionari stochast method learn sparsifi dictionari larg dataset sens matrix given. benefit train larg dataset, obtain compress sens (cs) system propos algorithm yield much better perform term signal recoveri accuraci exist ones. simul result natur imag demonstr effect suggest onlin algorithm compar exist methods.",
    "use low-dimension parametr signal gener power way enhanc perform signal process statist inference. popular wide explor type dimension reduct sparsity; anoth type gener model signal distributions. gener model base neural networks, gan variat auto-encoders, particularli perform gain applicability. paper studi spike matrix models, low-rank matrix observ noisi channel. problem spars structur spike attract broad attent past literature. here, replac sparsiti assumpt gener modelling, investig consequ statist algorithm properties. analyz bayes-optim perform specif gener model spike. contrast sparsiti assumption, observ region paramet statist perform superior best known algorithm performance. show analyz case approxim messag pass algorithm abl reach optim performance. also design enhanc spectral algorithm analyz perform threshold use random matrix theory, show superior classic princip compon analysis. complement theoret result illustr perform spectral algorithm spike come real datasets.",
    "deep neural network demonstr state-of-the-art perform mani classif tasks. however, inher capabl recogn predict wrong. sever effort recent past detect natur error suggest mechan pose addit energi requirements. address issue, propos ensembl classifi hidden layer enabl energi effici detect natur errors. particular, append relevant-featur base auxiliari cell (racs) class specif binari linear classifi train relev features. consensu rac use detect natur errors. base combin confid racs, classif termin early, therebi result energi effici detection. demonstr effect techniqu variou imag classif dataset cifar-10, cifar-100 tiny-imagenet.",
    "infecti diseas remain among top contributor human ill death worldwide, among mani diseas produc epidem wave infection. unavail specif drug ready-to-us vaccin prevent epidem make situat worse. forc public health officials, health care providers, policymak reli earli warn system gener reliabl accur forecast epidemics. accur forecast epidem assist stakehold tailor countermeasures, vaccin campaigns, staff scheduling, resourc allocation, situat hand, could translat reduct impact disease. unfortunately, past epidem (e.g., dengue, malaria, hepatitis, influenza, recent, covid-19) exhibit nonlinear non-stationari characterist due spread fluctuat base seasonal-depend variabl natur epidemics. analyz wide varieti epidem time seri dataset use maxim overlap discret wavelet transform (modwt) base autoregress neural network call ewnet. modwt techniqu effect character non-stationari behavior season depend epidem time seri improv forecast scheme autoregress neural network propos ensembl wavelet network framework. nonlinear time seri viewpoint, explor asymptot stationar propos ewnet model show asymptot behavior associ markov chain. also theoret investig effect learn stabil choic hidden neuron propos ewnet model.",
    "practic perspective, compar propos ewnet framework sever statistical, machin learning, deep learn model previous use epidem forecasting.",
    "appli neural network control dynam system shown great promises. however, critic yet challeng verifi safeti control system neural-network control loop. previou method verifi neural network control system limit specif activ functions. work, propos new reachabl analysi approach base bernstein polynomi verifi neural-network control system gener form activ functions, i.e., long ensur neural network lipschitz continuous. specifically, consid abstract feedforward neural network bernstein polynomi small subset inputs. quantifi error introduc abstraction, provid theoret error bound estim base theori bernstein polynomi practic sampl base error bound estimation, follow tight lipschitz constant estim approach base forward reachabl analysis. compar previou methods, approach address much broader set neural networks, includ heterogen neural network contain multipl type activ functions. experi result varieti benchmark show effect approach.",
    "common problem large-scal data analysi approxim matrix use combin specif sampl row columns, known cur decomposition. unfortunately, mani real-world environments, abil sampl specif individu row column matrix limit either system constraint cost. paper, consid matrix approxim sampl predefin \\emph{blocks} column (or rows) matrix. present algorithm sampl use column block provid novel guarante qualiti approximation. algorithm applic problem divers biometr data analysi distribut computing. demonstr effect propos algorithm comput block cur decomposit larg matric distribut set multipl node comput cluster, block correspond column (or rows) matrix store node, retriev much less overhead retriev individu column store across differ nodes. biometr setting, row correspond differ user column correspond users' biometr reaction extern stimuli, {\\em e.g.,}~watch video content, particular time instant. signific cost acquir user' reaction lengthi content sampl import scene approxim biometr response. individu time sampl use case cannot queri isol due lack context caus biometr reaction. instead, collect time segment ({\\em i.e.,} blocks) must present user.",
    "practic applic algorithm shown via experiment result use real-world user biometr data content test environment.",
    "recent advanc transfer learn made promis approach domain adapt via transfer learn representations. especi relev altern task limit sampl well-defin label data, common molecul data domain. make transfer learn ideal approach solv molecular learn tasks. adversari reprogram proven success method repurpos neural network altern tasks, work consid sourc altern task within domain. work, propos new algorithm, represent reprogram via dictionari learn (r2dl), adversari reprogram pretrain languag model molecular learn tasks, motiv leverag learn represent massiv state art languag models. adversari program learn linear transform dens sourc model input space (languag data) spars target model input space (e.g., chemic biolog molecul data) use k-svd solver approxim spars represent encod data, via dictionari learning. r2dl achiev baselin establish state art toxic predict model train domain-specif data outperform baselin limit training-data setting, therebi establish avenu domain-agnost transfer learn task molecul data.",
    "consid problem global optim unknown non-convex smooth function zeroth-ord feedback. setup, algorithm allow adapt queri underli function differ locat receiv noisi evalu function valu queri point (i.e. algorithm access zeroth-ord information). optim perform evalu expect differ function valu estim optimum true optimum. contrast classic optim setup, first-ord inform like gradient directli access optim algorithm. show classic minimax framework analysis, roughli character worst-cas queri complex optim algorithm setting, lead excess pessimist results. propos local minimax framework studi fundament difficulti optim smooth function adapt function evaluations, provid refin pictur intrins difficulti zeroth-ord optimization. show function fast level set growth around global minimum, care design optim algorithm identifi near global minim mani fewer queries. special case strongli convex smooth functions, impli converg rate match one develop zeroth-ord convex optim problems. end spectrum, worst-cas smooth function algorithm converg faster minimax rate estim entir unknown function $\\ell_\\infty$-norm. provid intuit effici algorithm attain deriv upper error bounds.",
    "aspect-bas sentiment analysi involv recognit call opinion target express (otes). automat extract otes, supervis learn algorithm usual employ train manual annot corpora. creation corpora labor-intens suffici larg dataset therefor usual avail narrow select languag domains. work, address lack avail annot data specif languag propos zero-shot cross-lingu approach extract opinion target expressions. leverag multilingu word embed share common vector space across variou languag incorpor convolut neural network architectur ote extraction. experi 5 languag give promis results: success train model annot data sourc languag perform accur predict target languag without ever use annot sampl target language. depend sourc target languag pairs, reach perform zero-shot regim 77% model train target languag data. furthermore, increas perform 87% baselin model train target languag data perform cross-lingu learn multipl sourc languages.",
    "covari matrix adapt evolut strategi (cma-es) popular method deal nonconvex and/or stochast optim problem gradient inform available. base cma-es, recent propos matrix adapt evolut strategi (ma-es) provid rather surpris result covari matrix associ oper (e.g., potenti unstabl eigendecomposition) replac cma-e updat transform matrix without loss performance. order simplifi ma-e reduc $\\mathcal{o}\\big(n^2\\big)$ time storag complex $\\mathcal{o}\\big(n\\log(n)\\big)$, present limited-memori matrix adapt evolut strategi (lm-ma-es) effici zeroth order large-scal optimization. algorithm demonstr state-of-the-art perform set establish large-scal benchmarks. explor algorithm problem gener adversari input (non-smooth) random forest classifier, demonstr surpris vulner classifier.",
    "multi-scenario learn (msl) enabl servic provid cater users' fine-grain demand separ servic differ user sectors, e.g., user' geograph region. scenario need optim multipl task-specif target e.g., click rate convers rate, known multi-task learn (mtl). recent solut msl mtl mostli base multi-g mixture-of-expert (mmoe) architecture. mmoe structur typic static design requir domain-specif knowledge, make less effect handl msl mtl. paper, propos novel automat expert select framework multi-scenario multi-task search, name aesm^{2}. aesm^{2} integr msl mtl unifi framework automat structur learning. specifically, aesm^{2} stack multi-task layer multi-scenario layers. hierarch design enabl us flexibl establish intrins connect differ scenarios, time also support high-level featur extract differ tasks. multi-scenario/multi-task layer, novel expert select algorithm propos automat identifi scenario-/task-specif share expert input. experi two real-world large-scal dataset demonstr effect aesm^{2} batteri strong baselines. onlin a/b test also show substanti perform gain multipl metrics. currently, aesm^{2} deploy onlin serv major traffic.",
    "seek deploy machin learn model beyond virtual control domains, critic analyz accuraci fact work time, model truli robust reliable. paper studi strategi implement adversari robustli train algorithm toward guarante safeti machin learn algorithms. provid taxonomi classifi adversari attack defenses, formul robust optim problem min-max set divid 3 subcategories, namely: adversari (re)training, regular approach, certifi defenses. survey recent import result adversari exampl generation, defens mechan adversari (re)train main defens perturbations. also survey mothod add regular term chang behavior gradient, make harder attack achiev objective. alternatively, we'v survey method formal deriv certif robust exactli solv optim problem approxim use upper lower bounds. addition, discuss challeng face recent algorithm present futur research perspectives.",
    "paper consid new famili variat distribut motiv sklar' theorem. famili base new copula-lik densiti hypercub non-uniform margin sampl efficiently, i.e. complex linear dimens state space. then, propos variat densiti suggest seen aris copula-lik densiti use base distribut hypercub gaussian quantil function spars rotat matric normal flows. latter correspond rotat margin complex $\\mathcal{o}(d \\log d)$. provid empir evid variat famili also approxim non-gaussian posterior benefici compar gaussian approximations. method perform larg compar state-of-the-art variat approxim standard regress classif benchmark bayesian neural networks.",
    "sample-effici domain adapt open problem robotics. paper, present affin transport -- variant optim transport, model map state transit distribut sourc target domain affin transformation. first, deriv affin transport framework; then, extend basic framework procrust align model arbitrari affin transformations. evalu method number openai gym sim-to-sim experi simul environments, well sim-to-r domain adapt task robot hit hockeypuck slide stop target position. experiment, evalu result transfer pair dynam domains. result show affin transport significantli reduc model adapt error comparison use original, non-adapt dynam model.",
    "fine-tun deep convolut neural network (cnn) often desired. paper provid overview publicli avail py-faster-rcnn-ft softwar librari use fine-tun vgg_cnn_m_1024 model custom subset microsoft common object context (m coco) dataset. example, improv procedur user look suitabl imag file dataset hand use demo program. implement randomli select imag contain least one object categori model fine-tuned.",
    "inspir strong correl label smooth regularization(lsr) knowledg distillation(kd), propos algorithm lsrkd train boost extend lsr method kd regim appli softer temperature. improv lsrkd teacher correction(tc) method, manual set constant larger proport right class uniform distribut teacher. improv perform lsrkd, develop self-distil method name memory-replay knowledg distil (mrkd) provid knowledg teacher replac uniform distribut one lsrkd. mrkd method penal kd loss current model' output distribut copies' train trajectory. prevent model learn far histor output distribut space, mrkd stabil learn find robust minimum. experi show lsrkd improv lsr perform consist cost, especi sever deep neural network lsr ineffectual. also, mrkd significantli improv singl model training. experi result confirm tc help lsrkd mrkd boost training, especi network failed. overall, lsrkd, mrkd, tc variant compar outperform lsr method, suggest broad applic kd methods.",
    "machin learn algorithm deploy edg devic must meet certain resourc constraint effici requirements. random vector function link (rvfl) network favor applic due simpl design train efficiency. propos modifi rvfl network avoid comput expens matrix oper training, thu expand network' rang potenti applications. modif replac least-squar classifi gener learn vector quantiz (glvq) classifier, employ simpl vector distanc calculations. glvq classifi also consid improv upon certain classif algorithm popularli use area hyperdimension computing. propos approach achiev state-of-the-art accuraci collect dataset uci machin learn repositori - higher previous propos rvfl networks. demonstr approach still achiev high accuraci sever limit train iter (use averag 21% least-squar classifi comput costs).",
    "adapt gradient method adam gain extrem popular due success train complex neural network less sensit hyperparamet tune compar sgd. however, recent shown adam fail converg might caus poor gener -- lead design new, sophist adapt method attempt gener well theoret reliable. technic report focu adabound, promising, recent propos optimizer. present stochast convex problem adabound provabl take arbitrarili long converg term factor account converg rate guarante luo et al. (2019). present new $o(\\sqrt t)$ regret guarante differ assumpt bound functions, provid empir result cifar suggest specif form momentum sgd match adabound' perform less hyperparamet lower comput costs.",
    "hybrid quantum-class algorithm propos potenti viabl applic quantum computers. particular exampl - variat quantum eigensolver, vqe - design determin global minimum energi landscap specifi quantum hamiltonian, make appeal need quantum chemistry. experiment realiz report recent year theoret estim effici subject intens effort. consid perform vqe techniqu hubbard-lik model describ one-dimension chain fermion compet nearest- next-nearest-neighbor interactions. find recov vqe solut allow one obtain correl function ground state consist exact result. also studi barren plateau phenomenon hamiltonian question find sever effect depend encod fermion qubits. result consist current knowledg barren plateau quantum optimization.",
    "information-theoret measur wide adopt design featur learn decis problems. inspir this, look relationship i) weak form inform loss shannon sens ii) oper loss minimum probabl error (mpe) sens consid famili lossi continu represent (features) continu observation. present sever result shed light interplay. first result offer lower bound weak form inform loss function respect oper loss adopt discret lossi represent (quantization) instead origin raw observation. this, main result show specif form vanish inform loss (a weak notion asymptot inform sufficiency) impli vanish mpe loss (or asymptot oper sufficiency) consid gener famili lossi continu representations. theoret find support observ select featur represent attempt captur inform suffici appropri learning, select rather conserv design principl intend goal achiev mpe classification. support last point, structur conditions, show possibl adopt altern notion inform suffici (strictli weaker pure suffici mutual inform sense) achiev oper suffici learning.",
    "strong correl neuron filter significantli weaken gener abil neural networks. inspir well-known tamm problem, propos novel divers regular method address issue, make normal weight vector neuron filter distribut hyperspher uniformli possible, maxim minim pairwis angl (mma). method easili exert effect plug mma regular term loss function neglig comput overhead. mma regular simple, efficient, effective. therefore, use basic regular method neural network training. extens experi demonstr mma regular abl enhanc gener abil variou modern model achiev consider perform improv cifar100 tinyimagenet datasets. addition, experi face verif show mma regular also effect featur learning. code avail at: https://github.com/wznpub/mma_regularization.",
    "nonlinear differenti equat rare admit closed-form solutions, thu requir numer time-step algorithm approxim solutions. further, mani system character multiscal physic exhibit dynam vast rang timescales, make numer integr comput expens due numer stiffness. work, develop hierarchi deep neural network time-stepp approxim flow map dynam system dispar rang time-scales. result model pure data-driven leverag featur multiscal dynamics, enabl numer integr forecast accur highli efficient. moreover, similar idea use coupl neural network-bas model classic numer time-steppers. multiscal hierarch time-step scheme provid import advantag current time-step algorithms, includ (i) circumv numer stiff due dispar time-scales, (ii) improv accuraci comparison lead neural-network architectures, (iii) effici long-tim simulation/forecast due explicit train slow time-scal dynamics, (iv) flexibl framework paralleliz may integr standard numer time-step algorithms. method demonstr wide rang nonlinear dynam systems, includ van der pol oscillator, lorenz system, kuramoto-sivashinski equation, fluid flow pass cylinder; audio video signal also explored. sequenc gener examples, benchmark algorithm state-of-the-art methods, lstm, reservoir computing, clockwork rnn. despit structur simplic method, outperform compet method numer integration.",
    "predict unschedul breakdown plasma etch equip reduc mainten cost product loss semiconductor industry. however, plasma etch complex procedur hard captur relev equip properti behavior singl physic model. machin learn offer altern predict upcom machin failur base relev data points. paper, describ three differ machin learn task use purpose: (i) predict time-to-failur (ttf), (ii) predict health state, (iii) predict ttf interv equipment. result show train machin learn model outperform benchmark resembl human judgment three tasks. suggest machin learn offer viabl altern current deploy plasma etch equip mainten strategi decis make processes.",
    "fixed-point iter heart numer comput often comput bottleneck real-tim applic typic need fast solut moder accuracy. present neural fixed-point acceler combin idea meta-learn classic acceler method automat learn acceler fixed-point problem drawn distribution. appli framework scs, state-of-the-art solver convex cone programming, design model loss function overcom challeng learn unrol optim acceler instabilities. work bring neural acceler optim problem express cvxpy. sourc code behind paper avail https://github.com/facebookresearch/neural-sc",
    "machin learn workflow develop anecdot regard iter process trial-and-error humans-in-the-loop. however, awar quantit evid corrobor popular belief. quantit character iter serv benchmark machin learn workflow develop practice, aid develop human-in-the-loop machin learn systems. end, conduct small-scal survey appli machin learn literatur five distinct applic domains. collect distil statist role iter within machin learn workflow development, report preliminari trend insight investigation, start point toward benchmark. base findings, final describ desiderata effect versatil human-in-the-loop machin learn system cater user divers domains.",
    "transfer reinforc learn aim improv learn perform target task use knowledg experienc sourc tasks. successor featur (sf) promin transfer mechan domain reward function chang tasks. reevalu expect return previous learn polici new target task transfer knowledge. limit factor sf framework assumpt reward linearli decompos successor featur reward weight vector. propos novel sf mechanism, $\\xi$-learning, base learn cumul discount probabl successor features. crucially, $\\xi$-learn allow reevalu expect return polici gener reward functions. introduc two $\\xi$-learn variations, prove convergence, provid guarante transfer performance. experiment evalu base $\\xi$-learn function approxim demonstr promin advantag $\\xi$-learn avail mechan gener reward functions, also case linearli decompos reward functions.",
    "collabor human requir rapidli adapt individu strengths, weaknesses, preferences. unfortunately, standard multi-ag reinforc learn techniques, self-play (sp) popul play (pp), produc agent overfit train partner gener well humans. alternatively, research collect human data, train human model use behavior cloning, use model train \"human-aware\" agent (\"behavior clone play\", bcp). approach improv gener agent new human co-players, involv oner expens step collect larg amount human data first. here, studi problem train agent collabor well human partner without use human data. argu crux problem produc divers set train partners. draw inspir success multi-ag approach competit domains, find surprisingli simpl approach highli effective. train agent partner best respons popul self-play agent past checkpoint taken throughout training, method call fictiti co-play (fcp). experi focu two-play collabor cook simul recent propos challeng problem coordin humans. find fcp agent score significantli higher sp, pp, bcp pair novel agent human partners. furthermore, human also report strong subject prefer partner fcp agent baselines.",
    "electr vehicl (evs) spread fast promis provid better perform comfort, all, help face climat change. despit success, cost still challenge. one expens compon ev lithium-ion batteries, becam standard energi storag wide rang applications. precis estim remain use life (rul) batteri pack open reus thu help reduc cost ev improv sustainability. correct rul estim use quantifi residu market valu batteri pack. custom decid sell batteri still value, i.e., exce end life target applic still reus second domain without compromis safeti reliability. paper, propos use deep learn approach base lstm autoencod estim rul li-ion batteries. compar propos far literature, employ measur ensur applic method also real deploy application. measur includ (1) avoid use non-measur variabl input, (2) employ appropri dataset wide variabl differ conditions, (3) use cycl defin rul.",
    "sever knee osteoarthr grade use 5-point kellgren-lawr (kl) scale healthi knee assign grade 0, subsequ grade 1-4 repres increas sever affliction. although sever method propos recent year develop model automat predict kl grade given radiograph, model develop evalu dataset sourc india. model fail perform well radiograph indian patients. paper, propos novel method use convolut neural network automat grade knee radiograph kl scale. method work two connect stages: first stage, object detect model segment individu knee rest image; second stage, regress model automat grade knee separ kl scale. train model use publicli avail osteoarthr initi (oai) dataset demonstr fine-tun model evalu dataset privat hospit significantli improv mean absolut error 1.09 (95% ci: 1.03-1.15) 0.28 (95% ci: 0.25-0.32). additionally, compar classif regress model built task demonstr regress outperform classification.",
    "china, stroke first lead caus death recent years. major caus long-term physic cognit impairment, bring great pressur nation public health system. evalu risk get stroke import prevent treatment stroke china. data set 2000 hospit stroke patient 2018 27583 resid year 2017 2020 analyz study. due data incompleteness, inconsistency, non-structur formats, miss valu raw data fill -1 abnorm class. clean features, three model risk level get stroke built use machin learn methods. import \"8+2\" factor china nation stroke prevent project (cspp) evalu via decis tree random forest models. except \"8+2\" factor import featur shap1 valu lifestyl information, demograph information, medic measur evalu rank via random forest model. furthermore, logist regress model appli evalu probabl get stroke differ risk levels. base censu data commun hospit shanxi province, investig differ risk factor get stroke rank interpret machin learn models. result show hypertens (systol blood pressure, diastol blood pressure), physic inact (lack sports), overweight (bmi) rank top three high-risk factor get stroke shanxi province.",
    "probabl get stroke person also predict via machin learn model.",
    "common techniqu gener b-mode ultrasound (us) imag delay sum (das) beamforming, signal receiv transduc array sampl appropri delay applied. necessit sampl rate exceed nyquist rate use larg number antenna element ensur suffici imag quality. recent propos method reduc sampl rate array size reli imag recoveri use iter algorithms, base compress sens (cs) finit rate innov (fri) frameworks. iter algorithm typic requir larg number iterations, make difficult use real-time. here, propos reconstruct method sub-nyquist sampl time spatial domain, base unfold ista algorithm, result effici interpret deep network. input network subsampl beamform signal summat delay frequenc domain, requir subset us signal store recovery. method allow reduc number array elements, sampl rate, comput time ensur high qualiti imag performance. use \\emph{in vivo} data demonstr propos method yield high-qual imag reduc data volum tradit use 36 times. term imag resolut contrast, techniqu outperform previous suggest method well da minimum-vari (mv) beamforming, pave way real-tim applic recoveri methods.",
    "recent work shown collabor filter-bas recommend system improv incorpor side information, natur languag reviews, way regular deriv product representations. motiv success approach, introduc two differ model review studi effect collabor filter performance. previou state-of-the-art approach base latent dirichlet alloc (lda) model reviews, model explor neural network based: bag-of-word product-of-expert model recurr neural network. demonstr increas flexibl offer product-of-expert model allow achiev state-of-the-art perform amazon review dataset, outperform lda-bas approach. however, interestingly, greater model power offer recurr neural network appear undermin model' abil act regular product representations.",
    "deep learn gain substanti popular recent years. develop mainli reli librari tool add deep learn capabl software. kind bug frequent found software? root caus bugs? impact bug have? stage deep learn pipelin bug prone? antipatterns? understand characterist bug deep learn softwar potenti foster develop better deep learn platforms, debug mechanisms, develop practices, encourag develop analysi verif frameworks. therefore, studi 2716 high-qual post stack overflow 500 bug fix commit github five popular deep learn librari caffe, keras, tensorflow, theano, torch understand type bugs, root caus bugs, impact bugs, bug-pron stage deep learn pipelin well whether common antipattern found buggi software. key find studi include: data bug logic bug sever bug type deep learn softwar appear 48% times, major root caus bug incorrect model paramet (ips) structur ineffici (si) show 43% times. also found bug usag deep learn librari common antipattern lead strong correl bug type among libraries.",
    "deep learn model shown great potenti image-bas diagnosi assist clinic decis making. time, increas number report rais concern potenti risk machin learn could amplifi exist health dispar due human bias embed train data. great import care investig extent bias may reproduc even amplifi wish build fair artifici intellig systems. seyyed-kalantari et al. advanc convers analys perform diseas classifi across popul subgroups. rais perform dispar relat underdiagnosi point concern; identifi area analysi believ deserv addit attention. specifically, wish highlight theoret practic difficulti associ assess model fair test data drawn bias distribut train data, especi sourc amount bias unknown.",
    "studi repeat persuas set sender receiver, time $t$, sender observ payoff-relev state drawn independ ident unknown prior distribution, share state inform receiver, myopic choos action. standard setting, sender seek persuad receiv choos action align sender' prefer select share inform state. however, contrast standard models, sender know prior, persuad gradual learn prior fly. studi sender' learn problem make persuas action recommend achiev low regret optim persuas mechan knowledg prior distribution. main posit result algorithm that, high probability, persuas across round achiev $o(\\sqrt{t\\log t})$ regret, $t$ horizon length. core philosophi behind design algorithm leverag robust sender' ignor prior. intuitively, time algorithm maintain set candid priors, choos persuas scheme simultan persuas them. demonstr effect algorithm, prove algorithm achiev regret better $\\omega(\\sqrt{t})$, even persuas requir significantli relaxed. therefore, algorithm achiev optim regret sender' learn problem term logarithm $t$.",
    "introduc causal markov decis process (c-mdps), new formal sequenti decis make combin standard mdp formul causal structur state transit reward functions. mani contemporari emerg applic area digit healthcar digit market benefit model c-mdp due causal mechan underli relationship intervent states/rewards. propos causal upper confid bound valu iter (c-ucbvi) algorithm exploit causal structur c-mdp improv perform standard reinforc learn algorithm take causal knowledg account. prove c-ucbvi satisfi $\\tilde{o}(hs\\sqrt{zt})$ regret bound, $t$ total time steps, $h$ episod horizon, $s$ cardin state space. notably, regret bound scale size actions/intervent ($a$), scale causal graph depend quantiti $z$ exponenti smaller $a$. extend c-ucbvi factor mdp setting, propos causal factor ucbvi (cf-ucbvi) algorithm, reduc regret exponenti term $s$. furthermore, show rl algorithm linear mdp problem also incorpor c-mdps. empir show benefit causal approach variou set valid algorithm theoret results.",
    "unman aerial vehicl (uav) pose major risk aviat safety, due neglig malici use. reason, autom detect track uav fundament task aerial secur systems. common technolog uav detect includ visible-band thermal infrar imaging, radio frequenc radar. recent advanc deep neural network (dnns) image-bas object detect open possibl use visual inform detect track task. furthermore, detect architectur implement backbon visual track systems, therebi enabl persist track uav incursions. date, comprehens perform benchmark exist appli dnn visible-band imageri uav detect tracking. end, three dataset vari environment condit uav detect tracking, compris total 241 video (331,486 images), assess use four detect architectur three track frameworks. best perform detector architectur obtain map 98.6% best perform track framework obtain mota 96.3%. cross-mod evalu carri visibl infrar spectrums, achiev maxim 82.8% map visibl imag train infrar modality. result provid first public multi-approach benchmark state-of-the-art deep learning-bas method give insight detect track architectur effect uav domain.",
    "studi asymptot behavior second-ord algorithm mix newton' method inerti gradient descent non-convex landscapes. show that, despit newtonian behavior methods, almost alway escap strict saddl points. also evid role play hyper-paramet method qualit behavior near critic points. theoret result support numer illustrations.",
    "consid problem detect anomali larg dataset. propos framework call partial identif captur intuit anomali easi distinguish overwhelm major point rel attribut values. formal intuition, propos geometr anomali measur point call pidscore, measur minimum densiti data point subcub contain point. present pidforest: random forest base algorithm find anomali base definition. show perform favor comparison sever popular anomali detect methods, across broad rang benchmarks. pidforest also provid succinct explan point label anomalous, provid set featur rang rel uncommon dataset.",
    "goal iarai competit traffic4cast predict city-wid traffic statu within 15-minut time window, base inform previou hour. traffic statu given multi-channel imag (one pixel roughli correspond 100x100 meters), one channel indic traffic volume, anoth one averag speed vehicles, third one rough heading. part work competition, evalu mani differ network architectures, analyz statist properti given data detail, thought transform problem abl take addit spatio-tempor context-inform account, street network, posit traffic lights, weather. document summar effort led best submission, give insight approach evaluated, work well imagined.",
    "reinforc learning, discount factor $\\gamma$ control agent' effect plan horizon. traditionally, paramet consid part mdp; however, deep reinforc learn algorithm tend becom unstabl effect plan horizon long, recent work refer $\\gamma$ hyper-paramet -- thu chang underli mdp potenti lead agent toward sub-optim behavior origin task. work, introduc \\emph{reward tweaking}. reward tweak learn surrog reward function $\\tild r$ discount set induc optim behavior origin finite-horizon total reward task. theoretically, show exist surrog reward lead optim origin task discuss robust approach. additionally, perform experi high-dimension continu control task show reward tweak guid agent toward better long-horizon return although plan short horizons.",
    "dss serv management, operations, plan level organ help make decisions, may rapidli chang easili specifi advance. data mine vital role extract import inform help decis make decis support system. integr data mine decis support system (dss) lead improv perform enabl tackl new type problems. artifici intellig method improv qualiti decis support, becom embed mani applic rang ant lock automobil brake day interact search engines. provid variou machin learn techniqu support data mining. classif one main valuabl task data mining. sever type classif algorithm suggested, test compar determin futur trend base unseen data. singl algorithm found superior other data sets. object paper compar variou classif algorithm frequent use data mine decis support systems. three decis tree base algorithms, one artifici neural network, one statistical, one support vector machin without ada boost one cluster algorithm test compar four data set differ domain term predict accuracy, error rate, classif index, comprehens train time. experiment result demonstr genet algorithm (ga) support vector machin base algorithm better term predict accuracy.",
    "svm without adaboost shall first choic context speed predict accuracy. adaboost improv accuraci svm cost larg train time.",
    "random convolut kernel transform (rocket) fast, efficient, novel approach time seri featur extraction, use larg number randomli initi convolut kernels, classif repres featur linear classifier, without train kernels. sinc kernel gener randomly, portion kernel may posit contribut perform model. hence, select import kernel prune redund less import one necessari reduc comput complex acceler infer rocket. select kernel combinatori optim problem. paper, kernel select process model optim problem population-bas approach propos select import kernels. approach evalu standard time seri dataset result show averag achiev similar perform origin model prune 60% kernels. cases, achiev similar perform use 1% kernels.",
    "integr renew energi sources, power grid oper need realist inform effect energi product consumpt assess grid stability. recently, research scenario plan benefit util gener adversari network (gans) gener model oper scenario planning. scenarios, oper examin tempor well spatial influenc differ energi sourc grid. analysi renew energi resourc affect grid enabl oper evalu stabil identifi potenti weak point limit transformer. however, due novelty, limit studi well gan model underli power distribution. analysi essenti because, e.g., especi extrem situat low high power gener requir evalu grid stability. conduct compar studi wasserstein distance, binary-cross-entropi loss, gaussian copula baselin appli two wind two solar dataset limit data compar previou studies. gan achiev good result consid limit amount data, wasserstein gan superior model tempor spatial relations, power distribution. besid evalu gener power distribut farms, essenti assess terrain specif distribut wind scenarios. terrain specif power distribut affect grid differ gener power magnitude.",
    "therefore, second study, show even simultan learn distribut wind park terrain specif patterns, gan capabl model individu also face limit data.",
    "recommend system play central role provid individu access inform services. paper focus collabor filtering, approach exploit share structur among mind-lik user similar items. particular, focu formal probabilist framework known markov random field (mrf). address open problem structur learn introduc sparsity-induc algorithm automat estim interact structur user items. item-item user-us correl network obtain by-product. large-scal experi movi recommend date match dataset demonstr power propos method.",
    "mine complex data form network increas interest mani scientif disciplines. network commun correspond dens connect subnetworks, often repres key function part real-world systems. work, propos silhouett commun detect (scd), approach detect communities, base cluster network node embeddings, i.e. real valu represent node deriv neighborhoods. investig perform propos scd approach 234 synthet networks, well real-lif social network. even though scd base form modular optimization, perform compar better state-of-the-art commun detect algorithms, infomap louvain algorithms. further, demonstr scd' output use along domain ontolog semant subgroup discovery, yield human-understand explan commun detect real-lif protein interact network. embedding-based, scd wide applic test out-of-the-box part mani exist network learn explor pipelines.",
    "mobil robot complex morpholog essenti travers rough terrain urban search & rescu mission (usar). sinc teleoper complex morpholog caus high cognit load operator, morpholog control autonomously. autonom control measur robot state surround terrain usual partial observable, thu data often incomplete. margin control miss measur evalu explicit safeti condition. safeti condit violated, tactil terrain explor body-mount robot arm gather miss data.",
    "propos simpl method identifi continu lie algebra symmetri dataset regress artifici neural network. propos take advantag $ \\mathcal{o}(\\epsilon^2)$ scale output variabl infinitesim symmetri transform input variables. symmetri transform gener post-training, methodolog reli sampl full represent space bin dataset, possibl fals identif minimised. demonstr method su(3)-symmetr (non-) linear $\\sigma$ model.",
    "given small corpu $\\mathcal d_t$ pertain limit set focus topics, goal train embed accur captur sens word topic spite limit size $\\mathcal d_t$. embed may use variou task involv $\\mathcal d_t$. popular strategi limit data set adapt pre-train embed $\\mathcal e$ train larg corpus. correct sens drift, fine-tuning, regularization, projection, pivot propos recently. among these, regular inform word' corpu frequenc perform well, improv upon use new regular base stabil cooccurr words. however, thorough comparison across ten topics, span three tasks, standard set hyper-parameters, reveal even best embed adapt strategi provid small gain beyond well-tun baselines, mani earlier comparison ignored. bold departur adapt pretrain embeddings, propos use $\\mathcal d_t$ probe, attend to, borrow fragment large, topic-rich sourc corpu (such wikipedia), need corpu use pretrain embeddings. step made scalabl practic suitabl indexing. reach surpris conclus even limit corpu augment use adapt embeddings, suggest non-domin sens inform may irrevoc obliter pretrain embed cannot salvag adaptation.",
    "neural network current domin machin learn commun good reasons. accuraci complex task imag classif unriv moment recent improv reason easi train. nevertheless, neural network lack robust interpretability. prototype-bas vector quantiz method hand known robust interpretable. reason, propos techniqu strategi merg approaches. contribut particularli highlight similar outlin construct prototype-bas classif layer multilay networks. additionally, provid alternative, prototype-based, approach classic convolut operation. numer result part report, instead focu lay establish strong theoret framework. publish framework respect theoret consider justif final numer experi hope jump-start incorpor prototype-bas learn neural network vice versa.",
    "paper focus detect anomali digit video broadcast (dvb) system providers' perspective. learn probabilist determinist real time automaton profil benign behavior encrypt control dvb control access system. profil use one-class classifier. anomal item test sequenc detect sequenc accept learn model.",
    "shown deep neural network prone overfit bias train data. toward address issue, meta-learn employ meta model correct train bias. despit promis performances, super slow train current bottleneck meta learn approaches. paper, introduc novel faster meta updat strategi (famus) replac expens step meta gradient comput faster layer-wis approximation. empir find famu yield reason accur also low-vari approxim meta gradient. conduct extens experi verifi propos method two tasks. show method abl save two-third train time still maintain compar achiev even better gener performance. particular, method achiev state-of-the-art perform synthet realist noisi labels, obtain promis perform long-tail recognit standard benchmarks.",
    "paper studi applic machin learn extract market impli featur histor risk neutral corpor bond yields. consid exampl hypothet illiquid fix incom market. choos surrog liquid market, appli denois autoencod algorithm field comput vision pattern recognit learn featur miss yield paramet histor impli data instrument trade chosen liquid market. result train machin learn algorithm compar output point in- time 2 dimension interpol algorithm known thin plate spline. finally, perform two algorithm compared.",
    "credit assign tradit recurr neural network usual involv back-propag long chain tie weight matrices. length chain scale linearli number time-step network run time-step. creat mani problems, vanish gradients, well studied. contrast, nnem' architectur recurr activ involv long chain activ (though architectur ntm util tradit recurr architectur controller). rather, extern store embed vector use time-step, messag pass previou time-steps. mean vanish gradient problem, necessari gradient path short. however, path extrem numer (one per embed vector memory) reus long time (until leav memory). thus, forward-pass inform memori must store entir durat memory. problemat addit storag far surpass actual memories, extent larg memori infeas back-propag high dimension settings. one way get around need hold onto forward-pass inform recalcul forward-pass whenev gradient inform available. however, observ larg store domain interest, direct reinstat forward pass cannot occur. instead, reli learn autoencod reinstat observation, use embed network recalcul forward-pass.",
    "sinc recalcul embed vector unlik perfectli match one store memory, tri 2 approxim util error gradient w.r.t. vector memory.",
    "dearth prescrib guidelin physician one key driver current opioid epidem unit states. work, analyz medic pharmaceut claim data draw insight characterist patient prone advers outcom initi synthet opioid prescription. toward end, propos gener model allow discoveri observ data subgroup demonstr enhanc diminish causal effect due treatment. approach model sub-popul mixtur distribution, use sparsiti enhanc interpretability, jointli learn nonlinear predictor potenti outcom better adjust confounding. approach lead human-interpret insight discov subgroups, improv practic util decis support",
    "human educ system train one student multipl experts. mixture-of-expert (moe) power spars architectur includ multipl experts. however, spars moe model hard implement, easi overfit, hardware-friendly. work, inspir human educ model, propos novel task, knowledg integration, obtain dens student model (ones) knowledg one spars moe. investig task propos gener train framework includ knowledg gather knowledg distillation. specifically, first propos singular valu decomposit knowledg gather (svd-kg) gather key knowledg differ pretrain experts. refin dens student model knowledg distil offset nois gathering. imagenet, one preserv $61.7\\%$ benefit moe. one achiev $78.4\\%$ top-1 accuraci $15$m parameters. four natur languag process datasets, one obtain $88.2\\%$ moe benefit outperform sota $51.7\\%$ use architectur train data. addition, compar moe counterpart, one achiev $3.7 \\times$ infer speedup due hardware-friendli architecture.",
    "covid-19 spread across globe immens rate left healthcar system incapacit diagnos test patient need rate. studi shown promis result detect covid-19 viral bacteri pneumonia chest x-rays. autom covid-19 test use medic imag speed test process patient health care system lack suffici number reverse-transcript polymeras chain reaction (rt-pcr) tests. supervis deep learn model convolut neural network (cnn) need enough label data class correctli learn task detection. gather label data cumbersom task requir time resourc could strain health care system radiologist earli stage pandem covid-19. study, propos random gener adversari network (randgan) detect imag unknown class (covid-19) known label class (normal viral pneumonia) without need label train data unknown class imag (covid-19). use largest publicli avail covid-19 chest x-ray dataset, covidx, compris normal, pneumonia, covid-19 imag multipl public databases. work, use transfer learn segment lung covidx dataset. next, show segment region interest (lungs) vital correctli learn task classification, specif dataset contain imag differ resourc case covidx dataset.",
    "finally, show improv result detect covid-19 case use gener model (randgan) compar convent gener adversari network (gans) anomali detect medic images, improv area roc curv 0.71 0.77.",
    "propos novel framework name vioc integr ontology-bas background knowledg form $n$-ball concept embed neural network base vision architecture. approach consist two compon - convert symbol knowledg ontolog continu space learn n-ball embed captur properti subsumpt disjointness, guid train infer vision model use learnt embeddings. evalu vioc use task few-shot imag classification, demonstr superior perform two standard benchmarks.",
    "container lightweight applic virtual technology, provid high environment consistency, oper system distribut portability, resourc isolation. exist mainstream cloud servic provid preval adopt contain technolog distribut system infrastructur autom applic management. handl autom deployment, maintenance, autoscaling, network container applications, contain orchestr propos essenti research problem. however, highli dynam divers featur cloud workload environ consider rais complex orchestr mechanisms. machin learn algorithm accordingli employ contain orchestr system behavior model predict multi-dimension perform metrics. insight could improv qualiti resourc provis decis respons chang workload complex environments. paper, present comprehens literatur review exist machin learning-bas contain orchestr approaches. detail taxonomi propos classifi current research common features. moreover, evolut machin learning-bas contain orchestr technolog year 2016 2021 design base object metrics. compar analysi review techniqu conduct accord propos taxonomies, emphasi key characteristics. finally, variou open research challeng potenti futur direct highlighted.",
    "author white paper met 16-17 januari 2020 new jersey institut technology, newark, nj, 2-day workshop brought togeth group heliophysicists, data providers, expert modelers, computer/data scientists. object discuss critic develop prospect applic machin and/or deep learn techniqu data analysis, model forecast heliophysics, shape strategi develop field. workshop combin set plenari session featur invit introductori talk interleav set open discuss sessions. outcom discuss encapsul white paper also featur top-level list recommend agre participants.",
    "consid problem learn classifi observ function data. here, data-point take form singl time-seri contain numer features. assum seri come binari label, problem learn predict label new come time-seri considered. hereto, notion {\\em margin} underli classic support vector machin extend continu version data. longitudin support vector machin also convex optim problem dual form deriv well. empir result specifi case signific test indic efficaci innov algorithm analyz long-term multivari data.",
    "supervis learn larg scale label dataset deep layer model made paradigm shift divers area learn recognition. however, approach still suffer gener issu presenc domain shift train test data distribution. regard, unsupervis domain adapt algorithm propos directli address domain shift problem. paper, approach problem transduct perspective. incorpor domain shift transduct target infer framework jointli solv asymmetr similar metric optim transduct target label assignment. also show model easili extend deep featur learn order learn featur discrimin target domain. experi show propos method significantli outperform state-of-the-art algorithm object recognit digit classif experi larg margin.",
    "mutual inform agent action environ state (mias) quantifi influenc agent environment. recently, found maxim mia use intrins motiv artifici agents. literature, term empower use repres maximum mia certain state. empower shown solv broad rang reinforc learn problems, calcul arbitrari dynam challeng problem reli estim mutual information. exist approaches, reli sampling, limit low dimension spaces, high-confid distribution-fre lower bound mutual inform requir exponenti number samples. work, develop novel approach estim empower unknown dynam visual observ only, without need sampl mias. core idea repres relat action sequenc futur state use stochast dynam model latent space specif form. allow us effici comput empower \"water-filling\" algorithm inform theory. construct embed deep neural network train sophist object function. experiment result show design embed preserv information-theoret properti origin dynamics.",
    "real-world applications, seldom case given observ evolv independ environment. social networks, users' behavior result peopl interact with, news feed, trend topics. natur language, mean phrase emerg combin words. gener medicine, diagnosi establish basi interact symptoms. here, propos new model, interact mix membership stochast block model (immsbm), investig role interact entiti (hashtags, words, memes, etc.) quantifi import within aforement corpora. find interact play import role corpora. infer tasks, take account lead averag rel chang respect non-interact model 150\\% probabl outcome. furthermore, role greatli improv predict power model. find suggest neglect interact model real-world phenomena might lead incorrect conclus drawn.",
    "learn strategi game (e.g. starcraft, poker) requir discoveri divers policies. often achiev iter train new polici exist ones, grow polici popul robust exploit. iter approach suffer two issu real-world games: a) finit budget, approxim best-respons oper iter need truncating, result under-train good-respons popul population; b) repeat learn basic skill iter wast becom intract presenc increasingli strong opponents. work, propos neural popul learn (neupl) solut issues. neupl offer converg guarante popul best-respons mild assumptions. repres popul polici within singl condit model, neupl enabl transfer learn across policies. empirically, show generality, improv perform effici neupl across sever test domains. interestingly, show novel strategi becom accessible, less, neural popul expands.",
    "hardware-bas acceler extens attempt facilit mani computationally-intens mathemat operations. paper propos fpga-bas architectur acceler convolut oper - complex expens comput step appear mani convolut neural network models. target design standard convolut operation, intend launch product edge-ai solution. project' purpos produc fpga ip core process convolut layer time. system develop deploy ip core variou fpga famili use verilog hdl primari design languag architecture. experiment result show singl comput core synthes simpl edg comput fpga board offer 0.224 gops. board fulli utilized, 4.48 gop achieved.",
    "despit superior convolut neural network demonstr time seri model forecasting, fulli explor design neural network architectur tune hyper-parameters. inspir increment construct strategi build random multilay perceptron, propos novel error-feedback stochast model (esm) strategi construct random convolut neural network (esm-cnn) time seri forecast task, build network architectur adaptively. esm strategi suggest random filter neuron error-feedback fulli connect layer increment ad steadili compens predict error construct process, filter select strategi introduc enabl esm-cnn extract differ size tempor features, provid help inform iter process prediction. perform esm-cnn justifi predict accuraci one-step-ahead multi-step-ahead forecast task respectively. comprehens experi synthet real-world dataset show propos esm-cnn outperform state-of-art random neural networks, also exhibit stronger predict power less comput overhead comparison train state-of-art deep neural network models.",
    "similar play fundament role mani areas, includ data mining, machin learning, statist variou appli domains. inspir success ensembl method flexibl trees, propos learn similar kernel call rpf-kernel random project forest (rpforests). theoret analysi reveal highli desir properti rpf-kernel: far-away (dissimilar) point low similar valu nearbi (similar) point would high similarity}, similar nativ interpret probabl point remain leaf node growth rpforests. learn rpf-kernel lead effect cluster algorithm--rpfcluster. wide varieti real benchmark datasets, rpfcluster compar favor k-mean clustering, spectral cluster state-of-the-art cluster ensembl algorithm--clust forests. approach simpl implement readili adapt geometri underli data. given desir theoret properti competit empir perform appli clustering, expect rpf-kernel applic mani problem unsupervis natur regular supervis weakli supervis settings.",
    "covid-19, diseas caus sars-cov-2 virus, declar pandem world health organization, report 18 million confirm case august 5, 2020. review, present overview recent studi use machin learn and, broadly, artifici intelligence, tackl mani aspect covid-19 crisis. identifi applic address challeng pose covid-19 differ scales, including: molecular, identifi new exist drug treatment; clinical, support diagnosi evalu prognosi base medic imag non-invas measures; societal, track epidem accompani infodem use multipl data sources. also review datasets, tools, resourc need facilit artifici intellig research, discuss strateg consider relat oper implement multidisciplinari partnership open science. highlight need intern cooper maxim potenti ai futur pandemics.",
    "iter new improv ocr solut enforc decis make come target right candid reprocessing. especi appli underli data collect consider size rather divers term fonts, languages, period public consequ ocr quality. articl captur effort nation librari luxembourg support target decisions. crucial order guarante low comput overhead reduc qualiti degrad risks, combin quantifi ocr improvement. particular, work explain methodolog librari respect text block level qualiti assessment. extens technique, regress model, abl take account enhanc potenti new ocr engine, also presented. mark promis approaches, especi cultur institut deal histor data lower quality.",
    "propos effici meta-algorithm bayesian estim problem base low-degre polynomials, semidefinit programming, tensor decomposition. algorithm inspir recent lower bound construct sum-of-squar relat method moments. focu sampl complex bound tight possibl (up addit lower-ord terms) often achiev statist threshold conjectur comput thresholds. algorithm recov best known bound commun detect spars stochast block model, widely-studi class estim problem commun detect graphs. obtain first recoveri guarante mixed-membership stochast block model (airoldi et el.) constant averag degre graphs---up conjectur comput threshold model. show algorithm exhibit sharp comput threshold stochast block model multipl commun beyond kesten--stigum bound---giv evid task may requir exponenti time. basic strategi algorithm strikingli simple: comput best-poss low-degre approxim moment posterior distribut paramet use robust tensor decomposit algorithm recov paramet approxim posterior moments.",
    "paper discuss novel framework multiclass learning, defin suitabl coding/decod strategy, name simplex coding, allow gener multipl class relax approach commonli use binari classification. framework, relax error analysi develop avoid constraint consid hypothes class. moreover, show set possibl deriv first provabl consist regular method training/tun complex independ number classes. tool convex analysi introduc use beyond scope paper.",
    "recent find indic over-parametrization, crucial success train deep neural networks, also introduc larg amount redundancy. tensor method potenti effici parametr over-complet represent leverag redundancy. paper, propos fulli parametr convolut neural network (cnns) singl high-order, low-rank tensor. previou work network tensor focus parametr individu layer (convolut fulli connected) only, perform tensor layer-by-lay separately. contrast, propos jointli captur full structur neural network parametr singl high-ord tensor, mode repres architectur design paramet network (e.g. number convolut blocks, depth, number stacks, input features, etc). parametr allow regular whole network drastic reduc number parameters. model end-to-end trainabl low-rank structur impos weight tensor act implicit regularization. studi case network rich structure, name fulli convolut network (fcns), propos parametr singl 8th-order tensor. show approach achiev superior perform small compress rates, attain high compress rate neglig drop accuraci challeng task human pose estimation.",
    "differenti privat model seek protect privaci data model train on, make import compon model secur privacy. time, data scientist machin learn engin seek use uncertainti quantif method ensur model use action possible. explor tension uncertainti quantif via dropout privaci conduct membership infer attack model without differenti privacy. find model larg dropout slightli increas model' risk succumb membership infer attack case includ differenti privat models.",
    "recent work reveal network embed techniqu enabl mani machin learn model handl divers downstream task graph structur data. however, previou method usual focu learn embed singl network, learn represent transfer multipl networks. hence, import design network embed algorithm support downstream model transfer differ networks, known domain adaptation. paper, propos novel domain adapt network embed framework, appli graph convolut network learn transfer embeddings. dane, node multipl network encod vector via share set learnabl paramet vector share align embed space. distribut embed differ network align adversari learn regularization. addition, dane' advantag learn transfer network embed guarante theoretically. extens experi reflect propos framework outperform state-of-the-art network embed baselin cross-network domain adapt tasks.",
    "cryptographi data scienc research grew exponenti internet boom. legaci encrypt techniqu forc user make trade-off usability, convenience, security. encrypt make valuabl data inaccessible, need decrypt time perform operation. billion dollar could saved, million peopl could benefit cryptographi method compromis usability, convenience, security. homomorph encrypt one paradigm allow run arbitrari oper encrypt data. enabl us run sophist machin learn algorithm without access underli raw data. thus, homomorph learn provid abil gain insight sensit data neglect due variou government organ privaci rules. paper, trace back idea homomorph learn formal pose ronald l. rivest len alderman \"can comput upon encrypt data?\" 1978 paper. gradual follow idea sprout brilliant mind shafi goldwasser, kristin lauter, dan bonch, toma sander, donald beaver, craig gentri address vital question. took 30 year collect effort final find answer \"yes\" import question.",
    "multi-label classif task assign subset label given queri instance. evalu predictions, set predict label need compar ground-truth label set associ instance, variou loss function propos purpose. addit assess predict accuracy, key concern regard foster analyz learner' abil captur label dependencies. paper, introduc new class loss function multi-label classification, overcom disadvantag commonli use loss ham subset 0/1. end, leverag mathemat framework non-addit measur integrals. roughli speaking, non-addit measur allow model import correct predict label subset (instead singl labels), therebi impact overal evaluation, flexibl way - give full import singl label entir label set, respectively, ham subset 0/1 rather extrem regard. present concret instanti class, compris ham subset 0/1 special cases, appear especi appeal model perspective. assess multi-label classifi term loss illustr empir study.",
    "traumat brain injuri caus variou type head impacts. however, due differ kinemat characteristics, mani brain injuri risk estim model generaliz across varieti impact human may sustain. current definit head impact subtyp base impact sourc (e.g., football, traffic accident), may reflect intrins kinemat similar impact across impact sources. investig potenti new definit impact subtyp base kinematics, 3,161 head impact variou sourc includ simulation, colleg football, mix martial arts, car race collected. appli k-mean cluster cluster impact 16 standard tempor featur head rotat kinematics. then, develop subtype-specif ridg regress model cumul strain damag (use threshold 15%), significantli improv estim accuraci compar baselin method mix impact differ sourc develop one model (r^2 0.7 0.9). investig effect kinemat features, present top three critic featur (maximum result angular acceleration, maximum angular acceler along z-axis, maximum linear acceler along y-axis) base regress accuraci use logist regress find critic point featur partit subtypes. studi enabl research defin head impact subtyp data-driven manner, lead generaliz brain injuri risk estimation.",
    "tensor compress sens (tcs) multidimension framework compress sens (cs), advantag term reduc amount storage, eas hardwar implement preserv multidimension structur signal comparison convent cs system. tc system, instead use random sens matrix predefin dictionary, average-cas perform improv employ optim multidimension sens matrix learn multilinear sparsifi dictionary. paper, propos joint optim approach sens matrix dictionari tc system. sens matrix design tcs, extend separ approach close form solut novel iter non-separ method propos multilinear dictionari fixed. addition, multidimension dictionari learn method take advantag multidimension structur derived, influenc sens matric taken account learn process. joint optim achiev via altern iter optim sens matrix dictionary. numer experi use synthet data real imag demonstr superior propos approaches.",
    "deep convolut neural network compris subclass deep neural network (dnn) constrain architectur leverag spatial tempor structur domain model. convolut network achiev best predict perform area speech imag recognit hierarch compos simpl local featur complex models. although dnn use drug discoveri qsar ligand-bas bioactiv predictions, none model benefit power convolut architecture. paper introduc atomnet, first structure-based, deep convolut neural network design predict bioactiv small molecul drug discoveri applications. demonstr appli convolut concept featur local hierarch composit model bioactiv chemic interactions. contrast exist dnn techniques, show atomnet' applic local convolut filter structur target inform success predict new activ molecul target previous known modulators. finally, show atomnet outperform previou dock approach divers set benchmark larg margin, achiev auc greater 0.9 57.8% target dude benchmark.",
    "intrus detect system (ids) essenti element come secur comput networks. despit huge research effort done field, handl sources' reliabl remain open issue. address problem, paper propos novel contextu discount method base sources' reliabl distinguish abil normal abnorm behavior. dempster-shaf theory, gener framework reason uncertainty, use construct evidenti classifier. nsl-kdd dataset, significantli revis improv version exist kddcup'99 dataset, provid basi assess perform new detect approach. give compar result kddtest+ dataset, approach outperform state-of-the-art method kddtest-21 dataset challenging.",
    "presenc bacteria fungi bloodstream patient abnorm lead life-threaten conditions. comput model base bidirect long short-term memori artifici neural network, explor assist doctor intens care unit predict whether examin blood cultur patient return positive. input use nine monitor clinic parameters, present time seri data, collect 2177 icu admiss ghent univers hospital. main goal determin gener machin learn method specific, tempor models, use creat earli detect system. preliminari research obtain area 71.95% precis recal curve, prove potenti tempor neural network context.",
    "learn privileg inform set recent attract lot attent within machin learn community, allow integr addit knowledg train process classifier, even come form data modal avail test time. here, show privileg inform natur treat nois latent function gaussian process classifi (gpc). is, contrast standard gpc setting, latent function nuisanc feature: becom natur measur confid train data modul slope gpc sigmoid likelihood function. extens experi public dataset show propos gpc method use privileg noise, call gpc+, improv standard gpc without privileg knowledge, also current state-of-the-art svm-base method, svm+. moreover, show advanc neural network deep learn method compress privileg information.",
    "gener dataset 200 gb 10^9 features, test recent b-bit minwis hash algorithm train large-scal logist regress svm. result confirm prior work that, compar vw hash algorithm (which varianc random projections), b-bit minwis hash substanti accur storage. example, mere 30 hash valu per data point, b-bit minwis hash achiev similar accuraci vw 2^14 hash valu per data point. demonstr preprocess cost b-bit minwis hash roughli order magnitud data load time. furthermore, use gpu, preprocess cost reduc small fraction data load time. minwis hash wide use industry, least context search. one reason popular one effici simul permut (e.g.,) univers hashing. words, need store permut matrix. paper, empir verifi practice, demonstr even use simplest 2-univers hash degrad learn performance.",
    "research shown convolut neural network contain signific redundancy, high classif accuraci obtain even weight activ reduc float point binari values. paper, present finn, framework build fast flexibl fpga acceler use flexibl heterogen stream architecture. util novel set optim enabl effici map binar neural network hardware, implement fulli connected, convolut pool layers, per-lay comput resourc tailor user-provid throughput requirements. zc706 embed fpga platform draw less 25 w total system power, demonstr 12.3 million imag classif per second 0.31 {\\mu} latenc mnist dataset 95.8% accuracy, 21906 imag classif per second 283 {\\mu} latenc cifar-10 svhn dataset respect 80.1% 94.9% accuracy. best knowledge, fastest classif rate report date benchmarks.",
    "airlin industri make use sophist revenu manag system maxim revenu decades. improv differ compon system focu numer studies, estim impact improv revenu overlook literatur despit practic importance. indeed, quantifi benefit chang system serv support invest decisions. challeng problem correspond differ gener valu valu would gener keep system before. latter observable. moreover, expect impact small rel value. paper, cast problem counterfactu predict unobserv revenue. impact revenu differ observ estim revenue. origin work lie innov applic econometr method propos macroeconom applic new problem setting. broadli applicable, approach benefit requir revenu data observ origin-destin pair network airlin day, chang system applied. report result use real large-scal data air canada. compar deep neural network counterfactu predict model econometr models. achiev respect 1% 1.1% error counterfactu revenu predictions, allow accur estim small impact (in order 2%).",
    "optim control problem natur aris mani scientif applic one wish steer dynam system certain initi state $\\mathbf{x}_0$ desir target state $\\mathbf{x}^*$ finit time $t$. recent advanc deep learn neural network-bas optim contribut develop method help solv control problem involv high-dimension dynam systems. particular, framework neural ordinari differenti equat (neural odes) provid effici mean iter approxim continu time control function associ analyt intract comput demand control tasks. although neural ode control shown great potenti solv complex control problems, understand effect hyperparamet network structur optim learn perform still limited. work aim address knowledg gap conduct effici hyperparamet optimization. end, first analyz truncat non-trunc backpropag time affect runtim perform abil neural network learn optim control functions. use analyt numer methods, studi role paramet initializations, optimizers, neural-network architecture. finally, connect result abil neural ode control implicitli regular control energy.",
    "network prune widely-us compress techniqu abl significantli scale overparameter model minim loss accuracy. paper show prune may creat exacerb dispar impacts. paper shed light factor caus disparities, suggest differ gradient norm distanc decis boundari across group respons critic issue. analyz factor detail, provid theoret empir support, propos simple, yet effective, solut mitig dispar impact caus pruning.",
    "whenev address specif object refer certain spatial location, use referenti deictic gestur usual accompani verbal description. especi point gestur necessari dissolv ambigu scene crucial import verbal commun may fail due environment condit two person simpli speak language. current increas advanc humanoid robot futur integr domest domains, develop gestur interfac complement human-robot interact scenario substanti interest. implement intuit gestur scenario still challeng point intent correspond object correctli recogn real-time. demand increas consid point gestur clutter environment, case households. also, human perform point mani differ way variat captured. research field often propos set geometr comput scale well number gestur objects, use specif marker predefin set point directions. paper, propos unsupervis learn approach model distribut point gestur use growing-when-requir (gwr) network. introduc interact scenario humanoid robot defin so-cal ambigu classes. implement hand object detect independ marker skeleton models, thu easili reproduced.",
    "evalu compar baselin comput vision approach gwr model show pointing-object associ well learn even case ambigu result close object proximity.",
    "work show leverag causal infer understand behavior complex learn system interact environ predict consequ chang system. predict allow human algorithm select chang improv short-term long-term perform systems. work illustr experi carri ad placement system associ bing search engine.",
    "low-rank learn attract much attent recent due efficaci rich varieti real-world tasks, e.g., subspac segment imag categorization. low-rank method incap captur low-dimension subspac supervis learn tasks, e.g., classif regression. paper aim learn discrimin low-rank represent (lrr) robust project subspac supervis manner. achiev goal, cast problem constrain rank minim framework adopt least squar regularization. naturally, data label structur tend resembl correspond low-dimension representation, deriv robust subspac project clean data low-rank learning. moreover, low-dimension represent origin data pair inform structur impos appropri constraint, e.g., laplacian regularizer. therefore, propos novel constrain lrr method. object function formul constrain nuclear norm minim problem, solv inexact augment lagrang multipli algorithm. extens experi imag classification, human pose estimation, robust face recoveri confirm superior method.",
    "spars neural network import achiev better gener enhanc comput efficiency. paper propos novel learn approach obtain spars fulli connect layer neural network (nns) automatically. design switcher neural network (snn) optim structur task neural network (tnn). snn take weight tnn input output use switch connect tnn. way, knowledg contain weight tnn explor determin import connect structur tnn consequently. snn tnn learn altern stochast gradient descent (sgd) optimization, target common objective. learning, achiev optim structur optim paramet tnn simultaneously. order evalu propos approach, conduct imag classif experi variou network structur datasets. network structur includ lenet, resnet18, resnet34, vggnet16 mobilenet. dataset includ mnist, cifar10 cifar100. experiment result show approach stabli lead spars well-perform fulli connect layer nns.",
    "prior studi unveil vulner deep neural network context adversari machin learning, lead great recent attent area. one interest question yet fulli explor bias-vari relationship adversari machin learning, potenti provid deeper insight behaviour. notion bia varianc one main approach analyz evalu gener reliabl machin learn model. although extens use machin learn models, well explor field deep learn even less explor area adversari machin learning. study, investig effect adversari machin learn bia varianc train deep neural network analyz adversari perturb affect gener network. deriv bias-vari trade-off classif regress applic base two main loss functions: (i) mean squar error (mse), (ii) cross-entropy. furthermore, perform quantit analysi simul real data empir evalu consist deriv bias-vari tradeoffs. analysi shed light deep neural network poor perform adversari perturb bias-vari point view type perturb would chang perform network.",
    "moreover, given new theoret findings, introduc new adversari machin learn algorithm lower comput complex well-known adversari machin learn strategi (e.g., pgd) provid high success rate fool deep neural network lower perturb magnitudes.",
    "semi-supervis variat autoencod (ssvaes) wide use model data effici learning. paper, question adequaci standard design sequenc ssvae task text classif exhibit two sourc overcomplex provid simplifications. simplif ssvae preserv theoret sound provid number practic advantag semi-supervis setup result train text classifier. simplif remov (i) kullback-liebl diverg object (ii) fulli unobserv latent variabl probabilist model. chang reliev user choos prior latent variables, make model smaller faster, allow better flow inform latent variables. compar simplifi version standard ssvae 4 text classif tasks. top above-ment simplification, experi show speed-up 26%, keep equival classif scores. code reproduc experi public.",
    "paper, focu latent modif gener 3d point cloud object model respect semant parts. differ exist method use separ network part gener assembly, propos singl end-to-end autoencod model handl gener modif semant parts, global shapes. propos method support part exchang 3d point cloud model composit differ part form new model directli edit latent representations. holist approach need part-bas train learn part represent introduc extra loss besid standard reconstruct loss. experi demonstr robust propos method differ object categori vari number points. method gener new model integr gener model gan vae work unannot point cloud integr segment module.",
    "machin learn model vulner adversari attacks. paper, consid scenario model distribut mani users, among malici user attempt attack anoth user. malici user probe uniqu copi model search adversari samples, present found sampl victim' model order replic attack. distribut differ copi model differ users, mitig attack wherein adversari sampl found one copi would work anoth copy. propos flexibl paramet rewrit method directli modifi model' parameters. method requir train abl gener larg number copies, copi induc differ set adversari samples. experiment studi show approach significantli mitig attack retain high accuracy.",
    "skeleton-bas human action recognit attract much attent preval access depth sensors. recently, graph convolut network (gcns) wide use task due power capabl model graph data. topolog adjac graph key factor model correl input skeletons. thus, previou method mainli focu design/learn graph topology. topolog learned, single-scal featur one transform exist layer networks. mani insights, multi-scal inform multipl set transformations, proven effect convolut neural network (cnns), investig gcns. reason that, due gap graph-structur skeleton data convent image/video data, challeng emb insight gcns. overcom gap, reinvent split-transform-merg strategi gcn skeleton sequenc processing. specifically, design simpl highli modular graph convolut network architectur skeleton-bas action recognition. network construct repeat build block aggreg multi-granular inform spatial tempor paths. extens experi demonstr network outperform state-of-the-art method signific margin 1/5 paramet 1/10 flops. code avail https://github.com/yellowtownhz/stigcn.",
    "learning-bas approach robot grasp use visual sensor typic requir collect larg size dataset, either manual label mani trial error robot manipul real simul world. propos simpler learning-from-demonstr approach abl detect object grasp mere singl demonstr use convolut neural network call graspnet. order increas robust decreas train time even further, leverag data previou demonstr quickli fine-tun grapnet new demonstration. present preliminari result grasp experi franka panda cobot train graspnet hundr train iterations.",
    "graph complet node attribut wide explor recently. practice, graph attribut partial node could avail other might entir missing. attribute-miss graph relat numer real-world applic limit studi investig correspond learn problems. exist graph learn method includ popular gnn cannot provid satisfi learn perform sinc specifi attribute-miss graphs. thereby, design new gnn graph burn issu graph learn community. paper, make shared-lat space assumpt graph develop novel distribut match base gnn call structure-attribut transform (sat) attribute-miss graphs. sat leverag structur attribut decoupl scheme achiev joint distribut model structur attribut distribut match techniques. could perform link predict task also newli introduc node attribut complet task. furthermore, practic measur introduc quantifi perform node attribut completion. extens experi seven real-world dataset indic sat show better perform method link predict node attribut complet tasks. code data avail online: https://github.com/xuchensjtu/sat-master-onlin",
    "rise fall artifici neural network well document scientif literatur comput scienc comput chemistry. yet almost two decad later, see resurg interest deep learning, machin learn algorithm base multilay neural networks. within last years, seen transform impact deep learn mani domains, particularli speech recognit comput vision, extent major expert practition field regularli eschew prior establish model favor deep learn models. review, provid introductori overview theori deep neural network uniqu properti distinguish tradit machin learn algorithm use cheminformatics. provid overview varieti emerg applic deep neural networks, highlight ubiqu broad applic wide rang challeng field, includ qsar, virtual screening, protein structur prediction, quantum chemistry, materi design properti prediction. review perform deep neural networks, observ consist outperform non-neur network state-of-the-art model across dispar research topics, deep neural network base model often exceed \"glass ceiling\" expect respect tasks. coupl matur gpu-acceler comput train deep neural network exponenti growth chemic data train network on, anticip deep learn algorithm valuabl tool comput chemistry.",
    "consid gradient descent like algorithm support vector machin (svm) train data relat form. gradient svm object effici comput known techniqu suffer ``subtract problem''. first show subtract problem surmount show comput constant approxim gradient svm object function $\\#p$-hard, even acycl joins. we, however, circumv subtract problem restrict attent stabl instances, intuit instanc nearli optim solut remain nearli optim point perturb slightly. give effici algorithm comput ``pseudo-gradient'' guarante converg stabl instanc rate compar achiev use actual gradient. believ result suggest sort stabil analysi would like yield use insight context design algorithm relat data learn problem subtract problem arises.",
    "mani import classif problems, object classification, speech recognition, machin translation, tackl supervis learn paradigm past, train corpora parallel input-output pair requir high cost. remov need parallel train corpora practic signific real-world applications, one main goal unsupervis learning. recently, encourag progress unsupervis learn solv classif problem made natur challeng clarified. article, review progress dissemin class promis new method facilit understand method machin learn researchers. particular, emphas key inform enabl success unsupervis learn - sequenti statist distribut prior labels. exploit sequenti statist make possibl estim paramet classifi without need pair input-output data. paper, first introduc concept caesar cipher decryption, motiv construct novel loss function unsupervis learn use throughout paper. use simpl repres binari classif task exampl deriv describ unsupervis learn algorithm step-by-step, easy-to-understand fashion. includ two cases, one bigram languag model sequenti statist use unsupervis paramet estimation, anoth simpler unigram languag model. cases, detail deriv step learn algorithm included.",
    "further, summari tabl compar comput step two case execut unsupervis learn algorithm learn binari classifiers.",
    "computer-aid breast cancer diagnosi mammographi limit inadequ data similar benign cancer masses. address this, propos sign graph regular deep neural network adversari augmentation, name \\textsc{diagnet}. firstly, use adversari learn gener posit neg mass-contain mammogram mass class. that, sign similar graph built upon expand data highlight discrimination. finally, deep convolut neural network train jointli optim sign graph regular classif loss. experi show \\textsc{diagnet} framework outperform state-of-the-art breast mass diagnosi mammography.",
    "propos reinforc learn (rl) approach comput express quasi-stationari distribution. base fixed-point formul quasi-stationari distribution, minim kl-diverg two markovian path distribut induc candid distribut true target distribution. solv challeng minim problem gradient descent, appli reinforc learn techniqu introduc reward valu functions. deriv correspond polici gradient theorem design actor-crit algorithm learn optim solut valu function. numer exampl finit state markov chain test demonstr new method.",
    "deep learn receiv much attent late due impress empir perform achiev train algorithms. consequently, need better theoret understand problem becom evid recent years. work, use unifi framework, show exist polyhedron encod simultan possibl deep neural network train problem aris given architecture, activ functions, loss function, sample-size. notably, size polyhedr represent depend linearli sample-size, better depend sever network paramet unlik (assum $p\\neq np$). additionally, use polyhedr represent obtain new better comput complex result train problem well-known neural network architectures. result provid new perspect train problem len polyhedr theori reveal strong structur aris problems.",
    "recent interest first-ord method linear program (lp). paper,w propos stochast algorithm use varianc reduct restart solv sharp primal-du problem lp. show propos stochast method exhibit linear converg rate solv sharp instanc high probability. addition, propos effici coordinate-bas stochast oracl unconstrain bilinear problems, $\\mathcal o(1)$ per iter cost improv complex exist determinist stochast algorithms. finally, show obtain linear converg rate nearli optim (upto $\\log$ terms) wide class stochast primal dual methods.",
    "gener adversari network (gans) extens carv open mani excit way tackl well known challeng medic imag analysi problem medic imag de-noising, reconstruction, segmentation, data simulation, detect classification. furthermore, abil synthes imag unpreced level realism also give hope chronic scarciti label data medic field resolv help gener models. review paper, broad overview recent literatur gan medic applic given, shortcom opportun propos method thoroughli discuss potenti futur work elaborated. review relev paper publish submiss date. quick access, import detail underli method, dataset perform tabulated. interact visual categor paper keep review alive, avail http://livingreview.in.tum.de/gans_for_medical_applications.",
    "wave interest appli machin learn studi dynam systems. present hamiltonian neural network solv differenti equat govern dynam systems. equation-driven machin learn method optim process network depend sole predict function without use ground truth data. model learn solut satisfy, arbitrarili small error, hamilton' equat and, therefore, conserv hamiltonian invariants. choic appropri activ function drastic improv predict network. moreover, error analysi deriv state numer error depend overal network performance. hamiltonian network employ solv equat nonlinear oscil chaotic henon-heil dynam system. systems, symplect euler integr requir two order evalu point hamiltonian network order achiev order numer error predict phase space trajectories.",
    "mani video depict people, interact inform us activities, relat one anoth cultur social setting. advanc human action recognition, research begun address autom recognit human-human interact video. main challeng stem deal consider variat record setting, appear peopl depict coordin perform interaction. survey provid summari challeng dataset address these, follow in-depth discuss relev vision-bas recognit detect methods. focu recent, promis work base deep learn convolut neural network (cnns). finally, outlin direct overcom limit current state-of-the-art analyz and, eventually, understand social human actions.",
    "sampl good neg exampl contrast learning? argu that, metric learning, contrast learn represent benefit hard neg sampl (i.e., point difficult distinguish anchor point). key challeng toward use hard neg contrast method must remain unsupervised, make infeas adopt exist neg sampl strategi use true similar information. response, develop new famili unsupervis sampl method select hard neg sampl user control hardness. limit case sampl result represent tightli cluster class, push differ class far apart possible. propos method improv downstream perform across multipl modalities, requir addit line code implement, introduc comput overhead.",
    "anomali detect play crucial role variou real-world applications, includ healthcar financ systems. owe limit number anomali label complex systems, unsupervis anomali detect method attract great attent recent years. two major challeng face exist unsupervis method are: (i) distinguish normal abnorm data transit field, normal abnorm data highli mix together; (ii) defin effect metric maxim gap normal abnorm data hypothesi space, built represent learner. end, work propos novel score network score-guid regular learn enlarg anomali score dispar normal abnorm data. score-guid strategy, represent learner gradual learn inform represent model train stage, especi sampl transit field. next propos score-guid autoencod (sg-ae), incorpor score network autoencod framework anomali detection, well three state-of-the-art models, demonstr effect transfer design. extens experi synthet real-world dataset demonstr state-of-the-art perform score-guid model (sgms).",
    "machin learn algorithm extens use make increasingli consequenti decis people, achiev optim predict perform longer focus. particularli import consider fair respect race, gender, sensit attribute. paper studi intersect fairness, intersect multipl sensit attribut considered. prior research mainli focus fair respect singl sensit attribute, intersect fair compar less studi despit critic import safeti modern machin learn systems. present comprehens framework audit achiev intersect fair classif problems: defin suit metric assess intersect fair data model output extend known single-attribut fair metrics, propos method robustli estim even intersect subgroup underrepresented. furthermore, develop post-process techniqu mitig detect intersect bia classif model. techniqu reli assumpt regard underli model preserv predict perform guarante level fairness. finally, give guidanc practic implementation, show propos method perform real-world dataset.",
    "binari perceptron simplest artifici neural network form $n$ input unit one output unit, neural state synapt weight restrict $\\pm 1$ values. task teacher--stud scenario infer hidden weight vector train set label patterns. previou effort passiv learn mode shown learn independ random pattern quit inefficient. consid activ onlin learn mode student design everi new ise train pattern. demonstr mathemat possibl achiev perfect (error-free) infer use $n$ design train patterns, comput unfeas larg systems. investig two bayesian statist design protocols, requir $2.3 n$ $1.9 n$ train patterns, respectively, achiev error-fre inference. train pattern instead design deduct reasoning, perfect infer achiev use $n\\!+\\!\\log_{2}\\!n$ samples. perform gap bayesian deduct design strategi may shorten futur work take account possibl ergod break version space binari perceptron.",
    "corrupt label class imbal commonli encount practic collect train data, easili lead over-fit deep neural network (dnns). exist approach allevi issu adopt sampl re-weight strategy, re-weight sampl design weight function. however, applic train data contain either one type data biases. practice, however, bias sampl corrupt label tail class commonli co-exist train data. handl simultan key under-explor problem. paper, find two type bias samples, though similar transient loss, distinguish trend characterist loss curves, could provid valuabl prior sampl weight assignment. motiv this, delv loss curv propos novel probe-and-alloc train strategy: probe stage, train network whole bias train data without intervention, record loss curv sampl addit attribute; alloc stage, feed result attribut newli design curve-percept network, name curvenet, learn identifi bia type sampl assign proper weight meta-learn adaptively. train speed meta learn also block application. solv it, propos method name skip layer meta optim (slmo) acceler train speed skip bottom layers. extens synthet real experi well valid propos method, achiev state-of-the-art perform multipl challeng benchmarks.",
    "paper, seek clinically-relev latent code repres spectrum macular disease. toward end, construct retina-vae, variat autoencoder-bas model accept patient profil vector (pvec) input. pvec compon includ clinic exam find demograph information. evalu model subspectrum retin maculopathies, particular, exud age-rel macular degeneration, central serou chorioretinopathy, polypoid choroid vasculopathy. three maculopathies, databas 3000 6-dimension pvec (1000 each) synthet gener base known diseas statist literature. databas use train vae gener latent vector representations. found train perform best 3-dimension latent vector architectur compar 2 4 dimension latents. additionally, 3d latent architecture, discov result latent vector strongli cluster spontan one 14 clusters. kmean use identifi member cluster inspect cluster properties. cluster suggest underli diseas subtyp may potenti respond better wors particular pharmaceut treatment anti-vascular endotheli growth factor variants. retina-va framework potenti yield new fundament insight mechan manifest disease. potenti facilit develop person pharmaceut gene therapies.",
    "propos version least-mean-squar (lms) algorithm spars system identification. algorithm call onlin linear bregman iter (olbi) deriv minim cumul predict error squar along l1-l2 norm regularizer. systemat treat non-differenti regular arriv simpl two-step iteration. demonstr olbi bia free compar oper exist spars lm algorithm rederiv onlin convex optim framework. perform converg analysi olbi white input signal deriv theoret express steadi state instantan mean squar deviat (msd). demonstr numer olbi improv perform lm type algorithm signal gener spars tap weights.",
    "due success bidirect encod represent transform (bert) natur languag process (nlp), multi-head attent transform preval computer-vis research (cv). however, still remain challeng research put forward complex task vision detect semant segmentation. although multipl transformer-bas architectur like detr vit-frcnn propos complet object detect task, inevit decreas discrimin accuraci bring comput effici caus enorm learn paramet heavi comput complex incur tradit self-attent operation. order allevi issues, present novel object detect architecture, name convolut vision transform base attent singl shot multibox detector (cvt-assd), built top convolut vision transorm (cvt) effici attent singl shot multibox detector (assd). provid comprehens empir evid show model cvt-assd lead good system effici perform pretrain large-scal detect dataset pascal voc ms coco. code releas public github repositori https://github.com/albert-jin/cvt-assd.",
    "deep learn research keen interest propos two new novel activ function boost network performance. good choic activ function signific consequ improv network performance. handcraft activ common choic neural network models. relu common choic deep learn commun due simplic though relu seriou drawbacks. paper, propos new novel activ function base approxim known activ function like leaki relu, call function smooth maximum unit (smu). replac relu smu, got 6.22% improv cifar100 dataset shufflenet v2 model.",
    "known fact train recurr neural network task long term depend challenging. one main reason vanish explod gradient problem, prevent gradient inform propag earli layers. paper propos simpl recurr architecture, fourier recurr unit (fru), stabil gradient aris train give us stronger express power. specifically, fru summar hidden state $h^{(t)}$ along tempor dimens fourier basi functions. allow gradient easili reach layer due fru' residu learn structur global support trigonometr functions. show fru gradient lower upper bound independ tempor dimension. also show strong express spars fourier basis, fru obtain strong express power. experiment studi also demonstr fewer paramet propos architectur outperform recurr architectur mani tasks.",
    "text-to-imag synthesi (t2i) aim gener photo-realist imag semant consist text descriptions. exist method usual built upon condit gener adversari network (gans) initi imag nois sentenc embedding, refin featur fine-grain word embed iteratively. close inspect gener imag reveal major limitation: even though gener imag holist match description, individu imag region part someth often recogniz consist word sentence, e.g. \"a white crown\". address problem, propos novel framework semantic-spati awar gan synthes imag input text. concretely, introduc simpl effect semantic-spati awar block, (1) learn semantic-adapt transform condit text effect fuse text featur imag features, (2) learn semant mask weakly-supervis way depend current text-imag fusion process order guid transform spatially. experi challeng coco cub bird dataset demonstr advantag method recent state-of-the-art approaches, regard visual fidel align input text description.",
    "studi implicit bia relu neural network train variant sgd step, label chang probabl $p$ random label (label smooth close variant procedure). experi demonstr label nois propel network spars solut follow sense: typic input, small fraction neuron active, fire pattern hidden layer sparser. fact, instances, appropri amount label nois sparsifi network reduc test error. turn theoret analysi sparsif mechanisms, focus extrem case $p=1$. show case, network wither anticip experiments, surprisingly, differ way depend learn rate presenc bias, either weight vanish neuron ceas fire.",
    "variat infer comput challeng model contain conjug non-conjug terms. method specif design conjug models, even though comput efficient, find difficult deal non-conjug terms. hand, stochastic-gradi method handl non-conjug term usual ignor conjug structur model might result slow convergence. paper, propos new algorithm call conjugate-comput variat infer (cvi) bring best two world togeth -- use conjug comput conjug term employ stochast gradient rest. deriv algorithm use stochast mirror-desc method mean-paramet space, express gradient step variat infer conjug model. demonstr algorithm' applic larg class model establish convergence. experiment result show method converg much faster method ignor conjug structur model.",
    "success convolut neural network (cnns) comput vision applic accompani signific increas comput memori costs, prohibit usag resource-limit environ mobil embed devices. end, research cnn compress recent becom emerging. paper, propos novel filter prune scheme, term structur sparsiti regular (ssr), simultan speedup comput reduc memori overhead cnns, well support variou off-the-shelf deep learn libraries. concretely, propos scheme incorpor two differ regular structur sparsiti origin object function filter pruning, fulli coordin global output local prune oper adapt prune filters. propos altern updat lagrang multipli (aulm) scheme effici solv optimization. aulm follow principl admm altern promot structur sparsiti cnn optim recognit loss, lead effici solver (2.5x recent work directli solv group sparsity-bas regularization). moreover, impos structur sparsity, onlin infer extrem memory-light, sinc number filter output featur map simultan reduced. propos scheme deploy varieti state-of-the-art cnn structur includ lenet, alexnet, vgg, resnet googlenet differ datasets. quantit result demonstr propos scheme achiev superior perform state-of-the-art methods.",
    "demonstr propos compress scheme task transfer learning, includ domain adapt object detection, also show excit perform gain state-of-the-arts.",
    "reinforc learn agent oper divers complex environ benefit structur decomposit behavior. often, address context hierarch reinforc learning, aim decompos polici lower-level primit options, higher-level meta-polici trigger appropri behavior given situation. however, meta-polici must still produc appropri decis states. work, propos polici design decompos primitives, similarli hierarch reinforc learning, without high-level meta-policy. instead, primit decid whether wish act current state. use information-theoret mechan enabl decentr decision: primit choos much inform need current state make decis primit request inform current state act world. primit regular use littl inform possible, lead natur competit specialization. experiment demonstr polici architectur improv flat hierarch polici term generalization.",
    "initi studi neural-network quantum state algorithm analyz continuous-vari lattic quantum system first quantization. simpl famili continuous-vari trial wavefuncton introduc natur gener restrict boltzmann machin (rbm) wavefunct introduc analyz quantum spin systems. virtu simplicity, variat mont carlo train algorithm develop ground state determin time evolut spin system natur analogu continuum. offer proof principl demonstr context ground state determin stoquast quantum rotor hamiltonian. result compar obtain partial differenti equat (pde) base scalabl eigensolvers. studi serv benchmark futur investig continuous-vari neural quantum state compared, point need consid deep network architectur sophist train algorithms.",
    "consid problem combinatori pure explor (cpe), deal find combinatori set arm high reward, reward individu arm unknown advanc must estim use arm pulls. previou algorithm problem, obtain sampl complex reduct mani cases, highli comput intensive, thu make impract even mildli larg problems. work, propos new cpe algorithm pac setting, comput light weight, easili appli problem ten thousand arms. achiev sinc propos algorithm requir small number combinatori oracl calls. algorithm base success accept arms, along elimin base combinatori structur problem. provid sampl complex guarante algorithm, demonstr experi use larg problems, wherea previou algorithm impract run problem even dozen arms. code algorithm experi provid https://github.com/noabdavid/csale.",
    "one crucial issu feder learn develop effici optim algorithms. current one requir full devic particip and/or impos strong assumpt convergence. differ widely-us gradient descent-bas algorithms, paper develop inexact altern direct method multipli (admm), comput communication-efficient, capabl combat stragglers' effect, converg mild conditions.",
    "comput cost high energi physic detector simul futur experiment facil go exceed current avail resources. overcom challenge, new idea surrog model use machin learn method explor replac comput expens components. additionally, differenti program propos complementari approach, provid control scalabl simul routines. document, new ongo effort surrog model differenti program appli detector simul discuss context 2021 particl physic commun plan exercis (`snowmass').",
    "import step toward explain deep imag classifi lie identif imag region contribut individu class score model' output. however, accur difficult task due black-box natur networks. exist approach find attribut either use activ gradient repeatedli perturb input. instead address challeng train second deep network, explainer, predict attribut pre-train black-box classifier, explanandum. attribut form mask show classifier-relev part image, mask rest. approach produc sharper boundary-precis mask compar salienc map gener methods. moreover, unlik exist approaches, capabl directli gener distinct class-specif masks. finally, propos method effici infer sinc take singl forward pass explain gener class-specif masks. show attribut superior establish method visual quantitatively, evalu pascal voc-2007 microsoft coco-2014 datasets.",
    "studi duel bandit weak utility-bas regret prefer arm total order carri observ featur vectors. order assum determin featur vectors, unknown prefer vector, known util function. structur introduc depend prefer pair arms, allow learn prefer one pair arm prefer anoth pair arms. propos algorithm set call compar best (ctb), show constant expect cumul weak utility-bas regret. provid bayesian interpret ctb, implement appropri small number arms, altern implement mani arm use input paramet satisfi decompos condition. demonstr numer experi ctb appropri input paramet outperform benchmark considered.",
    "given neural network, train data, threshold, known np-hard find weight neural network total error threshold. determin algorithm complex fundament problem precisely, show $\\exists\\mathbb r$-complete. mean problem equivalent, polynomial-tim reductions, decid whether system polynomi equat inequ integ coeffici real unknown solution. if, wide expected, $\\exists\\mathbb r$ strictli larger np, work impli problem train neural network even np. neural network usual train use variat backpropagation. result paper offer explan techniqu commonli use solv big instanc np-complet problem seem use task. exampl techniqu sat solvers, ip solvers, local search, dynam programming, name gener ones.",
    "understand dynam process govern perform function materi essenti design next gener materi tackl global energi environment challenges. mani process involv dynam individu atom small molecul condens phases, e.g. lithium ion electrolytes, water molecul membranes, molten atom interfaces, etc., difficult understand due complex local environments. work, develop graph dynam networks, unsupervis learn approach understand atom scale dynam arbitrari phase environ molecular dynam simulations. show import dynam inform learn variou multi-compon amorph materi systems, difficult obtain otherwise. larg amount molecular dynam data gener everyday nearli everi aspect materi design, approach provid broadli useful, autom tool understand atom scale dynam materi systems.",
    "lotteri ticket hypothesi conjectur everi larg neural network contain subnetwork that, train isolation, achiev compar perform larg network. even stronger conjectur proven recently: everi suffici overparameter network contain subnetwork that, random initialization, without training, achiev compar accuraci train larg network. latter result, however, reli number strong assumpt guarante polynomi factor size larg network compar target function. work, remov limit assumpt previou work provid significantli tighter bounds:th overparameter network need logarithm factor (in variabl depth) number neuron per weight target subnetwork.",
    "feder learn (fl) promis solut enabl mani ai applications, sensit dataset distribut client need collabor train global model. fl allow client particip train phase, govern central server, without share local data. one main challeng fl commun overhead, model updat particip client sent central server global train round. over-the-air comput (aircomp) recent propos allevi commun bottleneck model updat sent simultan multiple-access channel. however, simpl averag model updat via aircomp make learn process vulner random intend modif local model updat byzantin clients. paper, propos transmiss aggreg framework reduc effect attack preserv benefit aircomp fl. propos robust approach, central server divid particip client randomli group alloc transmiss time slot group. updat differ group aggreg use robust aggreg technique. extend approach handl case non-i.i.d. local data, resampl step ad robust aggregation. analyz converg propos approach case i.i.d. non-i.i.d. data demonstr propos algorithm converg linear rate neighborhood optim solution.",
    "experi real dataset provid confirm robust propos approach.",
    "multi-task learn (mtl), joint model train simultan make predict sever tasks. joint train reduc comput cost improv data efficiency; however, sinc gradient differ task may conflict, train joint model mtl often yield lower perform correspond single-task counterparts. common method allevi issu combin per-task gradient joint updat direct use particular heuristic. paper, propos view gradient combin step bargain game, task negoti reach agreement joint direct paramet update. certain assumptions, bargain problem uniqu solution, known nash bargain solution, propos use principl approach multi-task learning. describ new mtl optim procedure, nash-mtl, deriv theoret guarante convergence. empirically, show nash-mtl achiev state-of-the-art result multipl mtl benchmark variou domains.",
    "fall abnorm activ occur rarely, hard collect real data falls. is, therefore, difficult use supervis learn method automat detect falls. anoth challeng use machin learn method automat detect fall choic engin features. paper, propos use ensembl autoencod extract featur differ channel wearabl sensor data train normal activities. show tradit approach choos threshold maximum reconstruct error train normal data right way identifi unseen falls. propos two method automat tighten reconstruct error normal activ better identif unseen falls. present result two activ recognit dataset show efficaci propos method tradit autoencod model two standard one-class classif methods.",
    "paper, propos evebot, innovative, sequenc sequenc (seq2seq) based, fulli gener convers system diagnosi neg emot prevent depress posit suggest responses. system consist assembl deep-learn base models, includ bi-lstm base model detect neg emot user obtain psycholog counsel relat corpu train chatbot, anti-languag sequenc sequenc neural network, maximum mutual inform (mmi) model. adolesc reluct show neg emot physic interaction, tradit method emot analysi comfort method may work. therefore, system put emphasi use virtual platform detect sign depress anxiety, channel adolescents' stress mood, thu prevent emerg mental illness. launch integr chatbot system onto onlin platform real-world campu applications. one-month user study, observ better result increas posit public chatbot control group.",
    "studi wireless power transmiss energi sourc multipl energi harvest node aim maxim energi efficiency. sourc transmit energi node use one avail power level time slot node transmit inform back energi sourc use harvest energy. sourc channel state inform know whether receiv codeword given node success decod not. limit information, sourc learn optim power level maxim energi effici network. model problem stochast multi-arm bandit problem develop upper confid bound base algorithm, learn optim transmit power energi sourc maxim energi efficiency. numer result valid perform guarante propos algorithm show signific gain compar benchmark schemes.",
    "paper revisit classic problem classif misspecification. particular, studi problem learn halfspac massart nois rate $\\eta$. recent work, diakonikolas, goulekakis, tzamo resolv long-stand problem give first effici algorithm learn accuraci $\\eta + \\epsilon$ $\\epsilon > 0$. however, algorithm output complic hypothesis, partit space $\\text{poly}(d,1/\\epsilon)$ regions. give much simpler algorithm process resolv number outstand open questions:   (1) give first proper learner massart halfspac achiev $\\eta + \\epsilon$. also give improv bound sampl complex achiev polynomi time algorithms. (2) base (1), develop blackbox knowledg distil procedur convert arbitrarili complex classifi equal good proper classifier. (3) leverag simpl overlook connect evolvability, show sq algorithm requir super-polynomi mani queri achiev $\\mathsf{opt} + \\epsilon$. moreov studi gener linear model $\\mathbb{e}[y|\\mathbf{x}] = \\sigma(\\langl \\mathbf{w}^*, \\mathbf{x}\\rangle)$ odd, monotone, lipschitz function $\\sigma$. famili includ previous mention halfspac model special case, much richer includ fundament model like logist regression. introduc challeng new corrupt model gener massart noise, give gener algorithm learn setting. algorithm base small set core recip learn classifi presenc misspecification.",
    "final studi algorithm learn halfspac massart nois empir find exhibit appeal fair properties.",
    "work, propos open-world object detect method that, base image-capt pairs, learn detect novel object class along given set known classes. two-stag train approach first use location-guid image-capt match techniqu learn class label novel known class weakly-supervis manner second special model object detect task use known class annotations. show simpl languag model fit better larg contextu languag model detect novel objects. moreover, introduc consistency-regular techniqu better exploit image-capt pair information. method compar favor exist open-world detect approach data-efficient.",
    "residu network convolut layer wide use field machin learning. sinc effect extract featur input data stack multipl layers, achiev high accuraci mani applications. however, stack mani layer rais comput costs. address problem, propos network implosion, eras multipl layer residu network without degrad accuracy. key idea introduc prioriti term identifi import layer; select unimport layer accord prioriti eras training. addition, retrain network avoid critic drop accuraci layer erasure. theoret assess reveal erasur retrain scheme eras layer without accuraci drop, achiev higher accuraci possibl train scratch. experi show network implos can, classif cifar-10/100 imagenet, reduc number layer 24.00 42.86 percent without drop accuracy.",
    "echocardiographi becom routin use diagnosi cardiomyopathi abnorm cardiac blood flow. however, manual measur myocardi motion cardiac blood flow echocardiogram time-consum error-prone. comput algorithm automat track quantifi myocardi motion cardiac blood flow highli sought after, success due nois high variabl echocardiography. work, propos neural multi-scal self-supervis registr (nmsr) method autom myocardi cardiac blood flow dens tracking. nmsr incorpor two novel components: 1) util deep neural net parameter veloc field two imag frames, 2) optim paramet neural net sequenti multi-scal fashion account larg variat within veloc field. experi demonstr nmsr yield significantli better registr accuraci state-of-the-art methods, advanc normal tool (ants) voxelmorph, myocardi cardiac blood flow dens tracking. approach promis provid fulli autom method fast accur analys echocardiograms.",
    "describ pure image-bas method find geometr construct ruler compass euclidea geometr game. method base adapt mask r-cnn state-of-the-art imag process neural architectur ad tree-bas search procedur it. supervis setting, method learn solv 68 kind geometr construct problem first six level pack euclidea averag 92% accuracy. evalu new kind problems, method solv 31 68 kind euclidea problems. believ first time pure image-bas learn train solv geometr construct problem difficulty.",
    "trust reput manag (trm) play increasingli import role large-scal onlin environ multi-ag system (mas) internet thing (iot). one main object trm achiev accur trust assess entiti agent iot servic providers. however, encount accuracy-privaci dilemma identifi paper, propos framework call context-awar bernoulli neural network base reput assess (cobra) address challenge. cobra encapsul agent interact transactions, prone privaci leak, machin learn models, aggreg multipl model use bernoulli neural network predict trust score agent. cobra preserv agent privaci retain interact context via machin learn models, achiev accur trust predict fully-connect neural network alternative. cobra also robust secur attack agent inject fake machin learn models; notably, resist 51-percent attack. perform cobra valid experi use real dataset, simulations, also show cobra outperform state-of-the-art trm systems.",
    "cloud-bas machin learn servic (cmls) enabl organ take advantag advanc model pre-train larg quantiti data. main shortcom use services, however, difficulti keep transmit data privat secure. asymmetr encrypt requir data decrypt cloud, homomorph encrypt often slow difficult implement. propos one way scrambl deconvolut (owsd), deconvolution-bas scrambl framework offer advantag homomorph encrypt fraction comput overhead. extens evalu multipl imag dataset demonstr owsd' abil achiev near-perfect classif perform output vector cml suffici large. additionally, provid empir analysi robust approach.",
    "topic model structur topic model (stm) estim latent topic cluster within text. import step mani topic model applic explor relationship discov topic structur metadata associ text documents. method use estim relationship must take account topic structur directli observed, instead estim itself. author stm, instance, perform repeat ol regress sampl topic proport metadata covari use mont carlo sampl techniqu known method composition. paper, propos two improvements: first, replac ol appropri beta regression. second, suggest fulli bayesian approach instead current blend frequentist bayesian methods. demonstr improv methodolog explor relationship twitter post german member parliament (mps) differ metadata covariates.",
    "attribut map popular tool explain neural network predictions. assign import valu input dimens repres impact toward outcome, give intuit explan decis process. however, recent work discov vulner map impercept adversari changes, prove critic safety-relev domain healthcare. therefore, defin novel gener framework attribut robust (far) gener problem formul train model robust attributions. framework consist gener regular term train object minim maxim dissimilar attribut map local neighbourhood input. show far generalized, less constrain formul current exist train methods. propos two new instanti framework, aat advaat, directli optim robust attribut predictions. experi perform wide use vision dataset show method perform better compar current one term attribut robust gener applicable. final show method mitig undesir depend attribut robust train estim parameters, seem critic affect competitor methods.",
    "meta-learn approach enabl machin learn system adapt new task given exampl leverag knowledg relat tasks. however, larg number meta-train task still requir gener unseen task meta-testing, introduc critic bottleneck real-world problem come tasks, due variou reason includ difficulti cost construct tasks. recently, sever task augment method propos tackl issu use domain-specif knowledg design augment techniqu densifi meta-train task distribution. however, relianc domain-specif knowledg render method inapplic domains. manifold mixup base task augment method domain-agnostic, empir find ineffect non-imag domains. tackl limitations, propos novel domain-agnost task augment method, meta-interpolation, util express neural set function densifi meta-train task distribut use bilevel optimization. empir valid efficaci meta-interpol eight dataset span across variou domain imag classification, molecul properti prediction, text classif speech recognition. experimentally, show meta-interpol consist outperform relev baselines. theoretically, prove task interpol set function regular meta-learn improv generalization.",
    "build success machin learn (ml) systems, imper high qualiti data well tune learn models. one assess qualiti given dataset? strength weak model dataset revealed? new tool pyhard employ methodolog known instanc space analysi (isa) produc hard embed dataset relat predict perform multipl ml model estim instanc hard meta-features. space built observ distribut linearli regard hard classify. user visual interact embed multipl way obtain use insight data algorithm perform along individu observ dataset. show covid prognosi dataset analysi support identif pocket hard observ challeng ml model therefor worth closer inspection, delin region strength weak ml models.",
    "describ applic encoder-decod recurr neural network lstm unit attent gener headlin text news articles. find model quit effect concis paraphras news articles. furthermore, studi neural network decid input word pay attent to, specif identifi function differ neuron simplifi attent mechanism. interestingly, simplifi attent mechan perform better complex attent mechan held set articles.",
    "system lupu erythematosu (sle) rare autoimmun disord character unpredict cours flare remiss divers manifestations. lupu nephritis, one major diseas manifest sle organ damag mortality, key compon lupu classif criteria. accur identifi lupu nephriti electron health record (ehrs) would therefor benefit larg cohort observ studi clinic trial character patient popul critic recruitment, studi design, analysis. lupu nephriti recogn procedur code structur data, laboratori tests. however, critic inform document lupu nephritis, histolog report kidney biopsi prior medic histori narratives, requir sophist text process mine inform patholog report clinic notes. study, develop algorithm identifi lupu nephriti without natur languag process (nlp) use ehr data. develop four algorithms: rule-bas algorithm use structur data (baselin algorithm) three algorithm use differ nlp models. three nlp model base regular logist regress use differ set featur includ posit mention concept uniqu identifi (cuis), number appear cuis, mixtur three compon respectively. baselin algorithm best perform nlp algorithm extern valid dataset vanderbilt univers medic center (vumc).",
    "best perform nlp model incorpor featur structur data, regular express concepts, map cui improv f measur nmedw (0.41 vs 0.79) vumc (0.62 vs 0.96) dataset compar baselin lupu nephriti algorithm.",
    "paper, extend $\\beta$-cnmf two dimens deriv exact multipl updat factors. new updat gener correct nonneg matrix factor deconvolut previous propos schmidt m{\\o}rup. show simul updat lead monoton decreas $\\beta$-diverg term mean standard deviat correspond converg curv consist across common valu $\\beta$.",
    "switch dynam system express model class analysi time-seri data. mani field within natur engin sciences, system studi typic evolv continu time, natur consid continuous-tim model formul consist switch stochast differenti equat govern underli markov jump process. infer type model howev notori difficult, tractabl comput scheme rare. work, propos novel infer algorithm util markov chain mont carlo approach. present gibb sampler allow effici obtain sampl exact continuous-tim posterior processes. framework natur enabl bayesian paramet estimation, also includ estim diffus covariance, oftentim assum fix stochast differenti equat models. evalu framework model assumpt compar exist variat infer approach.",
    "graph represent learn (grl) becom central character structur complex network perform task link prediction, node classification, network reconstruction, commun detection. wherea numer gener grl model proposed, mani approach prohibit comput requir hamper large-scal network analysis, fewer abl explicitli account structur emerg multipl scales, explicitli respect import network properti homophili transitivity. paper propos novel scalabl graph represent learn method name hierarch block distanc model (hbdm). hbdm impos multiscal block structur akin stochast block model (sbm) account homophili transit accur approxim latent distanc model (ldm) throughout infer hierarchy. hbdm natur accommod unipartite, directed, bipartit network wherea hierarchi design ensur linearithm time space complex enabl analysi large-scal networks. evalu perform hbdm massiv network consist million nodes. importantly, find propos hbdm framework significantli outperform recent scalabl approach consid downstream tasks. surprisingly, observ superior perform even impos ultra-low two-dimension embed facilit accur direct hierarchical-awar network visual interpretation.",
    "paper address problem low-rank distanc matrix completion. problem amount recov miss entri distanc matrix dimens data embed space possibl unknown small compar number consid data points. focu high-dimension problems. recast consid problem optim problem set low-rank posit semidefinit matric propos two effici algorithm low-rank distanc matrix completion. addition, propos strategi determin dimens embed space. result algorithm scale high-dimension problem monoton converg global solut problem. finally, numer experi illustr good perform propos algorithm benchmarks.",
    "aspect sentiment classif (asc) aim determin sentiment express toward differ aspect sentence. state-of-the-art asc model achiev remark performance, recent shown suffer issu robustness. particularli two common scenarios: domain test train data differ (out-of-domain scenario) test data adversari perturb (adversari scenario), asc model may attend irrelev word neglect opinion express truli describ divers aspects. tackl challenge, paper, hypothes posit bia (i.e., word closer concern aspect would carri higher degre importance) crucial build robust asc model reduc probabl mis-attending. accordingly, propos two mechan captur posit bias, name position-bias weight position-bias dropout, flexibl inject exist model enhanc represent classification. experi conduct out-of-domain adversari dataset demonstr propos approach larg improv robust effect current models.",
    "address problem build agent whose goal learn execut out-of distribut (ood) multi-task instruct express tempor logic (tl) use deep reinforc learn (drl). recent work provid evid agent' neural architectur key featur drl agent learn solv ood task tl. yet, studi topic still infancy. work, propos new deep learn configur induct bias lead agent gener latent represent current goal, yield stronger gener performance. use latent-go network within neuro-symbol framework execut multi-task formally-defin instruct contrast perform propos neural network employ differ state-of-the-art (sota) architectur gener unseen instruct ood environments.",
    "present mechan comput sketch (succinct summary) complex modular deep network process inputs. sketch summar essenti inform input output network use quickli identifi key compon summari statist inputs. furthermore, sketch recurs unrol identifi sub-compon compon forth, captur potenti complic dag structure. sketch eras gracefully; even eras fraction sketch random, remaind still retain `high-weight' inform present origin sketch. sketch also organ repositori implicitli form `knowledg graph'; possibl quickli retriev sketch repositori relat sketch interest; arrang fashion, sketch also use learn emerg concept look new cluster sketch space. finally, scenario want learn ground truth deep network, show augment input/output pair sketch theoret make easier so.",
    "multi-task learn open challeng problem comput vision. typic way conduct multi-task learn deep neural network either handcraft scheme share initi layer branch adhoc point, separ task-specif network addit featur sharing/fus mechanism. unlik exist methods, propos adapt share approach, call adashare, decid share across task achiev best recognit accuracy, take resourc effici account. specifically, main idea learn share pattern task-specif polici select choos layer execut given task multi-task network. effici optim task-specif polici jointli network weights, use standard back-propagation. experi sever challeng divers benchmark dataset variabl number task well demonstr efficaci approach state-of-the-art methods. project page: https://cs-people.bu.edu/sunxm/adashare/project.html.",
    "gaussian process (gps) provid nonparametr represent functions. however, classic gp infer suffer high comput cost difficult design nonstationari gp prior practice. paper, propos spars gaussian process model, eigengp, base karhunen-loev (kl) expans gp prior. use nystrom approxim obtain data depend eigenfunct select eigenfunct evid maximization. select reduc number eigenfunct model provid nonstationari covari function. handl nonlinear likelihoods, develop effici expect propag (ep) infer algorithm, coupl expect maxim eigenfunct selection. eigenfunct gaussian kernel associ cluster sampl - includ label unlabel - select relev eigenfunct enabl eigengp conduct semi-supervis learning. experiment result demonstr improv predict perform eigengp altern state-of-the-art spars gp semisupervis learn method regression, classification, semisupervis classification.",
    "introduc attent unsupervis text (w)riter (autr), word level gener model natur language. use recurr neural network dynam attent canva memori mechan iter construct sentences. view state memori intermedi stage model place attention, gain insight construct sentences. demonstr autr learn meaning latent represent sentence, achiev competit log-likelihood lower bound whilst comput efficient. effect gener reconstruct sentences, well imput miss words.",
    "aggreg signal collect noisi sourc fundament problem mani domain includ crowd-sourcing, multi-ag planning, sensor networks, signal processing, voting, ensembl learning, feder learning. core question aggreg signal multipl sourc (e.g. experts) order reveal underli ground truth. full answer depend type signal, correl signals, desir output, problem common applic differenti sourc base qualiti weight accordingly. often assum differenti aggreg done single, accur central mechan agent (e.g. judge). complic model two ways. first, investig set singl judge, one multipl judges. second, given multi-ag interact judges, investig variou constraint judges' report space. build known result optim weight expert prove ensembl sub-optim mechan perform optim certain conditions. show empir ensembl approxim perform optim mechan broader rang conditions.",
    "physics-inform neural network (pinn) becom commonli use machin learn approach solv partial differenti equat (pde). but, face high-dimension second-ord pde problems, pinn suffer sever scalabl issu sinc loss includ second-ord derivatives, comput cost grow along dimens stack back-propagation. paper, develop novel approach significantli acceler train physics-inform neural networks. particular, parameter pde solut gaussian smooth model show that, deriv stein' identity, second-ord deriv effici calcul without back-propagation. discuss model capac provid varianc reduct method address key limit deriv estimation. experiment result show propos method achiev competit error compar standard pinn train two order magnitud faster.",
    "transfer oper perron--frobeniu koopman oper play import role global analysi complex dynam systems. eigenfunct oper use detect metast sets, project dynam onto domin slow processes, separ superimpos signals. extend transfer oper theori reproduc kernel hilbert space show oper relat hilbert space represent condit distributions, known condit mean embed machin learn community. moreover, numer method comput empir estim embed akin data-driven method approxim transfer oper extend dynam mode decomposit variants. one main benefit present kernel-bas approach method appli domain similar measur given kernel available. illustr result aid guid exampl highlight potenti applic molecular dynam well video text data analysis.",
    "detect concept drift well known problem affect product systems. however, two import issu frequent address literatur 1) detect drift label immedi available; 2) automat gener explan identifi possibl caus drift. example, fraud detect model onlin payment could show drift due hot sale item (with increas fals positives) due true fraud attack (with increas fals negatives) label available. paper propos samm, automat model monitor system data streams. samm detect concept drift use time space effici unsupervis stream algorithm gener alarm report summari event featur import explain it. samm evalu five real world fraud detect datasets, span period eight month total 22 million onlin transactions. evalu samm use human feedback domain experts, send 100 report gener system. result show samm abl detect anomal event model life cycl consid use domain experts. given results, samm roll next version feedzai' fraud detect solution.",
    "focu gener autoencoders, variat adversari autoencoders, jointli learn gener model alongsid infer model. gener autoencod train softli enforc prior latent distribut learn infer model. call distribut infer model map observ samples, learn latent distribution, may consist prior. formul markov chain mont carlo (mcmc) sampl process, equival iter decod encoding, allow us sampl learn latent distribution. since, gener model learn map learn latent distribution, rather prior, may use mcmc improv qualiti sampl drawn gener model, especi learn latent distribut far prior. use mcmc sampling, abl reveal previous unseen differ gener autoencod train either without denois criterion.",
    "studi deep neural network (dnns) infinite-width limit, via so-cal neural tangent kernel (ntk) approach, provid new insight dynam learning, generalization, impact initialization. one key dnn architectur remain kernelized, namely, recurr neural network (rnn). paper introduc studi recurr neural tangent kernel (rntk), provid new insight behavior overparametr rnns. key properti rntk greatli benefit practition abil compar input differ length. end, character rntk weight differ time step form output differ initi paramet nonlinear choices. synthet 56 real-world data experi demonstr rntk offer signific perform gain kernels, includ standard ntks, across wide array data sets.",
    "state-of-the-art contrast self-supervis learn (ssl) model produc result competit supervis counterparts, lack abil infer latent variables. contrast, prescrib latent variabl (lv) model enabl attribut uncertainty, induc task specif compression, gener allow interpret representations. work, introduc lv approxim larg scale contrast ssl models. demonstr addit improv downstream perform (result 96.42% 77.49% test top-1 fine-tun perform cifar10 imagenet respect resnet50) well produc highli compress represent (588x reduction) use interpretability, classif regress downstream tasks.",
    "ct mri two wide use clinic imag modal non-invas diagnosis. however, modal come certain problems. ct use harm ionis radiation, mri suffer slow acquisit speed. problem tackl undersampling, spars sampling. however, undersampl data lead lower resolut introduc artefacts. sever techniques, includ deep learn base methods, propos reconstruct data. however, undersampl reconstruct problem two modal alway consid two differ problem tackl separ differ research works. paper propos unifi solut spars ct undersampl radial mri reconstruction, achiev appli fourier transform-bas pre-process radial mri reconstruct modal use sinogram upsampl combin filter back-projection. primal-du network deep learn base method reconstruct sparsely-sampl ct data. paper introduc primal-du unet, improv primal-du network term accuraci reconstruct speed. propos method result averag ssim 0.932 perform spars ct reconstruct fan-beam geometri sparsiti level 16, achiev statist signific improv previou model, result 0.919. furthermore, propos model result 0.903 0.957 averag ssim reconstruct undersampl brain abdomin mri data acceler factor 16 - statist signific improv origin model, result 0.867 0.949.",
    "finally, paper show propos network improv overal imag quality, also improv imag qualiti regions-of-interest; well generalis better presenc needle.",
    "mani recent paper address read comprehension, exampl consist (question, passage, answer) tuples. presumably, model must combin inform question passag predict correspond answers. however, despit intens interest topic, hundr publish paper vy leaderboard dominance, basic question difficulti mani popular benchmark remain unanswered. paper, establish sensibl baselin babi, squad, cbt, cnn, who-did-what datasets, find question- passage-onli model often perform surprisingli well. $14$ $20$ babi tasks, passage-onli model achiev greater $50\\%$ accuracy, sometim match full model. interestingly, cbt provid $20$-sentenc stori last need compar accur prediction. comparison, squad cnn appear better-constructed.",
    "human abl perform myriad sophist task draw upon skill acquir prior experience. autonom agent capability, must abl extract reusabl skill past experi recombin new way subsequ tasks. furthermore, control complex high-dimension morphologies, humanoid bodies, task often requir coordin multipl skill simultaneously. learn discret primit everi combin skill quickli becom prohibitive. compos primit recombin creat larg varieti behavior suitabl model combinatori explosion. work, propos multipl composit polici (mcp), method learn reusabl motor skill compos produc rang complex behaviors. method factor agent' skill collect primitives, multipl primit activ simultan via multipl composition. flexibl allow primit transfer recombin elicit new behavior necessari novel tasks. demonstr mcp abl extract compos skill highli complex simul charact pre-train tasks, motion imitation, reus skill solv challeng continu control tasks, dribbl soccer ball goal, pick object transport target location.",
    "knowledg graph learn play critic role integr domain specif knowledg base deploy machin learn data mine model practice. exist method knowledg graph learn primarili focu model relat among entiti translat among relat entities, mani method abl handl zero-shot problems, new entiti emerge. paper, present new convolut neural network (cnn)-base dual-chain model. differ translat base methods, model, interact among relat entiti directli captur via cnn embeddings. moreover, secondari chain learn conduct simultan incorpor addit inform enabl better performance. also present extens model, incorpor descript entiti learn second set entiti embed descriptions. result, extend model abl effect handl zero-shot problems. conduct comprehens experiments, compar method 15 method 8 benchmark datasets. extens experiment result demonstr propos method achiev outperform state-of-the-art result knowledg graph learning, outperform method zero-shot problems. addition, method appli real-world biomed data abl produc result conform expert domain knowledge.",
    "studi propos two new dynam assign algorithm match refuge asylum seeker geograph local within host country. first, current implement multi-year pilot switzerland, seek maxim averag predict employ level (or measur outcom interest) refuge minimum-discord onlin assign algorithm. although propos algorithm achiev near-optim expect employ compar hindsight-optim solut (and improv upon statu quo procedur 40%), result period imbalanc alloc local time. lead undesir workload ineffici resettl resourc agents. address problem, second algorithm balanc goal improv refuge outcom desir even alloc time. perform propos method illustr use real refuge resettl data larg resettl agenc unit states. dataset, find alloc balanc algorithm achiev near-perfect balanc time small loss expect employ compar pure employment-maxim algorithm. addition, alloc balanc algorithm offer number ancillari benefit compar pure outcome-maximization, includ robust unknown arriv flow greater exploration.",
    "prove lower bound higher-ord method smooth non-convex finite-sum optimization. contribut threefold: first show determinist algorithm cannot profit finite-sum structur objective, simul pth-order regular method whole function construct exact gradient inform optim constant factors. show lower bound random algorithm compar best known upper bounds. address gap bounds, propos new second-ord smooth assumpt seen analogu first-ord mean-squar smooth assumption. prove suffici ensur state-of-the-art converg guarantees, allow sharper lower bound.",
    "propos distribut approach train deep neural network (dnns), guarante converg theoret great scalabl empirically: close 6 time faster instanc imagenet data set run 6 machines. propos scheme close optim scalabl term number machines, guarante converg optima undistribut setting. converg scalabl distribut set shown empir across differ dataset (timit imagenet) machin learn task (imag classif phonem extraction). converg analysi provid novel insight complex learn scheme, including: 1) layerwis convergence, 2) converg weight probability.",
    "unsupervis domain adapt address problem transfer knowledg well-label sourc domain unlabel target domain two domain distinct data distributions. thus, essenc domain adapt mitig distribut diverg two domains. state-of-the-art method practic idea either conduct adversari train minim metric defin distribut gaps. paper, propos new domain adapt method name adversari tight match (atm) enjoy benefit adversari train metric learning. specifically, first, propos novel distanc loss, name maximum densiti diverg (mdd), quantifi distribut divergence. mdd minim inter-domain diverg (\"match\" atm) maxim intra-class densiti (\"tight\" atm). then, address equilibrium challeng issu adversari domain adaptation, consid leverag propos mdd adversari domain adapt framework. last, tailor propos mdd practic learn loss report atm. empir evalu theoret analysi report verifi effect propos method. experiment result four benchmarks, classic large-scale, show method abl achiev new state-of-the-art perform evaluations. code dataset use paper avail {\\it github.com/lijin118/atm}.",
    "propos recurr neural network-bas spatio-tempor framework name maskgru detect track small object videos. mani develop area object track recent years, track small move object amid move object actor (such ball amid move player sport footage) continu difficult task. exist spatio-tempor networks, convolut gate recurr unit (convgrus), difficult train troubl accur track small object conditions. overcom difficulties, develop maskgru framework use weight sum intern hidden state produc convgru 3-channel mask track object' predict bound box hidden state use next time step underli convgru. believ techniqu incorpor mask hidden state weight sum two benefits: control effect explod gradient introduc attention-lik mechan network indic previou video frame object located. experi show maskgru outperform convgru track object small rel video resolut even presenc move objects.",
    "goal learn semant parser map natur languag utter execut program indirect supervis available: exampl label correct execut result, program itself. consequently, must search space program output correct result, misl spuriou programs: incorrect program coincident output correct result. connect two common learn paradigms, reinforc learn (rl) maximum margin likelihood (mml), present new learn algorithm combin strength both. new algorithm guard spuriou program combin systemat search tradit employ mml random explor rl, updat paramet probabl spread evenli across consist programs. appli learn algorithm new neural semant parser show signific gain exist state-of-the-art result recent context-depend semant pars task.",
    "hyperdimension comput promis novel paradigm low-pow embed machin learning. appli differ biomed applications, particularli epilept seizur detection. unfortunately, due differ data preparation, segmentation, encod strategies, perform metrics, result hard compare, make build upon knowledg difficult. thus, main goal work perform systemat assess hd comput framework detect epilept seizures, compar differ featur approach map hd vectors. precisely, test two previous implement featur well sever novel approach hd comput epilept seizur detection. evalu compar way, i.e., preprocess setup, ident perform measures. use two differ dataset order assess generaliz conclusions. systemat assess involv three primari aspect relev potenti wearabl implementations: 1) detect performance, 2) memori requirements, 3) comput complexity. analysi show signific differ detect perform approaches, also one highest perform might ideal wearabl applic due high memori comput requirements. furthermore, evalu post-process strategi adjust predict dynam epilept seizures, show perform significantli improv approach also post-processing, differ perform much smaller approaches.",
    "studi affect comput wild set underpin databases. exist multimod emot databas real-world condit small, limit number subject express singl language. meet requirement, collected, annotated, prepar releas new natur state video databas (call heu emotion). heu emot contain total 19,004 video clips, divid two part accord data source. first part contain video download tumblr, google, giphy, includ 10 emot two modal (facial express bodi posture). second part includ corpu taken manual movies, tv series, varieti shows, consist 10 emot three modal (facial expression, bodi posture, emot speech). heu emot far extens multi-mod emot databas 9,951 subjects. order provid benchmark emot recognition, use mani convent machin learn deep learn method evalu heu emotion. propos multi-mod attent modul fuse multi-mod featur adaptively. multi-mod fusion, recognit accuraci two part increas 2.19% 4.01% respect single-mod facial express recognition.",
    "repres data resid graph linear combin build block signal enabl effici insight visual statist analysi data, represent prove use regular signal process machin learn tasks. design collect build block signal -- formally, dictionari atom -- specif account underli graph structur well avail repres train signal activ area research last decade. article, survey particular class dictionari call local spectral graph filter frames, whose atom creat local spectral pattern differ region graph. show class encompass varieti approach spectral graph wavelet graph filter banks, focu two main question design spectral filter select center vertic pattern localized. throughout, emphas comput effici method ensur result transform invers appli data resid large, spars graphs. demonstr class transform method use signal process task denois non-linear approximation, provid code reader experi method new applic domains.",
    "invari equivari network use learn data symmetry, includ images, sets, point clouds, graphs. paper, consid invari equivari network symmetri finit groups. invari equivari network construct variou research use reynold operators. however, reynold oper comput expens order group larg use sum whole group, pose implement difficulty. overcom difficulty, consid repres reynold oper sum subset instead sum whole group. call subset reynold design, oper defin sum reynold design reduct reynold operator. example, case graph $n$ nodes, comput complex reduct reynold oper reduc $o(n^2)$, comput complex reynold oper $o(n!)$. construct learn model base reduct reynold oper call equivari invari reynold network (reynets) prove univers approxim property. reynold design equivari reynet deriv combinatori observ young diagrams, reynold design invari reynet deriv invari call reynold dimens defin set invari polynomials. numer experi show perform model compar state-of-the-art methods.",
    "despit achiev remark success variou domains, recent studi uncov vulner deep neural network adversari perturbations, creat concern model generaliz new threat prediction-evas misclassif stealthi reprogramming. among differ defens proposals, stochast network defens random neuron activ prune random perturb layer input shown promis attack mitigation. however, one critic drawback current defens robust enhanc cost notic perform degrad legitim data, e.g., larg drop test accuracy. paper motiv pursu better trade-off adversari robust test accuraci stochast network defenses. propos defens effici score (des), comprehens metric measur gain unsuccess attack attempt cost drop test accuraci defense. achiev better des, propos hierarch random switch (hrs), protect neural network novel random scheme. hrs-protect model contain sever block randomli switch channel prevent adversari exploit fix model structur paramet malici purposes. extens experi show hr superior defend state-of-the-art white-box adapt adversari misclassif attacks. also demonstr effect hr defend adversari reprogramming, first defens adversari programs. moreover, set averag de hr least 5x higher current stochast network defenses, valid significantli improv robustness-accuraci trade-off.",
    "studi adapt smoothly-vari ('easy') environ well-known onlin learn problem acquir inform expensive. problem label effici prediction, budget version predict expert advice, present onlin algorithm whose regret depend optim number label allow $q^*$ (the quadrat variat loss best action hindsight), along parameter-fre counterpart whose regret depend optim $q$ (the quadrat variat loss actions). quantiti significantli smaller $t$ (the total time horizon), yield improv existing, variation-independ result problem. extend analysi handl label effici predict bandit feedback, i.e., label effici bandits. work build upon framework optimist onlin mirror descent, leverag second order correct along care design hybrid regular encod constrain inform structur problem. consid reveal action-parti monitor game -- version label effici predict addit inform costs, gener known lie \\textit{hard} class game minimax regret order $t^{\\frac{2}{3}}$. provid strategi $\\mathcal{o}((q^*t)^{\\frac{1}{3}})$ bound reveal action games, along one $\\mathcal{o}((qt)^{\\frac{1}{3}})$ bound full class hard partial monitor games, strict improv current bounds.",
    "propos new architectur train methodolog gener adversari networks. current approach attempt learn transform nois sampl gener data sampl one shot. propos gener architecture, call $\\textit{chaingan}$, use two-step process. first attempt transform nois vector crude sample, similar tradit generator. next, chain networks, call $\\textit{editors}$, attempt sequenti enhanc sample. train unit independently, instead end-to-end backpropag entir chain. model robust, efficient, flexibl appli variou network architectures. provid rational choic experiment evalu model, achiev competit result sever datasets.",
    "last decades, psychologist develop sophist formal model human categor use simpl artifici stimuli. paper, use modern machin learn method extend work realm naturalist stimuli, enabl human categor studi complex visual domain evolv developed. show represent deriv convolut neural network use model behavior databas >300,000 human natur imag classifications, find group model base represent perform well, near reliabl human judgments. interestingly, group includ exemplar prototyp models, contrast domin exemplar model previou work. abl improv perform remain model preprocess neural network represent close captur human similar judgments.",
    "matrix approxim key element large-scal algebra machin learn approaches. recent propos method meka (si et al., 2014) effect employ two common assumpt hilbert spaces: low-rank properti inner product matrix obtain shift-invari kernel function data compact hypothesi mean inher block-clust structure. work, extend meka applic shift-invari kernel also non-stationari kernel like polynomi kernel extrem learn kernel. also address detail handl non-posit semi-definit kernel function within meka, either caus approxim intent use gener kernel functions. present lanczos-bas estim spectrum shift develop stabl posit semi-definit meka approximation, also usabl classic convex optim frameworks. furthermore, support find theoret consider varieti experi synthet real-world data.",
    "interspeech 2020 deep nois suppress (dns) challeng intend promot collabor research real-tim single-channel speech enhanc aim maxim subject (perceptual) qualiti enhanc speech. typic approach evalu nois suppress method use object metric test set obtain split origin dataset. perform good synthet test set, often model perform degrad significantli real recordings. also, convent object metric correl well subject test lab subject test scalabl larg test set. challenge, open-sourc larg clean speech nois corpu train nois suppress model repres test set real-world scenario consist synthet real recordings. also open-sourc onlin subject test framework base itu-t p.808 research reliabl test developments. evalu result use p.808 blind test set. result key learn challeng discussed. dataset script found quick access https://github.com/microsoft/dns-challenge.",
    "mani machin learn scenarios, supervis gold label avail consequ neural model cannot train directli maximum likelihood estim (mle). weak supervis scenario, metric-aug object employ assign feedback model outputs, use extract supervis signal training. present sever object two separ weakli supervis tasks, machin translat semant parsing. show object activ discourag neg output addit promot surrog gold structure. notion bipolar natur present ramp loss objectives, adapt neural models. show bipolar ramp loss object outperform non-bipolar ramp loss object minimum risk train (mrt) weakli supervis tasks, well supervis machin translat task. additionally, introduc novel token-level ramp loss objective, abl outperform even best sequence-level ramp loss weakli supervis tasks.",
    "effect implement sampling-bas probabilist infer often requir manual constructed, model-specif proposals. inspir recent progress meta-learn train learn agent gener unseen environments, propos meta-learn approach build effect generaliz mcmc proposals. parametr propos neural network provid fast approxim block gibb conditionals. learn neural propos gener occurr common structur motif across differ models, allow construct librari learn infer primit acceler infer unseen model model-specif train required. explor sever applic includ open-univers gaussian mixtur models, learn propos outperform hand-tun sampler, real-world name entiti recognit task, sampler yield higher final f1 score classic single-sit gibb sampling.",
    "solv analyt intract partial differenti equat (pdes) involv least one variabl defin unbound domain requir effici numer method accur resolv depend pde variabl sever order magnitude. unbound domain problem aris variou applic area solv problem import understand multi-scal biolog dynamics, resolv physic process long time scale distances, perform paramet infer engin problems. work, combin two class numer methods: (i) physics-inform neural network (pinns) (ii) adapt spectral methods. numer method develop take advantag abil physics-inform neural network easili implement high-ord numer scheme effici solv pdes. show recent introduc adapt techniqu spectral method integr pinn-bas pde solver obtain numer solut unbound domain problem cannot effici approxim standard pinns. number examples, demonstr advantag propos spectral adapt pinn (s-pinns) standard pinn approxim functions, solv pdes, estim model paramet noisi observ unbound domains.",
    "distinguish class time seri sampl dynam system common challeng system control engineering, exampl context health monitoring, fault detection, qualiti control. challeng increas underli model system known, measur nois present, long signal need interpreted. paper address issu new non parametr classifi base topolog signatures. model learn class weight kernel densiti estim (kdes) persist homolog diagram predict new trajectori label use sinkhorn diverg space diagram kde quantifi proximity. show approach accur discrimin state chaotic system close paramet space, perform robust noise.",
    "treatment cloud structur numer weather climat model often greatli simplifi make comput affordable. propos correct european centr medium-rang weather forecast 1d radiat scheme ecrad 3d cloud effect use comput cheap neural networks. 3d cloud effect learn differ ecrad' fast 1d triplecloud solver neglect 3d spartacu (speedi algorithm radi transfer cloud sides) solver includ five time comput expensive. typic error 20 % 30 % 3d signal, neural network improv tripleclouds' accuraci 1 % increas runtime. thus, rather emul whole spartacus, keep triplecloud unchang cloud-fre part atmospher 3d-correct elsewhere. focu compar small 3d correct instead entir signal allow us improv predict significantli assum similar signal-to-nois ratio both.",
    "averag lack biolog marker caus one year diagnost delay detect amyotroph later sclerosi (als). improv diagnost process automat voic assess base acoust analysi used. purpos work verifi sutabl sustain vowel phonat test automat detect patient als. propos enhanc procedur separ voic signal fundament period requir calcul perturb measur (such jitter shimmer). also propos method quantit assess patholog vibrato manifest sustain vowel phonation. study' experi show use propos acoust analysi methods, classifi base linear discrimin analysi attain 90.7\\% accuraci 86.7\\% sensit 92.2\\% specificity.",
    "knowledg distil (kd) one use techniqu light-weight neural networks. although neural network clear purpos embed dataset low-dimension space, exist knowledg quit far purpos provid limit information. argu good knowledg abl interpret embed procedure. paper propos method gener interpret embed procedur (iep) knowledg base princip compon analysis, distil base messag pass neural network. experiment result show student network train propos kd method improv 2.28% cifar100 dataset, higher perform state-of-the-art (sota) method. also demonstr embed procedur knowledg interpret via visual propos kd process. implement code avail https://github.com/sseung0703/iepkt.",
    "electr markets, retail broker want maxim profit alloc tariff profil end consumers. one object demand respons manag incentiv consum adjust consumpt overal electr procur wholesal market minimized, e.g. desir consum consum less peak hour cost procur broker wholesal market high. consid greedi solut maxim overal profit broker optim tariff profil allocation. in-turn requir forecast electr consumpt user tariff profiles. forecast problem challeng compar standard forecast problem due follow reasons: i. number possibl combin hourli tariff high retail may consid combin past result bias set tariff profil tri past, ii. profil alloc past user typic base certain policy. reason violat standard i.i.d. assumptions, need evalu new tariff profil exist custom histor data bias polici use past tariff allocation. work, consid sever scenario forecast optim conditions. leverag underli structur consum respond variabl tariff rate compar tariff across hour shift loads, propos suitabl induct bias design deep neural network base architectur forecast scenarios.",
    "specifically, leverag attent mechan permut equivari network allow desir process tariff profil learn tariff represent insensit bias data still repres task.",
    "contextu bandit algorithm becom popular onlin recommend system digg, yahoo! buzz, news recommend general. \\emph{offline} evalu effect new algorithm applic critic protect onlin user experi challeng due \"partial-label\" nature. common practic creat simul simul onlin environ problem hand run algorithm simulator. however, creat simul often difficult model bia usual unavoid introduced. paper, introduc \\emph{replay} methodolog contextu bandit algorithm evaluation. differ simulator-bas approaches, method complet data-driven easi adapt differ applications. importantly, method provid provabl unbias evaluations. empir result large-scal news articl recommend dataset collect yahoo! front page conform well theoret results. furthermore, comparison offlin replay onlin bucket evalu sever contextu bandit algorithm show accuraci effect offlin evalu method.",
    "studi problem concept induct visual reasoning, i.e., identifi concept hierarch relationship question-answ pair associ images; achiev interpret model via work induc symbol concept space. end, first design new framework name object-centr composit attent model (occam) perform visual reason task object-level visual features. then, come method induc concept object relat use clue attent pattern objects' visual featur question words. finally, achiev higher level interpret impos occam object repres induc symbol concept space. model design make easi adapt via first predict concept object relat project predict concept back visual featur space composit reason modul process normally. experi clevr gqa dataset demonstrate: 1) occam achiev new state art without human-annot function programs; 2) induc concept accur suffici occam achiev on-par perform object repres either visual featur induc symbol concept space.",
    "investig use deep neural network control complex nonlinear dynam systems, specif movement rigid bodi immers fluid. solv navier stoke equat two way coupling, give rise nonlinear perturb make control task challenging. neural network train unsupervis way act control desir characterist process learn differenti simulator. introduc set physic interpret loss term let network learn robust stabl interactions. demonstr control train canon set quiescent initi condit reliabl gener vari challeng environ previous unseen inflow condit forcing, although fluid inform input. further, show control train approach outperform varieti classic learn altern term evalu metric gener capabilities.",
    "condit neural network (clnn) exploit natur tempor sequenc sound signal repres spectrogram, variant mask condit neural network (mclnn) induc network learn frequenc band embed filterbank-lik spars network' link use binari mask. additionally, mask autom explor differ featur combin concurr analog handcraft optimum combin featur recognit task. evalu mclnn perform use urbansound8k dataset environment sounds. additionally, present collect manual record sound rail road traffic, yornoise, investig confus rate among machin gener sound possess low-frequ components. mclnn achiev competit result without augment use 12% trainabl paramet util equival model base state-of-the-art convolut neural network urbansound8k. extend urbansound8k dataset yornoise, experi shown common tonal properti affect classif performance.",
    "work concern gener target design rna, type genet macromolecul adopt complex structur influenc cellular activ functions. design larg scale complex biolog structur spur dedic graph-bas deep gener model techniques, repres key underappreci aspect comput drug discovery. work, investig principl behind repres gener differ rna structur modalities, propos flexibl framework jointli emb gener molecular structur along sequenc meaning latent space. equip deep understand rna molecular structures, sophist encod decod method oper molecular graph well junction tree hierarchy, integr strong induct bia rna structur regular fold mechan high structur validity, stabil divers gener rna achieved. also, seek adequ organ latent space rna molecular embed regard interact proteins, target optim use navig latent space search desir novel rna molecules.",
    "always-on tinyml percept task iot applic requir high energi efficiency. analog compute-in-memori (cim) use non-volatil memori (nvm) promis high effici also provid self-contain on-chip model storage. however, analog cim introduc new practic considerations, includ conduct drift, read/writ noise, fix analog-to-digit (adc) convert gain, etc. addit constraint must address achiev model deploy analog cim accept accuraci loss. work describ $\\textit{analognets}$: tinyml model popular always-on applic keyword spot (kws) visual wake word (vww). model architectur specif design analog cim, detail comprehens train methodology, retain accuraci face analog non-idealities, low-precis data convert infer time. also describ aon-cim, programmable, minimal-area phase-chang memori (pcm) analog cim accelerator, novel layer-seri approach remov cost complex interconnect associ fully-pipelin design. evalu analognet calibr simulator, well real hardware, find accuraci degrad limit 0.8$\\%$/1.2$\\%$ 24 hour pcm drift (8-bit) kws/vww. analognet run 14nm aon-cim acceler demonstr 8.58/4.37 tops/w kws/vww workload use 8-bit activations, respectively, increas 57.39/25.69 tops/w $4$-bit activations.",
    "paper describ motion plan network (mpnet), comput efficient, learning-bas neural planner solv motion plan problems. mpnet use neural network learn gener near-optim heurist path plan seen unseen environments. take environ inform raw point-cloud depth sensors, well robot' initi desir goal configur recurs call bidirect gener connect paths. addit find directli connect near-optim path singl pass, show worst-cas theoret guarante proven merg neural network strategi classic sample-bas planner hybrid approach still retain signific comput optim improvements. train mpnet models, present activ continu learn approach enabl mpnet learn stream data activ ask expert demonstr needed, drastic reduc data training. valid mpnet gold-standard state-of-the-art plan method varieti problem 2d 7d robot configur space challeng clutter environments, result show signific consist stronger perform metrics, motiv neural plan gener modern strategi solv motion plan problem efficiently.",
    "demonstr object track method 3d imag fix comput cost state-of-the-art performance. previou method predict transform paramet convolut layers. instead propos architectur includ either flatten convolut featur fulli connect layers, instead reli equivari filter preserv transform input output (e.g. rot./trans. input rotate/transl outputs). transform deriv close form output filters. method use applic requir low latency, real-tim tracking. demonstr model synthet augment adult brain mri, well fetal brain mri, intend use-case.",
    "design analyz minimax-optim algorithm onlin linear optim game player' choic unconstrained. player strive minim regret, differ loss loss post-hoc benchmark strategy. standard benchmark loss best strategi chosen bound compar set. comparison set adversary' gradient satisfi l_infin bounds, give valu game close form prove approach sqrt(2t/pi) -> infinity. interest algorithm result consid soft constraint comparator, rather restrict bound set. warmup, analyz game quadrat penalty. valu game exactli t/2, valu achiev perhap simplest onlin algorithm all: unproject gradient descent constant learn rate. deriv minimax-optim algorithm much softer penalti function. algorithm achiev good bound standard notion regret compar point, without need specifi compar set advance. valu game converg sqrt{e} ->infinity; give closed-form exact valu function t. result algorithm natur unconstrain invest bet scenarios, sinc guarante worst constant loss, allow exponenti reward \"easy\" adversary.",
    "work motiv common busi constraint onlin markets. firm respect advantag dynam price price experimentation, must limit number price chang (i.e., switches) within budget due variou practic reasons. studi classic price-bas network revenu manag problem distributionally-unknown setup, bandit knapsack problem. problems, decision-mak (without prior knowledg environment) finit initi inventori multipl resourc alloc finit time horizon. beyond classic resourc constraints, introduc addit switch constraint problems, restrict total number time decision-mak make switch action within fix switch budget. problems, show match upper lower bound optim regret, propos computationally-effici limited-switch algorithm achiev optim regret. work reveal surpris result: optim regret rate complet character piecewise-const function switch budget, depend number resourc constraint -- best knowledge, first time number resourc constraint shown play fundament role determin statist complex onlin learn problems. conduct comput experi examin perform algorithm numer setup wide use literature. compar benchmark algorithm literature, propos algorithm achiev promis perform clear advantag number incur switches.",
    "practically, firm benefit studi improv learn decision-mak perform simultan face resourc switch constraints.",
    "present new discrimin techniqu multiple-sourc adaptation, msa, problem. unlik previou work, reli densiti estim sourc domain, solut requir condit probabl easili accur estim unlabel data sourc domains. give detail analysi new technique, includ gener guarante base r\\'enyi divergences, learn bound condit maxent use estim condit probabl point belong sourc domain. show guarante compar favor deriv gener solution, use kernel densiti estimation. experi real-world applic demonstr new discrimin msa algorithm outperform previou gener solut well domain adapt baselines.",
    "greedi algorithm simplest heurist sequenti decis problem carelessli take local optim choic round, disregard advantag explor and/or inform gathering. theoretically, known sometim poor performances, instanc even linear regret (with respect time horizon) standard multi-arm bandit problem. hand, heurist perform reason well practic even sublinear, even near-optimal, regret bound specif linear contextu bayesian bandit models. build recent line work investig bandit set number arm rel larg simpl greedi algorithm enjoy highli competit performance, theori practice. first provid gener worst-cas bound regret greedi algorithm. combin arm subsampling, prove verifi near-optim worst-cas regret bound continuous, infinit many-arm bandit problems. moreover, shorter time spans, theoret rel suboptim greedi even reduced. consequence, subvers claim mani interest problem associ horizons, best compromis theoret guarantees, practic perform comput burden definit follow greedi heuristic. support claim mani numer experi show signific improv compar state-of-the-art, even moder long time horizon.",
    "fundament problem comput anim realiz purpos realist human movement given sufficiently-rich set motion captur clips. learn data-driven gener model human movement use autoregress condit variat autoencoders, motion vaes. latent variabl learn autoencod defin action space movement therebi govern evolut time. plan control algorithm use action space gener desir motions. particular, use deep reinforc learn learn control achiev goal-direct movements. demonstr effect approach multipl tasks. evalu system-design choic describ current limit motion vaes.",
    "deep learn (dl) vulner out-of-distribut adversari exampl result incorrect outputs. make dl robust, sever posthoc (or runtime) anomali detect techniqu detect (and discard) anomal sampl propos recent past. survey tri provid structur comprehens overview research anomali detect dl base applications. provid taxonomi exist techniqu base underli assumpt adopt approaches. discuss variou techniqu categori provid rel strength weak approaches. goal survey provid easier yet better understand techniqu belong differ categori research done topic. finally, highlight unsolv research challeng appli anomali detect techniqu dl system present high-impact futur research directions.",
    "propos novel parameter famili mix membership mallow model (m4) account variabl pairwis comparison gener heterogen popul noisi inconsist users. m4 model individu prefer user-specif probabilist mixtur share latent mallow components. key algorithm insight estim establish statist connect m4 topic model view pairwis comparison words, user documents. key insight lead us explor mallow compon separ structur leverag recent advanc separ topic discovery. separ appear overli restrictive, nevertheless show inevit outcom rel small number latent mallow compon world larg number items. develop algorithm base robust extreme-point identif convex polygon learn refer rankings, provabl consist polynomi sampl complex guarantees. demonstr new model empir competit current state-of-the-art approach predict real-world preferences.",
    "despit divers effort mine variou modal medic data, convers physician patient time care remain untap sourc insights. paper, leverag data extract structur inform might assist physician post-visit document electron health records, potenti lighten cleric burden. exploratori study, describ new dataset consist convers transcripts, post-visit summaries, correspond support evid (in transcript), structur labels. focu task recogn relev diagnos abnorm review organ system (ros). one methodolog challeng convers long (around 1500 words), make difficult modern deep-learn model use input. address challenge, extract noteworthi utterances---part convers like cite evid support summari sentence. find first filter (predicted) noteworthi utterances, significantli boost predict perform recogn diagnos ro abnormalities.",
    "take inspir natur languag embeddings, present astromer, transformer-bas model creat represent light curves. astrom train million macho r-band samples, easili fine-tun match specif domain associ downstream tasks. example, paper show benefit use pre-train represent classifi variabl stars. addition, provid python librari includ function employ work. librari includ pre-train model use enhanc perform deep learn models, decreas comput resourc achiev state-of-the-art results.",
    "number problem process sound natur language, well areas, reduc simultan read input sequenc write output sequenc gener differ length. well develop method produc output sequenc base entir known input. however, effici method enabl transform on-lin exist. paper introduc architectur learn reinforc make decis whether read token write anoth token. architectur abl transform potenti infinit sequenc on-line. experiment studi compar state-of-the-art method neural machin translation. produc slightli wors translat transformer, outperform autoencod attention, even though architectur translat text on-lin therebi solv difficult problem refer methods.",
    "internet thing (iot) devic becom cheaper powerful, research increasingli find solut scientif curios financi comput feasible. oper restrict power commun budgets, however, devic send highly-compress data. circumst common devic place away electr grid commun via satellite, situat particularli plausibl environment sensor networks. restrict complic potenti variabl commun budget, exampl solar-pow devic need expend less energi transmit data cloudi day. propos novel, topology-based, lossi compress method well-equip restrict yet variabl circumstances. technique, topolog signal compression, allow send compress signal util entireti variabl commun budget. demonstr algorithm' capabilities, perform entropi calcul well classif exercis increasingli topolog simplifi signal free-spoken digit dataset explor stabil result perform common baselines.",
    "consid distanc function condit distributions. focu wasserstein metric gaussian case known frechet incept distanc (fid). develop condit version metrics, analyz relat provid close form solut condit fid (cfid) metric. numer compar metric context perform evalu modern condit gener models. result show advantag cfid compar classic fid mean squar error (mse) measures. contrast fid, cfid use identifi failur realist output relat input generated. hand, compar mse, cfid use identifi failur singl realist output gener even though divers set equal probabl outputs.",
    "galaxi nearbi univers gravit bound cluster group galaxies. optic contents, optic richness, crucial understand co-evolut galaxi large-scal structur modern astronomi cosmology. determin optic rich challenging. propos self-supervis approach estim optic rich multi-band optic images. method use data properti multi-band optic imag pre-training, enabl learn featur represent larg unlabel dataset. appli propos method sloan digit sky survey. result show estim optic rich lower mean absolut error intrins scatter 11.84% 20.78%, respectively, reduc need label train data 60%. believ propos method benefit astronomi cosmology, larg number unlabel multi-band imag available, acquir imag label costly.",
    "surg popular supervis deep learning, desir reduc depend curated, label data set leverag vast quantiti unlabel data avail recent trigger renew interest unsupervis learn algorithms. despit significantli improv perform due approach identif disentangl latent representations, contrast learning, cluster optimisations, perform unsupervis machin learn still fall short hypothesis potential. machin learn previous taken inspir neurosci cognit scienc great success. however, mostli base adult learner access label vast amount prior knowledge. order push unsupervis machin learn forward, argu development scienc infant cognit might hold key unlock next gener unsupervis learn approaches. conceptually, human infant learn closest biolog parallel artifici unsupervis learning, infant must learn use represent unlabel data. contrast machin learning, new represent learn rapidli rel examples. moreover, infant learn robust represent use flexibl effici number differ task contexts. identifi five crucial factor enabl infants' qualiti speed learning, assess extent alreadi exploit machin learning, propos adopt factor give rise previous unseen perform level unsupervis learning.",
    "recently, machin learn (ml), artifici intellig (ai), convolut neural network (cnn) made huge progress broad applications, system deep learn structur larg number hyperparamet determin qualiti perform cnn ai systems. system may multi-object ml ai perform needs. key requir find optim hyperparamet structur multi-object robust optim cnn systems. paper propos gener taguchi approach effect determin optim hyperparamet structur multi-object robust optim cnn system via object perform vector norm. propos approach method appli cnn classif system origin resnet cifar-10 dataset demonstr validation, show propos method highli effect achiev optim accuraci rate origin resnet cifar-10.",
    "machin learn (ml) get appli security-crit sensit domains, grow need integr privaci outsourc ml computations. pragmat solut come trust execut environ (tees), use hardwar softwar protect isol sensit comput untrust softwar stack. however, isol guarante come price performance, compar untrust alternatives. paper initi studi high perform execut deep neural network (dnns) tee effici partit dnn comput trust untrust devices. build upon effici outsourc scheme matrix multiplication, propos slalom, framework secur deleg execut linear layer dnn tee (e.g., intel sgx sanctum) faster, yet untrusted, co-loc processor. evalu slalom run dnn intel sgx enclave, select deleg work untrust gpu. canon dnn (vgg16, mobilenet resnet variants) obtain 6x 20x increas throughput verifi inference, 4x 11x verifi privat inference.",
    "anticip human motion crowd scenario essenti develop intellig transport systems, social-awar robot advanc video surveil applications. key compon task repres inher multi-mod natur human path make social accept multipl futur human interact involved. end, propos gener architectur multi-futur trajectori predict base condit variat recurr neural network (c-vrnns). condit mainli reli prior belief maps, repres like move direct forc model consid past observ dynam gener futur positions. human interact model graph-bas attent mechan enabl onlin attent hidden state refin recurr estimation. corrobor model, perform extens experi publicly-avail dataset (e.g., eth/ucy, stanford drone dataset, stat sportvu nba, intersect drone dataset trajnet++) demonstr effect crowd scene compar sever state-of-the-art methods.",
    "expert system use enabl comput make recommend decisions. paper present use machin learn train expert system (mles) phish site detect fake news detection. topic share similar goal: design rule-fact network allow comput make explain decis like domain expert respect area. phish websit detect studi use mle detect potenti phish websit analyz site properti (like url length expir time). fake news detect studi use mle rule-fact network gaug news stori truth base factor emotion, speaker' polit affili status, job. two studi use differ mle network implementations, present compar herein. fake news studi util linear design phish project util complex connect structure. networks' input base commonli avail data sets.",
    "reinforc learn method requir care design involv reward function obtain desir action polici given task. absenc hand-craft reward functions, prior work topic propos sever method reward estim use expert state trajectori action pairs. however, case complet good action inform cannot obtain expert demonstrations. propos novel reinforc learn method agent learn intern model observ basi expert-demonstr state trajectori estim reward without complet learn dynam extern environ state-act pairs. intern model obtain form predict model given expert state distribution. reinforc learning, agent predict reward function differ actual state state predict intern model. conduct multipl experi environ vari complexity, includ super mario bro flappi bird games. show method success train good polici directli expert game-play videos.",
    "emot recognit becom import field research human-comput interact domain. latest advanc field show combin visual audio inform lead better result compar case use singl sourc inform separately. visual point view, human emot recogn analyz facial express person. precisely, human emot describ combin sever facial action units. paper, propos system abl recogn emot high accuraci rate real time, base deep convolut neural networks. order increas accuraci recognit system, analyz also speech data fuse inform come sources, i.e., visual audio. experiment result show effect propos scheme emot recognit import combin visual audio data.",
    "dynam network slice emerg promis fundament framework meet 5g' divers use cases. machin learn (ml) expect play pivot role effici control manag networks, work examin ml-base quality-of-transmiss (qot) estim problem dynam network slice context, slice meet differ qot requirement. examin ml-base qot framework aim find qot model/ fine-tun accord divers qot requirements. central distribut framework examin compar accord accuraci train time. show distribut qot model outperform central qot model, especi number divers qot requir increases.",
    "intrigu empir evid exist deep learn work well exoticschedul vari learn rate. paper suggest phenomenon may due batch normal bn, ubiquit provid benefit optim gener across standard architectures. follow new result shown bn weight decay momentum (in words, typic use case consid earlier theoret analys stand-alon bn. 1. train done use sgd momentum exponenti increas learn rate schedule, i.e., learn rate increas $(1 +\\alpha)$ factor everi epoch $\\alpha >0$. (precis statement paper.) best knowledg first time rate schedul success used, let alon highli success architectures. expected, train rapidli blow network weights, net stay well-behav due normalization. 2. mathemat explan success rate schedule: rigor proof equival standard set bn + sgd + standardr tune + weight decay + momentum. equival hold normal layer well, group normalization, layernormalization, instanc norm, etc. 3. worked-out toy exampl illustr linkag hyper-parameters. use either weight decay bn alon reach global minimum, converg fail used.",
    "sequenti data often possess hierarch structur complex depend subsequences, found utter dialogue. effort model kind gener process, propos neural network-bas gener architecture, latent stochast variabl span variabl number time steps. appli propos model task dialogu respons gener compar recent neural network architectures. evalu model perform automat evalu metric carri human evaluation. experi demonstr model improv upon recent propos model latent variabl facilit gener long output maintain context.",
    "propos analyz block coordin descent proxim algorithm (bcd-prox) simultan filter paramet estim ode models. show ode system d=40 dimensions, compar state-of-the-art methods, bcd-prox exhibit increas robust (to noise, paramet initialization, hyperparameters), decreas train times, improv accuraci filter state estim parameters. show bcd-prox use multistep numer discretizations, establish converg bcd-prox hypothes includ real system interest.",
    "face challeng imag classif tasks, often explain reason dissect image, point prototyp aspect one class another. mount evid class help us make final decision. work, introduc deep network architectur -- prototyp part network (protopnet), reason similar way: network dissect imag find prototyp parts, combin evid prototyp make final classification. model thu reason way qualit similar way ornithologists, physicians, other would explain peopl solv challeng imag classif tasks. network use image-level label train without annot part images. demonstr method cub-200-2011 dataset stanford car dataset. experi show protopnet achiev compar accuraci analog non-interpret counterpart, sever protopnet combin larger network, achiev accuraci par best-perform deep models. moreover, protopnet provid level interpret absent interpret deep models.",
    "markov random field use model high dimension distribut number appli areas. much recent interest devot reconstruct depend structur independ sampl markov random fields. analyz simpl algorithm reconstruct underli graph defin markov random field $n$ node maximum degre $d$ given observations. show mild non-degeneraci condit reconstruct gener graph high probabl use $\\theta(d \\epsilon^{-2}\\delta^{-4} \\log n)$ sampl $\\epsilon,\\delta$ depend local interactions. local interact $\\eps,\\delta$ order $\\exp(-o(d))$. result optim function $n$ multipl constant depend $d$ strength local interactions. result seem first result gener model guarante {\\em the} gener model reconstructed. furthermore, provid explicit $o(n^{d+2} \\epsilon^{-2}\\delta^{-4} \\log n)$ run time bound. case measur graph correl decay, run time $o(n^2 \\log n)$ fix $d$. also discuss effect observ noisi sampl show long nois level low, algorithm effective. hand, construct exampl larg nois impli non-identifi even gener nois interactions. finally, briefli show simpl cases, model hidden node also recovered.",
    "non-contact physiolog measur potenti provid low-cost, non-invas health monitoring. however, machin vision approach often limit avail divers annot video dataset result poor gener complex real-lif conditions. address challenges, work propos use synthet avatar display facial blood flow chang allow systemat gener sampl wide varieti conditions. result show train simul real video data lead perform gain challeng conditions. show state-of-the-art perform three larg benchmark dataset improv robust skin type motion.",
    "present vae architectur encod gener high dimension sequenti data, video audio. deep gener model learn latent represent data split static dynam part, allow us approxim disentangl latent time-depend featur (dynamics) featur preserv time (content). architectur give us partial control gener content dynam condit either one set features. experi artifici gener cartoon video clip voic recordings, show convert content given sequenc anoth one content swapping. audio, allow us convert male speaker femal speaker vice versa, video separ manipul shape dynamics. furthermore, give empir evid hypothesi stochast rnn latent state model effici compress gener long sequenc determinist ones, may relev applic video compression.",
    "convent rank system focu sole maxim util rank item users, fairness-awar rank system addit tri balanc exposur differ protect attribut gender race. achiev type group fair ranking, deriv new rank system base first principl distribut robustness. formul minimax game player choos distribut rank maxim util satisfi fair constraint adversari seek minim util match statist train data. show approach provid better util highli fair rank exist baselin methods.",
    "bootstrap provid simpl power mean assess qualiti estimators. however, set involv larg datasets, comput bootstrap-bas quantiti prohibit demanding. alternative, present bag littl bootstrap (blb), new procedur incorpor featur bootstrap subsampl obtain robust, comput effici mean assess estim quality. blb well suit modern parallel distribut comput architectur retain gener applicability, statist efficiency, favor theoret properti bootstrap. provid result extens empir theoret investig blb' behavior, includ studi statist correctness, large-scal implement performance, select hyperparameters, perform real data.",
    "present effici deep learn approach challeng task tumor segment multisequ mr images. recent years, convolut neural network (cnn) achiev state-of-the-art perform larg varieti recognit task medic imaging. consider comput cost cnns, larg volum mri typic process subvolumes, instanc slice (axial, coronal, sagittal) small 3d patches. paper introduc cnn-base model effici combin advantag short-rang 3d context long-rang 2d context. overcom limit specif choic neural network architectures, also propos merg output sever cascad 2d-3d model voxelwis vote strategy. furthermore, propos network architectur differ mr sequenc process separ subnetwork order robust problem miss mr sequences. finally, simpl effici algorithm train larg cnn model introduced. evalu method public benchmark brat 2017 challeng task multiclass segment malign brain tumors. method achiev good perform produc accur segment median dice score 0.918 (whole tumor), 0.883 (tumor core) 0.854 (enhanc core). approach natur appli variou task involv segment lesion organs.",
    "recurr neural network (rnns) becom state-of-the-art choic extract pattern tempor sequences. however, current rnn model ill-suit process irregularli sampl data trigger event gener continu time sensor neurons. data occur, example, input come novel event-driven artifici sensor gener sparse, asynchron stream event multipl convent sensor differ updat intervals. work, introduc phase lstm model, extend lstm unit ad new time gate. gate control parametr oscil frequenc rang produc updat memori cell small percentag cycle. even spars updat impos oscillation, phase lstm network achiev faster converg regular lstm task requir learn long sequences. model natur integr input sensor arbitrari sampl rates, therebi open new area investig process asynchron sensori event carri time information. also greatli improv perform lstm standard rnn applications, order-of-magnitud fewer comput runtime.",
    "paper inconsist results, i.e., made fail claim mistak use test criterion series. precisely, claim converg rate $\\mathcal{o}(1/t)$ sgd present theorem 1, corollari 1, theorem 2 corollari 2 wrongli deriv base lemma 5. lemma 5, correctli use test criterion series. hence, result lemma 5 valid. would like thank commun point mistake!",
    "introduc framework continuous--depth graph neural network (gnns). graph neural ordinari differenti equat (gdes) formal counterpart gnn input-output relationship determin continuum gnn layers, blend discret topolog structur differenti equations. propos framework shown compat variou static autoregress gnn models. result prove gener effect gdes: static set offer comput advantag incorpor numer method forward pass; dynam settings, hand, shown improv perform exploit geometri underli dynamics.",
    "identif segment breast mass mammogram face complex challenges, owe highli variabl natur malign densiti regard shape, contours, textur orientation. additionally, classifi typic suffer high class imbal region candidates, normal tissu region vastli outnumb malign masses. paper propos rigor segment method, support morpholog enhanc use grayscal linear filters. novel cascad ensembl support vector machin (svm) use effect tackl class imbal provid signific predictions. true posit rate (tpr) 0.35, 0.69 0.82, system gener 0.1, 0.5 1.0 fals positives/imag (fpi), respectively.",
    "recently, use sound measur metric artifici intellig becom subject interest academia, government, industry. effort toward measur differ phenomena gain traction ai community, illustr public sever influenti field report polici documents. metric design help decis taker inform fast-mov impact influenc key advanc artifici intellig gener machin learn particular. paper propos use newfound capabl ai technolog augment ai measur capabilities. train model classifi public relat ethic issu concerns. methodolog use expert, manual curat dataset train set evalu larg set research papers. finally, highlight implic ai metrics, particular contribut toward develop trust fair ai-bas tool technologies. keywords: ai ethics; ai fairness; ai measurement. ethic comput science.",
    "choic activ function larg effect perform neural network. attempt hand-engin novel activ functions, rectifi linear unit (relu) remain commonly-us practice. paper show evolutionari algorithm discov novel activ function outperform relu. tree-bas search space candid activ function defin explor mutation, crossover, exhaust search. experi train wide residu network cifar-10 cifar-100 imag dataset show approach effective. replac relu evolv activ function result statist signific increas network accuracy. optim perform achiev evolut allow custom activ function particular task; however, novel activ function shown generalize, achiev high perform across tasks. evolutionari optim activ function therefor promis new dimens metalearn neural networks.",
    "respons grow concern user privacy, feder learn emerg promis tool train statist model network devic keep data localized. feder learn method run train task directli user devic share raw user data third parties. however, current method still share model updates, may contain privat inform (e.g., one' weight height), train process. exist effort aim improv privaci feder learn make compromis one follow key areas: perform (particularli commun cost), accuracy, privacy. better optim trade-offs, propos \\textit{sketch algorithms} uniqu advantag provid privaci perform benefit maintain accuracy. evalu feasibl sketching-bas feder learn prototyp three repres learn models. initi find show possibl provid strong privaci guarante feder learn without sacrif perform accuracy. work highlight exist fundament connect privaci commun distribut settings, suggest import open problem surround theoret understanding, methodology, system design practical, privat feder learning.",
    "issu corefer resolut one frequent mention challeng inform extract biomed literature. thus, biomed genr long second research genr corefer resolut news domain, subject great deal research nlp general. recent year interest grown enorm lead develop number substanti datasets, domain-specif contextu languag models, sever architectures. paper review state-of-the-art corefer biomed domain particular attent recent developments.",
    "consid problem adapt place sensor along interv detect stochastically-gener events. present new formul problem continuum-arm bandit problem feedback form partial observ realis inhomogen poisson process. design solut method combin thompson sampl nonparametr infer via increasingli granular bayesian histogram deriv $\\tilde{o}(t^{2/3})$ bound bayesian regret $t$ rounds. coupl design effic optimis approach select action polynomi time. simul demonstr approach substanti lower less variabl regret competitor algorithms.",
    "promot secur privat artifici intellig (spai), review studi model secur data privaci dnns. model secur allow system behav intend without affect malici extern influenc compromis integr efficiency. secur attack divid base occur: attack occur training, known poison attack, occur infer (after training) term evas attack. poison attack compromis train process corrupt data malici examples, evas attack use adversari exampl disrupt entir classif process. defens propos attack includ techniqu recogn remov malici data, train model insensit data, mask model' structur paramet render attack challeng implement. furthermore, privaci data involv model train also threaten attack model-invers attack, dishonest servic provid ai applications. maintain data privacy, sever solut combin exist data-privaci techniqu proposed, includ differenti privaci modern cryptographi techniques. paper, describ notion methods, e.g., homomorph encryption, review advantag challeng implement deep-learn models.",
    "activ metric learn problem increment select high-util batch train data (typically, order triplets) annotate, order progress improv learn model metric input domain rapidli possible. standard approaches, independ assess inform triplet batch, suscept highli correl batch mani redund triplet henc low overal utility. recent work \\cite{kumari2020batch} propos batch-decorrel strategi metric learning, reli ad hoc heurist estim correl two triplet time. present novel batch activ metric learn method leverag maximum entropi principl learn least bias estim triplet distribut given set prior constraints. avoid redund triplets, method collect select batch maximum joint entropy, simultan captur inform diversity. take advantag submodular joint entropi function construct tractabl solut use effici greedi algorithm base gram-schmidt orthogon provabl $\\left( 1 - \\frac{1}{e} \\right)$-optimal. approach first batch activ metric learn method defin unifi score balanc inform divers entir batch triplets. experi sever real-world dataset demonstr algorithm robust, gener well differ applic input modalities, consist outperform state-of-the-art.",
    "quality-divers (qd) algorithms, map-elit (me) particular, proven use broad rang applic includ enabl real robot recov quickli joint damage, solv strongli decept maze task evolv robot morpholog discov new gaits. however, present implement map-elit qd algorithm seem limit low-dimension control far fewer paramet modern deep neural network models. paper, propos leverag effici evolut strategi (es) scale map-elit high-dimension control parameter larg neural networks. design evalu new hybrid algorithm call map-elit evolut strategi (me-es) post-damag recoveri difficult high-dimension control task tradit fails. additionally, show me-e perform effici exploration, par state-of-the-art explor algorithm high-dimension control task strongli decept rewards.",
    "consid semi-supervis classif part avail data unlabeled. unlabel data use classif problem make assumpt relat behavior regress function margin distribution. seeger (2000) propos well-known \"cluster assumption\" reason one. propos mathemat formul assumpt method base densiti level set estim take advantag achiev fast rate converg number unlabel exampl number label examples.",
    "hierarch cluster graph fundament task data mine machin learn applic domain phylogenetics, social network analysis, inform retrieval. specifically, consid recent popular object function hierarch cluster due dasgupta. previou algorithm (approximately) minim object function requir linear time/spac complexity. mani applic underli graph massiv size make comput challeng process graph even use linear time/spac algorithm. result, strong interest design algorithm perform global comput use sublinear resources. focu work studi hierarch cluster massiv graph three well-studi model sublinear comput focu space, time, communication, respectively, primari resourc optimize: (1) (dynamic) stream model edg present stream, (2) queri model graph queri use neighbor degre queries, (3) mpc model graph edg partit sever machin connect via commun channel. design sublinear algorithm hierarch cluster three model above. heart algorithm result view object term cut graph, allow us use relax notion cut sparsifi hierarch cluster introduc small distort object function.",
    "main algorithm contribut show cut sparsifi desir form effici construct queri model mpc model. complement algorithm result establish nearli match lower bound rule possibl design better algorithm models.",
    "tl;dr: no, cannot, least averag standard archiv problems. assess whether use six smooth algorithm (move average, exponenti smoothing, gaussian filter, savitzky-golay filter, fourier approxim recurs median sieve) could automat appli time seri classif problem preprocess step improv perform three benchmark classifi (1-nearest neighbour euclidean dynam time warp distances, rotat forest). found signific improv unsmooth data even set smooth paramet cross validation. claim smooth worth. import role exploratori analysi help specif classif problem domain knowledg exploited. observ automat applic help cannot explain improv time seri classif algorithm baselin classifi simpli function absenc smoothing.",
    "neural network model size dramat increased, interest variou techniqu reduc paramet count acceler execution. activ area research field sparsiti - encourag zero valu paramet discard storag computations. research focus high level sparsity, challeng univers maintain model accuraci well achiev signific speedup modern matrix-math hardware. make sparsiti adopt practical, nvidia amper gpu architectur introduc sparsiti support matrix-math units, tensor cores. present design behavior spars tensor cores, exploit 2:4 (50%) sparsiti pattern lead twice math throughput dens matrix units. also describ simpl workflow train network satisfi 2:4 sparsiti pattern requir maintain accuracy, verifi wide rang common task model architectures. workflow make easi prepar accur model effici deploy spars tensor cores.",
    "address visual reloc problem predict locat camera orient pose (6dof) given input scene. propos method base human determin locat use visibl landmarks. defin anchor point uniformli across rout map propos deep learn architectur predict relev anchor point present scene well rel offset respect it. relev anchor point need nearest anchor point ground truth location, might visibl due pose. henc propos multi task loss function, discov relev anchor point, without need ground truth it. valid effect approach experi cambridgelandmark (larg scale outdoor scenes) well 7 scene (indoor scenes) use variouscnn featur extractors. method improv median error indoor well outdoor local dataset compar previou best deep learn model known posenet (with geometr re-project loss) use featur extractor. improv median error local specif case street scene, 8m.",
    "describ submit system zerospeech challeng 2019. current challeng theme address difficulti construct speech synthes without text phonet label requir system (1) discov subword unit unsupervis way, (2) synthes speech target speaker' voice. moreover, system also balanc discrimin score abx, bit-rat compress rate, natur intellig construct voice. tackl problem achiev best trade-off, util vector quantiz variat autoencod (vq-vae) multi-scal codebook-to-spectrogram (code2spec) invert train mean squar error adversari loss. vq-vae extract speech latent space, forc map nearest codebook produc compress representation. next, invert gener magnitud spectrogram target voice, given codebook vector vq-vae. experiments, also investig sever cluster algorithms, includ k-mean gmm, compar vq-vae result abx score bit rates. propos approach significantli improv intellig (in cer), mos, discrimin abx score compar offici zerospeech 2019 baselin even topline.",
    "preserv perform train model remov uniqu characterist mark train data point challenging. recent research usual suggest retrain model scratch remain train data refin model revert model optim mark data points. unfortunately, asid comput inefficiency, approach inevit hurt result model' gener abil sinc remov uniqu characterist also discard share (and possibl contributive) information. address perform degrad problem, paper present novel approach call perform unchang model augmentation~(puma). propos puma framework explicitli model influenc train data point model' gener abil respect variou perform criteria. complement neg impact remov mark data reweight remain data optimally. demonstr effect puma framework, compar multipl state-of-the-art data remov techniqu experiments, show puma effect effici remov uniqu characterist mark train data without retrain model 1) fool membership attack, 2) resist perform degradation. addition, puma estim data import operation, show could serv debug mislabel data point effici exist approaches.",
    "terahertz (thz) sens promis imag technolog wide varieti differ applications. extract interpret physic meaning paramet applications, however, requir solv invers problem model function determin paramet need fit measur data. sinc underli optim problem nonconvex costli solve, propos learn predict suitabl paramet measur data directly. precisely, develop model-bas autoencod encod network predict suitabl paramet decod fix physic meaning model function, train encod network unsupervis way. illustr numer result network 140 time faster classic optim techniqu make predict slightli higher object values. use predict start point local optim techniqu allow us converg better local minima twice fast optim without network-bas initialization.",
    "present first provabl converg two-timescal off-polici actor-crit algorithm (cof-pac) function approximation. key cof-pac introduct new critic, emphasi critic, train via gradient emphasi learn (gem), novel combin key idea gradient tempor differ learn emphat tempor differ learning. help emphasi critic canon valu function critic, show converg cof-pac, critic linear actor nonlinear.",
    "centaur half-human, half-ai decision-mak ai' goal complement human. so, ai must abl recogn goal constraint human mean help them. present novel formul interact human ai sequenti game agent model use bayesian best-respons models. show case ai' problem help bounded-r human make better decis reduc bayes-adapt pomdp. simul experiments, consid instanti framework human subject optimist ai' futur behaviour. result show equip model human, ai infer human' bound nudg toward better decisions. discuss way machin learn improv upon limit well help human. identifi novel trade-off centaur partial observ tasks: ai' action accept human, machin must make sure belief suffici aligned, align belief might costly. present preliminari theoret analysi trade-off depend task structure.",
    "simplest form, traffic flow predict problem restrict predict singl time-step future. multi-step traffic flow predict extend set-up case predict multipl time-step futur base finit histori interest. problem significantli difficult single-step variant known suffer degrad predict time step increases. paper, two approach improv multi-step traffic flow predict perform recurs multi-output set introduced. particular, model allow recurs predict approach take account tempor context term time-step index make predict introduced. addition, condit gener adversari network-bas data augment method propos improv predict perform multi-output setting. experi real-world traffic flow dataset show two method improv multi-step traffic flow predict recurs multi-output settings, respectively.",
    "paper, present applic 2-d convolut neural network (2-d cnns) design perform featur extract classif stage singl organ solv highlight problems. method use network light cnn instead deep take raw acceler signal input. use light cnns, everi one optim specif element, increas accuraci make network faster perform. also, new framework propos decreas data requir train phase. verifi method qatar univers grandstand simul (qugs) benchmark data provid structur dynam team. result show improv accuraci methods, run time adequ real-tim applications.",
    "imag attribut -- match imag back trust sourc -- emerg tool fight onlin misinformation. deep visual fingerprint model recent explor purpose. however, robust tini input perturb known adversari examples. first illustr gener valid adversari imag easili caus incorrect imag attribution. describ approach prevent impercept adversari attack deep visual fingerprint models, via robust contrast learning. propos train procedur leverag train $\\ell_\\infty$-bound adversari examples, conceptu simpl incur small comput overhead. result model substanti robust, accur even unperturb images, perform well even databas million images. particular, achiev 91.6% standard 85.1% adversari recal $\\ell_\\infty$-bound perturb manipul imag compar 80.1% 0.0% prior work. also show robust gener type impercept perturb unseen training. finally, show train adversari robust imag compar model detect editori chang match images.",
    "autom statist model challeng problem artifici intelligence. automat statistician take first step direction, employ kernel search algorithm gaussian process (gp) provid interpret statist model regress problems. howev scale due $o(n^3)$ run time model selection. propos scalabl kernel composit (skc), scalabl kernel search algorithm extend automat statistician bigger data sets. so, deriv cheap upper bound gp margin likelihood sandwich margin likelihood variat lower bound . show upper bound significantli tighter lower bound thu use model selection.",
    "voicefilter-lit speaker-condit voic separ model play crucial role improv speech recognit speaker verif suppress overlap speech non-target speakers. however, one limit voicefilter-lite, speaker-condit speech model general, model usual limit singl target speaker. undesir smart home devic support multipl enrol users. order extend benefit person multipl users, previous develop attention-bas speaker select mechan appli voicefilter-lite. however, origin multi-us voicefilter-lit model suffer signific perform degrad compar single-us models. paper, devis seri experi improv multi-us voicefilter-lit model. incorpor dual learn rate schedul use feature-wis linear modul (film) condit model attend speaker embedding, success close perform gap multi-us single-us voicefilter-lit model single-speak evaluations. time, new model also easili extend support number users, significantli outperform previous publish model multi-speak evaluations.",
    "model brain dynam better understand control complex behavior underli variou cognit brain function interest engineers, mathematicians, physicist last sever decades. motiv develop comput effici model brain dynam use design control-theoret neurostimul strategies, develop novel data-driven approach long short-term memori (lstm) neural network architectur predict tempor dynam complex system extend long time-horizon future. contrast recent lstm-base dynam model approach make use multi-lay perceptron linear combin layer output layers, architectur use singl fulli connect output layer reversed-ord sequence-to-sequ map improv short time-horizon predict accuraci make multi-timestep predict dynam behaviors. demonstr efficaci approach reconstruct regular spike burst dynam exhibit experimentally-valid 9-dimension hodgkin-huxley model hippocamp ca1 pyramid neurons. simulations, show lstm neural network predict multi-tim scale tempor dynam underli variou spike pattern reason accuracy. moreover, result show predict improv increas predict time-horizon multi-timestep deep lstm neural network.",
    "give short introduct cough detect effort undertaken last decad describ solut automat cough detect develop aiocar portabl spirometri system. contrast popular analysi sound audio recordings, fulli base approach airflow signal only. system intend use larg varieti environ differ patients, train valid algorithm use aiocare-collect data larg databas spirometri curv nhane databas american nation center health statistics. train differ classifiers, logist regression, feed-forward artifici neural network, support vector machine, random forest choos one best performance. ann solut select final classifier. classif result test set (aiocar data) are: 0.86 (sensitivity), 0.91 (specificity), 0.91 (accuracy) 0.88 (f1 score). classif methodolog develop studi robust detect cough event spirometri measurements. far know, solut present work first fulli reproduc descript automat cough detect algorithm base total airflow signal first cough detect implement commerci spirometri system published.",
    "present novel multistream network learn robust eye represent gaze estimation. first creat synthet dataset contain eye region mask detail visibl eyebal iri use simulator. perform eye region segment u-net type model later use gener eye region mask real-world eye images. next, pretrain eye imag encod real domain self-supervis contrast learn learn gener eye representations. finally, pretrain eye encoder, along two addit encod visibl eyebal region iris, use parallel multistream framework extract salient featur gaze estim real-world images. demonstr perform method eyediap dataset two differ evalu set achiev state-of-the-art results, outperform exist benchmark dataset. also conduct addit experi valid robust self-supervis network respect differ amount label data use training.",
    "pedestrian trajectori predict activ research area recent work undertaken emb accur model pedestrian social interact contextu complianc dynam spatial graphs. however, exist work reli spatial assumpt scene dynamics, entail signific challeng adapt graph structur unknown environ onlin system. addition, lack assess approach relat model impact predict performance. fill gap, propos social trajectori recommender-g graph recurr neighborhood network, (str-ggrnn), use data-driven adapt onlin neighborhood recommend base contextu scene featur pedestrian visual cues. neighborhood recommend achiev onlin nonneg matrix factor (nmf) construct graph adjac matric predict pedestrians' trajectories. experi base widely-us dataset show method outperform state-of-the-art. best perform model achiev 12 cm ade $\\sim$15 cm fde eth-uci dataset. propos method take 0.49 second sampl total 20k futur trajectori per frame.",
    "develop new data-driven paradigm rapid inference, model simul physic transport phenomena deep learning. use condit gener adversari network (cgan), train model direct gener solut steadi state heat conduct incompress fluid flow pure observ without knowledg underli govern equations. rather use iter numer method approxim solut constitut equations, cgan learn directli gener solut phenomena, given arbitrari boundari condit domain, high test accuraci (mae$<$1\\%) state-of-the-art comput performance. cgan framework use learn causal model directli experiment observ underli physic model complex unknown.",
    "success kernel-bas learn method depend choic kernel. recently, kernel learn method propos use data select appropri kernel, usual combin set base kernels. introduc new algorithm kernel learn combin {\\em continu set base kernels}, without common step discret space base kernels. demonstr new method achiev state-of-the-art perform across varieti real-world datasets. furthermore, explicitli demonstr import combin right dictionari kernels, problemat method base finit set base kernel chosen priori. method first approach work continu parameter kernels. however, show method requir substanti less comput previou approaches, amen multipl dimension parameter base kernels, demonstrate.",
    "background: develop activ tradit perform manually, make code commits, opening, managing, close issu increasingli subject autom mani oss projects. specifically, activ often perform tool react event run specif times. refer autom tool bot and, mani softwar mine scenario relat develop product code qualiti desir identifi bot order separ action action individuals. aim: find autom way identifi bot code commit bots, character type bot base activ patterns. method result: propos biman, systemat approach detect bot use author names, commit messages, file modifi commit, project associ ommits. test data, valu auc-roc 0.9. also character bot base time pattern code commit type file modified, found primarili work document file web pages, file preval html javascript ecosystems. compil shareabl dataset contain detail inform 461 bot found (all 1000 commits) 13,762,430 commit created.",
    "accountability, requisit respons ai, facilit transpar mechan audit explainability. however, prior work suggest success mechan may limit global north contexts; understand limit current intervent vari socio-polit condit crucial help policymak facilit wider accountability. so, examin mediat account exist interact vulner user 'high-risk' ai system global south setting. report qualit studi 29 financially-stress user instant loan platform india. found user experienc intens feel indebted 'boon' instant loans, perceiv huge oblig toward loan platforms. user fulfil oblig accept harsh term conditions, over-shar sensit data, pay high fee unknown unverifi lenders. user demonstr depend loan platform persist behavior despit risk harm abuse, recur debts, discrimination, privaci harms, self-harm them. instead enrag loan platforms, user assum respons neg experiences, thu releas high-pow loan platform account obligations. argu account shape platform-us power relations, urg caution policymak adopt pure technic approach foster algorithm accountability. instead, call situat intervent enhanc agenc users, enabl meaning transparency, reconfigur designer-us relations, prompt critic reflect practition toward wider accountability. conclud implic respons deploy ai fintech applic india beyond.",
    "actuat grow attent person healthcar pandemic, popular e-health proliferating. nowadays, enhanc medic diagnosi via machin learn model highli effect mani aspect e-health analytics. nevertheless, classic cloud-based/centr e-health paradigms, data central store server facilit model training, inevit incur privaci concern high time delay. distribut solut like decentr stochast gradient descent (d-sgd) propos provid safe time diagnost result base person devices. however, method like d-sgd subject gradient vanish issu usual proceed slowli earli train stage, therebi imped effect effici training. addition, exist method prone learn model bias toward user dens data, compromis fair provid e-health analyt minor groups. paper, propos decentr block coordin descent (d-bcd) learn framework better optim deep neural network-bas model distribut decentr devic e-health analytics. benchmark experi three real-world dataset illustr effect practic propos d-bcd, addit simul studi showcas strong applic d-bcd real-lif e-health scenarios.",
    "present data-driven optim framework redesign polic patrol zone urban environment. object rebal polic workload among geograph area reduc respons time emerg calls. develop stochast model polic emerg respons integr multipl data sources, includ polic incid reports, demograph surveys, traffic data. use stochast model, optim zone redesign plan use mixed-integ linear programming. propos design implement atlanta polic depart march 2019. analyz data zone redesign, show new design reduc respons time high prioriti 911 call 5.8\\% imbal polic workload among differ zone 43\\%.",
    "industri bin pick solutions, pose workpiec local match cad model point cloud obtain 3d sensor. distinguish flat workpiec bottom bin point cloud impos challeng local workpiec lead wrong phantom detections. paper, propos framework solv problem automat segment workpiec region non-workpiec region point cloud data. done real time appli fulli convolut neural network train simul real data. real data label novel techniqu automat gener ground truth label real point clouds. along real time workpiec segmentation, framework also help improv number detect workpiec estim correct object poses. moreover, decreas comput time approxim 1s due reduct search space object pose estimation.",
    "binomi devianc svm hing loss function two wide use loss function machin learning. mani similar them, also strength deal differ type data. work, introduc new exponenti famili base convex relax hing loss function use soft class-separ parameters. new family, denot soft-svm, allow us prescrib gener linear model effect bridg logist regress svm classification. new model interpret avoid data separ issues, attain good fit predict perform automat adjust data label separ via soft parameter. result confirm empir simul case studi compar regular logistic, svm, soft-svm regress conclud propos model perform well term classif predict errors.",
    "discuss notion \"discret function bases\" particular focu discret basi deriv legendr delay network (ldn). character perform base delay comput task, fix tempor convolut neural networks. network use fix tempor convolut conceptu simpl yield state-of-the-art result task psmnist. main result   (1) present numer stabl algorithm construct matrix dlop l o(qn)   (2) legendr delay network (ldn) use form discret function basi basi transform matrix h o(qn). (3) q < 300, convolv ldn basi onlin lower run-tim complex convolv arbitrari fir filters. (4) slide window transform exist base (haar, cosine, fourier) requir o(q) oper per sampl o(n) memory. (5) lti system similar ldn construct mani discret function bases; ldn system superior term finit impuls response. (6) compar discret function base linearli decod delay signal repres respect bases. result depict figur 20. overall, decod error similar. ldn basi highest fourier cosin base smallest errors. (7) fourier cosin base featur uniform decod error delays. base use signal repres well fourier domain.",
    "(8) neural network experi suggest fix tempor convolut outperform learn convolutions. basi choic critical; roughli observ perform trend delay task. (9) ldn right choic small q, o(q) euler updat feasible, low o(q) memori requir importance.",
    "recent year wit rise popular natur languag process (nlp) relat field artifici intellig (ai) machin learn (ml). mani onlin cours resourc avail even without strong background field. often student curiou specif topic quit know begin studying. answer question \"what one learn first,\" appli embedding-bas method learn prerequisit relat cours concept domain nlp. introduc lecturebank, dataset contain 1,352 english lectur file collect univers cours classifi accord exist taxonomi well 208 manually-label prerequisit relat topics, publicli available. dataset use educ purpos lectur prepar organ well applic read list generation. additionally, experi neural graph-bas network non-neur classifi learn prerequisit relat dataset.",
    "propos meta-learn techniqu offlin discoveri physics-inform neural network (pinn) loss functions. extend earlier work meta-learning, develop gradient-bas meta-learn algorithm address divers task distribut base parametr partial differenti equat (pdes) solv pinns. furthermore, base new theori identifi two desir properti meta-learn loss pinn problems, enforc propos new regular method use specif parametr loss function. comput examples, meta-learn loss employ test time address regress pde task distributions. result indic signific perform improv achiev use shared-among-task offline-learn loss function even out-of-distribut meta-testing. case, solv test task belong task distribut use meta-training, also employ pinn architectur differ pinn architectur use meta-training. better understand capabl limit propos method, consid variou parametr loss function describ differ algorithm design option may affect meta-learn performance.",
    "prove exact relationship optim denois function data distribut case addit gaussian noise, show denois implicitli model structur data allow exploit unsupervis learn representations. result gener known relationship [2], valid limit small corrupt noise.",
    "tradit sequenti multi-object attent model reli recurr mechan infer object relations. propos relat extens (r-sqair) one attent model (sqair) endow modul strong relat induct bia comput parallel pairwis interact infer objects. two recent propos relat modul studi task unsupervis learn videos. demonstr gain sequenti relat mechanisms, also term combinatori generalization.",
    "formul learn binari autoencod biconvex optim problem learn pairwis correl encod decod bits. among possibl algorithm use information, find autoencod reconstruct input worst-cas optim loss. optim decod singl layer artifici neurons, emerg entir minimax loss minimization, weight learn convex optimization. reflect competit experiment results, demonstr binari autoencod done effici convey inform pairwis correl optim fashion.",
    "low-rank nonsmooth matrix optim problem captur mani fundament task statist machin learning. signific progress made recent year develop effici method \\textit{smooth} low-rank optim problem avoid maintain high-rank matric comput expens high-rank svds, advanc nonsmooth problem slow paced. paper consid standard convex relax problems. mainly, prove \\textit{strict complementarity} condit rel mild assumpt nonsmooth object written maximum smooth functions, approxim variant two popular \\textit{mirror-prox} methods: euclidean \\textit{extragradi method} mirror-prox \\textit{matrix exponenti gradient updates}, initi \"warm-start\", converg optim solut rate $o(1/t)$, requir two \\textit{low-rank} svd per iteration. moreover, extragradi method also consid relax version strict complementar yield trade-off rank svd requir radiu ball need initi method. support theoret result empir experi sever nonsmooth low-rank matrix recoveri tasks, demonstr plausibl strict complementar assumption, effici converg propos low-rank mirror-prox variants.",
    "learn algorithm need bia gener perform better random guessing. examin flexibl (expressivity) bias algorithms. express algorithm adapt chang train data, alter outcom base chang input. measur express use information-theoret notion entropi algorithm outcom distributions, demonstr trade-off bia expressivity. degre algorithm bias degre outperform uniform random sampling, also degre becom inflexible. deriv bound relat bia expressivity, prove necessari trade-off inher tri creat strongli perform yet flexibl algorithms.",
    "data cluster instrument tool area energi resourc management. one problem convent cluster take final use cluster data account, may lead suboptim use energi comput resources. cluster data use decision-mak entity, turn signific gain obtain tailor cluster scheme final task perform decision-mak entity. key good final perform automat extract import attribut data space inher relev subsequ decision-mak entity, partit data space base attribut instead partit data space base predefin convent metrics. purpose, formul framework decision-mak orient cluster propos algorithm provid decision-bas partit data space good repres decisions. appli novel framework algorithm typic problem real-tim price power consumpt scheduling, obtain sever insight analyt result express best repres price profil real-tim price signific reduct term requir cluster perform power consumpt schedul shown simulations.",
    "graph neural network (gnns) achiev tremend success varieti real-world applic reli fix graph data input. however, initi input graph might optim term specif downstream tasks, inform scarcity, noise, adversari attacks, discrep distribut graph topology, features, groundtruth labels. paper, propos bi-level optimization-bas approach learn optim graph structur via directli learn person pagerank propag matrix well downstream semi-supervis node classif simultaneously. also explor low-rank approxim model reduc time complexity. empir evalu show superior efficaci robust propos model baselin methods.",
    "wide adopt convolut neural network (cnns) applic decision-mak uncertainti fundamental, brought great deal attent abil model accur quantifi uncertainti predictions. previou work combin cnn gaussian process (gps) develop assumpt predict probabl model well-calibrated. paper show that, fact, current combin cnn gp miscalibrated. propos novel combin consider outperform previou approach aspect, achiev state-of-the-art perform imag classif tasks.",
    "recent deep learn (dl) model move beyond static network architectur dynam ones, handl data network structur chang everi example, sequenc variabl lengths, trees, graphs. exist dataflow-bas program model dl---both static dynam declaration---eith cannot readili express dynam models, ineffici due repeat dataflow graph construct processing, difficulti batch execution. present cavs, vertex-centr program interfac optim system implement dynam dl models. cav repres dynam network structur static vertex function $\\mathcal{f}$ dynam instance-specif graph $\\mathcal{g}$, perform backpropag schedul execut $\\mathcal{f}$ follow depend $\\mathcal{g}$. cav bypass expens graph construct preprocess overhead, allow use static graph optim techniqu pre-defin oper $\\mathcal{f}$, natur expos batch execut opportun differ graphs. experi compar cav two state-of-the-art framework dynam nn (tensorflow fold dynet) demonstr efficaci approach: cav achiev near one order magnitud speedup train variou dynam nn architectures, ablat demonstr contribut propos batch memori manag strategies.",
    "forecast stock price interpret time seri predict problem, long short term memori (lstm) neural network often use due architectur specif built solv problems. paper, consid design trade strategi perform portfolio optim use lstm stock price predict four differ companies. custom loss function use train lstm increas profit earned. moreover, propos data driven approach optim select window length multi-step predict length, consid addit analyst call technic indic multi-stack bidirect lstm strengthen addit attent units. find lstm model custom loss function improv perform train bot regress baselin arima, addit analyst call improv perform certain datasets.",
    "consid convex optim problem subject larg number constraints. focu stochast problem object take form expect valu feasibl set intersect larg number convex sets. propos class algorithm perform stochast gradient descent random feasibl updat simultaneously. everi iteration, algorithm sampl number project point onto randomli select small subset constraints. three feasibl updat scheme considered: averag random project points, project onto distant sample, project onto special polyhedr set construct base sampl points. prove almost sure converg algorithms, analyz iterates' feasibl error optim error, respectively. provid new converg rate benchmark stochast first-ord optim mani constraints. rate analysi numer experi reveal algorithm use polyhedral-set project scheme effici one within known algorithms.",
    "deep reinforc learn (drl) algorithm increasingli employ last decad solv variou decision-mak problem autonom drive robotics. however, algorithm face great challeng deploy safety-crit environ sinc often exhibit erron behavior lead potenti critic errors. one way assess safeti drl agent test detect possibl fault lead critic failur execution. rais question effici test drl polici ensur correct adher safeti requirements. exist work test drl agent use adversari attack perturb state action agent. however, attack often lead unrealist state environment. main goal test robust drl agent rather test complianc agents' polici respect requirements. due huge state space drl environments, high cost test execution, black-box natur drl algorithms, exhaust test drl agent impossible. paper, propos search-bas test approach reinforc learn agent (starla) test polici drl agent effect search fail execut agent within limit test budget. use machin learn model dedic genet algorithm narrow search toward faulti episodes.",
    "appli starla deep-q-learn agent wide use benchmark show significantli outperform random test detect fault relat agent' policy. also investig extract rule character faulti episod drl agent use search results. rule use understand condit agent fail thu assess deploy risks.",
    "introduc new kind linear transform name deform butterfli (debut) gener convent butterfli matric adapt variou input-output dimensions. inherit fine-to-coarse-grain learnabl hierarchi tradit butterfli deploy neural networks, promin structur sparsiti debut layer constitut new way network compression. appli debut drop-in replac standard fulli connect convolut layers, demonstr superior homogen neural network render favor properti light weight low infer complexity, without compromis accuracy. natur complexity-accuraci tradeoff aris myriad deform debut layer also open new room analyt practic research. code appendix publicli avail at: https://github.com/ruilin0212/debut.",
    "stream social media provid real-tim glimps extrem weather impacts. however, volum stream data make mine inform challeng emerg managers, polici makers, disciplinari scientists. explor effect data learn approach mine filter inform stream social media data hurrican irma' landfal florida, usa. use 54,383 twitter messag (out 784k geoloc messages) 16,598 user sept. 10 - 12, 2017 develop 4 independ model filter data relevance: 1) geospati model base forc condit place time tweet, 2) imag classif model tweet includ images, 3) user model predict reliabl tweeter, 4) text model determin text relat hurrican irma. four model independ tested, combin quickli filter visual tweet base user-defin threshold submodel. envis type filter visual routin use base model data captur noisi sourc twitter. data subsequ use polici makers, environment managers, emerg managers, domain scientist interest find tweet specif attribut use differ stage disast (e.g., preparedness, response, recovery), detail research.",
    "shape instanti predict 3d shape dynam target one 2d imag import real-tim intra-op navigation. previously, gener shape instanti framework propos manual imag segment gener 2d statist shape model (ssm) kernel partial least squar regress (kplsr) learn relationship 2d 3d ssm 3d shape prediction. paper, two-stag shape instanti improv one-stage. pointoutnet 19 convolut layer three fully-connect layer use network structur chamfer distanc use loss function predict 3d target point cloud singl 2d image. propos one-stag shape instanti algorithm, spontan image-to-point cloud train infer achieved. dataset 27 right ventricl (rv) subjects, indic 609 experiments, use valid propos one-stag shape instanti algorithm. averag point cloud-to-point cloud (pc-to-pc) error 1.72mm achieved, compar plsr-base (1.42mm) kplsr-base (1.31mm) two-stag shape instanti algorithm.",
    "describ induct logic program (ilp) approach call learn failures. approach, ilp system (the learner) decompos learn problem three separ stages: generate, test, constrain. gener stage, learner gener hypothesi (a logic program) satisfi set hypothesi constraint (constraint syntact form hypotheses). test stage, learner test hypothesi train examples. hypothesi fail entail posit exampl entail neg example. hypothesi fails, then, constrain stage, learner learn constraint fail hypothesi prune hypothesi space, i.e. constrain subsequ hypothesi generation. instance, hypothesi gener (entail neg example), constraint prune generalis hypothesis. hypothesi specif (doe entail posit examples), constraint prune specialis hypothesis. loop repeat either (i) learner find hypothesi entail posit none neg examples, (ii) hypothes test. introduc popper, ilp system implement approach combin answer set program prolog. popper support infinit problem domains, reason list numbers, learn textual minim programs, learn recurs programs.",
    "experiment result three domain (toy game problems, robot strategies, list transformations) show (i) constraint drastic improv learn performance, (ii) popper outperform exist ilp systems, term predict accuraci learn times.",
    "mani neural network prune approach consist sever iter train prune steps, seemingli lose signific amount perform prune recov subsequ retrain phase. recent work renda et al. (2020) le & hua (2021) demonstr signific learn rate schedul retrain phase propos specif heurist choos schedul imp (han et al., 2015). place find context result li et al. (2020) regard train model within fix train budget demonstr that, consequently, retrain phase massiv shorten use simpl linear learn rate schedule. go step further, propos similarli impos budget initi dens train phase show result simpl effici method capabl outperform significantli complex heavili parameter state-of-the-art approach attempt sparsifi network training. find advanc understand retrain phase, broadli question belief one aim avoid need retrain reduc neg effect 'hard' prune incorpor sparsif process standard training.",
    "autism one import neurolog disord lead problem person' social interactions. improv brain imag technolog techniqu help us build brain structur function networks. find network topolog pattern group (autism healthi control) aid us achiev autism disord screen model. present study, util genet algorithm extract discrimin sub-network repres differ two group better. fit evalu phase, sub-network, machin learn model train use variou entropi featur sub-network perform measured. proper model perform impli extract good discrimin sub-network. network entropi use network topolog descriptors. evalu result indic accept perform propos screen method base extract discrimin sub-network machin learn model succeed obtain maximum accuraci 73.1% structur network ucla dataset, 82.2% function network ucla dataset, 66.1% function network abid datasets.",
    "adapt gradient method adam shown effect train deep neural network (dnns) track second moment gradient comput individu learn rates. differ exist methods, make use recent first moment gradient comput individu learn rate per iteration. motiv behind dynam variat first moment gradient may provid use inform obtain learn rates. refer new method rapidli adapt moment estim (rame). theoret converg determinist rame studi use analysi similar one use [1] adam. experiment result train number dnn show promis perform rame w.r.t. converg speed gener perform compar stochast heavy-bal (shb) method, adam, rmsprop.",
    "variou neural network model propos tackl combinatori optim problem travel salesman problem (tsp). exist learning-bas tsp method adopt simpl set train test data independ ident distributed. however, exist literatur fail solv tsp instanc train test data differ distributions. concretely, find differ train test distribut result difficult tsp instances, i.e., solut obtain model larg gap optim solution. tackl problem, work, studi learning-bas tsp method train test data differ distribut use adaptive-hardness, i.e., difficult tsp instanc solver. problem challeng non-trivi (1) defin hard measur quantitatively; (2) effici continu gener suffici hard tsp instanc upon model training; (3) fulli util instanc differ level hard learn power tsp solver. solv challenges, first propos principl hard measur quantifi hard tsp instances. then, propos hardness-adapt gener gener instanc differ hardness. propos curriculum learner fulli util instanc train tsp solver. experi show hardness-adapt gener gener instanc ten time harder exist methods, propos method achiev signific improv state-of-the-art model term optim gap.",
    "melanoma, one danger type skin cancer, re-sult high mortal rate. earli detect resect two key point success cure. recent research use artifici intellig classifi melanoma nevu compar assess algorithm dermatologists. however, imbal sensit specif measur affect perform exist models. studi propos method use deep convolut neural network aim detect melanoma binari classif problem. involv 3 key features, name custom batch logic, custom loss function reform fulli connect layers. train dataset kept date includ 17,302 imag melanoma nevus; largest dataset far. model perform compar 157 dermatologist 12 univers hospit germani base mclass-d dataset. model outperform 157 dermatologist achiev state-of-the-art perform auc 94.4% sensit 85.0% specif 95.0% use predict threshold 0.5 mclass-d dataset 100 dermoscop images. moreover, threshold 0.40858 show balanc measur compar researches, promisingli applic medic diagnosis, sensit 90.0% specif 93.8%.",
    "non-neg matrix factor (nmf) previous shown use decomposit multivari data. interpret factor new way use gener miss attribut test data. provid joint optim scheme miss attribut well nmf factors. prove monoton converg algorithms. present classif result case miss attributes.",
    "consid problem learn linear subspac data corrupt outliers. classic approach typic design case subspac dimens small rel ambient dimension. approach work dual represent subspac henc aim find orthogon complement; such, particularli suitabl subspac whose dimens close ambient dimens (subspac high rel dimension). pose problem comput normal vector inlier subspac non-convex $\\ell_1$ minim problem sphere, call dual princip compon pursuit (dpcp) problem. provid theoret guarante everi global solut dpcp vector orthogon complement inlier subspace. moreover, relax non-convex dpcp problem recurs linear program whose solut shown converg finit number step vector orthogon subspace. particular, inlier subspac hyperplane, solut recurs linear program converg global minimum non-convex dpcp problem finit number steps. also propos algorithm base altern minim iter re-weight least squares, suitabl deal large-scal data.",
    "experi synthet data show propos method abl handl outlier higher rel dimens current state-of-the-art methods, experi context three-view geometri problem comput vision suggest propos method use even superior altern tradit ransac-bas approach comput vision applications.",
    "knowledg base complet (kbc) aim automat infer miss fact exploit inform alreadi present knowledg base (kb). promis approach kbc emb knowledg latent space make predict learn embeddings. however, exist embed model subject least one follow limitations: (1) theoret inexpressivity, (2) lack support promin infer pattern (e.g., hierarchies), (3) lack support kbc higher-ar relations, (4) lack support incorpor logic rules. here, propos spatio-transl embed model, call boxe, simultan address limitations. box emb entiti points, relat set hyper-rectangl (or boxes), spatial character basic logic properties. seemingli simpl abstract yield fulli express model offer natur encod mani desir logic properties. box captur inject rule rich class rule languages, go well beyond individu infer patterns. design, box natur appli higher-ar kbs. conduct detail experiment analysis, show box achiev state-of-the-art performance, benchmark knowledg graph gener kbs, empir show power integr logic rules.",
    "recent research demonstr superfici well-train machin learn (ml) model highli vulner adversari examples. ml techniqu becom popular solut cyber-phys system (cpss) applic research literatures, secur applic concern. however, current studi adversari machin learn (aml) mainli focu pure cyberspac domains. risk adversari exampl bring cp applic well investigated. particular, due distribut properti data sourc inher physic constraint impos cpss, widely-us threat model state-of-the-art aml algorithm previou cyberspac research becom infeasible. studi potenti vulner ml appli cpss propos constrain adversari machin learn (conaml), gener adversari exampl satisfi intrins constraint physic systems. first summar differ aml cpss aml exist cyberspac system propos gener threat model conaml. design best-effort search algorithm iter gener adversari exampl linear physic constraints. evalu algorithm simul two typic cpss, power grid water treatment system. result show conaml algorithm effect gener adversari exampl significantli decreas perform ml model even practic constraints.",
    "process manufactur industries, larg push produc higher qualiti product ensur maximum effici processes. requir approach effect detect resolv disturb ensur optim operations. control system compens mani type disturbances, chang process still cannot handl adequately. therefor import develop monitor system effect detect identifi fault quickli resolv operators. paper, novel probabilist fault detect identif method propos adopt newli develop deep learn approach use bayesian recurr neural networks~(brnns) variat dropout. brnn model gener model complex nonlinear dynamics. moreover, compar tradit statistic-bas data-driven fault detect identif methods, propos brnn-base method yield uncertainti estim allow simultan fault detect chemic processes, direct fault identification, fault propag analysis. outstand perform method demonstr contrast (dynamic) princip compon analysis, wide appli industry, benchmark tennesse eastman process~(tep) real chemic manufactur dataset.",
    "algorithm decis make process affect mani aspect lives. standard tool machin learning, classif regression, subject bia data, thu direct applic off-the-shelf tool could lead specif group unfairli discriminated. remov sensit attribut data solv problem \\textit{dispar impact} aris non-sensit attribut sensit attribut correlated. here, studi fair machin learn algorithm avoid dispar impact make decision. inspir two-stag least squar method wide use field economics, propos two-stag algorithm remov bia train data. propos algorithm conceptu simple. unlik exist fair algorithm design classif tasks, propos method abl (i) deal regress tasks, (ii) combin explanatori attribut remov revers discrimination, (iii) deal numer sensit attributes. perform fair propos algorithm evalu simul synthet real-world datasets.",
    "statist machin learn model evalu valid put work. convent k-fold mont carlo cross-valid (mccv) procedur use pseudo-random sequenc partit instanc k subsets, usual caus subsampl bias, inflat gener error jeopard reliabl effect cross-validation. base order systemat sampl theori statist low-discrep sequenc theori number theory, propos new k-fold cross-valid procedur replac pseudo-random sequenc best-discrep sequence, ensur low subsampl bia lead precis expected-prediction-error estimates. experi 156 benchmark dataset three classifi (logist regression, decis tree naiv bayes) show general, cross-valid procedur extrud subsampl bia mccv lower epe around 7.18% varianc around 26.73%. comparison, stratifi mccv reduc epe varianc mccv around 1.58% 11.85% respectively. leave-one-out (loo) lower epe around 2.50% varianc much higher cv procedure. comput time cross-valid procedur 8.64% mccv, 8.67% stratifi mccv 16.72% loo. experi also show approach benefici dataset character rel small size larg aspect ratio. make approach particularli pertin solv bioscienc classif problems. propos systemat subsampl techniqu could gener machin learn algorithm involv random subsampl mechanism.",
    "weird galaxi outlier either unknown uncommon featur make differ normal sample. galaxi interest may provid new insight current theories, use form new theori process universe. interest outlier often found accident, becom increasingli difficult futur big survey gener enorm amount data. give need machin learn detect techniqu find interest weird objects. work, inspect galaxi spectra third data releas galaxi mass assembl survey look weird outli galaxi use two differ outlier detect techniques. first, appli distance-bas unsupervis random forest galaxi spectra use flux valu input features. spectra high outlier score inspect divid differ categori blends, quasi-stellar objects, bpt outliers. also experi reconstruction-bas outlier detect method use variat autoencod compar result two differ methods. last, appli dimension reduct techniqu output method inspect cluster similar spectra. find unsupervis method extract import featur data use find mani differ type outliers.",
    "altern direct method multipli (admm) distribut version wide use machin learning. iter admm, model updat use local privat data model exchang among agent impos critic privaci concerns. despit pioneer work reliev concerns, differenti privat admm still confront mani research challenges. example, guarante differenti privaci (dp) reli premis optim local problem perfectli attain admm iteration, may never happen practice. model train dp admm may low predict accuracy. paper, address concern propos novel (improved) plausibl differenti privat admm algorithm, call pp-admm ipp-admm. pp-admm, agent approxim solv perturb optim problem formul local privat data iteration, perturb approxim solut gaussian nois provid dp guarantee. improv model accuraci convergence, improv version ipp-admm adopt spars vector techniqu (svt) determin agent updat neighbor current perturb solution. agent calcul differ current solut last iteration, differ larger threshold, pass solut neighbors; otherwis solut discarded. moreover, propos track total privaci loss zero-concentr dp (zcdp) provid gener perform analysis.",
    "experi real-world dataset demonstr privaci guarantee, propos algorithm superior state art term model accuraci converg rate.",
    "recent advanc neural algorithm reason graph neural network (gnns) prop notion algorithm alignment. broadly, neural network better learn execut reason task (in term sampl complexity) individu compon align well target algorithm. specifically, gnn claim align dynam program (dp), gener problem-solv strategi express mani polynomial-tim algorithms. however, align truli demonstr theoret quantified? show, use method categori theori abstract algebra, exist intric connect gnn dp, go well beyond initi observ individu algorithm bellman-ford. expos connection, easili verifi sever prior find literature, produc better-ground gnn architectur edge-centr tasks, demonstr empir result clr algorithm reason benchmark. hope exposit serv foundat build stronger algorithm align gnns.",
    "emot express inher multimod -- integr facial behavior, speech, gaze -- automat recognit often limit singl modality, e.g. speech phone call. previou work propos crossmod emot embed improv monomod recognit performance, despit importance, explicit represent gaze included. propos new approach emot recognit incorpor explicit represent gaze crossmod emot embed framework. show method outperform previou state art audio-onli video-onli emot classif popular one-minut gradual emot recognit dataset. furthermore, report extens ablat experi provid detail insight perform differ state-of-the-art gaze represent integr strategies. result underlin import gaze emot recognit also demonstr practic highli effect approach leverag gaze inform task.",
    "dynam neural network toolkit pytorch, dynet, chainer offer flexibl implement model cope data vari dimens structure, rel toolkit oper static declar comput (e.g., tensorflow, cntk, theano). however, exist toolkit - static dynam - requir develop organ comput batch necessari exploit high-perform algorithm hardware. batch task gener difficult, becom major hurdl architectur becom complex. paper, present algorithm, implement dynet toolkit, automat batch operations. develop simpli write minibatch comput aggreg singl instanc computations, batch algorithm seamlessli execut them, fly, use comput effici batch operations. varieti tasks, obtain throughput similar obtain manual batches, well compar speedup single-inst learn architectur impract batch manually.",
    "sequenti user behavior model play crucial role onlin user-ori services, product purchasing, news feed consumption, onlin advertising. perform sequenti model heavili depend scale qualiti histor behaviors. however, number user behavior inher follow long-tail distribution, seldom explored. work, argu focus tail user could bring benefit address long tail issu learn transferr paramet optim featur perspectives. specifically, propos gradient align optim adopt adversari train scheme facilit knowledg transfer head tail. method also deal cold-start problem new users. moreover, could directli adapt variou well-establish sequenti models. extens experi four real-world dataset verifi superior framework compar state-of-the-art baselines.",
    "surg number machin learn method analyz product kept retail shelv images. deep learn base comput vision method use detect product retail shelv classifi them. however, differ size variant product look exactli visual method differenti look rel size product shelves. make process deciph size base variant use comput vision algorithm alon impractical. work, propos method ascertain size variant product downstream task object detector extract product shelf classifi determin product brand. product variant determin task assign product variant product brand base size bound box brand predict classifier. gradient boost base method work well product whose face clear distinct, nois accommod neural network method propos case product stack irregularly.",
    "energy-bas models, a.k.a. energi networks, perform infer optim energi function, typic parametr neural network. allow one captur potenti complex relationship input outputs. learn paramet energi function, solut optim problem typic fed loss function. key challeng train energi network lie comput loss gradients, typic requir argmin/argmax differentiation. paper, build upon gener notion conjug function, replac usual bilinear pair gener energi function, propos gener fenchel-young losses, natur loss construct learn energi networks. loss enjoy mani desir properti gradient comput effici without argmin/argmax differentiation. also prove calibr excess risk case linear-concav energies. demonstr loss multilabel classif imit learn tasks.",
    "explor power hybrid model differenti privaci (dp), user desir guarante local model dp other content receiv trusted-cur model guarantees. particular, studi util hybrid model estim comput mean arbitrari real-valu distribut bound support. curat know distribution' variance, design hybrid estim that, realist dataset paramet settings, achiev constant factor improv natur baselines. analyt character estimator' util parameter problem set paramet choices. distribution' varianc unknown, design heurist hybrid estim analyz compar baselines. find often perform better baselines, sometim almost well known-vari estimator. answer question estimator' util affect users' data drawn distribution, rather distribut depend trust model preference. concretely, examin implic two groups' distribut diverg show cases, estim maintain fairli high utility. demonstr hybrid estim incorpor sub-compon complex, higher-dimension applications. finally, propos new privaci amplif notion hybrid model emerg due interact groups, deriv correspond amplif result hybrid estimators.",
    "tutori survey paper metric learning. algorithm divid spectral, probabilistic, deep metric learning. first start definit distanc metric, mahalanobi distance, gener mahalanobi distance. spectral methods, start method use scatter data, includ first spectral metric learning, relev method fisher discrimin analysis, relev compon analysi (rca), discrimin compon analysi (dca), fisher-hs method. then, large-margin metric learning, imbalanc metric learning, local linear metric adaptation, adversari metric learn covered. also explain sever kernel spectral method metric learn featur space. also introduc geometr metric learn method riemannian manifolds. probabilist methods, start collaps class input featur space explain neighborhood compon analysi methods, bayesian metric learning, inform theoret methods, empir risk minim metric learning. deep learn methods, first introduc reconstruct autoencod supervis loss function metric learning. then, siames network variou loss functions, triplet mining, triplet sampl explained. deep discrimin analysi methods, base fisher discrimin analysis, also reviewed. finally, introduc multi-mod deep metric learning, geometr metric learn neural networks, few-shot metric learning.",
    "recent era predict enzym class unknown protein one challeng task bioinformatics. day day number protein increas result predict enzym class give new opportun bioinformat scholars. prime object articl implement machin learn classif techniqu featur select predict also find appropri classif techniqu function prediction. articl seven differ classif techniqu like crt, quest, chaid, c5.0, ann (artifici neural network), svm bayesian implement 4368 protein data extract uniprotkb databank categori six differ class. protein data high dimension sequenc data contain maximum 48 features.to manipul high dimension sequenti protein data differ classif technique, spss use experiment tool. differ classif techniqu give differ result everi model show data imbalanc class c4, c5 c6. imbalanc data affect perform model. three class precis recal valu less negligible. experiment result highlight c5.0 classif techniqu accuraci suit protein featur classif predictions. c5.0 classif techniqu give 95.56% accuraci also give high precis recal value. finally, conclud featur select use function prediction.",
    "nois contrast learn popular techniqu unsupervis represent learning. approach, represent obtain via reduct supervis learning, given notion semant similarity, learner tri distinguish similar (positive) exampl collect random (negative) examples. success modern contrast learn pipelin reli mani paramet choic data augmentation, number neg examples, batch size; however, limit understand paramet interact affect downstream performance. focu disambigu role one parameters: number neg examples. theoretically, show exist collision-coverag trade-off suggest optim number neg exampl scale number underli concept data. empirically, scrutin role number neg nlp vision tasks. nlp task, find result broadli agre theory, vision experi murkier perform sometim even insensit number negatives. discuss plausibl explan behavior suggest futur direct better align theori practice.",
    "consid quickest chang detect problem paramet pre- post- chang distribut unknown, prevent use classic simpl hypothesi testing. without addit assumptions, optim solut tractabl reli minimax robust variant objective. consequence, chang point might detect late practic applic (in economics, health care mainten instance). avail constant complex techniqu typic solv relax version problem, deepli reli specif probabl distribut and/or precis addit knowledge. consid total differ approach leverag theoret asymptot properti optim solut deriv new scalabl approxim algorithm near optim perform runs~in~$\\mathcal{o}(1)$, adapt even complex markovian settings.",
    "embodi convers agent benefit abl accompani speech gestures. although mani data-driven approach gestur gener propos recent years, still unclear whether system consist gener gestur convey meaning. investig gestur properti (phase, category, semantics) predict speech text and/or audio use contemporari deep learning. extens experiments, show gestur properti relat gestur mean (semant category) predict text featur (time-align fasttext embeddings) alone, prosod audio features, rhythm-rel gestur properti (phase) hand predict audio featur better text. result encourag indic possibl equip embodi agent content-wis meaning co-speech gestur use machine-learn model.",
    "recently, deep learn approach variou network architectur achiev signific perform improv exist iter reconstruct method variou imag problems. however, still unclear deep learn architectur work specif invers problems. address issues, show long-searched-for miss link convolut framelet repres signal convolv local non-loc bases. convolut framelet origin develop gener theori low-rank hankel matrix approach invers problems, paper extend idea obtain deep neural network use multilay convolut framelet perfect reconstruct (pr) rectilinear linear unit nonlinear (relu). analysi also show popular deep network compon residu block, redund filter channels, concaten relu (crelu) inde help achiev pr, pool unpool layer augment high-pass branch meet pr condition. moreover, chang number filter channel bias, control shrinkag behavior neural network. discoveri lead us propos novel theori deep convolut framelet neural network.",
    "use numer experi variou invers problems, demonstr deep convolut framelet network show consist improv exist deep architectures.thi discoveri suggest success deep learn magic power black-box, rather come power novel signal represent use non-loc basi combin data-driven local basis, inde natur extens classic signal process theory.",
    "recent advanc self-supervis learn (ssl) larg close gap supervis imagenet pretraining. despit success method primarili appli unlabel imagenet images, show margin gain train larger set uncur images. hypothes current ssl method perform best icon images, struggl complex scene imag mani objects. analyz contrast ssl method show poor visual ground receiv poor supervisori signal train scene images. propos contrast attention-supervis tuning(cast) overcom limitations. cast use unsupervis salienc map intellig sampl crops, provid ground supervis via grad-cam attent loss. experi coco show cast significantli improv featur learn ssl method scene images, experi show cast-train model robust chang backgrounds.",
    "grow attent learning-to-learn new task use examples, meta-learn wide use numer problem few-shot classification, reinforc learning, domain generalization. however, meta-learn model prone overfit suffici train task meta-learn generalize. although exist approach dropout wide use address overfit problem, method typic design regular model singl task supervis training. paper, introduc simpl yet effect method allevi risk overfit gradient-bas meta-learning. specifically, gradient-bas adapt stage, randomli drop gradient inner-loop optim paramet deep neural networks, augment gradient improv gener new tasks. present gener form propos gradient dropout regular show term sampl either bernoulli gaussian distribution. valid propos method, conduct extens experi analysi numer comput vision tasks, demonstr gradient dropout regular mitig overfit problem improv perform upon variou gradient-bas meta-learn frameworks.",
    "fabric process variat significantli influenc perform yield nano-scal electron photon circuits. stochast spectral method achiev great success quantifi impact process variations, suffer curs dimensionality. recently, low-rank tensor method develop mitig issue, two fundament challeng remain open: automat determin tensor rank adapt pick inform simul samples. paper propos novel tensor regress method address two challenges. use $\\ell_{q}/ \\ell_{2}$ group-spars regular determin tensor rank. result optim problem effici solv via altern minim solver. also propos two-stag adapt sampl method reduc simul cost. method consid explor exploit via estim voronoi cell volum nonlinear measur respectively. propos model verifi synthet realist circuit benchmarks, method well captur uncertainti caus 19 100 random variabl 100 600 simul samples.",
    "recent crowdsourc becom de facto platform distribut collect human comput wide rang task applic inform retrieval, natur languag process machin learning. current crowdsourc platform limit area qualiti control. effort ensur good qualiti done experiment manag number worker need reach good results. propos simpl model adapt qualiti control crowdsourc multiple-choic task call \\emph{bandit survey problem}. model relat to, technic differ well-known multi-arm bandit problem. present sever algorithm problem, support analysi simulations. approach base experi conduct relev evalu larg commerci search engine.",
    "observ sub-structures, like annular gaps, dust emiss protoplanetari disk, often interpret signatur embed planets. fit model planetari gap observ featur use custom simul empir relat reveal characterist hidden planets. however, custom fit often impract owe increas sampl size complex disk-planet interaction. paper introduc architectur dpnnet-2.0, second seri dpnnet \\citep{aud20}, design use convolut neural network ( cnn, specif resnet50) predict exoplanet mass directli simul imag protoplanetari disk host singl planet. dpnnet-2.0 addit consist multi-input framework use cnn multi-lay perceptron (a class artifici neural network) process imag disk paramet simultaneously. enabl dpnnet-2.0 train use imag directly, ad option consid disk paramet (disk viscosities, disk temperatures, disk surfac densiti profiles, dust abundances, particl stoke numbers) gener disk-planet hydrodynam simul inputs. work provid requir framework first step toward use comput vision (implement cnn) directli extract mass exoplanet planetari gap observ dust-surfac densiti map telescop atacama larg (sub-)millimet array.",
    "studi use stack generalization, two-step process combin machin learn methods, call meta super learners, improv perform algorithm step one (bi minim error rate individu algorithm reduc bia learn set) step two input result meta learner stack blend output (demonstr improv perform weakest algorithm learn better). method essenti enhanc cross-valid strategy. although process use great comput resources, result perform metric resampl fraud data show increas system cost justified. fundament key fraud data inher systemat and, yet, optim resampl methodolog identified. build test har account permut algorithm sampl set pair demonstr complex, intrins data structur thoroughli tested. use compar analysi fraud data appli stack gener provid use insight need find optim mathemat formula use imbalanc fraud data sets.",
    "mani machin learn (ml) approach wide use gener bioclimat model predict geograph rang organ function climate. applic predict rang shift organism, rang invas speci influenc climat chang import paramet understand impact climat change. however, success machin learning-bas approach depend number factors. safe said particular ml techniqu effect applic success techniqu predominantli depend applic type problem, use understand behavior ensur inform choic techniques. paper present comprehens review machin learning-bas bioclimat model gener analys factor influenc success models. consid wide use statist techniques, discuss also includ convent statist techniqu use bioclimat modelling.",
    "paper, propos method reconstruct 3d model base continu sensori input. robot draw extrem larg data real world use variou sensors. however, sensori input usual noisi high-dimension data. difficult time consum robot process use raw data robot tri construct 3d model. hence, need method extract use inform sensori inputs. address problem method util concept object semant hierarchi (osh). differ previou work use hierarchi framework, extract motion inform use deep belief network techniqu instead appli classic comput vision approaches. train two larg set random dot imag (10,000) translat rotated, respectively, success extract sever base explain translat rotat motion. base translat rotat bases, background subtract becom possibl use object semant hierarchy.",
    "cross-speak style transfer crucial applic multi-styl express speech synthesi scale. requir target speaker expert express style collect correspond record model training. however, perform exist style transfer method still far behind real applic needs. root caus mainli twofold. firstly, style embed extract singl refer speech hardli provid fine-grain appropri prosodi inform arbitrari text synthesize. secondly, model content/text, prosody, speaker timbr usual highli entangled, therefor realist expect satisfi result freeli combin components, transfer speak style speakers. paper, propos cross-speak style transfer text-to-speech (tts) model explicit prosodi bottleneck. prosodi bottleneck build kernel account speak style robustly, disentangl prosodi content speaker timbre, therefor guarante high qualiti cross-speak style transfer. evalu result show propos method even achiev on-par perform sourc speaker' speaker-depend (sd) model object measur prosody, significantli outperform cycl consist gmvae-bas baselin object subject evaluations.",
    "autom visual understand divers open world demand comput vision model gener well minim custom specif tasks, similar human vision. comput vision foundat models, train diverse, large-scal dataset adapt wide rang downstream tasks, critic mission solv real-world comput vision applications. exist vision foundat model clip, align, wu dao 2.0 focu mainli map imag textual represent cross-mod share representation, introduc new comput vision foundat model, florence, expand represent coars (scene) fine (object), static (images) dynam (videos), rgb multipl modal (caption, depth). incorpor univers visual-languag represent web-scal image-text data, florenc model easili adapt variou comput vision tasks, classification, retrieval, object detection, vqa, imag caption, video retriev action recognition. moreover, florenc demonstr outstand perform mani type transfer learning: fulli sampl fine-tuning, linear probing, few-shot transfer zero-shot transfer novel imag objects. properti critic vision foundat model serv gener purpos vision tasks. florenc achiev new state-of-the-art result major 44 repres benchmarks, e.g., imagenet-1k zero-shot classif top-1 accuraci 83.74 top-5 accuraci 97.18, 62.4 map coco fine tuning, 80.36 vqa, 87.8 kinetics-600.",
    "sepsi danger condit lead caus patient mortality. treat sepsi highli challenging, individu patient respond differ medic intervent univers agreed-upon treatment sepsis. work, explor use continu state-spac model-bas reinforc learn (rl) discov high-qual treatment polici sepsi patients. quantit evalu reveal blend treatment strategi discov rl clinician follow, obtain improv policies, potenti allow better medic treatment sepsis.",
    "accur diagnost skin lesion critic task classif dermoscop images. research, form new type imag features, call hybrid features, stronger discrimin abil singl method features. studi involv new techniqu inject handcraft featur featur transfer fulli connect layer convolut neural network (cnn) model train process. base literatur review now, studi examin investig impact classif perform inject handcraft featur cnn model train process. addition, also investig impact segment mask effect overal classif performance. model achiev 92.3% balanc multiclass accuracy, 6.8% better typic singl method classifi architectur deep learning.",
    "coronaviru caus hundr thousand deaths. fatal could decreas everi patient could get suitabl treatment healthcar system. machin learning, especi comput vision method base deep learning, help healthcar profession diagnos treat covid-19 infect case efficiently. hence, infect patient get better servic healthcar system decreas number death caus coronavirus. research propos method segment infect lung region ct image. purpose, convolut neural network attent mechan use detect infect area complex patterns. attent block improv segment accuraci focus inform part image. furthermore, gener adversari network gener synthet imag data augment expans small avail datasets. experiment result show superior propos method compar exist procedures.",
    "articl contain propos add coinduct comput apparatu natur languag understanding. this, argue, provid basi realistic, comput sound, scalabl model natur languag dialogue, syntax semantics. given bottom up, induct constructed, semant syntact structur brittle, seemingli incap adequ repres mean longer sentenc realist dialogues, natur languag understand need new foundation. coinduction, use top constraints, success use design oper system program languages. moreover, implicitli present text mining, machin translation, attempt model intension modalities, provid evid works. articl show high level formal uses. sinc coinduct induct coexist, provid common languag conceptu model research natur languag understanding. particular, opportun seem emerg research compositionality. articl show sever exampl joint appear induct coinduct natur languag processing. argu known individu limit induct coinduct overcom empir set combin two methods. see open problem provid theori joint use.",
    "camera lidar process revolution sinc introduct deep learning, radar process still reli classic tools. paper, introduc deep learn approach radar processing, work directli radar complex data. overcom lack radar label data, reli train radar calibr data introduc new radar augment techniques. evalu method radar 4d detect task demonstr superior perform compar classic approach keep real-tim performance. appli deep learn radar data sever advantag elimin need expens radar calibr process time enabl classif detect object almost zero-overhead.",
    "time seri forecast relev task perform sever real-world scenario product sale analysi predict energi demand. given accuraci performance, currently, recurr neural network (rnns) model choic task. despit success time seri forecasting, less attent paid make rnn trustworthy. example, rnn natur provid uncertainti measur predictions. could extrem use practic sever case e.g. detect predict might complet wrong due unusu pattern time series. whittl sum-product network (wspns), promin deep tractabl probabilist circuit (pcs) time series, assist rnn provid meaning probabl uncertainti measure. aim, propos recown, novel architectur employ rnn discrimin variant wspn call condit wspn (cwspns). also formul log-likelihood ratio score better estim uncertainti tailor time seri whittl likelihoods. experiments, show recown accur trustworthi time seri predictors, abl \"know know\".",
    "studi riemannian langevin algorithm problem sampl distribut densiti $\\nu$ respect natur measur manifold metric $g$. assum target densiti satisfi log-sobolev inequ respect metric prove manifold gener unadjust langevin algorithm converg rapidli $\\nu$ hessian manifolds. allow us reduc problem sampl non-smooth (constrained) densiti ${\\bf r}^n$ sampl smooth densiti appropri manifolds, need access gradient log-density, this, turn, sampl natur brownian motion manifold. main analyt tool (1) extens self-concord manifolds, (2) stochast approach bound smooth manifolds. special case approach sampl isoperimetr densiti restrict polytop use metric defin logarithm barrier.",
    "mani real-world applications, often need handl variou deploy scenarios, resourc constraint superclass interest correspond group class dynam specified. effici deploy deep model divers deploy scenario new challenge. previou na approach seek design architectur class simultaneously, may optim individu superclasses. straightforward solut search architectur scratch deploy scenario, howev computation-intens impractical. address this, present novel gener framework, call elast architectur search (eas), permit instant special runtim divers superclass variou resourc constraints. end, first propos effect train over-parameter network via superclass dropout strategi training. way, result model robust subsequ superclass drop infer time. base well-train over-parameter network, propos effici architectur gener obtain promis architectur within singl forward pass. experi three imag classif dataset show ea abl find compact network better perform remark order magnitud faster state-of-the-art na methods, e.g., outperform ofa (once-for-all) 1.3% top-1 accuraci budget around 361m #madd imagenet-10. critically, ea abl find compact architectur within 0.1 second 50 deploy scenarios.",
    "advanc multi-ag reinforc learn (marl) enabl sequenti decis make rang excit multi-ag applic cooper ai autonom driving. explain agent decis crucial improv system transparency, increas user satisfaction, facilit human-ag collaboration. however, exist work explain reinforc learn mostli focu single-ag set suitabl address challeng pose multi-ag environments. present novel method gener two type polici explan marl: (i) polici summar agent cooper task sequence, (ii) languag explan answer queri agent behavior. experiment result three marl domain demonstr scalabl methods. user studi show gener explan significantli improv user perform increas subject rate metric user satisfaction.",
    "random forest (rf) ensembl supervis machin learn techniqu develop breiman decad ago. compar ensembl techniques, prove accuraci superiority. mani researchers, however, believ still room enhanc improv perform accuracy. explain why, past decade, mani extens rf extens employ varieti techniqu strategi improv certain aspect(s) rf. sinc proven empiricallthat ensembl tend yield better result signific divers among constitu models, object paper twofold. first, investig data cluster (a well known divers technique) appli identifi group similar decis tree rf order elimin redund tree select repres group (cluster). second, like divers repres use produc extens rf term club-drf much smaller size rf, yet perform least good rf, mostli exhibit higher perform term accuracy. latter refer known techniqu call ensembl pruning. experiment result 15 real dataset uci repositori prove superior propos extens tradit rf. experi achiev least 95% prune level retain outperform rf accuracy.",
    "large-scal graph becom increasingli prevalent, pose signific comput challeng process, extract analyz larg graph data. graph coarsen one popular techniqu reduc size graph maintain essenti properties. despit rich graph coarsen literature, limit explor data-driven method field. work, leverag recent progress deep learn graph graph coarsening. first propos framework measur qualiti coarsen algorithm show depend goal, need care choos laplac oper coars graph associ projection/lift operators. motiv observ current choic edg weight coars graph may sub-optimal, parametr weight assign map graph neural network train improv coarsen qualiti unsupervis way. extens experi synthet real networks, demonstr method significantli improv common graph coarsen method variou metrics, reduct ratios, graph sizes, graph types. gener graph larger size ($25\\times$ train graphs), adapt differ loss (differenti non-differentiable), scale much larger graph previou work.",
    "non-neg blind sourc separ (bss) rais interest variou field research, testifi wide literatur topic non-neg matrix factor (nmf). context, fundament sourc estim present divers order effici retrieved. sparsiti known enhanc contrast sourc produc robust approaches, especi noise. paper introduc new algorithm order tackl blind separ non-neg spars sourc noisi measurements. first show sparsiti non-neg constraint care appli sought-aft solution. fact, improperli constrain solut unlik stabl therefor sub-optimal. propos algorithm, name ngmca (non-neg gener morpholog compon analysis), make use proxim calculu techniqu provid properli constrain solutions. perform ngmca compar state-of-the-art algorithm demonstr numer experi encompass wide varieti settings, neglig paramet tuning. particular, ngmca shown provid robust nois perform well synthet mixtur real nmr spectra.",
    "use databas systems, design storag engin data model directli affect perform databas perform queries. therefore, user databas need select storag engin design data model accord workload encountered. however, hybrid workload, queri set databas dynam changing, design optim storag structur also changing. motiv this, propos automat storag structur select system base learn cost, use dynam select optim storag structur databas hybrid workloads. system, introduc machin learn method build cost model storag engine, column-ori data layout gener algorithm. experiment result show propos system choos optim combin storag engin data model accord current workload, greatli improv perform default storag structure. system design compat differ storag engin easi use practic applications.",
    "propuls system electrif revolut undergo automot industry. electrifi propuls system improv energi effici reduc depend fossil fuel. however, batteri electr vehicl experi degrad process vehicl operation. research consid batteri degrad energi consumpt battery/ supercapacitor electr vehicl still lacking. studi propos q-learning-bas strategi minim batteri degrad energi consumption. besid q-learning, two heurist energi manag method also propos optim use particl swarm optim algorithm. vehicl propuls system model first presented, sever factor batteri degrad model consid experiment valid help genet algorithm. result analysis, q-learn first explain optim polici map learning. then, result vehicl without ultracapacitor use baseline, compar result vehicl ultracapacitor use q-learning, two heurist method energi manag strategies. learn valid drive cycles, result indic q-learn strategi slow batteri degrad 13-20% increas vehicl rang 1.5-2% compar baselin vehicl without ultracapacitor.",
    "gener model variat autoencod (vae) gener adversari network (gan) proven incred power gener synthet data preserv statist properti util real-world datasets, especi context imag natur languag text. nevertheless, now, success demonstr appli either method gener use physiolog sensori data. state-of-the-art techniqu context achiev limit success. present physiogan, gener model produc high fidel synthet physiolog sensor data readings. physiogan consist encoder, decoder, discriminator. evalu physiogan state-of-the-art techniqu use two differ real-world datasets: ecg classif activ recognit motion sensor datasets. compar physiogan baselin model accuraci class condit gener also sampl divers sampl novelti synthet datasets. prove physiogan gener sampl higher util gener model show classif model train synthet data gener physiogan 10% 20% decreas classif accuraci rel classif model train real data. furthermore, demonstr use physiogan sensor data imput creat plausibl results.",
    "complex deep learn (dl) model increases, comput requir increas accordingly. deploy convolut neural network (cnn) involv two phases: train inference. infer task typic take place resource-constrain devices, lot research explor field low-pow infer custom hardwar accelerators. hand, train compute- memory-intens primarili perform power-hungri gpu large-scal data centres. cnn train fpga nascent field research. primarili due lack tool easili prototyp deploy variou hardwar and/or algorithm techniqu power-effici cnn training. work present barista, autom toolflow provid seamless integr fpga train cnn within popular deep learn framework caffe. best knowledge, tool allow versatil rapid deploy hardwar algorithm fpga-bas train cnns, provid necessari infrastructur research development.",
    "paper, propos novel spatiotempor convolut dens network (stdnet) address video-bas crowd count problem, contain decomposit 3d convolut 3d spatiotempor dilat dens convolut allevi rapid growth model size caus conv3d layer. moreover, sinc dilat convolut extract multiscal features, combin dilat convolut channel attent block enhanc featur representations. due error occur difficulti label crowds, especi videos, imprecis standard-inconsist label may lead poor converg model. address issue, propos new patch-wis regress loss (prl) improv origin pixel-wis loss. experiment result three video-bas benchmarks, i.e., ucsd, mall worldexpo'10 datasets, show stdnet outperform image- video-bas state-of-the-art methods. sourc code releas \\url{https://github.com/stdnet/stdnet}.",
    "darpa lifelong learn machin (l2m) program seek yield advanc artifici intellig (ai) system capabl learn (and improving) continuously, leverag data one task improv perform another, comput sustain way. perform program develop system capabl perform divers rang functions, includ autonom driving, real-tim strategy, drone simulation. system featur divers rang characterist (e.g., task structure, lifetim duration), immedi challeng face program' test evalu team measur system perform across differ settings. document, develop close collabor darpa program performers, outlin formal construct character perform agent perform lifelong learn scenarios.",
    "converg rate final perform common deep learn model significantli benefit heurist learn rate schedules, knowledg distillation, skip connections, normal layers. absenc theoret underpinnings, control experi aim explain strategi aid understand deep learn landscap train dynamics. exist approach empir analysi reli tool linear interpol visual dimension reduction, limitations. instead, revisit analysi heurist len recent propos method loss surfac represent analysis, viz., mode connect canon correl analysi (cca), hypothes reason success heuristics. particular, explor knowledg distil learn rate heurist (cosine) restart warmup use mode connect cca. empir analysi suggest that: (a) reason often quot success cosin anneal evidenc practice; (b) effect learn rate warmup prevent deeper layer creat train instability; (c) latent knowledg share teacher primarili disburs deeper layers.",
    "problem learn direct acycl graph (dag) markov equival equival problem find permut variabl induc sparsest graph. without addit assumptions, task known np-hard. build minimum degre algorithm spars choleski decomposition, util dag-specif problem structure, introduc effici algorithm find spars permutations. show jointli gaussian distributions, method depth $w$ run $o(p^{w+3})$ time. compar method $w = 1$ algorithm find spars elimin order undirect graphs, show take advantag dag-specif problem structur lead signific improv discov permutation. also compar algorithm provabl consist causal structur learn algorithms, pc algorithm, ges, gsp, show method achiev compar perform shorter runtime. thus, method use causal structur discovery. finally, show exist dens graph method achiev almost perfect performance, unlik exist causal structur learn algorithms, situat algorithm achiev good perform good runtim limit spars graphs.",
    "present novel dynam recommend model focus user interact past turn rel inact recently. make effect recommend time-sensit cold-start user critic maintain user base recommend system. due spars recent interactions, challeng captur users' current prefer precisely. sole reli histor interact may also lead outdat recommend misalign recent interests. propos model leverag histor current user-item interact dynam factor user' (latent) prefer time-specif time-evolv represent jointli affect user behaviors. latent factor interact optim item embed achiev accur time recommendations. experi real-world data help demonstr effect propos time-sensit cold-start recommend model.",
    "project, success designed, implemented, deploy test novel fpga acceler algorithm neural network training. algorithm develop independ studi option. train method base altern direct method multipli algorithm, strong parallel characterist avoid procedur matrix invers problemat hardwar design employ lsmr. intermedi stage, fulli implement admm-lsmr method c languag feed-forward neural network flexibl number layer hidden size. demonstr method oper fixed-point arithmet without compromis accuracy. next, devis fpga acceler version algorithm use intel fpga sdk opencl perform extens optimis stage follow success deploy program intel arria 10 gx fpga. fpga acceler program show 6 time speed compar equival cpu implement achiev promis accuracy.",
    "learn social media data embed deep model attract extens research interest well boom lot applications, link prediction, classification, cross-mod search. however, social imag contain link inform multimod content (e.g., text description, visual content), simpli employ embed learnt network structur data content result sub-optim social imag representation. paper, propos novel social imag embed approach call deep multimod attent network (dman), employ deep model jointli emb multimod content link information. specifically, effect captur correl multimod contents, propos multimod attent network encod fine-granular relat imag region textual words. leverag network structur embed learning, novel siamese-triplet neural network propos model link among images. joint deep model, learnt embed captur multimod content nonlinear network information. extens experi conduct investig effect approach applic multi-label classif cross-mod search. compar state-of-the-art imag embeddings, propos dman achiev signific improv task multi-label classif cross-mod search.",
    "detect local imag manipul necessari counter malici use imag edit techniques. accordingly, essenti distinguish authent tamper region analyz intrins statist image. focu jpeg compress artifact left imag acquisit editing. propos convolut neural network (cnn) use discret cosin transform (dct) coefficients, compress artifact remain, local imag manipulation. standard cnn cannot learn distribut dct coeffici convolut throw away spatial coordinates, essenti dct coefficients. illustr design train neural network learn distribut dct coefficients. furthermore, introduc compress artifact trace network (cat-net) jointli use imag acquisit artifact compress artifacts. significantli outperform tradit deep neural network-bas method detect local tamper regions.",
    "encoder-decod recurr neural network model (rnn seq2seq) achiev great success ubiquit area comput applications. shown success model data tempor spatial depend translat predict tasks. study, propos embed approach visual interpret represent data models. furthermore, show embed effect method unsupervis learn util estim optim model training. particular, demonstr embed space project decod state rnn seq2seq model train sequenc predict organ cluster captur similar differ dynam sequences. perform correspond unsupervis cluster spatio-tempor featur employ time-depend problem tempor segmentation, cluster dynam activity, self-supervis classification, action recognition, failur prediction, etc. test demonstr applic embed methodolog time-sequ 3d human bodi poses. show methodolog provid high-qual unsupervis categor movements.",
    "describ system term choic result cost reward offer promis free algorithm design programm specifi choic made; implementations, choic realiz optim techniqu and, increasingly, machine-learn methods. studi approach programming-languag perspective. defin two small languag support decision-mak abstractions: one choic rewards, addit probabilities. give oper denot semantics. case second languag consid three denot semantics, vari degre correl possibl program valu expect rewards. oper semant combin usual semant standard construct optim space possibl execut strategies. denot semantics, compositional, reli select monad, handl choice, augment auxiliari monad handl effects, reward probability. establish adequaci theorem two semant coincid cases. also prove full abstract base types, vari notion observ probabilist case correspond variou degre correlation. present axiom choic combin reward probability, establish complet base type case reward without probability.",
    "design voltage-control oscil (vco) inductor labori time-consum task convent done manual human experts. paper, propos framework autom design vco inductors, use reinforc learn (rl). formul problem sequenti procedure, wire segment drawn one another, complet inductor created. employ rl agent learn draw inductor meet certain target specifications. light need tweak target specif throughout circuit design cycle, also develop variant agent learn quickli adapt draw new inductor moder differ target specifications. empir result show propos framework success automat gener vco inductor meet exceed target specification.",
    "amort infer allow latent-vari model train via variat learn scale larg datasets. qualiti approxim infer determin two factors: a) capac variat distribut match true posterior b) abil recognit network produc good variat paramet datapoint. examin approxim infer variat autoencod term factors. find diverg true posterior often due imperfect recognit networks, rather limit complex approxim distribution. show due partli gener learn accommod choic approximation. furthermore, show paramet use increas express approxim play role gener infer rather simpli improv complex approximation.",
    "transcript profil microarray obtain gene express use facilit cancer diagnosis. propos deep gener machin learn architectur (call deepcancer) learn featur unlabel microarray data. model use conjunct convent classifi perform classif tissu sampl either cancer non-cancerous. propos model test two differ clinic datasets. evalu demonstr deepcanc model achiev high precis score, significantli control fals posit fals neg scores.",
    "data poison attack aim manipul model behavior distort train data. previously, aggregation-bas certifi defense, deep partit aggreg (dpa), propos mitig threat. dpa predict aggreg base classifi train disjoint subset data, thu restrict sensit dataset distortions. work, propos improv certifi defens gener poison attacks, name finit aggregation. contrast dpa, directli split train set disjoint subsets, method first split train set smaller disjoint subset combin duplic build larger (but disjoint) subset train base classifiers. reduc worst-cas impact poison sampl thu improv certifi robust bounds. addition, offer altern view method, bridg design determinist stochast aggregation-bas certifi defenses. empirically, propos finit aggreg consist improv certif mnist, cifar-10, gtsrb, boost certifi fraction 3.05%, 3.87% 4.77%, respectively, keep clean accuraci dpa's, effect establish new state art (pointwise) certifi robust data poisoning.",
    "present hindsight network credit assign (hnca), novel learn method stochast neural networks, work assign credit neuron' stochast output base influenc output immedi children network. prove hnca provid unbias gradient estim reduc varianc compar reinforc estimator. also experiment demonstr advantag hnca reinforc contextu bandit version mnist. comput complex hnca similar backpropagation. believ hnca help stimul new way think credit assign stochast comput graphs.",
    "discov distinct featur relat data help us uncov valuabl knowledg crucial variou tasks, e.g., classification. neuroimaging, featur could help understand, classify, possibl prevent brain disorders. model introspect highli perform overparameter deep learn (dl) model could help find featur relations. however, achiev high-perform level dl model requir numer label train sampl ($n$) rare avail mani fields. paper present pre-train method involv graph convolutional/neur network (gcns/gnns), base maxim mutual inform two high-level embed input sample. mani recent propos pre-train method pre-train one mani possibl network architecture. sinc almost everi dl model ensembl multipl networks, take high-level embed two differ network model --a convolut graph network--. learn high-level graph latent represent help increas perform downstream graph classif task bypass need high number label data samples. appli method neuroimag dataset classifi subject healthi control (hc) schizophrenia (sz) groups. experi show pre-train model significantli outperform non-pre-train model requir $50\\%$ less data similar performance.",
    "inform system enabl mani organiz process everi industry. effici effect use inform technolog creat unintend byproduct: misus exist user somebodi imperson - insid threat. detect insid threat may possibl thorough analysi electron logs, captur user behaviors, take place. however, log usual larg unstructured, pose signific challeng organizations. study, use deep learning, specif long short term memori (lstm) recurr network enabl detection. demonstr large, anonym dataset lstm use sequenc natur data reduc search space make work secur analyst effective.",
    "studi classif perform kronecker-structur model two asymptot regim develop algorithm separable, fast compact k- dictionari learn better classif represent multidimension signal exploit structur signal. first, studi classif perform term divers order pairwis geometri subspaces. deriv exact express divers order function signal subspac dimens k- model. next, studi classif capacity, maximum rate number class grow signal dimens goe infinity. describ fast algorithm kronecker-structur learn discrimin dictionari (k-sld2). finally, evalu empir classif perform k- model synthet data, show agre divers order analysis. also evalu perform k-sld2 synthet real-world dataset show k-sld2 balanc compact signal represent good classif performance.",
    "consid problem learn play first-person shooter (fps) video game use raw screen imag observ keyboard input actions. high-dimension observ type applic lead prohibit need train data model-fre methods, deep q-network (dqn), recurr variant drqn. thus, recent work focus learn low-dimension represent may reduc need data. paper present new effici method learn representations. salient segment consecut frame detect optic flow, cluster base featur descriptors. cluster typic correspond differ discov categori objects. segment detect new frame classifi base nearest clusters. categori relev given task, import categori defin correl occurr agent' performance. result encod vector indic object frame locations, use side input drqn. experi game doom provid good evid benefit approach.",
    "hybrid system identif key tool achiev reliabl model cyber-phys system data. piecewis affin model guarante univers approximation, local linear equival class hybrid system. still, pwa identif challeng problem, requir concurr solut regress classif tasks. work, focu identif piecewis auto regress exogen input model arbitrari region (npwarx), thu restrict polyhedr domains, character discontinu maps. end, propos method base probabilist mixtur model, discret state repres multinomi distribut condit input regressors. architectur conceiv follow mixtur expert concept, develop within machin learn field. achiev nonlinear partitioning, parametr discrimin function use neural network. then, paramet arx submodel classifi concurr estim maxim likelihood overal model use expect maximization. propos method demonstr nonlinear piece-wis problem discontinu maps.",
    "paper describ detect malici execut file base static analysi binari content. stage pre-process clean data extract differ area execut file analyzed. method encod categor attribut execut file considered, way reduc featur field dimens select characterist featur order effect repres sampl binari execut file train classifiers. ensembl train approach appli order aggreg forecast classifier, ensembl classifi variou featur group execut file attribut creat order subsequ develop system detect malici file uninsul environment.",
    "dynam system ubiquit often model use non-linear system govern equations. numer solut procedur mani dynam system exist sever decades, slow due high-dimension state space dynam system. thus, deep learning-bas reduc order model (roms) interest one famili algorithm along line base koopman theory. paper extend recent develop adversari koopman model (balakrishnan \\& upadhyay, arxiv:2006.05547) stochast space, koopman oper appli probabl distribut latent encod encoder. specifically, latent encod system model gaussian, advanc time use auxiliari neural network output two koopman matric $k_{\\mu}$ $k_{\\sigma}$. adversari gradient loss use found lower predict errors. reduc koopman formul also undertaken koopman matric assum tridiagon structure, yield predict compar baselin model full koopman matrices. efficaci stochast koopman model demonstr differ test problem chaos, fluid dynamics, combustion, reaction-diffus models. propos model also appli set koopman matric condit input paramet gener appli simul state lithium-ion batteri time. koopman model discuss studi promis wide rang problem considered.",
    "introduc threaten markov decis process (tmdps) extens classic markov decis process framework reinforc learn (rl). tmdp allow suport decis maker potenti oppon rl context. also propos level-k think scheme result novel learn approach deal tmdps. introduc framework deriv theoret results, relev empir evid given via extens experiments, show benefit account adversari rl agent learn",
    "sinc first coronaviru case identifi u.s. jan. 21, 1 million peopl u.s. confirm case covid-19. infecti respiratori diseas spread rapidli across 3000 counti 50 state u.s. exhibit evolutionari cluster complex trigger patterns. essenti understand complex spacetim intertwin propag diseas accur predict smart extern intervent carri out. paper, model propag covid-19 spatio-tempor point process propos gener intensity-fre model track spread disease. adopt gener adversari imit learn framework learn model parameters. comparison tradit likelihood-bas learn methods, imit learn framework need prespecifi intens function, allevi model-misspecification. moreover, adversari learn procedur bypass difficult-to-evalu integr involv likelihood evaluation, make model infer scalabl data variables. showcas dynam learn perform covid-19 confirm case u.s. evalu social distanc polici base learn gener model.",
    "deepen penetr variabl energi resourc creat unpreced challeng system oper (sos). issu merit special attent precipit net load ramps, requir so flexibl capac dispos maintain supply-demand balanc times. judici procur deploy flexibl capacity, tool forecast net load ramp may great assist sos. end, propos methodolog forecast magnitud start time daili primari three-hour net load ramps. perform extens analysi identifi factor influenc net load draw identifi factor develop forecast methodolog har long short-term memori model. demonstr effect propos methodolog caiso system use compar assess select benchmark base variou evalu metrics.",
    "self-supervis featur represent shown use supervis classification, few-shot learning, adversari robustness. show featur obtain use self-supervis learn compar to, better than, supervis learn domain gener comput vision. introduc new self-supervis pretext task predict respons gabor filter bank demonstr multi-task learn compat pretext task improv domain gener perform compar train individu task alone. featur learnt self-supervis obtain better gener unseen domain compar supervis counterpart larger domain shift train test distribut even show better local abil object interest. self-supervis featur represent also combin domain gener method boost performance.",
    "clinic practice, anisotrop volumetr medic imag low through-plan resolut commonli use due short acquisit time lower storag cost. nevertheless, coars resolut may lead difficulti medic diagnosi either physician computer-aid diagnosi algorithms. deep learning-bas volumetr super-resolut (sr) method feasibl way improv resolution, convolut neural network (cnn) core. despit recent progress, method limit inher properti convolut operators, ignor content relev cannot effect model long-rang dependencies. addition, exist method use pseudo-pair volum train evaluation, pseudo low-resolut (lr) volum gener simpl degrad high-resolut (hr) counterparts. however, domain gap pseudo- real-lr volum lead poor perform method practice. paper, build first public real-pair dataset rplhr-ct benchmark volumetr sr, provid baselin result re-impl four state-of-the-art cnn-base methods. consid inher shortcom cnn, also propos transform volumetr super-resolut network (tvsrn) base attent mechanisms, dispens convolut entirely. first research use pure transform ct volumetr sr. experiment result show tvsrn significantli outperform baselin psnr ssim. moreover, tvsrn method achiev better trade-off imag quality, number parameters, run time. data code avail https://github.com/smilenaxx/rplhr-ct.",
    "graph represent learn resurg trend research subject owe widespread use deep learn euclidean data, inspir variou creativ design neural network non-euclidean domain, particularli graphs. success graph neural network (gnn) static setting, approach practic scenario graph dynam evolves. exist approach typic resort node embed use recurr neural network (rnn, broadli speaking) regul embed learn tempor dynamics. method requir knowledg node full time span (includ train testing) less applic frequent chang node set. extrem scenarios, node set differ time step may complet differ. resolv challenge, propos evolvegcn, adapt graph convolut network (gcn) model along tempor dimens without resort node embeddings. propos approach captur dynam graph sequenc use rnn evolv gcn parameters. two architectur consid paramet evolution. evalu propos approach task includ link prediction, edg classification, node classification. experiment result indic gener higher perform evolvegcn compar relat approaches. code avail \\url{https://github.com/ibm/evolvegcn}.",
    "paper, shown auto-encod use optim reconstruct significantli outperform convent auto-encoder. optim reconstruct use condit mean input given features, maximum entropi prior distribution. optim reconstruct network, call determinist project beli network (d-pbn), resembl standard reconstruct network, special non-linear mist iter solved. method, seen gener maximum entropi imag reconstruction, extend multipl layers. experiments, mean squar reconstruct error reduc factor two. perform improv diminish deeper networks, input data unconstrain valu (gaussian assumption).",
    "paper investig adversary' eas attack gener adversari exampl real-world scenarios. address three key requir practic attack real-world: 1) automat constrain size shape attack appli stickers, 2) transform-robustness, i.e., robust attack environment physic variat viewpoint light changes, 3) support attack white-box, also black-box hard-label scenarios, adversari attack proprietari models. work, propos graphite, effici gener framework gener attack satisfi three key requirements. graphit take advantag transform-robustness, metric base expect transform (eot), automat gener small mask optim gradient-fre optimization. graphit also flexibl easili trade-off transform-robustness, perturb size, queri count black-box settings. gtsrb model hard-label black-box setting, abl find attack possibl 1,806 victim-target class pair averag 77.8% transform-robustness, perturb size 16.63% victim images, 126k queri per pair. digital-onli attack achiev transform-robust requirement, graphit abl find success small-patch attack averag 566 queri 92.2% victim-target pairs. graphit also abl find success attack use perturb modifi small area input imag patchguard, recent propos defens patch-bas attacks.",
    "era inform explos prompt accumul tremend amount time-seri data, includ stationari non-stationari time-seri data. state-of-the-art algorithm achiev decent perform deal stationari tempor data. however, tradit algorithm tackl stationari time-seri appli non-stationari seri like forex trading. paper investig applic model improv accuraci forecast futur trend non-stationari time-seri sequences. particular, focu identifi potenti model investig effect recogn pattern histor data. propos combin \\rebuttal{the} seq2seq model base rnn, along attent mechan enrich set featur extract via dynam time warp zigzag peak valley indicators. custom loss function evalu metric design focu predict sequence' peak valley points. result show model predict 4-hour futur trend high accuraci forex dataset, crucial realist scenario assist foreign exchang trade decis making. provid evalu effect variou loss functions, evalu metrics, model variants, compon model performance.",
    "paper introduc analyz learn scenario \\emph{coupl nonlinear dimension reduction}, combin two major step machin learn pipeline: project onto manifold subsequ supervis learning. first, present new gener bound scenario and, second, introduc algorithm follow bounds. gener error bound base care analysi empir rademach complex relev hypothesi set. particular, show upper bound rademach complex $\\widetild o(\\sqrt{\\lambda_{(r)}/m})$, $m$ sampl size $\\lambda_{(r)}$ upper bound ky-fan $r$-norm associ kernel matrix. give upper lower bound guarante term ky-fan $r$-norm, strongli justifi definit hypothesi set. best knowledge, first learn guarante problem coupl dimension reduction. analysi learn guarante appli sever special cases, use fix kernel supervis dimension reduct unsupervis learn kernel dimension reduct follow supervis learn algorithm. base theoret analysis, suggest structur risk minim algorithm consist coupl fit low dimension manifold separ function manifold.",
    "amidst toolbox softwar scalabl probabilist machin learn spe- cial focu (massive) stream data. toolbox support flexibl model languag base probabilist graphic model latent variabl tempor dependencies. specifi model learnt larg data set use parallel distribut implementa- tion bayesian learn algorithm either stream batch data. algorithm base flexibl variat messag pass scheme, support discret continu- ou variabl wide rang probabl distributions. amidst also leverag exist function algorithm interfac softwar tool flink, spark, moa, weka, r hugin. amidst open sourc toolbox written java avail http://www.amidsttoolbox.com apach softwar licens version 2.0.",
    "tensor network method key ingredi advanc condens matter physic recent spark interest machin learn commun abil compactli repres high-dimension objects. tensor network method exampl use effici learn linear model exponenti larg featur space [stoudenmir schwab, 2016]. work, deriv upper lower bound vc dimens pseudo-dimens larg class tensor network model classification, regress completion. upper bound hold linear model parameter arbitrari tensor network structures, deriv lower bound common tensor decomposit models~(cp, tensor train, tensor ring tucker) show tight gener upper bound. result use deriv gener bound appli classif low rank matric well linear classifi base commonli use tensor decomposit models. corollari results, obtain bound vc dimens matrix product state classifi introduc [stoudenmir schwab, 2016] function so-cal bond dimension~(i.e. tensor train rank), answer open problem list cirac, garre-rubio p\\'erez-garc\\'ia [cirac et al., 2019].",
    "domain adapt (da) aim transfer knowledg label-rich sourc domain relat label-scarc target domain. convent da strategi align featur distribut two domains. recently, increas research focus self-train semi-supervis algorithm explor data structur target domain. however, bulk depend larg confid sampl order build reliabl pseudo labels, prototyp cluster centers. repres target data structur way would overlook huge low-confid samples, result sub-optim transfer bias toward sampl similar sourc domain. overcom issue, propos novel contrast learn method process low-confid samples, encourag model make use target data structur instanc discrimin process. specific, creat posit neg pair use low-confid samples, re-repres origin featur classifi weight rather directli util them, better encod task-specif semant information. furthermore, combin cross-domain mixup augment propos contrast loss. consequently, domain gap well bridg contrast learn intermedi represent across domains. evalu propos method unsupervis semi-supervis da settings, extens experiment result benchmark reveal method effect achiev state-of-the-art performance. code found https://github.com/zhyx12/mixlrco.",
    "defin disentangl far class-differ data point are, rel distanc among class-similar data points. maxim disentangl represent learning, obtain transform featur represent class membership data point preserved. class membership data point preserved, would featur represent space nearest neighbour classifi cluster algorithm would perform well. take advantag method learn better natur languag representation, employ text classif text cluster tasks. disentanglement, obtain text represent better-defin cluster improv text classif performance. approach test classif accuraci high 90.11% test cluster accuraci 88% ag news dataset, outperform baselin model -- without train trick regularization.",
    "chest x-ray one commonli use technolog medic diagnosis. mani deep learn model propos improv autom abnorm detect task type data. paper, propos differ approach base imag inpaint adversari train first introduc goodfellow et al. configur context encod model task train 1.1m 128x128 imag healthi x-rays. goal model reconstruct miss central 64x64 patch. model learn inpaint healthi tissue, test perform imag without abnormalities. discuss motiv result consid psnr, mse ssim score evalu metrics. addition, conduct 2afc observ studi show half time expert unabl distinguish real imag one reconstruct use model. comput visual pixel-wis differ sourc reconstruct images, highlight abnorm simplifi detect classif tasks.",
    "introduc librari geometr voxel featur cad surfac recognition/retriev tasks. featur includ local version intrins volum (the usual 3d volume, surfac area, integr mean gaussian curvature) close relat quantities. also comput haar wavelet statist distribut featur aggreg raw voxel features. appli featur object classif esb data set demonstr accur result small number shallow decis trees.",
    "detect emerg topic receiv renew interest motiv rapid growth social networks. convent term-frequency-bas approach may appropri context, inform exchang text also images, urls, videos. focu social aspect these networks. is, link user gener dynam intent unintent replies, mentions, retweets. propos probabl model mention behaviour social network user, propos detect emerg new topic anomali measur model. combin propos mention anomali score recent propos change-point detect techniqu base sequenti discount normal maximum likelihood (sdnml), kleinberg' burst model. aggreg anomali score hundr users, show detect emerg topic base reply/ment relationship social network posts. demonstr techniqu number real data set gather twitter. experi show propos mention-anomaly-bas approach detect new topic least earli convent term-frequency-bas approach, sometim much earlier keyword ill-defined.",
    "provid larger step-siz restrict gradient descent base algorithm (almost surely) avoid strict saddl points. particular, consid twice differenti (non-convex) object function whose gradient lipschitz constant l whose hessian well-behaved. prove probabl initi condit gradient descent step-siz 2/l converg strict saddl point, given one uniformli random initialization, zero. extend previou result sharp limit impos convex case. addition, argument hold case learn rate schedul given, either continu decay rate piece-wis constant schedule.",
    "advanc convolut neural network (cnns) variou vision applic attract lot attention. yet major cnn unabl satisfi strict requir real-world deployment. overcom this, recent popular network prune effect method reduc redund models. however, rank filter accord \"importance\" differ prune criteria may inconsistent. one filter could import accord certain criterion, unnecessari accord anoth one, indic criterion partial view comprehens \"importance\". motivation, propos novel framework integr exist filter prune criteria explor criteria diversity. propos framework contain two stages: criteria cluster filter import calibration. first, condens prune criteria via layerwis cluster base rank \"importance\" score. second, within cluster, propos calibr factor adjust signific select blend candid search optim blend criterion via evolutionari algorithm. quantit result cifar-100 imagenet benchmark show framework outperform state-of-the-art baselines, regrad compact model perform pruning.",
    "previou video salient object detect (vsod) approach mainli focus design fanci network achiev perform improvements. however, slow-down develop deep learn techniqu recently, may becom difficult anticip anoth breakthrough via fanci network solely. end, paper propos univers learn scheme get 3\\% perform improv state-of-the-art (sota) methods. major highlight method resort \"motion quality\"---a brand new concept, select sub-group video frame origin test set construct new train set. select frame new train set contain high-qual motions, salient object larg probabl success detect \"target sota method\"---th one want improve. consequently, achiev signific perform improv use new train set start new round network training. new round training, vsod result target sota method appli pseudo train objectives. novel learn scheme simpl yet effective, semi-supervis methodolog may larg potenti inspir vsod commun future.",
    "graph neural network achiev remark result problem structur data come black-box predictors. transfer exist explan techniques, occlusion, fail even remov singl node edg lead drastic chang graph. result graph differ train examples, caus model confus wrong explanations. thus, argu explic must use graph compliant distribut underli train data. coin properti distribut compliant explan (dce) present novel contrast gnn explan (coge) techniqu follow paradigm. experiment studi support efficaci coge.",
    "work theoret studi stochast neural networks, main type neural network use. prove width optim stochast neural network tend infinity, predict varianc train set decreas zero. theori justifi common intuit ad stochast model help regular model introduc averag effect. two common exampl theori relev neural network dropout bayesian latent variabl model special limit. result thu help better understand stochast affect learn neural network potenti design better architectur practic problems.",
    "process discoveri aim learn process model observ process behavior. user' perspective, discoveri algorithm work like black box. besid paramet tuning, interact user algorithm. interact process discoveri allow user exploit domain knowledg guid discoveri process. previously, increment discoveri approach introduc model, consid construction, get increment extend user-select process behavior. paper introduc novel approach addit allow user freez model part within model construction. frozen sub-model alter increment approach new behavior ad model. user thu steer discoveri algorithm. experi show freez sub-model lead higher qualiti models.",
    "use featur extract use deep convolut neural network (cnn) combin writer-depend (wd) svm classifi result signific improv perform handwritten signatur verif (hsv) compar previou state-of-the-art methods. work investig whether use cnn featur provid good result writer-independ (wi) hsv context, base dichotomi transform combin use svm writer-independ classifier. experi perform brazilian gpd dataset show (i) propos approach outperform wi-hsv method literature, (ii) global threshold scenario, propos approach abl outperform writer-depend method cnn featur brazilian dataset, (iii) user threshold scenario, result similar obtain writer-depend method cnn features.",
    "uncertainti machin learn gener taught gener knowledg machin learn cours curricula. paper propos short curriculum cours uncertainti machin learning, complement cours select use cases, aim trigger discuss let student play concept uncertainti program setting. use case cover concept output uncertainty, bayesian neural network weight distributions, sourc uncertainty, distribut detection. expect curriculum set use case motiv commun adopt import concept cours safeti ai.",
    "data-intens scienc increasingli reliant real-tim process capabl machin learn workflows, order filter analyz extrem volum data collected. especi true energi intens frontier particl physic bandwidth raw data exceed 100 tb/ heterogeneous, high-dimension data sourc hundr million individu sensors. paper, introduc new data-driven approach design optim high-throughput data filter trigger system use physic facil like larg hadron collid (lhc). concretely, goal design data-driven filter system minim run-tim cost determin data event keep, preserv (and potenti improv upon) distribut output gener hand-design trigger system. introduc key insight interpret predict model cost-sensit learn order account non-loc ineffici current paradigm construct cost-effect data filter trigger model compromis physic coverage.",
    "deep learn continu push state-of-the-art perform semant segment color (i.e., rgb) imagery; however, lack annot data mani remot sens sensor (i.e. hyperspectr imageri (hsi)) prevent research take advantag recent success. sinc gener sensor specif dataset time intens cost prohibitive, remot sens research embrac deep unsupervis featur extraction. although method push state-of-the-art perform current hsi benchmarks, mani tool readili access mani researchers. letter, introduc softwar pipeline, call earthmapper, semant segment non-rgb remot sens imagery. includ self-taught spatial-spectr featur extraction, variou standard deep learn classifiers, undirect graphic model post-processing. evalu earthmapp indian pine pavia univers dataset releas code public use.",
    "feder learn enabl entiti collabor learn share predict model keep train data locally. prevent data collect aggreg and, therefore, mitig associ privaci risks. however, still remain vulner variou secur attack malici particip aim degrad gener model, insert backdoors, infer participants' train data. paper present new feder learn scheme provid differ trade-off robustness, privacy, bandwidth efficiency, model accuracy. scheme use bias quantiz model updat henc bandwidth efficient. also robust state-of-the-art backdoor well model degrad attack even larg proport particip node malicious. propos practic differenti privat extens scheme protect whole dataset particip entities. show extens perform effici non-priv robust scheme, even stringent privaci requir less robust model degrad backdoor attacks. suggest possibl fundament trade-off differenti privaci robustness.",
    "formant track investig studi use tracker base dynam program (dp) deep neural net (dnns). use dp approach, six formant estim method first compared. six method includ linear predict (lp) algorithms, weight lp algorithm recent develop quasi-clos phase forward-backward (qcp-fb) method. qcp-fb gave best perform comparison. therefore, novel formant track approach, combin benefit deep learn signal process base qcp-fb, proposed. approach, formant predict dnn-base tracker speech frame refin use peak all-pol spectrum comput qcp-fb frame. result show propos dnn-base tracker perform better detect rate estim error lowest three formant compar refer formant trackers. compar popular wavesurfer, example, propos tracker gave reduct 29%, 48% 35% estim error lowest three formants, respectively.",
    "enhanc spatio-tempor observ distribut energi resourc (ders) crucial achiev secur effici oper distribut grids. paper put forth joint recoveri framework residenti load leverag complimentari strength heterogen measur real time. propos framework integr low-resolut smart meter data collect everi load node fast-sampl feeder-level measur limit number distribut phasor measur units. address lack data, exploit two key characterist load ders, name spars chang due infrequ activ applianc electr vehicl (evs) locat depend solar photovolta (pv) generation. accordingly, meaning regular term introduc cast convex load recoveri problem, simplifi reduc comput complexity. load recoveri solut util identifi ev charg event load node infer total behind-the-met pv output. numer test use real-world data demonstr effect propos approach enhanc visibl grid-edg ders.",
    "deep neural network (dnns) alreadi becom crucial comput approach reveal spatial pattern human brain; however, three major shortcom util dnn detect spatial pattern function magnet reson signals: 1). fulli connect architectur increas complex network structur difficult optim vulner overfitting; 2). requir larg train sampl result eras individual/minor pattern featur extraction; 3). hyperparamet requir tune manually, time-consuming. therefore, propos novel deep nonlinear matrix factor name deep matrix approxim nonlinear decomposit (demand) work take advantag shallow linear model, e.g., spars dictionari learn (sdl) dnns. first, propos demand employ non- connect multilayer-stack architectur easier optim compar canon dnns; furthermore, due effici architecture, train demand avoid overfit enabl recognit individual/minor featur base small dataset individu data; finally, novel rank estim techniqu introduc tune hyperparamet demand automatically. moreover, propos demand valid four peer methodolog via real function magnet reson imag data human brain. short, valid result demonstr demand reveal reproduc meta, canonical, sub-spati featur human brain effici peer methodologies.",
    "real world applic requir deal stochast like sensor nois predict uncertainty, formal specif desir behavior inher probabilistic. despit promis formal verif ensur reliabl neural networks, progress direct probabilist specif limited. direction, first introduc gener formul probabilist specif neural networks, captur probabilist network (e.g., bayesian neural networks, mc-dropout networks) uncertain input (distribut input aris sensor nois perturbations). propos gener techniqu verifi specif gener notion lagrangian duality, replac standard lagrangian multipli \"function multipliers\" arbitrari function activ given layer. show optim choic function multipli lead exact verif (i.e., sound complet verification), specif form multipliers, develop tractabl practic verif algorithms. empir valid algorithm appli bayesian neural network (bnns) mc dropout networks, certifi properti adversari robust robust detect out-of-distribut (ood) data. task abl provid significantli stronger guarante compar prior work -- instance, vgg-64 mc-dropout cnn train cifar-10, improv certifi auc (a verifi lower bound true auc) robust ood detect (on cifar-100) $0\\% \\rightarrow 29\\%$. similarly, bnn train mnist, improv robust accuraci $60.2\\% \\rightarrow 74.6\\%$.",
    "further, novel specif -- distribut robust ood detect -- improv certifi auc $5\\% \\rightarrow 23\\%$.",
    "design effect emerg respons manag (erm) system respond incid road accid major problem face communities. addit respond frequent incid day (about 240 million emerg medic servic call 5 million road accid us year), system also support respons natur hazards. recently, consist interest build decis support optim tool help emerg respond provid effici effect response. includ number principl subsystem implement earli incid detection, incid likelihood forecast strateg resourc alloc dispatch policies. paper, highlight key challeng provid overview approach develop team collabor commun partners.",
    "reinforc learn (rl) capabl manag wireless, energy-harvest iot node solv problem autonom manag non-stationary, resource-constrain settings. show state-of-the-art policy-gradi approach rl appropri iot domain outperform previou approaches. due abil model continu observ action spaces, well improv function approxim capability, new approach abl solv harder problems, permit reward function better align actual applic goals. show reward function use policy-gradi approach learn capabl policies, lead behavior appropri iot node less manual design effort, increas level autonomi iot.",
    "train error deep neural network degrad depth network increases, residu network appear exception. show main reason lyapunov stabil gradient descent algorithm: arbitrarili chosen step size, equilibria gradient descent like remain stabl parametr residu networks. present architectur pair residu network approxim larg class function decompos convex concav part. paramet model shown chang littl training, imperfect optim prevent overfit data lead solut small lipschitz constants, provid clue gener deep networks.",
    "risk human astronaut interplanetari distanc caus slow limit commun drive scientist pursu autonom approach explor distant planets, mars. portion explor mar conduct autonom collect analysi martian data spacecraft mar rover mar express orbiter. autonomi use mar explor spacecraft earth analyz data collect vehicl mainli consist machin learning, field artifici intellig algorithm collect data self-improv data. addit applic machin learn techniqu mar explor potenti resolv commun limit human risk interplanetari exploration. addition, analyz mar data machin learn potenti provid greater understand mar numer domain climate, atmosphere, potenti futur habitation. explor util machin learn techniqu mar exploration, paper first summar gener featur phenomena mar provid gener overview planet, elabor upon uncertainti mar would benefici explor understand, summar everi current previou usag machin learn techniqu explor mars, explor implement machin learn util futur mar explor missions, explor machin learn techniqu use earthli domain provid solut previous describ uncertainti mars.",
    "propos simpl recurs data-bas partit scheme produc piecewise-const piecewise-linear densiti estim intervals, show scheme determin optim $l_1$ minimax rate discret nonparametr classes.",
    "state-of-the-art machin learn model vulner small input perturb adversari constructed. adversari train one effect approach defend examples. show linear regress problems, adversari train formul convex problem. fact use show $\\ell_\\infty$-adversari train produc spars solut mani similar lasso method. similarly, $\\ell_2$-adversari train similar ridg regression. use robust regress framework analyz understand similar also point differences. finally, show adversari train behav differ regular method estim overparameter model (i.e., model paramet datapoints). minim sum three term regular solution, unlik lasso ridg regression, sharpli transit interpol mode. show suffici mani featur suffici small regular parameters, learn model perfectli interpol train data still exhibit good out-of-sampl performance.",
    "despit achiev strong perform semi-supervis node classif task, graph neural network (gnns) vulner adversari attacks, similar deep learn models. exist research focu develop either robust gnn model attack detect method adversari attack graphs. however, littl research attent paid potenti practic immun adversari attack graphs. paper, propos formul graph adversari immun problem, i.e., vaccin afford fraction node pairs, connect unconnected, improv certifi robust graph admiss adversari attack. propos effect algorithm, call advimmune, optim meta-gradi discret way circumv comput expens combinatori optim solv adversari immun problem. experi conduct two citat network one social network. experiment result demonstr propos advimmun method remark improv ratio robust node 12%, 42%, 65%, afford immun budget 5% edges.",
    "optim way deep reinforc learn (drl) agent explor learn set skill achiev uniform distribut states. follow this,w introduc distop, new model simultan learn divers skill focus improv reward skills. distop progress build discret topolog environ use unsupervis contrast loss, grow network goal-condit policy. use topology, state-independ hierarch polici select agent keep discov skill state space. turn, newli visit state allow improv learnt represent learn loop continues. experi emphas distop agnost ground state represent agent discov topolog environ whether state high-dimension binari data, images, propriocept inputs. demonstr paradigm competitiveon mujoco benchmark state-of-the-art algorithm single-task dens reward divers skill discovery. combin two aspects, showthat distop achiev state-of-the-art perform comparison hierarch reinforc learn (hrl) reward sparse. believ distop open new perspect show bottom-up skill discoveri combin represent learn unlock explor challeng drl.",
    "social media platform provid environ peopl freeli engag discussions. unfortunately, also enabl sever problems, onlin harassment. recently, googl jigsaw start project call perspective, use machin learn automat detect toxic language. demonstr websit also launched, allow anyon type phrase interfac instantan see toxic score [1]. paper, propos attack perspect toxic detect system base adversari examples. show adversari subtli modifi highli toxic phrase way system assign significantli lower toxic score it. appli attack sampl phrase provid perspect websit show consist reduc toxic score level non-tox phrases. exist adversari exampl harm toxic detect system serious undermin usability.",
    "identifi featur leak inform sensit attribut key challeng design inform obfusc mechanisms. paper, propos framework identifi information-leak featur via inform densiti estimation. here, featur whose inform densiti exceed pre-defin threshold deem information-leak features. featur identified, sequenti pass target obfusc mechan provabl leakag guarante term $\\mathsf{e}_\\gamma$-divergence. core mechan reli data-driven estim trim inform densiti propos novel estimator, name trim inform densiti estim (tide). use tide implement mechan three real-world datasets. approach use data-driven pipelin design obfusc mechan target specif features.",
    "deploy deep neural network applic requir high throughput extrem low latenc sever comput challenge, exacerb ineffici map comput hardware. present novel method design neural network topolog directli map highli effici fpga implementation. exploit equival artifici neuron quantiz inputs/output truth tables, train quantiz neural network directli convert netlist truth tables, subsequ deploy highli pipelinable, massiv parallel fpga circuit. however, neural network topolog requir care consider sinc hardwar cost truth tabl grow exponenti neuron fan-in. obtain smaller network whole netlist placed-and-rout onto singl fpga, deriv fan-in driven hardwar cost model guid topolog design, combin high sparsiti low-bit activ quantiz limit neuron fan-in. evalu approach two task high intrins throughput requir high-energi physic network intrus detection. show combin sparsiti low-bit activ quantiz result high-spe circuit small logic depth low lut cost, demonstr competit accuraci less 15 ns infer latenc throughput hundr million infer per second.",
    "soften label train dataset respect data represent frequent use improv train deep neural network (dnns). practic studi way leverag privileg inform distribut data, well-train learner soft classif output first obtain prior gener privileg information. solv chicken-egg problem, propos colam framework co-learn dnn soft label altern minim two object - (a) train loss subject soft label (b) object learn improv soft label - one end-to-end train procedure. perform extens experi compar propos method seri baselines. experi result show colam achiev improv perform mani task better test classif accuracy. also provid qualit quantit analys explain colam work well.",
    "work present onlin learning-bas control method improv trajectori track unman aerial vehicl use deep learn expert knowledge. propos method requir exact model system controlled, robust variat system dynam well oper uncertainties. learn divid two phases: offlin (pre-)train onlin (post-)training. former, convent control perform set trajectori and, base input-output dataset, deep neural network (dnn)-base control trained. latter, train dnn, mimic convent controller, control system. unlik exist paper literature, network still train differ set trajectori use train phase dnn. thank rule-base, contain expert knowledge, propos framework learn system dynam oper uncertainti real-time. experiment result show propos onlin learning-bas approach give better trajectori track perform compar offlin train network.",
    "ionic liquid (ils) import solvent sustain process predict activ coeffici (acs) solut il needed. recently, matrix complet method (mcms), transformers, graph neural network (gnns) shown high accuraci predict ac binari mixtures, superior well-establish models, e.g., cosmo-r unifac. gnn particularli promis learn molecular graph-to-properti relationship without pretraining, typic requir transformers, are, unlik mcms, applic molecul includ training. ils, however, gnn applic current missing. herein, present gnn predict temperature-depend infinit dilut ac solut ils. train gnn databas includ 40,000 ac valu compar state-of-the-art mcm. gnn mcm achiev similar high predict performance, gnn addit enabl high-qual predict ac solut contain il solut consid training.",
    "paper, studi equal design multiple-input multiple-output (mimo) orthogon frequenc divis multiplex (ofdm) system insuffici cyclic prefix (cp). particular, signal detect perform sever impair inter-carri interfer (ici) inter-symbol interfer (isi) multipath delay spread exceed length cp. tackl problem, deep learning-bas equal propos approxim maximum likelihood detection. inspir depend adjac subcarriers, comput effici joint detect scheme developed. employ propos equalizer, iter receiv also construct detect perform evalu simul measur multipath channels. result reveal propos receiv achiev signific perform improv compar two tradit baselin schemes.",
    "effici motion compens predict modern video codec highli depend avail refer pictures. occlus non-linear motion pose challeng motion compens often result high bit rate predict error. propos gener artifici refer pictur use deep recurr neural networks. conceptually, refer pictur time instanc current code pictur gener previous reconstruct convent refer pictures. base artifici refer pictures, propos complet code pipelin base hevc. use artifici refer pictur motion compens prediction, averag bd-rate gain 1.5% hevc achieved.",
    "kalman filter requir true paramet model solv optim state estim recursively. expect maxim (em) algorithm applic estim paramet model avail kalman filtering, em-kf algorithm. improv precis em-kf algorithm, author present state estim method combin long-short term memori network (lstm), transform em-kf algorithm framework encoder-decod sequenc sequenc (seq2seq). simul linear mobil robot model demonstr new method accurate. sourc code paper avail https://github.com/zshicode/deep-learning-based-state-estimation.",
    "linear system bedrock virtual numer computation. machin learn pose specif challeng solut system due scale, characterist structure, stochast central role uncertainti field. unifi earlier work propos class probabilist linear solver jointli infer matrix, invers solut matrix-vector product observations. class emerg fundament set desiderata constrain space possibl algorithm recov method conjug gradient certain conditions. demonstr incorpor prior spectral inform order calibr uncertainti experiment showcas potenti solver machin learning.",
    "job radiat oncologist deliv x-ray beam point toward tumor time avoid stomach intestines. mr-linac (magnet reson imag linear acceler systems), oncologist visual posit tumor allow precis dose accord tumor cell presenc vari day day. current job outlin posit stomach intestin adjust x-ray beam direct dose deliveri tumor avoid organs. time-consum labor-intens process easili prolong treatment 15 minut hour day unless deep learn method autom segment process. paper discuss autom segment process use deep learn make process faster allow patient get effect treatment.",
    "condit neural processes~(cnps) bridg neural network probabilist infer approxim function stochast process meta-learn settings. given batch non-{\\it i.i.d} function instantiations, cnp jointli optim in-instanti observ predict cross-instanti meta-represent adapt within gener reconstruct pipeline. challeng tie togeth two target distribut function observ scale high-dimension noisi spaces. instead, nois contrast estim might abl provid robust represent learn distribut match object combat inher limit gener models. light this, propos equip cnp 1) align predict encod ground-truth observation, 2) decoupl meta-represent adapt gener reconstruction. specifically, two auxiliari contrast branch set hierarchically, name in-instanti tempor contrast learning~({\\tt tcl}) cross-instanti function contrast learning~({\\tt fcl}), facilit local predict align global function consistency, respectively. empir show {\\tt tcl} captur high-level abstract observations, wherea {\\tt fcl} help identifi underli functions, turn provid effici representations. model outperform cnp variant evalu function distribut reconstruct paramet identif across 1d, 2d high-dimension time-series.",
    "paper propos framework call watt implementing, comparing, recombin open-end learn (oel) algorithms. motiv modular algorithm flexibility, watt atom compon oel system promot studi direct comparison approaches. examin implement three oel algorithms, paper introduc modul framework. hope watt enabl benchmark explor new type oel algorithms. repo avail \\url{https://github.com/aadharna/watts}",
    "ubiquit natur chatbot interact user gener enorm amount data. improv chatbot use data? self-feed chatbot improv ask natur languag feedback user dissatisfi respons use feedback addit train sample. however, user feedback case contain extran sequenc hinder use train sample. work, propos gener adversari model convert noisi feedback plausibl natur respons conversation. generator' goal convert feedback respons answer user' previou utter fool discrimin distinguish feedback natur responses. show augment origin train data modifi feedback respons improv origin chatbot perform 69.94% 75.96% rank correct respons personachat dataset, larg improv given origin model alreadi train 131k samples.",
    "present prototyp automat page turn system work directli real scores, i.e., sheet images, without symbol representation. system base multi-mod neural network architectur observ complet sheet imag page input, listen incom music performance, predict correspond posit image. use posit estim system, use simpl heurist trigger page turn event certain locat within sheet imag reached. proof concept combin system actual machin physic turn page command.",
    "estim mutual inform (mi) condit mutual inform (cmi) set sampl long-stand problem. recent line work area leverag approxim power artifici neural network shown improv convent methods. one import challeng new approach need obtain, given origin dataset, differ set sampl distribut accord specif product densiti function. particularli challeng estim cmi. paper, introduc new technique, base k nearest neighbor (k-nn), perform resampl deriv high-confid concentr bound sampl average. techniqu employ train neural network classifi cmi estim accordingly. propos three estim use techniqu prove consistency, make comparison similar approach literature, experiment show improv estim cmi term accuraci varianc estimators.",
    "due recent technic scientif advances, wealth inform hidden unstructur text data offline/onlin narratives, research articles, clinic reports. mine data properly, attribut innat ambiguity, word sens disambigu (wsd) algorithm avoid number difficulti natur languag process (nlp) pipeline. however, consid larg number ambigu word one languag technic domain, may encount limit constraint proper deploy exist wsd models. paper attempt address problem one-classifier-per-one-word wsd algorithm propos singl bidirect long short-term memori (blstm) network consid sens context sequenc work ambigu word collectively. evalu senseval-3 benchmark, show result model compar top-perform wsd algorithms. also discuss appli addit modif allevi model fault need train data.",
    "propos off-lin approach explicitli encod tempor pattern spatial differ type images, namely, gramian angular field markov transit fields. enabl use techniqu comput vision featur learn classification. use tile convolut neural network learn high-level featur individu gaf, mtf, gaf-mtf imag 12 benchmark time seri dataset two real spatial-tempor trajectori datasets. classif result approach competit state-of-the-art approach type data. analysi featur weight learn cnn explain approach works.",
    "consid stochast second-ord method minim smooth strongly-convex function interpol condit satisfi over-parameter models. condition, show regular subsampl newton method (r-ssn) achiev global linear converg adapt step-siz constant batch-size. grow batch size subsampl gradient hessian, show r-ssn converg quadrat rate local neighbourhood solution. also show r-ssn attain local linear converg famili self-concord functions. furthermore, analyz stochast bfg algorithm interpol set prove global linear convergence. empir evalu stochast l-bfg \"hessian-free\" implement r-ssn binari classif synthetic, linearly-separ dataset real dataset kernel mapping. experiment result demonstr fast converg methods, term number iter wall-clock time.",
    "recent find shown neural network gener also over-parametr regim zero train error. surprising, sinc complet tradit machin learn wisdom. empir studi fortifi find domain fine-grain imag classification. show larg convolut neural network million weight learn hand train sampl without imag augmentation, explicit regular pretraining. train architectur resnet018, resnet101 vgg19 subset difficult benchmark dataset caltech101, cub_200_2011, fgvcaircraft, flowers102 stanfordcar 100 class more, perform comprehens compar studi draw implic practic applic cnns. finally, show vgg19 140 million weight learn distinguish airplan motorbik 95% accuraci 20 sampl per class.",
    "deep neural network gener well unseen data though number paramet often far exce number train examples. recent propos complex measur provid insight understand generaliz neural network perspect pac-bayes, robustness, overparametrization, compress on. work, advanc understand relat network' architectur generaliz compress perspective. use tensor analysis, propos seri intuitive, data-depend easily-measur properti tightli character compress generaliz neural networks; thus, practice, gener bound outperform previou compression-bas ones, especi neural network use tensor weight kernel (e.g. cnns). moreover, intuit measur provid insight design neural network architectur properti favor better/guarante generalizability. experiment result demonstr propos measur properties, gener error bound match trend test error well. theoret analysi provid justif empir success limit widely-us tensor-bas compress approaches. also discov improv compress robust current neural network incorpor tensor oper via propos layer-wis structure.",
    "convolut neural network (cnns) commonli use imag classification. salienc method exampl approach use interpret cnn post hoc, identifi relev pixel predict follow gradient flow. even though cnn correctli classifi images, underli salienc map could erron mani cases. result skeptic valid model interpretation. propos novel approach train trustworthi cnn penal paramet choic result inaccur salienc map gener training. add penalti term inaccur salienc map produc predict label correct, penalti term accur salienc map produc predict label incorrect, regular term penal overli confid salienc maps. experi show increas classif performance, user engagement, trust.",
    "consid well-studi problem decompos vector time seri signal compon differ characteristics, smooth, periodic, nonnegative, sparse. propos simpl gener framework compon defin loss function (which includ constraints), signal decomposit carri minim sum loss compon (subject constraints). loss function neg log-likelihood densiti signal component, method coincid maximum posteriori probabl (map) estimation; also includ mani interest cases. give two distribut optim method comput decomposition, find optim decomposit compon class loss function convex, good heurist not. method requir mask proxim oper compon loss functions, gener well-known proxim oper handl miss entri argument. method distributed, i.e., handl compon separately. deriv tractabl method evalu mask proxim oper loss function that, knowledge, appear literature.",
    "need interpret deep learn (dl) model led, past years, prolifer work concern issue. among strategi aim shed light inform repres intern dl models, one consist extract symbol rule-bas machin connectionist model suppos approxim well behaviour. order better understand reason approxim strategi are, need know comput complex measur qualiti approximation. article, prove comput result relat problem extract finit state machin (fsm) base model train rnn languag models. precisely, we'll show following: (a) gener weight rnn-lm singl hidden layer relu activation: - equival problem pdfa/pfa/wfa weight first-ord rnn-lm undecidable; - corollary, distanc problem languag gener pdfa/pfa/wfa weight rnn-lm recursive; -the intersect dfa cut languag weight rnn-lm undecidable; - equival pdfa/pfa/wfa weight rnn-lm finit support exp-hard; (b) consist weight rnn-lm comput activ function: - tcheybechev distanc approxim decidable; - tcheybechev distanc approxim finit support np-hard. moreover, reduct techniqu 3-sat make latter fact easili generaliz rnn architectur (e.g. lstms/rnns), rnn finit precision.",
    "reservoir comput type dynam system arrang computation. typically, reservoir comput construct connect larg number nonlinear node network includ recurr connections. order achiev accur results, reservoir usual contain hundr thousand nodes. high dimension make difficult analyz reservoir comput use tool dynam system theory. additionally, need creat connect larg number nonlinear node make difficult design build analog reservoir comput faster consum less power digit reservoir computers. demonstr reservoir comput may divid two parts; small set nonlinear node (the reservoir), separ set time-shift reservoir output signals. time-shift output signal serv increas rank memori reservoir computer, set nonlinear node may creat embed input dynam system. use time-shift techniqu obtain excel perform opto-electron delay-bas reservoir comput small number virtual nodes. nonlinear node required, construct reservoir comput becom much easier, delay-bas reservoir comput oper much higher speeds.",
    "lpcnet effici vocod combin linear predict deep neural network modul keep comput complex low. work, present two techniqu reduc complexity, aim low-cost lpcnet vocoder-bas neural text-to-speech (tts) system. techniqu are: 1) sample-bunching, allow lpcnet gener one audio sampl per inference; 2) bit-bunching, reduc comput final layer lpcnet. propos bunch techniques, lpcnet, conjunct deep convolut tt (dctts) acoust model, show 2.19x improv baselin run-tim run mobil device, less 0.1 decreas tt mean opinion score (mos).",
    "non linear regress approach consist specif regress model incorpor latent process, allow variou polynomi regress model activ preferenti smoothly, introduc paper. model paramet estim maximum likelihood perform via dedic expecation-maxim (em) algorithm. experiment studi use simul real data set reveal good perform propos approach.",
    "over-parameter models, deepnet convnets, form class model routin adopt wide varieti applications, bayesian infer desir extrem challenging. variat infer offer tool tackl challeng scalabl way degre flexibl approximation, over-parameter model challeng due over-regular properti variat objective. inspir literatur kernel methods, particular structur approxim distribut random matrices, paper propos walsh-hadamard variat infer (whvi), use walsh-hadamard-bas factor strategi reduc parameter acceler computations, thu avoid over-regular issu variat objective. extens theoret empir analys demonstr whvi yield consider speedup model reduct compar techniqu carri approxim infer over-parameter models, ultim show advanc kernel method translat advanc approxim bayesian inference.",
    "propos complet unsupervis method understand audio scene observ random microphon arrang decompos scene constitu sourc rel presenc microphone. end, formul neural network architectur interpret nonneg tensor factor multi-channel audio recording. cluster learn network paramet correspond channel content, learn sources' individu spectral dictionari activ pattern time. method allow us leverag deep learn advanc like end-to-end training, also allow stochast minibatch train feasibl decompos realist audio scene intract decompos use standard methods. neural network architectur easili extens kind tensor factorizations.",
    "consid onlin learn minim regret unknown, episod markov decis process (mdps) continu state actions. develop variant ucrl posterior sampl algorithm employ nonparametr gaussian process prior gener across state action spaces. transit reward function true mdp member associ reproduc kernel hilbert space function induc symmetr psd kernel (frequentist setting), show algorithm enjoy sublinear regret bounds. bound term explicit structur paramet kernels, name novel gener inform gain metric kernel bandit, highlight influenc transit reward function structur learn performance. result applic multidimension state action space composit kernel structures, gener result literatur kernel bandits, adapt control parametr linear dynam system quadrat costs.",
    "propos improv estim multi-task averag problem, whose goal joint estim mean multipl distribut use separate, independ data sets. naiv approach take empir mean data set individually, wherea propos method exploit similar tasks, without relat inform known advance. first, data set, similar neighbor mean determin data multipl testing. naiv estim shrunk toward local averag neighbors. prove theoret approach provid reduct mean squar error. improv signific dimens input space large, demonstr \"bless dimensionality\" phenomenon. applic approach estim multipl kernel mean embeddings, play import role mani modern applications. theoret result verifi artifici real world data.",
    "nowaday govern privat agenc use remot sens imageri wide rang applic militari applic farm development. imag may panchromatic, multispectral, hyperspectr even ultraspectr terra bytes. remot sens imag classif one amongst signific applic world remot sensing. number imag classif algorithm prove good precis classifi remot sens data. but, late, due increas spatiotempor dimens remot sens data, tradit classif algorithm expos weak necessit research field remot sens imag classification. effici classifi need classifi remot sens imag extract information. experi supervis unsupervis classification. compar differ classif method performances. found mahalanobi classifi perform best classification.",
    "deep-learning-bas method differ applic shown vulner adversari examples. exampl make deploy model safety-crit task questionable. use deep neural network invers problem solver gener much excit medic imag includ ct mri, recent similar vulner also demonstr tasks. show invers problem solvers, one analyz studi effect adversari measurement-space, instead signal-spac previou work. paper, propos modifi train strategi end-to-end deep-learning-bas invers problem solver improv robustness. introduc auxiliari network gener adversari examples, use min-max formul build robust imag reconstruct networks. theoretically, show linear reconstruct scheme min-max formul result singular-value(s) filter regular solution, suppress effect adversari exampl occur ill-condit measur matrix. find linear network use propos min-max learn scheme inde converg solution. addition, non-linear compress sens (cs) reconstruct use deep networks, show signific improv robust use propos approach methods. complement theori experi cs two differ dataset evalu effect increas perturb train networks. find behavior ill-condit well-condit measur matric qualit different.",
    "product recommend system reli embed method repres variou features. imped challeng practic larg embed matrix incur substanti memori footprint serv number featur grow time. propos similarity-awar embed matrix compress method call saec address challenge. saec cluster similar featur within field reduc embed matrix size. saec also adopt fast cluster optim base featur frequenc drastic improv cluster time. implement evalu saec numerous, product distribut machin learn system tencent, 10-day worth featur data qq mobil browser. testb experi show saec reduc number embed vector two order magnitude, compress embed size ~27x, deliv auc log loss performance.",
    "object pose estim multipl import applications, robot grasp augment reality. present new method estim 6d pose object improv upon accuraci current propos still use real-time. method use rgb-d data input segment object estim pose. use neural network multipl head identifi object scene, gener appropri mask estim valu translat vector quaternion repres objects' rotation. head leverag pyramid architectur use featur extract featur fusion. conduct empir evalu use two common dataset area, compar state-of-the-art approaches, illustr capabl mpf6d. method use real-tim low infer time high accuracy.",
    "paper, present novel audio synthesizer, caesynth, base condit autoencoder. caesynth synthes timbr real-tim interpol refer sound share latent featur space, control pitch independently. show train condit autoencod base accuraci timbr classif togeth adversari regular pitch content allow timbr distribut latent space effect stabl timbr interpol pitch conditioning. propos method applic creation music cue also explor audio afford mix realiti base novel timbr mixtur environment sounds. demonstr experi caesynth achiev smooth high-fidel audio synthesi real-tim timbr interpol independ yet accur pitch control music cue well audio afford environment sound. python implement along gener sampl share online.",
    "recov spars condit independ graph data fundament problem machin learn wide applications. popular formul problem $\\ell_1$ regular maximum likelihood estimation. mani convex optim algorithm design solv formul recov graph structure. recently, surg interest learn algorithm directli base data, case, learn map empir covari spars precis matrix. however, challeng task case, sinc symmetr posit definit (spd) sparsiti matrix easi enforc learn algorithms, direct map data precis matrix may contain mani parameters. propos deep learn architecture, glad, use altern minim (am) algorithm model induct bias, learn model paramet via supervis learning. show glad learn compact effect model recov spars graph data.",
    "address challeng learn deep gener model (e.g.,th blurri variat auto-encod instabl train gener adversari networks, propos novel deep gener model, name wasserstein-wasserstein auto-encod (wwae). formul wwae minim penal optim transport target distribut gener distribution. notic prior $p_z$ aggreg posterior $q_z$ latent code z well captur gaussians, propos wwae util closed-form squar wasserstein-2 distanc two gaussian optim process. result, wwae suffer sampl burden comput effici leverag reparameter trick. numer result evalu multipl benchmark dataset includ mnist, fashion- mnist celeba show wwae learn better latent structur vae gener sampl better visual qualiti higher fid score vae gans.",
    "amount varieti energet research increases, machin awar topic identif necessari streamlin futur research pipelines. makeup automat topic identif process consist creat document represent perform classification. however, implement process energet research impos new challenges. energet dataset contain mani scientif term necessari understand context document may requir complex document representations. secondly, predict classif must understand trust chemist within pipeline. work, studi trade-off predict accuraci interpret implement three document embed method vari comput complexity. accuraci results, also introduc local interpret model-agnost explan (lime) predict provid local understand predict valid classifi decis team energet experts. studi carri novel label energet dataset creat valid team energet experts.",
    "work rigor analys assumpt inher black-box optimis hyper-paramet tune tasks. result bayesmark benchmark indic heteroscedast non-stationar pose signific challeng black-box optimisers. base findings, propos heteroscedast evolutionari bayesian optimis solver (hebo). hebo perform non-linear input output warping, admit exact margin log-likelihood optimis robust valu learn parameters. demonstr hebo' empir efficaci neurip 2020 black-box optimis challenge, hebo place first. upon analysis, observ hebo significantli outperform exist black-box optimis 108 machin learn hyperparamet tune task compris bayesmark benchmark. find indic major hyper-paramet tune task exhibit heteroscedast non-stationarity, multi-object acquisit ensembl pareto front solut improv queri configurations, robust acquisit maximis afford empir advantag rel non-robust counterparts. hope find may serv guid principl practition bayesian optimisation. code made avail https://github.com/huawei-noah/hebo.",
    "structur predict energi network (spens) simple, yet express famili structur predict model (belang mccallum, 2016). energi function candid structur output given deep network, predict form gradient-bas optimization. paper present end-to-end learn spens, energi function discrimin train back-propag gradient-bas prediction. experience, approach substanti accur structur svm method belang mccallum (2016), allow us use sophist non-convex energies. provid collect techniqu improv speed, accuracy, memori requir end-to-end spens, demonstr power method 7-scene imag denois conll-2005 semant role label tasks. both, inexact minim non-convex spen energi superior baselin method use simplist energi function minim exactly.",
    "extend concept transfer learning, wide appli modern machin learn algorithms, emerg context hybrid neural network compos classic quantum elements. propos differ implement hybrid transfer learning, focu mainli paradigm pre-train classic network modifi augment final variat quantum circuit. approach particularli attract current era intermediate-scal quantum technolog sinc allow optim pre-process high dimension data (e.g., images) state-of-the-art classic network emb select set highli inform featur quantum processor. present sever proof-of-concept exampl conveni applic quantum transfer learn imag recognit quantum state classification. use cross-platform softwar librari pennylan experiment test high-resolut imag classifi two differ quantum computers, respect provid ibm rigetti.",
    "revisit notion individu fair propos dwork et al. central challeng operation approach difficulti elicit human specif similar metric. paper, propos operation individu fair reli human specif distanc metric. instead, propos novel approach elicit leverag side-inform equal deserv individu counter subordin social groups. model knowledg fair graph, learn unifi pairwis fair represent (pfr) data captur data-driven similar individu pairwis side-inform fair graph. elicit fair judgment varieti sources, includ human judgment two real-world dataset recidiv predict (compas) violent neighborhood predict (crime & communities). experi show pfr model operation individu fair practic viable.",
    "multilingu sequenc label task predict label sequenc use singl unifi model multipl languages. compar reli multipl monolingu models, use multilingu model benefit smaller model size, easier onlin serving, generaliz low-resourc languages. however, current multilingu model still underperform individu monolingu model significantli due model capac limitations. paper, propos reduc gap monolingu model unifi multilingu model distil structur knowledg sever monolingu model (teachers) unifi multilingu model (student). propos two novel kd method base structure-level information: (1) approxim minim distanc student' teachers' structur level probabl distributions, (2) aggreg structure-level knowledg local distribut minim distanc two local probabl distributions. experi 4 multilingu task 25 dataset show approach outperform sever strong baselin stronger zero-shot generaliz baselin model teacher models.",
    "approxim bayesian comput (abc) set techniqu bayesian infer likelihood intract sampl model possible. work present simpl yet effect abc algorithm base combin two classic abc approach --- regress abc sequenti abc. key idea rather learn posterior directly, first target anoth auxiliari distribut learn accur exist methods, subsequ learn desir posterior help gaussian copula. process, complex model chang adapt accord data hand. experi synthet dataset well three real-world infer task demonstr propos method fast, accurate, easi use.",
    "spike neural network (snn), individu neuron oper autonom commun neuron sparingli asynchron via spike signals. characterist render massiv parallel hardwar implement snn potenti power computer, albeit non von neumann one. one guarante snn comput solv import problem reliably? paper, formul mathemat model one snn configur spars code problem featur extraction. moder well-defin assumption, prove snn inde solv spars coding. best knowledge, first rigor result kind.",
    "exploit ultrafast irregular time seri gener laser delay feedback, previous demonstr scalabl algorithm solv multi-arm bandit (mab) problem util time-divis multiplex laser chao time series. although algorithm detect arm highest reward expectation, correct recognit order arm term reward expect achievable. here, present algorithm degre explor adapt control base confid interv repres estim accuraci reward expectations. demonstr numer approach improv arm order recognit accuraci significantly, along reduc depend reward environments, total reward almost maintain compar convent mab methods. studi appli sector order inform critical, effici alloc resourc inform commun technology.",
    "gener paraphrases, is, differ variat sentenc convey meaning, import yet challeng task nlp. automat gener paraphras util mani nlp task like question answering, inform retrieval, convers system name few. paper, introduc iter refin gener paraphras within vae base gener framework. current sequenc gener model lack capabl (1) make improv sentenc generated; (2) rectifi error made decoding. propos techniqu iter refin output use multipl decoders, one attend output sentenc gener previou decoder. improv current state art result significantli - 9% 28% absolut increas meteor score quora question pair mscoco dataset respectively. also show qualit exampl re-decod approach gener better paraphras compar singl decod rectifi error make improv paraphras structure, induc variat introduc new semant coher information.",
    "address six differ classif task relat fine-grain build attributes: construct type, number floors, pitch geometri roof, facad material, occup class. tackl remot build analysi problem becam possibl recent due grow large-scal dataset urban scenes. end, introduc new benchmark dataset, consist 49426 imag (top-view street-view) 9674 buildings. photo assembled, togeth geometr metadata. dataset showcas variou real-world challenges, occlusions, blur, partial visibl objects, broad spectrum buildings. propos new project pool layer, creat unified, top-view represent top-view side view high-dimension space. allow us util build imageri metadata seamlessly. introduc layer improv classif accuraci -- compar highli tune baselin model -- indic suitabl build analysis.",
    "scientif literatur grow exponentially, profession abl cope current amount publications. text mine provid past method retriev extract inform text; however, approach ignor tabl figures. research done mine tabl data still integr approach mine would consid complex challeng table. research examin method extract numer (number patients, age, gender distribution) textual (advers reactions) inform tabl clinic literature. present requir analysi templat integr methodolog inform extract tabl clinic domain contain 7 steps: (1) tabl detection, (2) function processing, (3) structur processing, (4) semant tagging, (5) pragmat processing, (6) cell select (7) syntact process extraction. approach perform f-measur rang 82 92%, depend variable, task complexity.",
    "consid distribut reinforc learn set multipl agent separ explor environ commun experi central server. however, $\\alpha$-fract agent adversari report arbitrari fake information. critically, adversari agent collud fake data sizes. desir robustli identifi near-optim polici underli markov decis process presenc adversari agents. main technic contribut weighted-clique, novel algorithm robust mean estim batch problem, handl arbitrari batch sizes. build upon new estimator, offlin setting, design byzantine-robust distribut pessimist valu iter algorithm; onlin setting, design byzantine-robust distribut optimist valu iter algorithm. algorithm obtain near-optim sampl complex achiev superior robust guarante prior works.",
    "move object detect (mod) critic task autonom vehicl move object repres higher collis risk static ones. trajectori ego-vehicl plan base futur state detect move objects. quit challeng ego-mot model compens abl understand motion surround objects. work, propos real-tim end-to-end cnn architectur mod util spatio-tempor context improv robustness. construct novel time-awar architectur exploit tempor motion inform embed within sequenti imag addit explicit motion map use optic flow images.w demonstr impact algorithm kitti dataset obtain improv 8% rel baselines. compar algorithm state-of-the-art method achiev competit result kitti-mot dataset term accuraci three time better run-time. propos algorithm run 23 fp standard desktop gpu target deploy embed platforms.",
    "mani popular linear classifiers, logist regression, boosting, svm, train optim margin-bas risk function. traditionally, risk function comput base label dataset. develop novel techniqu estim risk use unlabel data margin label distribution. prove propos risk estim consist high-dimension dataset demonstr synthet real-world data. particular, show estim use evalu classifi transfer learning, train classifi label data whatsoever.",
    "rise interest studi robust deep neural network classifi adversaries, advanc attack defenc techniqu activ developed. however, recent work focus discrimin classifiers, model condit distribut label given inputs. paper, propos investig deep bay classifier, improv classic naiv bay condit deep gener models. develop detect method adversari examples, reject input low likelihood gener model. experiment result suggest deep bay classifi robust deep discrimin classifiers, propos detect method effect mani recent propos attacks.",
    "feder learn (fl) excit new paradigm enabl train global model data gener local client nodes, without move client data central server. perform fl multi-access edg comput (mec) network suffer slow converg due heterogen stochast fluctuat comput power commun link qualiti across clients. recent work, code feder learn (cfl), propos mitig straggler speed train linear regress task assign redund comput mec server. code redund cfl comput exploit statist properti comput commun delays. develop codedfedl address difficult task extend cfl distribut non-linear regress classif problem multioutput labels. key innov work exploit distribut kernel embed use random fourier featur transform train task distribut linear regression. provid analyt solut load allocation, demonstr signific perform gain codedfedl experi benchmark dataset use practic network parameters.",
    "recent convolut neural network (cnns) led impress perform often suffer poor calibration. tend overconfident, model confid alway reflect underli true ambigu hardness. paper, propos angular visual hard (avh), score given normal angular distanc sampl featur embed target classifi measur sampl hardness. valid score in-depth extens scientif study, observ cnn model highest accuraci also best avh scores. agre earlier find state-of-art model improv classif harder examples. observ train dynam avh vastli differ compar train loss. specifically, avh quickli reach plateau sampl even though train loss keep improving. suggest need design better loss function target harder exampl effectively. also find avh statist signific correl human visual hardness. finally, demonstr benefit avh varieti applic self-train domain adapt domain generalization.",
    "optim feedback control given markov decis process (mdp) principl synthes valu polici iteration. however, system dynam reward function unknown, learn agent must discov optim control via direct interact environment. interact data gather commonli lead diverg toward danger uninform region state space unless addit regular measur taken. prior work propos bound inform loss measur kullback-leibl (kl) diverg everi polici improv step elimin instabl learn dynamics. paper, consid broader famili $f$-divergences, concret $\\alpha$-divergences, inherit benefici properti provid polici improv step close form time yield correspond dual object polici evaluation. entrop proxim polici optim view give unifi perspect compat actor-crit architectures. particular, common least-squar valu function estim coupl advantage-weight maximum likelihood polici improv shown correspond pearson $\\chi^2$-diverg penalty. actor-crit pair aris variou choic penalty-gener function $f$. concret instanti framework $\\alpha$-divergence, carri asymptot analysi solut differ valu $\\alpha$ demonstr effect diverg function choic common standard reinforc learn problems.",
    "report signific discoveri link deep convolut neural network (cnn) biolog vision fundament particl physics. model inform propag cnn propos via analog optic system, boson particl (i.e. photons) concentr 2d spatial resolut imag collaps focal point $1\\time 1=1$. 3d space $(x,y,t)$ defin $(x,y)$ coordin imag plane cnn layer $t$, princip ray $(0,0,t)$ run direct inform propag optic axi imag center pixel locat $(x,y)=(0,0)$, sharpest possibl spatial focu limit circl confus imag plane. novel insight model princip optic ray $(0,0,t)$ geometr equival medial vector posit orthant $i(x,y) \\in r^{n+}$ $n$-channel activ space, e.g. along greyscal (or luminance) vector $(t,t,t)$ $rgb$ colour space. inform thu concentr energi potenti $e(x,y,t)=\\|i(x,y,t)\\|^2$, which, particularli bottleneck layer $t$ gener cnns, highli concentr symmetr spatial origin $(0,0,t)$ exhibit well-known \"sombrero\" potenti boson particle. symmetri broken classification, bottleneck layer gener pre-train cnn model exhibit consist class-specif bia toward angl $\\theta \\in u(1)$ defin simultan imag plane activ featur space. initi observ valid hypothesi gener pre-train cnn activ map bare-bon memory-bas classif scheme, train tuning.",
    "train scratch use random $u(1)$ class label lead improv classif cases.",
    "work, give new parallel algorithm problem maxim non-monoton diminish return submodular function subject cardin constraint. desir accuraci $\\epsilon$, algorithm achiev $1/e - \\epsilon$ approxim use $o(\\log{n} \\log(1/\\epsilon) / \\epsilon^3)$ parallel round function evaluations. approxim guarante nearli match best approxim guarante known problem sequenti set number parallel round nearly-optim constant $\\epsilon$. previou algorithm achiev wors approxim guarante use $\\omega(\\log^2{n})$ parallel rounds. experiment evalu suggest algorithm obtain solut whose object valu nearli match valu obtain state art sequenti algorithms, outperform previou parallel algorithm number parallel rounds, iterations, solut quality.",
    "due mass advanc ubiquit technolog nowadays, new pervas method come practic provid new innov featur stimul research new human-comput interactions. paper present hand gestur recognit method util smartphone' built-in speaker microphones. propos system emit ultrason sonar-bas signal (inaud sound) smartphone' stereo speakers, receiv smartphone' microphon process via convolut neural network (cnn) hand gestur recognition. data augment techniqu propos improv detect accuraci three dual-channel input fusion method compared. first method merg dual-channel audio singl input spectrogram image. second method adopt earli fusion concaten dual-channel spectrograms. third method adopt late fusion two convect input branch process dual-channel spectrogram output merg last layers. experiment result demonstr promis detect accuraci six gestur present publicli avail dataset accuraci 93.58\\% baseline.",
    "emerg complex life earth often attribut arm race ensu huge number organ compet finit resources. present artifici intellig research environment, inspir human game genr mmorpg (massiv multiplay onlin role-play games, a.k.a. mmos), aim simul set microcosm. mmorpg real world alike, environ persist support larg variabl number agents. environ well suit studi large-scal multiag interaction: requir agent learn robust combat navig polici presenc larg popul attempt same. baselin experi reveal popul size magnifi incentiv develop skill behavior result agent outcompet agent train smaller populations. show polici agent unshar weight natur diverg fill differ nich order avoid competition.",
    "histori deep learn shown human-design problem-specif network greatli improv classif perform gener neural models. practic cases, however, choos optim architectur given task remain challeng problem. recent architecture-search method abl automat build neural model strong perform fail fulli appreci interact neural architectur weights. work investig problem disentangl role neural structur edg weights, show well-train architectur may need link-specif fine-tun weights. compar perform weight-fre network (in case binari network {0, 1}-valu weights) random, weight-agnostic, prune standard fulli connect networks. find optim weight-agnost network, use novel comput effici method translat hard architecture-search problem feasibl optim problem.mor specifically, look optim task-specif architectur optim configur binari network {0, 1}-valu weights, found approxim gradient descent strategy. theoret converg guarante propos algorithm obtain bound error gradient approxim practic perform evalu two real-world data sets. measur structur similar differ architectures, use novel spectral approach allow us underlin intrins differ real-valu network weight-fre architectures.",
    "time complet design cycl complex system rang consum electron hyperson vehicl reli rapid simulation-bas prototyping. latter typic involv high-dimension space possibl correl control variabl (cvs) quantiti interest (qois) non-gaussian possibl multimod distributions. develop model-agnostic, moment-independ global sensit analysi (gsa) reli differenti mutual inform rank effect cv qois. data requir information-theoret approach gsa met replac comput intens compon physics-bas model deep neural network surrogate. subsequently, gsa use explain network predictions, surrog deploy close design loops. view uncertainti quantif method interrog surrogate, framework compat wide varieti black-box models. demonstr surrogate-driven mutual inform gsa provid use distinguish rank two applic interest energi storage. consequently, information-theoret gsa provid \"outer loop\" acceler product design identifi least sensit input direct perform subsequ optim appropri reduc paramet subspaces.",
    "paper propos canc, co-teach activ nois cancel method, appli spatial comput address deep learn train extrem noisi labels. deep learn algorithm success spatial comput land build footprint recognition. howev lot nois exist ground truth label due label collect spatial comput satellit imagery. exist method deal extrem label nois conduct clean sampl select util remain samples. techniqu wast due cost data retrieval. propos canc algorithm conserv high-cost train sampl also provid activ label correct better improv robust deep learn extrem noisi labels. demonstr effect canc build footprint recognit spatial computing.",
    "paramet reduct import topic deep learn due ever-increas size deep neural network model need train run resourc limit machines. despit mani effort area, rigor theoret guarante exist neural net compress method work. paper, provid provabl guarante hashing-bas paramet reduct method neural nets. first, introduc neural net compress scheme base random linear sketch (which usual implement effici via hashing), show sketch (smaller) network abl approxim origin network input data come smooth well-condit low-dimension manifold. sketch network also train directli via back-propagation. next, studi previous propos hashednet architectur show optim landscap one-hidden-lay hashednet local strong convex properti similar normal fulli connect neural network. complement theoret result empir verifications.",
    "calibr reservoir model observ transient data fluid pressur rate key task obtain predict model flow transport behaviour earth' subsurface. model calibr task, commonli refer \"histori matching\", formalis ill-pos invers problem aim find underli spatial distribut petrophys properti explain observ dynam data. use gener adversari network pretrain geostatist object-bas model repres distribut rock properti synthet model hydrocarbon reservoir. dynam behaviour reservoir fluid model use transient two-phas incompress darci formulation. invert underli reservoir properti first model properti distribut use pre-train gener model use adjoint equat forward problem perform gradient descent latent variabl control output gener model. addit dynam observ data, includ well rock-typ constraint introduc addit object function. contribut show synthet test case, abl obtain solut invers problem optimis latent variabl space deep gener model, given set transient observ non-linear forward problem.",
    "fashion landmark function key point defin clothes, corner neckline, hemline, cuff. recent introduc effect visual represent fashion imag understanding. however, detect fashion landmark challeng due background clutters, human poses, scales. remov variations, previou work usual assum bound box cloth provid train test addit annotations, expens obtain inapplic practice. work address unconstrain fashion landmark detection, cloth bound box provid train test. end, present novel deep landmark network (dlan), bound box landmark jointli estim train iter end-to-end manner. dlan contain two dedic modules, includ select dilat convolut handl scale discrepancies, hierarch recurr spatial transform handl background clutters. evalu dlan, present large-scal fashion landmark dataset, name unconstrain landmark databas (uld), consist 30k images. statist show uld challeng exist dataset term imag scales, background clutters, human poses. extens experi demonstr effect dlan state-of-the-art methods. dlan also exhibit excel gener across differ cloth categori modalities, make extrem suitabl real-world fashion analysis.",
    "structur semant sentenc represent abstract mean represent (amrs) potenti use variou nlp tasks. however, qualiti automat pars vari greatli jeopard usefulness. mitig model accur rate amr qualiti absenc costli gold data, allow us inform downstream system incorpor parse' trustworthi select among differ candid parses. work, propos transfer amr graph domain images. allow us creat simpl convolut neural network (cnn) imit human judg task rate graph quality. experi show method rate qualiti accur strong baselines, sever qualiti dimensions. moreover, method prove effici reduc incur energi consumption.",
    "secur assess among fundament function power system operator. sheer complex power system exceed buses, however, make extrem comput demand task. emerg deep learn method abl handl immens amount data, infer valuabl inform appear promis alternative. paper two main contributions. first, inspir remark perform convolut neural network imag processing, repres first time power system snapshot 2-dimension images, thu take advantag wide rang deep learn method avail imag processing. second, train deep neural network larg databas nesta 162-bu system assess n-1 secur small-sign stability. find approach 255 time faster standard small-sign stabil assessment, correctli determin unsaf point 99% accuracy.",
    "filter key compon modern convolut neural network (cnns). however, sinc cnn usual over-parameterized, pre-train network alway contain invalid (unimportant) filters. filter rel small $l_{1}$ norm contribut littl output (\\textbf{reason}). filter prune remov invalid filter effici consideration, tend reactiv improv represent capabl cnns. paper, introduc filter graft (\\textbf{method}) achiev goal. activ process graft extern inform (weights) invalid filters. better perform grafting, develop novel criterion measur inform filter adapt weight strategi balanc graft inform among networks. graft operation, network fewer invalid filter compar initi state, enpow model represent capacity. meanwhile, sinc graft oper reciproc network involved, find graft may lose inform valid filter improv invalid filters. gain univers improv valid invalid filters, compens graft distil (\\textbf{cultivation}) overcom drawback graft . extens experi perform classif recognit task show superior method. code avail \\textcolor{black}{\\emph{https://github.com/fxmeng/filter-grafting}}.",
    "model compress method reduc model complex premis maintain accept performance, thu promot applic deep neural network resourc constrain environments. despit great success, select suitabl compress method design detail compress scheme difficult, requir lot domain knowledg support, friendli non-expert users. make user easili access model compress scheme best meet needs, paper, propos automc, effect automat tool model compression. automc build domain knowledg model compress deepli understand characterist advantag compress method differ settings. addition, present progress search strategi effici explor pareto optim compress scheme accord learn prior knowledg combin histor evalu information. extens experiment result show automc provid satisfi compress scheme within short time, demonstr effect automc.",
    "major challeng real-world reinforc learn (rl) sparsiti reward feedback. often, avail intuit spars reward function indic whether task complet partial fully. however, lack care designed, fine grain feedback impli exist rl algorithm fail learn accept polici reason time frame. larg number explor action polici perform get use feedback learn from. work, address challeng problem develop algorithm exploit offlin demonstr data gener sub-optim behavior polici faster effici onlin rl spars reward settings. propos algorithm, call learn onlin guidanc offlin (logo) algorithm, merg polici improv step addit polici guidanc step use offlin demonstr data. key idea obtain guidanc - imit - offlin data, logo orient polici manner sub-optim policy, yet abl learn beyond approach optimality. provid theoret analysi algorithm, provid lower bound perform improv learn episode. also extend algorithm even challeng incomplet observ setting, demonstr data contain censor version true state observation.",
    "demonstr superior perform algorithm state-of-the-art approach number benchmark environ spars reward censor state. further, demonstr valu approach via implement logo mobil robot trajectori track obstacl avoidance, show excel performance.",
    "model daytim chang high resolut photographs, e.g., re-rend scene differ illumin typic day, night, dawn, challeng imag manipul task. present high-resolut daytim translat (hidt) model task. hidt combin gener image-to-imag model new upsampl scheme allow appli imag translat high resolution. model demonstr competit result term commonli use gan metric human evaluation. importantly, good perform come result train dataset still landscap imag daytim label available. result avail https://saic-mdal.github.io/hidt/.",
    "recently, deep learn base imag deblur well developed. however, exploit detail imag featur deep learn framework alway requir mass parameters, inevit make network suffer high comput burden. solv problem, propos lightweight multiinform fusion network (lmfn) imag deblurring. propos lmfn design encoder-decod architecture. encod stage, imag featur reduc variou smallscal space multi-scal inform extract fusion without larg amount inform loss. then, distil network use decod stage, allow network benefit residu learn remain suffici lightweight. meanwhile, inform fusion strategi distil modul featur channel also carri attent mechanism. fuse differ inform propos approach, network achiev state-of-the-art imag deblur result smaller number paramet outperform exist method model complexity.",
    "present deep reinforc learning-bas artifici intellig agent could provid optim develop plan given basic descript reservoir rock/fluid properti minim comput cost. artifici intellig agent, compris convolut neural network, provid map given state reservoir model, constraints, econom condit optim decis (drill/do drill well location) taken next stage defin sequenti field develop plan process. state reservoir model defin use paramet appear govern equat two-phas flow. feedback loop train process refer deep reinforc learn use train artifici intellig agent capability. train entail million flow simul vari reservoir model descript (structural, rock fluid properties), oper constraints, econom conditions. paramet defin reservoir model, oper constraints, econom condit randomli sampl defin rang applicability. sever algorithm treatment introduc enhanc train artifici intellig agent. appropri training, artifici intellig agent provid optim field develop plan instantli new scenario within defin rang applicability. approach advantag tradit optim algorithm (e.g., particl swarm optimization, genet algorithm) gener use find solut specif field develop scenario typic generaliz differ scenarios.",
    "consid problem learn stabil state nois probabl approxim correct (pac) framework aaronson (2007) learn quantum states. noiseless setting, algorithm problem recent given rocchetto (2018), noisi case left open. motiv approach nois toler classic learn theory, introduc statist queri (sq) model pac-learn quantum states, prove algorithm model inde resili common form noise, includ classif depolar noise. prove exponenti lower bound learn stabil state sq model. even outsid sq model, prove learn stabil state nois gener hard learn pariti nois (lpn) use classic examples. result posit problem learn stabil state natur quantum analogu classic problem learn parities: easi noiseless setting, seemingli intract even simpl form noise.",
    "work introduc expertmatcher, method autom deep learn model select use autoencoders. specifically, interest perform infer data sourc distribut across mani client use pretrain expert ml network central server. expertmatch assign relev model(s) central server given client' data representation. allow resource-constrain client develop countri util relev ml model given task without evalu perform ml model. method gener benefici setup local client numer central expert ml models.",
    "light-weight convolut neural network (cnns) de-facto mobil vision tasks. spatial induct bias allow learn represent fewer paramet across differ vision tasks. however, network spatial local. learn global representations, self-attention-bas vision trans-form (vits) adopted. unlik cnns, vit heavy-weight. paper, ask follow question: possibl combin strength cnn vit build light-weight low latenc network mobil vision tasks? toward end, introduc mobilevit, light-weight general-purpos vision transform mobil devices. mobilevit present differ perspect global process inform transformers, i.e., transform convolutions. result show mobilevit significantli outperform cnn- vit-bas network across differ task datasets. imagenet-1k dataset, mobilevit achiev top-1 accuraci 78.4% 6 million parameters, 3.2% 6.2% accur mobilenetv3 (cnn-based) deit (vit-based) similar number parameters. ms-coco object detect task, mobilevit 5.7% accur mobilenetv3 similar number parameters. sourc code open-sourc avail at: https://github.com/apple/ml-cvnet",
    "feder learn emerg learn paradigm allow train model sampl distribut across larg network client respect privaci commun restrictions. despit success, feder learn face sever challeng relat decentr nature. work, develop novel algorithm procedur theoret speedup guarante simultan handl two hurdles, name (i) data heterogeneity, i.e., data distribut vari substanti across clients, (ii) system heterogeneity, i.e., comput power client could differ significantly. method reli idea represent learn theori find global common represent use clients' data learn user-specif set paramet lead person solut client. furthermore, method mitig effect straggler adapt select client base comput characterist statist significance, thu achieving, first time, near optim sampl complex provabl logarithm speedup. experiment result support theoret find show superior method altern person feder scheme system data heterogen environments.",
    "long short-term memori (lstm) recurr neural network (rnns) reli gate signals, driven function weight sum least 3 components: (i) one adapt weight matrix multipli incom extern input vector sequence, (ii) one adapt weight matrix multipli previou memory/st vector, (iii) one adapt bia vector. effect, augment simpl recurr neural network (srnns) structur addit \"memori cell\" incorpor 3 gate signals. standard lstm structur compon encompass redund overli increas parameterization. paper, system introduc variant lstm rnns, refer slim lstms. variant express aggress reduc parameter achiev comput save and/or speedup (training) performance---whil necessarili retain (valid accuracy) perform compar standard lstm rnn.",
    "multi-task learn (mtl) model shown robustness, effectiveness, effici transfer learn knowledg across tasks. real industri applic web content classification, multipl classif task predict input text web article. however, serv time, exist multitask transform model prompt adaptor base approach need conduct n forward pass n task o(n) comput cost. tackl problem, propos scalabl method achiev stronger perform close o(1) comput cost via one forward pass. illustr real applic usage, releas multitask dataset news topic style classification. experi show propos method outperform strong baselin glue benchmark news dataset. code dataset publicli avail https://bit.ly/mtop-code.",
    "collabor learn allow particip jointli train model without data sharing. updat model parameters, central server broadcast model paramet clients, client send updat direct gradient server. data leav client device, commun gradient paramet leak client' privacy. attack infer clients' privaci gradient paramet develop prior work. simpl defens dropout differenti privaci either fail defend attack serious hurt test accuracy. propos practic defens call double-blind collabor learn (dbcl). high-level idea appli random matrix sketch paramet (aka weights) re-gener random sketch iteration. dbcl prevent client conduct gradient-bas privaci infer effect attacks. dbcl work attacker' perspective, sketch effect random nois outweigh signal. notably, dbcl much increas comput commun cost hurt test accuraci all.",
    "increasingli complex, non-linear world-earth system model use describ dynam biophys earth system socio-econom socio-cultur world human societi interactions. identifi pathway toward sustain futur model inform polici maker wider public, e.g. pathway lead robust mitig danger anthropogen climat change, challeng wide investig task field climat research broader earth system science. problem particularli difficult constraint avoid transgress planetari boundari social foundat need taken account. work, propos combin recent develop machin learn techniques, name deep reinforc learn (drl), classic analysi trajectori world-earth system. base concept agent-environ interface, develop agent gener abl act learn variabl manag environ model earth system. demonstr potenti framework appli drl algorithm two styliz world-earth system models. conceptually, explor therebi feasibl find novel global govern polici lead safe oper space constrain certain planetari socio-econom boundaries. artifici intellig agent learn time specif mix tax carbon emiss subsidi renew crucial relev find world-earth system trajectori sustain long term.",
    "paper, propos attention-bas classifi predict multipl emot given sentence. model imit human' two-step procedur sentenc understand effect repres classifi sentences. emoji-to-mean preprocess extra lexicon utilization, improv model performance. train evalu model data provid semeval-2018 task 1-5, sentenc sever label among 11 given sentiments. model achiev 5-th/1-th rank english/spanish respectively.",
    "gener graph-structur data requir learn underli distribut graphs. yet, challeng problem, previou graph gener method either fail captur permutation-invari properti graph cannot suffici model complex depend node edges, crucial gener real-world graph molecules. overcom limitations, propos novel score-bas gener model graph continuous-tim framework. specifically, propos new graph diffus process model joint distribut node edg system stochast differenti equat (sdes). then, deriv novel score match object tailor propos diffus process estim gradient joint log-dens respect component, introduc new solver system sde effici sampl revers diffus process. valid graph gener method divers datasets, either achiev significantli superior competit perform baselines. analysi show method abl gener molecul lie close train distribut yet violat chemic valenc rule, demonstr effect system sde model node-edg relationships. code avail https://github.com/harryjo97/gdss.",
    "adapt stochast gradient method adagrad gain popular particular train deep neural networks. commonli use studi variant maintain diagon matrix approxim second order inform accumul past gradient use tune step size adaptively. certain situat full-matrix variant adagrad expect attain better performance, howev high dimens comput impractical. present ada-lr radagrad two comput effici approxim full-matrix adagrad base random dimension reduction. abl captur depend featur achiev similar perform full-matrix adagrad much smaller comput cost. show regret ada-lr close regret full-matrix adagrad up-to exponenti smaller depend dimens diagon variant. empirically, show ada-lr radagrad perform similarli full-matrix adagrad. task train convolut neural network well recurr neural networks, radagrad achiev faster converg diagon adagrad.",
    "paper studi offlin imit learn (il) agent learn imit expert demonstr without addit onlin environ interactions. instead, learner present static offlin dataset state-action-next state transit tripl potenti less profici behavior policy. introduc model-bas il offlin data (milo): algorithm framework util static dataset solv offlin il problem effici theori practice. theory, even behavior polici highli sub-optim compar expert, show long data behavior polici provid suffici coverag expert state-act trace (and necess global coverag entir state-act space), milo provabl combat covari shift issu il. complement theori results, also demonstr practic implement approach mitig covari shift benchmark mujoco continu control tasks. demonstr behavior polici whose perform less half expert, milo still success imit extrem low number expert state-act pair tradit offlin il method behavior clone (bc) fail completely. sourc code provid https://github.com/jdchang1/milo.",
    "present systemat effici solut observ enhanc root-caus diagnosi post-silicon system-on-chip (socs) valid divers usag scenarios. model specif interact flow typic applic messag selection. method messag select optim flow specif coverag trace buffer utilization. defin diagnosi problem identifi buggi trace outlier bug-fre trace inliers/norm behaviors, use unsupervis learn algorithm outlier detection. instead direct applic machin learn algorithm trace data use signal raw features, use featur engin transform raw featur sophist featur use domain specif operations. engin featur highli relev diagnosi task gener appli across hardwar designs. present debug root caus analysi subtl post-silicon bug industry-scal opensparc t2 soc. achiev trace buffer util 98.96\\% flow specif coverag 94.3\\% (average). diagnosi method abl diagnos 66.7\\% bug took 847$\\times$ less diagnosi time compar manual debug diagnosi precis 0.769.",
    "fundament question regard galact center excess (gce) whether underli structur point-lik smooth. debate, often frame term millisecond pulsar annihil dark matter (dm) origin emission, await conclus resolution. work weigh problem use bayesian graph convolut neural networks. simul data, neural network (nn) abl reconstruct flux inner galaxi emiss compon averag $\\sim$0.5%, compar non-poissonian templat fit (nptf). appli actual $\\textit{fermi}$-lat data, find nn estim flux fraction background templat consist nptf; however, gce almost entir attribut smooth emission. suggestive, claim definit resolut gce, nn tend underestim flux point-sourc peak near 1$\\sigma$ detect threshold. yet techniqu display robust number systematics, includ reconstruct inject dm, diffus mismodeling, unmodel north-south asymmetries. nn hint smooth origin gce present, refin argu bayesian deep learn well place resolv dm mystery.",
    "visibl light communication~(vlc) system provid illumin data communication, also indoor monitor servic effect differ event creat receiv optic signal properli tracked. purpose, channel state inform vlc receiv comput equal subcarri ofdm signal also reus train unsupervis learn classifier. way, differ cluster creat collect csi data, could map relev event to-be-monitor indoor environments, presenc new object given posit chang posit given object. compar supervis learn algorithms, propos approach need add tag train data, simplifi notabl implement machin learn classifier. practic valid monitor approach done aid software-defin vlc link base ofdm, copi intens modul signal come phosphor-convert led captur pair photodetectors~(pds). perform evalu experiment vlc-base monitor demo achiev posit accuraci few-centimeter-range, without necess deploy larg number sensor and/or ad vlc-enabl sensor object to-be-tracked.",
    "estim value-at-risk (var) larg portfolio asset import task financi institutions. joint log-return asset price often project latent space much smaller dimension, use variat autoencod (vae) estim var natur suggestion. ensur bottleneck structur autoencod learn sequenti data, use tempor vae (tempvae) avoid auto-regress structur observ variables. however, low signal- to-nois ratio financi data combin auto-prun properti vae typic make use vae prone posterior collapse. therefore, propos use anneal regular mitig effect. result, auto-prun tempva work properli also result excel estim result var beat classic garch-typ histor simul approach appli real data.",
    "broaden adopt machin learn enterpris increas pressur strict govern cost-effect performance, particular common consequenti step model storag inference. rdbm provid natur start point, given matur infrastructur fast data access processing, along support enterpris featur (e.g., encryption, auditing, high-availability). take advantag above, need address key concern: in-rdbm score ml model match (outperform?) perform dedic frameworks? answer posit build raven, system leverag nativ integr ml runtim (i.e., onnx runtime) deep within sql server, unifi intermedi represent (ir) enabl advanc cross-optim ml db operators. optim space, discov excit research opportun combin db/compiler/ml thinking. initi evalu real data demonstr perform gain 5.5x nativ integr ml sql server, 24x cross-optimizations--w demonstr raven live confer talk.",
    "graph embed essenti graph mine tasks. preval graph data real-world applications, mani method propos recent year learn high-qual graph embed vector variou type graphs. however, exist method usual randomli select neg sampl origin graph enhanc train data without consid noise. addition, method focu explicit graph structur cannot fulli captur complex semant edg variou relationship asymmetry. order address issues, propos robust gener framework adversari graph embed base gener adversari networks. inspir gener adversari network, propos robust gener framework adversari graph embedding, name age. age gener fake neighbor node enhanc neg sampl implicit distribution, enabl discrimin gener jointli learn node' robust gener representation. base framework, propos three model handl three type graph data deriv correspond optim algorithms, i.e., ug-ag dg-age undirect direct homogen graphs, respectively, hin-ag heterogen inform networks. extens experi show method consist significantli outperform exist state-of-the-art method across multipl graph mine tasks, includ link prediction, node classification, graph reconstruction.",
    "introduc recurr neural network model work memori combin short-term long-term components. e short-term compon model use gate reservoir model train hold valu input stream gate signal on. e long-term compon model use conceptor order store inner tempor pattern (that correspond values). combin two compon obtain model inform go long-term memori short-term memori vice-versa show standard oper conceptor allow combin long-term memori describ effect short-term memory.",
    "calcul effici schedul work neural network compil difficult task. mani paramet account posit advers affect schedul depend configur - work share distribut targets, subdivis tensor fit memory, toggl enabl optimizations, etc. traditionally, neural network compil determin set valu build graph choic choos path minim 'cost'. choic correspond cost usual determin algorithm craft engin deep knowledg target platform. however, amount option avail compil large, difficult ensur model consist produc optim schedul scenarios, whilst still complet compil accept timeframe. paper present 'vpunn' - neural network-bas cost model train low-level task profil consist outperform state-of-the-art cost model intel' line vpu processors.",
    "propos cluster-bas quantiz method convert pre-train full precis weight ternari weight minim impact accuracy. addition, also constrain activ 8-bit thu enabl sub 8-bit full integ infer pipeline. method use smaller cluster n filter common scale factor minim quantiz loss, also maxim number ternari operations. show cluster size n=4 resnet-101, achiev 71.8% top-1 accuracy, within 6% best full precis result replac ~85% multipl 8-bit accumulations. use method 4-bit weight achiev 76.3% top-1 accuraci within 2% full precis result. also studi impact size cluster perform accuracy, larger cluster size n=64 replac ~98% multipl ternari oper introduc signific drop accuraci necessit fine tune paramet retrain network lower precision. address also train low-precis resnet-50 8-bit activ ternari weight pre-initi network full precis weight achiev 68.9% top-1 accuraci within 4 addit epochs. final quantiz model run full 8-bit comput pipeline, potenti 16x improv perform compar baselin full-precis models.",
    "depth percept key compon autonom system interact real world, deliveri robots, warehous robots, self-driv cars. task autonom robot 3d object recognition, simultan local map (slam), path plan navigation, requir form 3d spatial information. depth percept long-stand research problem comput vision robot long history. mani approach use deep learning, rang structur motion, shape-from-x, monocular, binocular, multi-view stereo, yield accept results. however, sever shortcom method requir expens hardware, need supervis train data, ground truth data comparison, disregard occlusion. order address shortcomings, work propos new deep convolut gener adversari network architecture, call y-gan, use data three camera estim depth map frame multi-camera video stream.",
    "regress models, wide use engin applic financi forecasting, vulner target malici attack train data poisoning, adversari manipul predictions. previou work attempt address problem reli assumpt natur attack/attack overestim knowledg learner, make impractical. introduc novel local intrins dimension (lid) base measur call n-lid measur local deviat given data point' lid respect neighbors. show n-lid distinguish poison sampl normal sampl propos n-lid base defens approach make assumpt attacker. extens numer experi benchmark datasets, show propos defens mechan outperform state art defens term predict accuraci (up 76% lower mse compar undefend ridg model) run time.",
    "paper provid theoret explan cluster aspect nonneg matrix factor (nmf). prove even without impos orthogon sparsiti constraint basi and/or coeffici matrix, nmf still give cluster results, thu provid theoret support mani works, e.g., xu et al. [1] kim et al. [2], show superior standard nmf cluster method.",
    "learn raw data input, thu limit need manual featur engineering, one key compon mani success applic machin learn methods. machin learn problem often formul data natur translat vector represent suitabl classifiers, data sources, exampl cybersecurity, natur repres divers file unifi hierarch structure, xml, json, protocol buffers. convert data vector (tensor) represent gener done manual featur engineering, laborious, lossy, prone human bia import particular features. mill jsongrind tandem libraries, fulli autom conversion. start arbitrari set json samples, creat differenti machin learn model capabl infer json sampl raw form.",
    "domain shift, mismatch train test data characteristics, caus signific degrad predict perform multi-sourc imag scenarios. medic imaging, heterogen population, scanner acquisit protocol differ site present signific domain shift challeng limit widespread clinic adopt machin learn models. harmon method aim learn represent data invari differ preval tool address domain shift, typic result degrad predict accuracy. paper take differ perspect problem: embrac disharmoni data design simpl effect framework tackl domain shift. key idea, base theoret arguments, build pretrain classifi sourc data adapt model new data. classifi fine-tun intra-sit domain adaptation. also tackl situat access ground-truth label target data; show one use auxiliari task adaptation; task employ covari age, gender race easi obtain nevertheless correl main task. demonstr substanti improv intra-sit domain adapt inter-sit domain gener large-scal real-world 3d brain mri dataset classifi alzheimer' diseas schizophrenia.",
    "glioblastoma common malign brain tumor adults. approxim 200000 peopl die year glioblastoma world. glioblastoma patient median surviv 12 month optim therapi 4 month without treatment. glioblastoma appear heterogen necrot mass irregular peripher enhancement, surround vasogen edema. current standard care includ surgic resection, radiotherapi chemotherapy, requir accur segment brain tumor subregions. effect treatment planning, vital identifi methyl statu promot methylguanin methyltransferas (mgmt), posit prognost factor chemotherapy. however, current method brain tumor segment tedious, subject scalable, current techniqu determin methyl statu mgmt promot involv surgic invas procedures, expens time consuming. henc press need develop autom tool segment brain tumor non-invas method predict methyl statu mgmt promoter, facilit better treatment plan improv surviv rate. creat integr diagnost solut power artifici intellig automat segment brain tumor subregion predict mgmt promot methyl status, use brain mri scans. ai solut proven larg dataset perform exceed current standard field test data teach file local neuroradiologists. solution, physician submit brain mri images, get segment methyl predict minutes, guid brain tumor patient effect treatment plan ultim improv surviv time.",
    "sol open-sourc librari scalabl onlin learn algorithms, particularli suitabl learn high-dimension data. librari provid famili regular spars onlin learn algorithm large-scal binari multi-class classif task high efficiency, scalability, portability, extensibility. sol implement c++, provid collect easy-to-us command-lin tools, python wrapper librari call user developers, well comprehens document beginn advanc users. sol practic machin learn toolbox, also comprehens experiment platform onlin learn research. experi demonstr sol highli effici scalabl large-scal machin learn high-dimension data.",
    "work describ develop differ model detect patronis condescend languag within extract news articl part semev 2022 competit (task-4). work explor differ model base pre-train roberta languag model coupl lstm cnn layers. best model achiev 15$^{th}$ rank f1-score 0.5924 subtask-a 12$^{th}$ subtask-b macro-f1 score 0.3763.",
    "address challeng open problem learn effect latent space symbol music data gener music modeling. focu leverag adversari regular flexibl natur mean imbu variat autoencod context inform concern music genr style. paper, show gaussian mixtur take account music metadata inform use effect prior autoencod latent space, introduc first music adversari autoencod (musae). empir analysi larg scale benchmark show model higher reconstruct accuraci state-of-the-art model base standard variat autoencoders. also abl creat realist interpol two music sequences, smoothli chang dynam differ tracks. experi show model organis latent space accordingli low-level properti music pieces, well emb latent variabl high-level genr inform inject prior distribut increas overal performance. allow us perform chang gener piec principl way.",
    "need precis estim student' academ perform emphas increas amount attent paid intellig tutor system (its). however, sinc label academ performance, test scores, collect outsid its, obtain label costly, lead label-scarc problem bring challeng take machin learn approach academ perform prediction. end, inspir recent advanc pre-train method natur languag process community, propos dpa, transfer learn framework discrimin pre-train task academ perform prediction. dpa pre-train two models, gener discriminator, fine-tun discrimin academ perform prediction. dpa' pre-train phase, sequenc interact token mask provid gener train reconstruct origin sequence. then, discrimin take interact sequenc mask token replac generator' outputs, train predict origin token sequence. compar previou state-of-the-art gener pre-train method, dpa sampl efficient, lead fast converg lower academ perform predict error. conduct extens experiment studi real-world dataset obtain multi-platform applic show dpa outperform previou state-of-the-art gener pre-train method reduct 4.05% mean absolut error robust increas label-scarcity.",
    "artifici intellig (ai) / machin learn (ml)-base system wide sought-aft commerci solut autom augment core busi services. intellig system improv qualiti servic offer support scalabl automation. paper describ experi engin exploratori system assess qualiti essay suppli custom special recruit support service. problem domain challeng open-end customer-suppli sourc text consider scope ambigu error, make model analysi hard build. also need incorpor special busi domain knowledg intellig process systems. address challenges, experi exploit number cloud-bas machin learn model compos application-specif process pipeline. design allow modif underli algorithm data improv techniqu becom available. describ design, main challeng faced, name keep check qualiti control models, test softwar deploy comput expens ml model cloud.",
    "often observ probabilist predict given machin learn model disagre averag actual outcom specif subset data, also known issu miscalibration. respons unreli practic machin learn systems. example, onlin advertising, ad receiv click-through rate predict 0.1 popul user actual click rate 0.15. cases, probabilist predict fix system deployed. paper, first introduc new evalu metric name field-level calibr error measur bia predict sensit input field decision-mak concerns. show exist post-hoc calibr method limit improv new field-level metric non-calibr metric auc score. end, propos neural calibration, simpl yet power post-hoc calibr method learn calibr make full use field-awar inform valid set. present extens experi five large-scal datasets. result show neural calibr significantli improv uncalibr predict common metric neg log-likelihood, brier score auc, well propos field-level calibr error.",
    "scientif collabor almost everi disciplin mainli driven need share knowledge, expertise, pool resources. scienc becom complex encourag scientist involv collabor research project order better address challenges. highli interdisciplinari field rapidli evolv scientif landscape, artifici intellig call research special profil cover divers set skill expertise. understand gender aspect scientif collabor paramount importance, especi field artifici intellig attract larg investments. use social network analysis, natur languag processing, machin learn focus artifici intellig public period 2000 2019, work, comprehens investig effect sever drive factor acquir key posit scientif collabor network gender lens. found that, regardless gender, scientif perform term quantiti impact play crucial possess \"social researcher\" network. however, subtl differ observ femal male research acquir \"local influencer\" role.",
    "paper, introduc new task control text edition, take input long text, question, target answer, output minim modifi text, fit target answer. task import mani situations, chang conditions, consequences, properti legal document, chang key inform event news text. challenging, hard obtain parallel corpu training, need first find text posit chang decid chang them. construct new dataset wikibioct task base exist dataset wikibio (origin creat table-to-text generation). use wikibioct training, manual label test set testing. also propos novel evalu metric novel method solv new task. experiment result test set show propos method good fit novel nlp task.",
    "present work address theoret practic question domain deep learn high frequenc trading. state-of-the-art model random models, logist regressions, lstms, lstm equip attent mask, cnn-lstm mlp review compar tasks, featur space dataset, cluster accord pairwis similar perform metrics. underli dimens model techniqu henc investig understand whether intrins limit order book' dynamics. observ multilay perceptron perform compar better state-of-the-art cnn-lstm architectur indic dynam spatial tempor dimens good approxim lob' dynamics, necessarili true underli dimensions.",
    "vulner machin learn model adversari perturb motiv signific amount research broad umbrella adversari machin learning. sophist attack may caus learn algorithm learn decis function make decis poor predict performance. context, grow bodi literatur use local intrins dimension (lid), local metric describ minimum number latent variabl requir describ data point, detect adversari sampl subsequ mitig effects. research date tend focu use lid practic defenc method often without fulli explain lid detect adversari samples. paper, deriv lower-bound upper-bound lid valu perturb data point demonstr bounds, particular lower-bound, posit correl magnitud perturbation. hence, demonstr data point perturb larg amount would larg lid valu compar unperturb samples, thu justifi use prior literature. furthermore, empir valid demonstr valid bound benchmark datasets.",
    "anomali detect necessari proper safe oper large-scal system consist multipl devices, networks, and/or plants. system often character pair multivari datasets. detect anomali system local element(s) associ anomaly, one would need estim score quantifi anomal entir system well elements. however, trivial estim score consid chang relationship elements, strongli correl other. moreover, necessari estim score entir system element singl framework, order identifi relationship among score local element associ anomaly. here, develop new method quantifi anomal entir system element simultaneously. purpos paper threefold. first one propos new anomali detect method: doubl kernel score (dks). dk unifi framework entire-system anomali score element-wis anomali scoring. therefore, dk allow conduct simultan 1) anomali detect entir system 2) local identifi faulti element respons system anomaly. second purpos propos new kernel function: matrix kernel. matrix kernel defin gener matrices, might differ dimensions, allow conduct anomali detect system number element chang time. third purpos demonstr effect propos method experimentally.",
    "evalu propos method synthet real time seri data. result demonstr dk abl detect anomali local element associ successfully.",
    "one techniqu improv retriev effect search engin expand document term relat repres documents' content.from perspect question answer system, might compris question document potenti answer. follow observation, propos simpl method predict queri issu given document expand predict vanilla sequence-to-sequ model, train use dataset consist pair queri relev documents. combin method highly-effect re-rank component, achiev state art two retriev tasks. latency-crit regime, retriev result alon (without re-ranking) approach effect comput expens neural re-rank much faster.",
    "sensor-bas percept vehicl becom preval import enhanc road safety. autonom drive system use cameras, lidar, radar detect surround objects, human-driven vehicl use assist driver. however, environment percept individu vehicl limit coverag and/or detect accuracy. example, vehicl cannot detect object occlud moving/stat obstacles. paper, present cooper percept scheme deep reinforc learn enhanc detect accuraci surround objects. use deep reinforc learn select data transmit, scheme mitig network load vehicular commun network enhanc commun reliability. design, test, verifi cooper percept scheme, develop cooper & intellig vehicl simul (civs) platform, integr three softwar components: traffic simulator, vehicl simulator, object classifier. evalu scheme decreas packet loss therebi increas detect accuraci 12%, compar baselin protocol.",
    "propos novel graph convolut neural network could construct coarse, spars latent point cloud dense, raw point cloud. novel non-isotrop convolut oper defin irregular geometries, model reconstruct origin point cloud latent cloud fine details. furthermore, propos even possibl perform particl simul use latent cloud encod simul particl cloud (e.g. fluids), acceler particl simul process. model test shapenetcor dataset auto-encod limit latent dimens test synthesi dataset fluid simulation. also compar model state-of-the-art models, sever visual done intuit understand model.",
    "recommend system extens studi mani literatur past ubiquit onlin advertisement, shop industry/e-commerce, queri suggest search engines, friend recommend social networks. moreover, restaurant/music/product/movie/news/app recommend applic recommend system. small percent improv ctr predict accuraci mention add million dollar revenu advertis industry. click-through-r (ctr) predict special version recommend system goal predict whether user go click recommend item. content-bas recommend approach take account past histori user' behavior, i.e. recommend product user reaction them. so, person model recommend right item right user right time key build model. hand, so-cal collabor filter approach incorpor click histori user similar particular user, therebi help recommend come confid predict particular user leverag wider knowledg user share tast connect network users. project, interest build ctr predictor use graph neural network complement onlin learn algorithm model dynam interactions.",
    "frame problem binari classif task, evalu system offlin model (gnn, deep factor machines) test-auc 0.7417 onlin learn model test-auc 0.7585 use sub-sampl version criteo public dataset consist 10,000 data points.",
    "constrain real-world scenario challeng costli gener data, disciplin method acquir inform new data point fundament import effici train machin learn (ml) models. activ learn (al) subfield ml focus develop method iter econom acquir data strateg queri new data point use particular task. here, introduc pyrelational, open sourc librari al research. describ modular toolkit compat divers ml framework (e.g. pytorch, scikit-learn, tensorflow, jax). furthermore, help acceler research develop field, librari implement number publish method provid api access wide-rang benchmark dataset al task configur base exist literature. librari supplement expans set tutorials, demos, document help user get started. perform experi pyrel collect benchmark dataset showcas consider economi al provide. pyrel maintain use modern softwar engin practic - inclus contributor code conduct - promot long term librari qualiti utilisation.",
    "real-world scenarios, mani large-scal dataset often contain inaccur labels, i.e., noisi labels, may confus model train lead perform degradation. overcom issue, label nois learn (lnl) recent attract much attention, variou method propos design unbias risk estim noise-fre dataset combat label noise. among them, trend work base loss decomposit centroid estim (ldce) shown promis performance. however, exist lnl method base ldce design binari classification, directli extend multi-class situations. paper, propos novel multi-class robust learn method ldce, term \"mc-ldce\". specifically, decompos commonli adopt loss (e.g., mean squar loss) function label-depend part label-independ part, former influenc label noise. further, defin new form data centroid, transform recoveri problem label-depend part centroid estim problem. finally, critic examin mathemat expect clean data centroid given observ noisi set, centroid estim help build unbias risk estim multi-class learning. propos mc-ldce method gener applic differ type (i.e., linear nonlinear) classif models. experiment result five public dataset demonstr superior propos mc-ldce repres lnl method tackl multi-class label nois problem.",
    "even though well known relev comput problem differ algorithm may perform better differ class problem instances, research still focu determin singl best algorithm configur base aggreg result average. paper, propos integ program base approach build decis tree algorithm select problem. techniqu allow autom three crucial decisions: (i) discern import problem featur determin problem classes; (ii) group problem class (iii) select best algorithm configur class. evalu new approach, extens comput experi execut use linear program algorithm implement coin-or branch & cut solver across comprehens set instances, includ miplib benchmark instances. result exceed expectations. select singl best paramet set across instanc decreas total run time 22%, approach decreas total run time 40% averag across 10-fold cross valid experiments. result indic method gener quit well overfit.",
    "neural messag pass basic featur extract unit graph-structur data take account impact neighbor node featur network propag one layer next. model process interact particl system attract repuls forc allen-cahn forc aris model phase transition. system reaction-diffus process separ particl differ clusters. induc allen-cahn messag pass (acmp) graph neural network numer iter solut constitut messag pass propagation. mechan behind acmp phase transit particl enabl format multi-clust thu gnn predict node classification. acmp propel network depth hundr layer theoret proven strictli posit lower bound dirichlet energy. thu provid deep model gnn circumv common gnn problem oversmoothing. experi variou real node classif datasets, possibl high homophili difficulty, show gnn acmp achiev state art perform decay dirichlet energy.",
    "graphs, social networks, word co-occurr networks, commun networks, occur natur variou real-world applications. analyz yield insight structur society, language, differ pattern communication. mani approach propos perform analysis. recently, method use represent graph node vector space gain traction research community. survey, provid comprehens structur analysi variou graph embed techniqu propos literature. first introduc embed task challeng scalability, choic dimensionality, featur preserved, possibl solutions. present three categori approach base factor methods, random walks, deep learning, exampl repres algorithm categori analysi perform variou tasks. evalu state-of-the-art method common dataset compar perform one another. analysi conclud suggest potenti applic futur directions. final present open-sourc python librari developed, name gem (graph embed methods, avail https://github.com/palash1992/gem), provid present algorithm within unifi interfac foster facilit research topic.",
    "model base neural network machin learn see rise popular space physics. particular, forecast geomagnet indic neural network model becom popular field study. model evalu metric root-mean-squar error (rmse) pearson correl coefficient. however, classic metric sometim fail captur crucial behavior. show classic metric lacking, train neural network, use long short-term memori network, make forecast disturb storm time index origin time $t$ forecast horizon 1 6 hours, train omniweb data. inspect model' result correl coeffici rmse indic perform compar latest publications. however, visual inspect show predict made neural network behav similarli persist model. work, new method propos measur whether two time seri shift time respect other, persist model output versu observation. new measure, base dynam time warping, capabl identifi result made persist model show promis result confirm visual observ neural network' output. finally, differ methodolog train neural network explor order remov persist behavior results.",
    "present optim complet distil (ocd), train procedur optim sequenc sequenc model base edit distance. ocd efficient, hyper-paramet own, requir pretrain joint optim condit log-likelihood. given partial sequenc gener model, first identifi set optim suffix minim total edit distance, use effici dynam program algorithm. then, posit gener sequence, use target distribut put equal probabl first token optim suffixes. ocd achiev state-of-the-art perform end-to-end speech recognition, wall street journal librispeech datasets, achiev $9.3\\%$ wer $4.5\\%$ wer respectively.",
    "explain graph neural network (gnns) aim answer \"whi gnn made certain prediction? \", crucial interpret model prediction. featur attribut framework distribut gnn' predict input featur (e.g., edges), identifi influenti subgraph explanation. evalu explan (i.e., subgraph importance), standard way audit model predict base subgraph solely. however, argu distribut shift exist full graph subgraph, caus out-of-distribut problem. furthermore, in-depth causal analysis, find ood effect act confounder, bring spuriou associ subgraph import model prediction, make evalu less reliable. work, propos deconfound subgraph evalu (dse) assess causal effect explanatori subgraph model prediction. distribut shift gener intractable, employ front-door adjust introduc surrog variabl subgraphs. specifically, devis gener model gener plausibl surrog conform data distribution, thu approach unbias estim subgraph importance. empir result demonstr effect dse term explan fidelity.",
    "larg capac neural network enabl learn complex functions. avoid overfitting, network howev requir lot train data expens time-consum collect. common practic approach attenu overfit use network regular techniques. propos novel regular method progress penal magnitud activ training. combin activ signal produc neuron given layer form represent input imag featur space. propos regular represent last featur layer classif layers. method' effect gener analyz label random test cumul ablations. experiment result show advantag approach comparison commonly-us regular standard benchmark datasets.",
    "learn commun order share state inform activ problem area multi-ag reinforc learn (marl). credit assign problem, non-stationar commun environ creation influenc agent major challeng within research field need overcom order learn valid commun protocol. paper introduc novel multi-ag counterfactu commun learn (macc) method adapt counterfactu reason order overcom credit assign problem commun agents. secondly, non-stationar commun environ learn commun q-function overcom creat commun q-function use action polici agent q-function action environment. additionally, social loss function introduc order creat influenc agent requir learn valid commun protocol. experi show macc abl outperform state-of-the-art baselin four differ scenario particl environment.",
    "bank credit rate classifi bank differ level base publicli disclos intern information, serv import input financi risk management. however, domain expert vagu idea explor compar differ bank credit rate schemes. loos connect subject quantit analysi difficulti determin appropri indic weight obscur understand bank credit ratings. furthermore, exist model fail consid bank type appli unifi indic weight set banks. propos ratingvi assist expert explor compar differ bank credit rate schemes. support interact infer indic weight bank involv domain knowledg consid bank type analysi loop. conduct case studi real-world bank data verifi efficaci ratingvis. expert feedback suggest approach help better understand differ rate schemes.",
    "studi complex optim highli smooth convex functions. posit integ $p$, want find $\\epsilon$-approxim minimum convex function $f$, given oracl access function first $p$ derivatives, assum $p$th deriv $f$ lipschitz. recently, three independ research group (jiang et al., plmr 2019; gasnikov et al., plmr 2019; bubeck et al., plmr 2019) develop new algorithm solv problem $\\tilde{o}(1/\\epsilon^{\\frac{2}{3p+1}})$ oracl call constant $p$. known optim (up log factors) determinist algorithms, known lower bound random algorithm match bound. prove new lower bound match bound (up log factors), hold random algorithms, also quantum algorithms.",
    "stochast block model (sbm) one wide use gener model network data. mani continuous-tim dynam network model built upon assumpt sbm: edg event pair node condit independ given block commun memberships, prevent reproduc higher-ord motif triangl commonli observ real networks. propos multivari commun hawk (mulch) model, extrem flexibl community-bas model continuous-tim network introduc depend node pair use structur multivari hawk processes. fit model use spectral cluster likelihood-bas local refin procedure. find propos mulch model far accur exist model predict gener tasks.",
    "social bot refer autom account social network make attempt behav like human. graph neural network (gnns) massiv appli field social bot detection, huge amount domain expertis prior knowledg heavili engag state-of-th art approach design dedic neural network architectur specif classif task. involv overs node network layer model design, however, usual caus over-smooth problem lack embed discrimination. paper, propos rosgas, novel reinforc self-supervis gnn architectur search framework adapt pinpoint suitabl multi-hop neighborhood number layer gnn architecture. specifically, consid social bot detect problem user-centr subgraph embed classif task. exploit heterogen inform network present user connect leverag account metadata, relationships, behavior featur content features. rosga use multi-ag deep reinforc learn (rl) mechan navig search optim neighborhood network layer learn individu subgraph embed target user. nearest neighbor mechan develop acceler rl train process, rosga learn discrimin subgraph embed aid self-supervis learning. experi 5 twitter dataset show rosga outperform state-of-the-art approach term accuracy, train effici stability, better gener handl unseen samples.",
    "optim deep neural network (dnns) often suffer ill-condit problem. observ scaling-bas weight space symmetri properti rectifi nonlinear network caus neg effect. therefore, propos constrain incom weight neuron unit-norm, formul optim problem obliqu manifold. simpl yet effici method refer project base weight normal (pbwn) also develop solv problem. pbwn execut standard gradient updates, follow project updat weight back obliqu manifold. propos method properti regular collabor well commonli use batch normal technique. conduct comprehens experi sever widely-us imag dataset includ cifar-10, cifar-100, svhn imagenet supervis learn state-of-the-art convolut neural networks, inception, vgg residu networks. result show method abl improv perform dnn differ architectur consistently. also appli method ladder network semi-supervis learn permut invari mnist dataset, method outperform state-of-the-art methods: obtain test error 2.52%, 1.06%, 0.91% 20, 50, 100 label samples, respectively.",
    "articl review evalu model network evolut base notion structur diversity. show divers underli theme three principl network evolution: preferenti attach model, connect link prediction. show three cases, domin trend toward shrink divers apparent, theoret empirically. previou work, mani kind differ data model networks: social structure, navig structure, transport infrastructure, communication, etc. almost type network static structures, instead dynam system chang continuously. thus, import question concern trend observ network interpret term exist network models. show articl numer network characterist follow statist signific trend go either down, trend predict consid notion diversity. work extend previou work observ shrink network diamet measur cluster coefficient, power-law expon random walk return probability, justifi preferenti attach model link predict algorithms. evalu hypothesi experiment use divers collect twenty-seven tempor evolv real-world network datasets.",
    "natur languag process (nlp), import detect relationship two sequenc gener sequenc token given anoth observ sequence. call type problem model sequenc pair sequenc sequenc (seq2seq) map problems. lot research devot find way tackl problems, tradit approach reli combin hand-craft features, align models, segment heuristics, extern linguist resources. although great progress made, tradit approach suffer variou drawbacks, complic pipeline, labori featur engineering, difficulti domain adaptation. recently, neural network emerg promis solut mani problem nlp, speech recognition, comput vision. neural model power train end end, generalis well unseen examples, framework easili adapt new domain. aim thesi advanc state-of-the-art seq2seq map problem neural networks. explor solut three major aspects: investig neural model repres sequences, model interact sequences, use unpair data boost perform neural models. aspect, propos novel model evalu efficaci variou task seq2seq mapping.",
    "recurr neural network (rnns) base automat speech recognit nowaday becom preval mobil devic smart phones. however, previou rnn compress techniqu either suffer hardwar perform overhead due irregular signific accuraci loss due preserv regular hardwar friendliness. work, propos rtmobil leverag novel block-bas prune approach compil optim acceler rnn infer mobil devices. propos rtmobil first work achiev real-tim rnn infer mobil platforms. experiment result demonstr rtmobil significantli outperform exist rnn hardwar acceler method term infer accuraci time. compar prior work fpga, rtmobil use adreno 640 embed gpu gru improv energy-effici 40$\\times$ maintain infer time.",
    "condit gener adversari network (cgans) wide research gener class condit imag use singl generator. however, convent cgan techniques, still challeng gener learn condition-specif features, sinc standard convolut layer weight use regardless condition. paper, propos novel convolut layer, call condit convolut layer, directli gener differ featur map employ weight adjust depend conditions. specifically, condit convolut layer, weight condit simpl effect way filter-wis scale channel-wis shift operations. contrast convent methods, propos method singl gener effect handl condition-specif characteristics. experiment result cifar, lsun imagenet dataset show gener propos condit convolut layer achiev higher qualiti condit imag gener standard convolut layer.",
    "residu reinforc learn (rl) propos way solv challeng robot task adapt control action convent feedback control maxim reward signal. extend residu formul learn visual input spars reward use demonstrations. learn images, propriocept input spars task-complet reward relax requir access full state features, object target positions. addition, replac base control polici learn demonstr remov depend hand-engin control favour dataset demonstrations, provid non-experts. experiment evalu simul manipul task 6-dof ur5 arm 28-dof dexter hand demonstr residu rl demonstr abl gener unseen environ condit flexibl either behavior clone rl fine-tuning, capabl solv high-dimensional, sparse-reward task reach rl scratch.",
    "detect code clone crucial variou softwar engin tasks. particular, code clone detect signific use context analyz fix bug larg scale applications. however, prior works, machin learning-bas clone detection, may caus consider amount fals positives. paper, propos twin-finder, novel, closed-loop approach pointer-rel code clone detect integr machin learn symbol execut techniqu achiev precision. twin-find introduc clone verif mechan formal verifi two clone sampl inde clone feedback loop automat gener formal rule tune machin learn algorithm reduc fals positives. experiment result show twin-find swiftli identifi 9x code clone compar tree-bas clone detector, deckard remov averag 91.69% fals positives.",
    "sever method triclust three dimension data requir specif cluster size dimension. introduc certain degre arbitrariness. address issue, propos new method, name multi-slic cluster (msc) 3-order tensor data set. analyse, dimens tensor mode, spectral decomposit tensor slice, i.e. matrix. thus, defin similar measur matrix slice threshold (precision) parameter, that, identifi cluster. intersect partial cluster provid desir triclustering. effect algorithm shown synthet real-world data sets.",
    "frank-wolf algorithm method constrain optim reli linear minimizations, oppos projections. therefore, motiv put forward larg bodi work frank-wolf algorithm comput advantag solv linear minim instead projections. however, discuss support advantag often succinct incomplete. paper, review complex bound task sever set commonli use optimization. project method onto $\\ell_p$-ball, $p\\in\\left]1,2\\right[\\cup\\left]2,+\\infty\\right[$, birkhoff polytop also proposed.",
    "reinforc learn (rl) algorithm achiev state-of-the-art perform variou challeng tasks, easili encount catastroph forget interfer face lifelong stream information. paper, propos scalabl lifelong rl method dynam expand network capac accommod new knowledg prevent past memori perturbed. use dirichlet process mixtur model non-stationari task distribution, captur task related estim likelihood task-to-clust assign cluster task model latent space. formul prior distribut mixtur chines restaur process (crp) instanti new mixtur compon needed. updat expans mixtur govern bayesian non-parametr framework expect maxim (em) procedure, dynam adapt model complex without explicit task boundari heuristics. moreover, use domain random techniqu train robust prior paramet initi task model mixture, thu result model better gener adapt unseen tasks. extens experi conduct robot navig locomot domains, show method success facilit scalabl lifelong rl outperform relev exist methods.",
    "statist machin learn widespread applic variou domains. method includ probabilist algorithms, markov chain monte-carlo (mcmc), reli gener random number probabl distributions. algorithm comput expens convent processors, yet statist properties, name interpret uncertainti quantif (uq) compar deep learning, make attract altern approach. therefore, hardwar special adopt address shortcom convent processor run applications. paper, propos high-throughput acceler markov random field (mrf) inference, power model repres wide rang applications, use mcmc gibb sampling. propos tile architectur take advantag near-memori computing, memori optim tailor semant mrf. additionally, propos novel hybrid on-chip/off-chip memori system log scheme effici support uq. memori system design specif mrf model applic applic use probabilist algorithms. addition, dramat reduc off-chip memori bandwidth requirements. implement fpga prototyp propos architectur use high-level synthesi tool achiev 146mhz frequenc acceler 32 function unit intel arria 10 fpga. compar prior work fpga, acceler achiev 26x speedup. furthermore, propos memori system log scheme support uq reduc off-chip bandwidth 71% two applications.",
    "asic analysi 15nm show design 2048 function unit run 3ghz outperform gpu implement motion estim stereo vision nvidia rtx2080ti 120x-210x, occupi 7.7% area.",
    "paper propos transit motion tensor, data-driven framework creat novel physic accur transit outsid motion dataset. enabl simul charact adopt new motion skill effici robustli without modifi exist ones. given sever physic simul control special differ motions, tensor serv tempor guidelin transit them. queri tensor transit best fit user-defin preferences, creat unifi control capabl produc novel transit solv complex task may requir multipl motion work coherently. appli framework quadrup bipeds, perform quantit qualit evalu transit quality, demonstr capabl tackl complex motion plan problem follow user control directives.",
    "introduc delenox (deep learn novelti explorer), system autonom creat artifact constrain space accord evolv interesting criterion. delenox proce altern phase explor transformation. explor phases, version novelti search augment constraint handl search maxim divers artifact use given distanc function. transform phases, deep learn autoencod learn compress variat found artifact lower-dimension space. newli train encod use basi new distanc function, transform criteria next explor phase. current paper, appli delenox creation spaceship suitabl use two-dimension arcade-styl comput games, repres problem procedur content gener games. also situat delenox relat distinct exploratori transform creativity, relat schmidhuber' theori creativ drive compress progress.",
    "consid fair represent learn perspective, optim predictors, top data representation, ensur invari respect differ sub-groups. specifically, formul intuit bi-level optimization, represent learn outer-loop, invari optim group predictor updat inner-loop. moreover, propos bi-level object demonstr fulfil suffici rule, desir variou practic scenario commonli studi fair learning. besides, avoid high comput memori cost differenti inner-loop bi-level objective, propos implicit path align algorithm, reli solut inner optim implicit differenti rather exact optim path. analyz error gap implicit approach empir valid propos method classif regress settings. experiment result show consist better trade-off predict perform fair measurement.",
    "shap explan aim identifi featur contribut differ model predict specif input versu background distribution. recent studi shown manipul malici adversari produc arbitrari desir explanations. however, exist attack focu sole alter black-box model itself. paper, propos complementari famili attack leav model intact manipul shap explan use stealthili bias sampl data point use approxim expect w.r.t background distribution. context fair audit, show attack reduc import sensit featur explain differ outcom groups, remain undetected. result highlight manipul shap explan encourag auditor treat post-hoc explan skepticism.",
    "kalman filter key tool time-seri forecast analysis. show depend predict kalman filter past decay exponentially, whenev process nois non-degenerate. therefore, kalman filter may approxim regress recent observations. surprisingly, also show process nois essenti exponenti decay. process noise, may happen forecast depend past uniformly, make forecast difficult. base insight, devis on-lin algorithm improp learn linear dynam system (lds), consid recent observations. use decay result provid first regret bound w.r.t. kalman filter within learn lds. is, compar result algorithm best, hindsight, kalman filter given signal. also, algorithm practical: per-upd run-tim linear regress depth.",
    "common techniqu compress neural network comput $k$-rank $\\ell_2$ approxim $a_{k,2}$ matrix $a\\in\\mathbb{r}^{n\\tim d}$ correspond fulli connect layer (or embed layer). here, $d$ number neuron layer, $n$ number next one, $a_{k,2}$ store $o((n+d)k)$ memori instead $o(nd)$. $\\ell_2$-approxim minim sum everi entri power $p=2$ matrix $a - a_{k,2}$, among everi matrix $a_{k,2}\\in\\mathbb{r}^{n\\tim d}$ whose rank $k$. comput effici via svd, $\\ell_2$-approxim known sensit outlier (\"far-away\" rows). hence, machin learn use e.g. lasso regression, $\\ell_1$-regularization, $\\ell_1$-svm use $\\ell_1$-norm. paper suggest replac $k$-rank $\\ell_2$ approxim $\\ell_p$, $p\\in [1,2]$. provid practic provabl approxim algorithm comput $p\\geq1$, base modern techniqu comput geometry. extens experiment result glue benchmark compress bert, distilbert, xlnet, roberta confirm theoret advantage. example, approach achiev $28\\%$ compress roberta' embed layer $0.63\\%$ addit drop accuraci (without fine-tuning) averag task glue, compar $11\\%$ drop use exist $\\ell_2$-approximation. open code provid reproduc extend results.",
    "bitcoin, ever-grow popularity, demonstr extrem price volatil sinc origin. volatility, togeth decentralis nature, make bitcoin highli subject specul trade compar tradit assets. paper, propos multimod model predict extrem price fluctuations. model take input varieti correl assets, technic indicators, well twitter content. in-depth study, explor whether social media discuss gener public bitcoin predict power extrem price movements. dataset 5,000 tweet per day contain keyword `bitcoin' collect 2015 2021. dataset, call prebit, made avail online. hybrid model, use sentence-level finbert embeddings, pretrain financi lexicons, captur full content tweet feed model understand way. combin embed convolut neural network, built predict model signific market movements. final multimod ensembl model includ nlp model togeth model base candlestick data, technic indic correl asset prices. ablat study, explor contribut individu modalities. finally, propos backtest trade strategi base predict model vari predict threshold show use build profit trade strategi reduc risk `hold' move averag strategy.",
    "model-agnost meta learn (maml) current one domin approach few-shot meta-learning. albeit effectiveness, optim maml challeng due innat bilevel problem structure. specifically, loss landscap maml much complex possibl saddl point local minim empir risk minim counterpart. address challenge, leverag recent invent sharpness-awar minim develop sharpness-awar maml approach term sharp-maml. empir demonstr sharp-maml computation-effici variant outperform popular exist maml baselin (e.g., $+12\\%$ accuraci mini-imagenet). complement empir studi converg rate analysi gener bound sharp-maml. best knowledge, first empir theoret studi sharpness-awar minim context bilevel learning. code avail https://github.com/mominabbass/sharp-maml.",
    "studi gener properti ridg regress random featur statist learn framework. show first time $o(1/\\sqrt{n})$ learn bound achiev $o(\\sqrt{n}\\log n)$ random featur rather $o({n})$ suggest previou results. further, prove faster learn rate show might requir random features, unless sampl accord possibl problem depend distribution. result shed light statist comput trade-off larg scale kernel learning, show potenti effect random featur reduc comput complex keep optim gener properties.",
    "on-devic machin learn (ml) enabl train process exploit massiv amount user-gener privat data samples. enjoy benefit, inter-devic commun overhead minimized. end, propos feder distil (fd), distribut model train algorithm whose commun payload size much smaller benchmark scheme, feder learn (fl), particularli model size large. moreover, user-gener data sampl like becom non-iid across devices, commonli degrad perform compar case iid dataset. cope this, propos feder augment (faug), devic collect train gener model, therebi augment local data toward yield iid dataset. empir studi demonstr fd faug yield around 26x less commun overhead achiev 95-98% test accuraci compar fl.",
    "hybrid machin learn quantum physic caus essenti impact methodolog fields. inspir quantum potenti neural network, propos solv potenti schroding equat provid eigenstate, combin metropoli sampl deep neural network, dub metropoli potenti neural network (mpnn). loss function propos explicitli involv energi optim accur evaluation. benchmark harmon oscil hydrogen atom, mpnn show excel accuraci stabil predict potenti satisfi schroding equation, also eigen-energy. propos could potenti appli ab-initio simulations, invers solv partial differenti equat physic beyond.",
    "recurr neural network architectur excel process sequenc model depend differ timescales. recent introduc recurr weight averag (rwa) unit captur long term depend far better lstm sever challeng tasks. rwa achiev appli attent input comput weight averag full histori computations. unfortunately, rwa cannot chang attent assign previou timesteps, struggl carri consecut task task chang requirements. present recurr discount attent (rda) unit build rwa addit allow discount past. empir compar model rwa, lstm gru unit sever challeng tasks. task singl output rwa, rda gru unit learn much quicker lstm better performance. multipl sequenc copi task rda unit learn task three time quickli lstm gru unit rwa fail learn all. wikipedia charact predict task lstm perform best follow close rda unit. overal rda unit perform well sampl effici larg varieti sequenc tasks.",
    "hypothesi test import problem applic target localization, clinic trial etc. mani activ hypothesi test strategi oper two phases: explor phase verif phase. explor phase, select experi moder level confid true hypothesi achieved. subsequ experi design aim improv confid level hypothesi desir level. paper, focu verif phase. confid measur defin activ hypothesi test formul confid maxim problem infinite-horizon average-reward partial observ markov decis process (pomdp) setting. problem maxim confid condit particular hypothesi refer hypothesi verif problem. relationship hypothesi test verif problem established. verif problem formul markov decis process (mdp). optim solut verif mdp character simpl heurist adapt strategi verif propos base zero-sum game interpret kullback-leibl divergences. demonstr numer experi heurist perform better scenario compar exist method literature.",
    "studi propos effici neural network convolut layer classifi significantli class-imbalanc clinic data. data curat nation health nutrit examin survey (nhanes) goal predict occurr coronari heart diseas (chd). major exist machin learn model use class data vulner class imbal even adjust class-specif weights, simpl two-lay cnn exhibit resili imbal fair harmoni class-specif performance. order obtain signific improv classif accuraci supervis learn settings, common practic train neural network architectur massiv data thereafter, test result network compar smaller amount data. however, given highli imbalanc dataset, often challeng achiev high class 1 (true chd predict rate) accuraci test data size increases. adopt two-step approach: first, employ least absolut shrinkag select oper (lasso) base featur weight assess follow majority-vot base identif import features. next, import featur homogen use fulli connect layer, crucial step pass output layer success convolut stages. also propos train routin per epoch, akin simul anneal process, boost classif accuracy.",
    "despit 35:1 (non-chd:chd) ratio nhane dataset, investig confirm propos cnn architectur classif power 77% correctli classifi presenc chd 81.8% absenc chd case test data, 85.70% total dataset. ( (<1920 characters)pleas check paper full abstract)",
    "learn perform robot manipul polici challeng due high-dimension continu action complex physics-bas dynamics. allevi intellig choic action space. oper space control (osc) use effect task-spac control manipulation. nonetheless, strength depend underli model fidelity, prone failur model errors. work, propos osc adapt robust (oscar), data-driven variant osc compens model error infer relev dynam paramet onlin trajectories. oscar decompos dynam learn task-agnost task-specif phases, decoupl dynam depend robot extrins due environment. structur enabl robust zero-shot perform out-of-distribut rapid adapt signific domain shift addit finetuning. evalu method varieti simul manipul problems, find substanti improv array control baselines. result information, pleas visit https://cremebrule.github.io/oscar-web/.",
    "modern neural network architectur often gener well despit contain mani paramet size train dataset. paper explor gener capabl neural network train via gradient descent. develop data-depend optim gener theori leverag low-rank structur jacobian matrix associ network. result help demystifi train gener easier clean structur dataset harder noisi unstructur dataset well network size affect evolut train test error training. specifically, use control knob split jacobian spectum \"information\" \"nuisance\" space associ larg small singular values. show inform space learn fast one quickli train model zero train loss also gener well. nuisanc space train slower earli stop help gener expens bias. also show overal gener capabl network control well label vector align inform space. key featur result even constant width neural net provabl gener suffici nice datasets.",
    "conduct variou numer experi deep network corrobor theoret find demonstr that: (i) jacobian typic neural network exhibit low-rank structur larg singular valu mani small one lead low-dimension inform space, (ii) inform space learn fast label vector fall space, (iii) label nois fall nuisanc space imped optimization/generalization.",
    "optim acceler techniqu momentum play key role state-of-the-art machin learn algorithms. recently, gener vector sequenc extrapol techniques, regular nonlinear acceler (rna) scieur et al., propos shown acceler fix point iterations. contrast rna comput extrapol coeffici (approximately) set gradient object function zero extrapol point, propos direct approach, call direct nonlinear acceler (dna). dna, aim minim (an approxim of) function valu extrapol point instead. adopt regular approach regular design prevent model enter region function approxim less precise. comput cost dna compar rna, direct approach significantli outperform rna synthet real-world datasets. focu paper convex problems, obtain encourag result acceler train neural networks.",
    "understand structur dynam large-scal social, biolog technolog network, may use discov behavior role repres main connect pattern present time. paper, propos scalabl non-parametr approach automat learn structur dynam network individu nodes. role may repres structur behavior pattern center star, peripher nodes, bridg node connect differ communities. novel approach learn appropri structur role dynam arbitrari network track chang time. particular, uncov specif global network dynam local node dynam technological, communication, social network. identifi interest node network pattern stationari non-stationari roles, spikes/step role-membership (perhap indic anomalies), increasing/decreas role trends, among mani others. result indic node network distinct connect pattern non-stationari evolv consider time. overall, experi demonstr effect approach fast mine track dynam larg networks. furthermore, dynam structur represent provid basi build sophist model tool fast explor larg dynam networks.",
    "danger adversari attack unprotect uncrew aerial vehicl (uav) agent oper public growing. adopt ai-bas techniqu specif deep learn (dl) approach control guid uav benefici term perform add concern regard safeti techniqu vulner adversari attack caus chanc collis go agent becom confused. paper propos innov approach base explain dl method build effici detector protect dl scheme thu uav adopt potenti attacks. agent adopt deep reinforc learn (drl) scheme guidanc planning. form train deep determinist polici gradient (ddpg) prioritis experi replay (per) drl scheme utilis artifici potenti field (apf) improv train time obstacl avoid performance. adversari attack gener fast gradient sign method (fgsm) basic iter method (bim) algorithm reduc obstacl cours complet rate 80\\% 35\\%. realist synthet environ uav explain drl base plan guidanc includ obstacl adversari attack built. two adversari attack detector proposed. first one adopt convolut neural network (cnn) architectur achiev accuraci detect 80\\%. second detector develop base long short term memori (lstm) network achiev accuraci 91\\% much faster comput time compar cnn base detector.",
    "gener adversari net (gans) promis techniqu model distribut samples. howev well known gan train suffer instabl due natur maximin formulation. paper, explor way tackl instabl problem dualiz discriminator. start linear discrimin case conjug dualiti provid mechan reformul saddl point object maxim problem, gener discrimin 'dual gan' act concert. demonstr extend intuit non-linear formulations. gan linear discrimin approach abl remov instabl training, gan nonlinear discrimin approach provid altern commonli use gan train algorithm.",
    "present extens open neural network exchang (onnx) intermedi represent format repres arbitrary-precis quantiz neural networks. first introduc support low precis quantiz exist onnx-bas quantiz format leverag integ clipping, result two new backward-compat variants: quantiz oper format clip quantize-clip-dequant (qcdq) format. introduc novel higher-level onnx format call quantiz onnx (qonnx) introduc three new oper -- quant, bipolarquant, trunc -- order repres uniform quantization. keep qonnx ir high-level flexible, enabl target wider varieti platforms. also present util work qonnx, well exampl usag finn hls4ml toolchains. finally, introduc qonnx model zoo share low-precis quantiz neural networks.",
    "find cluster well-connect node graph extens studi problem graph-bas data analysis. mani applications, larg number distinct graph cluster object function algorithm alreadi propos analyzed. aid practition determin best cluster approach use differ applications, present new techniqu automat learn set cluster resolut parameters. paramet control size structur commun form optim gener object function. begin formal notion paramet fit function, measur well fix input cluster approxim solv gener cluster object specif resolut paramet value. reason assumptions, suit two key graph cluster applications, paramet fit function effici minim use bisection-lik method, yield resolut paramet fit well exampl clustering. view framework type single-shot hyperparamet tuning, abl learn good resolut paramet singl example. gener approach appli learn resolut paramet local global graph cluster objectives. demonstr util sever experi real-world data help learn resolut paramet given exampl clustering.",
    "work show differenti activ function necessari error backpropagation. deriv activ function replac iter tempor differenc use fix random feedback alignment. use fix random synapt feedback align iter tempor differenc transform tradit error backpropag biolog plausibl approach learn deep neural network architectures. big step toward integr stdp-base error backpropag deep learning.",
    "consid use machin learn hypothesi test emphasi target detection. classic model-bas solut reli compar likelihoods. sensit imperfect model often comput expensive. contrast, data-driven machin learn often robust yield classifi fix comput complexity. learn detector usual provid high accuraci low complex constant fals alarm rate (cfar) requir mani applications. close gap, propos add term loss function promot similar distribut detector null hypothesi scenario. experi show approach lead near cfar detector similar accuraci competitors.",
    "explor much learn noisi label audio music tagging. experi show care annot label result highest figur merit, even high amount noisi label contain enough inform success learning. artifici corrupt curat data allow us quantiz contribut noisi labels.",
    "refer express gener (reg) task gener contextu appropri refer entities. limit exist reg system reli entity-specif supervis training, mean cannot handl entiti seen training. study, address two ways. first, propos task setup specif test reg system' abil gener entiti seen training. second, propos profile-bas deep neural network model, profilereg, encod local context extern profil entiti gener refer realizations. model gener token learn choos gener pronouns, gener fix vocabulary, copi word profile. evalu model three differ split webnlg dataset, show outperform competit baselin set accord automat human evaluations.",
    "motivation: digit patholog laboratori digit slide scanner advanc deep learn approach object histolog assess result rapid progress field comput patholog (cpath) wide-rang applic medic pharmaceut research well clinic workflows. however, estim robust cpath model variat input imag open problem signific impact down-stream practic applicability, deploy accept approaches. furthermore, develop domain-specif strategi enhanc robust model prime import well. implement availability: work, propos first domain-specif robust evalu enhanc toolbox (reet) comput patholog applications. provid suit algorithm strategi enabl robust assess predict model respect special imag transform staining, compression, focusing, blurring, chang spatial resolution, bright variations, geometr chang well pixel-level adversari perturbations. furthermore, reet also enabl effici robust train deep learn pipelin comput pathology. reet implement python avail follow url: https://github.com/alexjfoote/reetoolbox. contact: fayyaz.minhas@warwick.ac.uk",
    "hybrid privat infer (pi) protocol, synergist util multi-parti comput (mpc) homomorph encryption, one promin techniqu pi. however, even state-of-the-art pi protocol bottleneck non-linear layers, especi activ functions. although standard non-linear activ function gener higher model accuracy, must process via costli garbled-circuit mpc primitive. polynomi activ process via beaver' multipl tripl mpc primit incur sever accuraci drop far. paper, propos accuraci preserv low-degre polynomi activ function (aespa) exploit hermit expans relu basis-wis normalization. appli aespa popular ml models, vggnet, resnet, pre-activ resnet, show infer accuraci compar standard model relu activation, achiev superior accuraci prior low-degre polynomi studies. appli all-relu baselin state-of-the-art delphi pi protocol, aespa show 42.1x 28.3x lower onlin latenc commun cost.",
    "practice, often explicit constraint represent decis accept applic machin learning. exampl may legal requir decis must favour particular group. altern represent data must identifi information. address two relat issu learn flexibl represent minim capabl adversari critic. adversari tri predict relev sensit variabl representation, minim perform adversari ensur littl inform represent sensit variable. demonstr adversari approach two problems: make decis free discrimin remov privat inform images. formul adversari model minimax problem, optim minimax object use stochast gradient altern min-max optimizer. demonstr abil provid discrimin free represent standard test problems, compar previou state art method fairness, show statist signific improv across cases. flexibl method shown via novel problem: remov annot images, unalign train exampl annot unannot images, priori knowledg form annot provid model.",
    "merit ensembl learn lie differ output mani individu model singl input, i.e., divers base models. high qualiti divers achiev model special differ subset whole dataset. moreover, model explicitli know subset specialized, opportun aris improv diversity. paper, propos advanc ensembl method, call auxiliari class base multipl choic learn (amcl), ultim special model framework multipl choic learn (mcl). advanc amcl origin three novel techniqu control framework differ directions: 1) concept auxiliari class provid distinct inform labels, 2) strategy, name memory-bas assignment, determin associ input models, 3) featur fusion modul achiev gener features. demonstr perform method compar variant mcl methods, conduct extens experi imag classif segment tasks. overall, perform amcl exce other public dataset train variou network member ensembles.",
    "gener linear estim (gle) problems, seek estim signal observ linear transform follow component-wise, possibl nonlinear noisy, channel. bayesian optim setting, gener approxim messag pass (gamp) known achiev optim perform gle. however, perform significantli degrad whenev mismatch assum true gener model, situat frequent encount practice. paper, propos new algorithm, name gener approxim survey propag (gasp), solv gle presenc prior model mis-specifications. prototyp example, consid phase retriev problem, show gasp outperform correspond gamp, reduc reconstruct threshold and, certain choic parameters, approach bayesian optim performance. furthermore, present set state evolut equat exactli character dynam gasp high-dimension limit.",
    "learning-aug algorithm -- which, tradit algorithm augment machine-learn predict -- emerg framework go beyond worst-cas analysis. overarch goal design algorithm perform near-optim predict accur yet retain certain worst-cas guarante irrespect accuraci predictions. framework success appli onlin problem cach predict use allevi uncertainties. paper introduc studi set learning-aug algorithm util predict parsimoniously. consid cach problem -- extens studi learning-aug set -- show one achiev quantit similar result use sublinear number predictions.",
    "novel fast semi-automat method segmentation, locat count blood cell imag proposed. method, threshold use separ nucleu parts. also use hough transform circl locat center white cells. locat count red cell perform use templat matching. make use find local maxima, label mean valu comput order shrink area obtain appli hough transform templat matching, singl pixel repres locat region. propos method fast comput number locat white cell accurately. also capabl locat count red cell small error.",
    "graph neural network (gnns) wide use variou graph-rel problem node classif graph classification, superior perform mainli establish natur node featur available. however, well understood gnn work without natur node features, especi regard variou way construct artifici ones. paper, point two type artifici node features,i.e., posit structur node features, provid insight appropri certain tasks,i.e., posit node classification, structur node classification, graph classification. extens experiment result 10 benchmark dataset valid insights, thu lead practic guidelin choic differ artifici node featur gnn non-attribut graphs. code avail https://github.com/zjzijielu/gnn-exp/.",
    "paper, propos neural phrase-to-phras machin translat (np$^2$mt). model use phrase attent mechan discov relev input (source) segment use decod gener output (target) phrases. also design effici dynam program algorithm decod segment allow model train faster exist neural phrase-bas machin translat method huang et al. (2018). furthermore, method natur integr extern phrase dictionari decoding. empir experi show method achiev compar perform state-of-th art method benchmark datasets. however, train test data differ distribut domains, method perform better.",
    "counterfactu regret minim (cfr) lead framework solv larg imperfect-inform games. converg equilibrium iter travers game tree. order deal extrem larg games, abstract typic appli run cfr. abstract game solv tabular cfr, solut map back full game. process problemat aspect abstract often manual domain specific, abstract algorithm may miss import strateg nuanc game, chicken-and-egg problem determin good abstract requir knowledg equilibrium game. paper introduc deep counterfactu regret minimization, form cfr obviat need abstract instead use deep neural network approxim behavior cfr full game. show deep cfr principl achiev strong perform larg poker games. first non-tabular variant cfr success larg games.",
    "increas demand scalabl algorithm capabl cluster analyz larg time seri datasets. kohonen self-organ map (som) type unsupervis artifici neural network visual cluster complex data, reduc dimension data, select influenti features. like cluster methods, som requir measur similar input data (in work time series). dynam time warp (dtw) one measure, top perform given accommod distort align time series. despit use clustering, dtw limit practic quadrat runtim complex length time seri data. address this, present new dtw-base cluster method, call somtim (a self-organ map time series), scale better run faster dtw-base cluster algorithms, similar perform accuracy. comput perform somtim stem abil prune unnecessari dtw comput som' train phase. also implement similar prune strategi k-mean comparison one top perform cluster algorithms. evalu prune effectiveness, accuracy, execut time scalabl 112 benchmark time seri dataset univers california, riversid classif archive. show similar accuracy, speed-up achiev somtim k-mean 1.8x average; however, rate vari 1x 18x depend dataset. somtim k-mean prune 43% 50% total dtw computations, respectively.",
    "appli somtim natur languag convers data collect part larg healthcar cohort studi patient-clinician seriou ill convers demonstr algorithm' util complex, tempor sequenc phenomena.",
    "multi task learn (mtl) effici leverag use inform contain multipl relat task help improv gener perform tasks. articl conduct larg dimension analysi simpl but, shall see, extrem power care tuned, least squar support vector machin (lssvm) version mtl, regim dimens $p$ data number $n$ grow larg rate. mild assumpt input data, theoret analysi mtl-lssvm algorithm first reveal \"suffici statistics\" exploit algorithm interact work. result demonstrate, strike consequence, standard approach mtl-lssvm larg suboptimal, lead sever effect neg transfer impair easili corrected. correct turn improv mtl-lssvm algorithm benefit addit data, theoret perform also analyzed. evidenc theoret sustain numer recent works, larg dimension result robust broad rang data distributions, present experi corroborate. specifically, articl report systemat close behavior theoret empir perform popular datasets, strongli suggest applic propos care tune mtl-lssvm method real data. fine-tun fulli base theoret analysi particular requir cross valid procedure. besides, report perform real dataset almost systemat outperform much elabor less intuit state-of-the-art multi-task transfer learn methods.",
    "studi learn dynam represent emerg recurr neural network train integr one multipl tempor signals. combin analyt numer investigations, character condit rnn n neuron learn integr d(n) scalar signal arbitrari duration. show, linear relu neurons, intern state live close d-dimension manifold, whose shape relat activ function. neuron therefor carries, variou degrees, inform valu integrals. discuss deep analog result concept mix select forg comput neuroscientist interpret cortic recordings.",
    "deep network success use classif model yield state-of-the-art result train larg number label samples. models, however, usual much less suit semi-supervis problem tendenc overfit easili train small amount data. work explor new train object target semi-supervis regim small subset label data. criterion base deep metric embed distanc relat within set label samples, togeth constraint embed unlabel set. final learn represent discrimin euclidean space, henc use subsequ nearest-neighbor classif use label samples.",
    "feature-bas student-teach learning, train method encourag student' hidden featur mimic teacher network, empir success transfer knowledg pre-train teacher network student network. furthermore, recent empir result demonstr that, teacher' featur boost student network' gener even student' input sampl corrupt noise. however, lack theoret insight method transfer knowledg success heterogen tasks. analyz method theoret use deep linear networks, experiment use nonlinear networks. identifi three vital factor success method: (1) whether student train zero train loss; (2) knowledg teacher clean-input problem; (3) teacher decompos knowledg hidden features. lack proper control three factor lead failur student-teach learn method.",
    "stock trend forecasting, forecast stock prices' futur trends, play essenti role investment. stock market share inform stock price highli correlated. sever method recent propos mine share inform stock concept (e.g., technology, internet retail) extract web improv forecast results. however, previou work assum connect stock concept stationary, neglect dynam relev stock concepts, limit forecast results. moreover, exist method overlook invalu share inform carri hidden concepts, measur stocks' common beyond manual defin stock concepts. overcom shortcom previou work, propos novel stock trend forecast framework adequ mine concept-ori share inform predefin concept hidden concepts. propos framework simultan util stock' share inform individu inform improv stock trend forecast performance. experiment result real-world task demonstr effici framework stock trend forecasting. invest simul show framework achiev higher invest return baselines.",
    "recent success munchausen reinforc learn (m-rl) featur implicit kullback-leibl (kl) regular augment reward function logarithm current stochast policy. though signific improv shown boltzmann softmax policy, tsalli sparsemax polici considered, augment lead flat learn curv almost everi problem considered. show due mismatch convent logarithm non-logarithm (generalized) natur tsalli entropy. draw inspir tsalli statist literature, propos correct mismatch m-rl help $q$-logarithm/exponenti functions. propos formul lead implicit tsalli kl regular maximum tsalli entropi framework. show formul m-rl achiev superior perform benchmark problem shed light gener m-rl variou entrop indic $q$.",
    "classifi hand-written digit letter taken big leap introduct convnets. however, constrain hardwar time necessari train model would high. main contribut twofold. first, extens test end-to-end vanilla neural network (mlp) approach pure numpi without pre-process featur extract done beforehand. second, show basic data mine oper significantli improv perform model term comput time, without sacrif much accuracy. illustr claim simpler variant extend mnist dataset, call balanc emnist dataset. experi show that, without data mining, get increas gener perform use hidden layer regular techniques, best model achiev 84.83% accuraci test dataset. use dimension reduct done pca abl increas figur 85.08% 10% origin featur space, reduc memori size need 64%. finally, ad method remov possibl harm train sampl like deviat mean help us still achiev 84% test accuraci 32.8% origin memori size train set. compar favor major literatur result obtain similar architectures. although approach get outshin state-of-the-art models, scale (alexnet, vggnet) train 50% dataset.",
    "web applic real-tim emot recognit psychologist psychiatrist presented. mental health effect covid-19 quarantin need handl societi emot impacted. human micro-express describ genuin emot captur convolut neural network (cnn) models. challeng implement poor perform part societi comput low speed internet connection, i.e., improv comput effici reduc data transfer. valid comput effici premise, compar cnn architectur results, collect floating-point oper per second (flops), number paramet (np) accuraci mobilenet, peleenet, extend deep neural network (ednn), inception- base deep neural network (idnn) propos residu mobile-bas network model (resmonet). also, compar train model result term main memori util (mmu) respons time complet emot (rte) recognition. besides, design data transfer includ raw data emot basic patient information. web applic evalu system usabl scale (sus) util questionnair psychologist psychiatrists. resmonet model gener reduc np, flops, mmu results, ednn overcom resmonet 0.01sec rte. optim model impact accuracy, therefor idnn ednn 0.02 0.05 accur model respectively. finally, accord psychologist psychiatrists, web applic good usabl (73.8 100) util (3.94 5).",
    "polar code theoret achiev competit frame error rates. practice, perform may depend chosen decod procedure, well paramet commun system deploy upon. consequence, design effici polar code specif context quickli becom challenging. paper, introduc methodolog consist train deep neural network predict frame error rate polar code base frozen bit construct sequence. introduc algorithm base project gradient descent leverag gradient neural network function gener promis frozen bit sequences. showcas gener dataset abil propos methodolog produc code effici use train neural networks, even latter select among effici ones.",
    "cross-lingu word embed transfer knowledg languages: model train high-resourc languag predict low-resourc languages. introduc clime, interact system quickli refin cross-lingu word embed given classif problem. first, clime rank word salienc downstream task. then, user mark similar keyword nearest neighbor embed space. finally, clime updat embed use annotations. evalu clime identifi health-rel text four low-resourc languages: ilocano, sinhalese, tigrinya, uyghur. embed refin clime captur nuanc word semant higher test accuraci origin embeddings. clime often improv accuraci faster activ learn baselin easili combin activ learn improv results.",
    "traffic flow forecast crucial task urban computing. challeng aris traffic flow often exhibit intrins latent spatio-tempor correl cannot identifi extract spatial tempor pattern traffic data separately. argu correl univers play pivot role traffic flow. put forward {spacetim interv learning} paradigm explicitli captur correl unifi analysi spatial tempor features. unlik state-of-the-art methods, restrict particular road network, model univers spatio-tempor correl transfer citi cities. end, propos new spacetim interv learn framework construct local-spacetim context traffic sensor compris data neighbor within close time points. base idea, introduc local spacetim neural network (stnn), employ novel spacetim convolut attent mechan learn univers spatio-tempor correlations. propos stnn captur local traffic patterns, depend specif network structure. result, train stnn model appli unseen traffic networks. evalu propos stnn two public real-world traffic dataset simul dataset dynam networks. experi result show stnn improv predict accuraci 4% state-of-the-art methods, also effect handl case traffic network undergo dynam chang well superior gener capability.",
    "usag amount inform avail internet increas past decade. digit lead need autom answer system extract fruit inform redund transit knowledg sources. system design cater promin answer giant knowledg sourc user queri use natur languag understand (nlu) thu emin depend question-answering(qa) field. question answer involv limit step like map user question pertin query, retriev relev information, find best suitabl answer retriev inform etc. current improv deep learn model evinc compel perform improv tasks. review work, research direct qa field analyz base type question, answer type, sourc evidence-answer, model approach. detail follow open challeng field like automat question generation, similar detect and, low resourc avail language. end, survey avail dataset evalu measur presented.",
    "recently, deep learn approach becom main research frontier biolog imag reconstruct enhanc problem thank high performance, along ultra-fast infer times. however, due difficulti obtain match refer data supervis learning, increas interest unsupervis learn approach need pair refer data. particular, self-supervis learn gener model success use variou biolog imag applications. paper, overview approach coher perspect context classic invers problems, discuss applic biolog imaging, includ electron, fluoresc deconvolut microscopy, optic diffract tomographi function neuroimaging.",
    "machin learn recent emerg promis approach studi complex phenomena character rich datasets. particular, data-centr approach lend possibl automat discov structur experiment dataset manual inspect may miss. here, introduc interpret unsupervised-supervis hybrid machin learn approach, hybrid-correl convolut neural network (hybrid-ccnn), appli experiment data gener use programm quantum simul base rydberg atom arrays. specifically, appli hybrid-ccnn analyz new quantum phase squar lattic programm interactions. initi unsupervis dimension reduct cluster stage first reveal five distinct quantum phase regions. second supervis stage, refin phase boundari character phase train fulli interpret ccnn extract relev correl phase. characterist spatial weight snippet correl specif recogn phase captur quantum fluctuat striat phase identifi two previous undetect phases, rhombic boundary-ord phases. observ demonstr combin programm quantum simul machin learn use power tool detail explor correl quantum state matter.",
    "studi problem consist recov sparsiti pattern regress paramet vector correl observ govern determinist miss data pattern use lasso. consid case observ dataset censor deterministic, non-uniform filter. recov sparsiti pattern dataset determinist miss structur arguabl challeng recov uniformly-at-random scenario. paper, propos effici algorithm miss valu imput util topolog properti censorship filter. provid novel theoret result exact recoveri sparsiti pattern use propos imput strategy. analysi show that, certain statist topolog conditions, hidden sparsiti pattern recov consist high probabl polynomi time logarithm sampl complexity.",
    "study, present dynam graph represent learn model weight graph accur predict network capac connect viewer live video stream event. propos egad, neural network architectur captur graph evolut introduc self-attent mechan weight consecut graph convolut networks. addition, account fact neural architectur requir huge amount paramet train, thu increas onlin infer latenc neg influenc user experi live video stream event. address problem high onlin infer vast number parameters, propos knowledg distil strategy. particular, design distil loss function, aim first pretrain teacher model offlin data, transfer knowledg teacher smaller student model less parameters. evalu propos model link predict task three real-world datasets, gener live video stream events. event last 80 minut viewer exploit distribut solut provid compani hive stream ab. experi demonstr effect propos model term link predict accuraci number requir parameters, evalu state-of-the-art approaches. addition, studi distil perform propos model term compress ratio differ distil strategies, show propos model achiev compress ratio 15:100, preserv high link predict accuracy.",
    "reproduct purposes, evalu dataset implement publicli avail https://stefanosantaris.github.io/egad.",
    "studi search problem solv perform gradient descent bound convex polytop domain show class equal intersect two well-known classes: ppad pls. main underli technic contribution, show comput karush-kuhn-tuck (kkt) point continu differenti function domain $[0,1]^2$ ppad $\\cap$ pls-complete. first natur problem shown complet class. result also impli class cl (continu local search) - defin daskalaki papadimitri \"natural\" counterpart ppad $\\cap$ pl contain mani interest problem - equal ppad $\\cap$ pls.",
    "object paper develop predict model classifi brazilian legal proceed three possibl class status: (i) archiv proceedings, (ii) activ proceedings, (iii) suspend proceedings. problem' resolut intend assist public privat institut manag larg portfolio legal proceedings, provid gain scale efficiency. paper, legal proceed made sequenc short text call \"motions.\" combin sever natur languag process (nlp) machin learn techniqu solv problem. although work portugues nlp, challeng due lack resources, approach perform remark well classif task, achiev maximum accuraci .93 top averag f1 score .89 (macro) .93 (weighted). furthermore, could extract interpret pattern learn one model besid quantifi pattern relat classif task. interpret step import among machin learn legal applic give us excit insight black-box model make decisions.",
    "one fundament task graph theory, subgraph match crucial task mani fields, rang inform retrieval, comput vision, biology, chemistri natur languag processing. yet subgraph match problem remain np-complet problem. studi propos end-to-end learning-bas approxim method subgraph match task, call subgraph match network (sub-gmn). propos sub-gmn firstli use graph represent learn map node node-level embedding. combin metric learn attent mechan model relationship match node data graph queri graph. test perform propos method, appli method two databases. use two exist methods, gnn fgnn baselin comparison. experi show that, dataset 1, averag accuraci sub-gmn 12.21\\% 3.2\\% higher gnn fgnn respectively. averag run time sub-gmn run 20-40 time faster fgnn. addition, averag f1-score sub-gmn experi dataset 2 reach 0.95, demonstr sub-gmn output correct node-to-nod matches. compar previou gnns-base method subgraph match task, propos sub-gmn allow vari queri data graph test/appl stage, previou gnns-base method find match subgraph data graph test/appl queri graph use train stage.",
    "anoth advantag propos sub-gmn output list node-to-nod matches, exist end-to-end gnn base method cannot provid match node pairs.",
    "amongst varieti approach aim make learn procedur neural network effective, scientif commun develop strategi order exampl accord estim complexity, distil knowledg larger networks, exploit principl behind adversari machin learning. differ idea recent proposed, name friendli training, consist alter input data ad automat estim perturbation, goal facilit learn process neural classifier. transform progress fades-out long train proceeds, complet vanishes. work revisit extend idea, introduc radic differ novel approach inspir effect neural gener context adversari machin learning. propos auxiliari multi-lay network respons alter input data make easier handl classifi current stage train procedure. auxiliari network train jointli neural classifier, thu intrins increas 'depth' classifier, expect spot gener regular data alter process. effect auxiliari network progress reduc end training, fulli drop classifi deploy applications. refer approach neural friendli training.",
    "extend experiment procedur involv sever dataset differ neural architectur show neural friendli train overcom origin propos friendli train technique, improv gener classifier, especi case noisi data.",
    "due direct relev post-disast operations, meter read civil refus collection, uncertain capacit arc rout problem (ucarp) import optimis problem. stochast model critic studi accur repres real-world determinist counterparts. although extens studi solv rout problem uncertainty, consid ucarp, none consid collabor vehicl handl neg effect uncertainty. paper propos novel solut construct procedur (scp) gener solut ucarp within collaborative, multi-vehicl framework. consist two type collabor activities: one vehicl unexpectedli expend capac (\\emph{rout failure}), refil process. then, propos genet program hyper-heurist (gphh) algorithm evolv rout polici use within collabor framework. experiment studi show new heurist vehicl collabor gp-evolv rout polici significantli outperform compar state-of-the-art algorithm commonli studi test problems. shown especi true instanc larger number task vehicles. clearli show advantag vehicl collabor handl uncertain environment, effect newli propos algorithm.",
    "reinforc learning, graph laplacian prove valuabl tool task-agnost setting, applic rang skill discoveri reward shaping. recently, learn laplacian represent frame optim temporally-contrast object overcom comput limit larg (or continuous) state spaces. however, approach requir uniform access state state space, overlook explor problem emerg represent learn process. work, propos altern method abl recover, non-uniform-prior setting, express desir properti laplacian representation. combin represent learn skill-bas cover policy, provid better train distribut extend refin representation. also show simpl augment represent object learn tempor abstract improv dynamics-awar help exploration. find method succe altern laplacian non-uniform set scale challeng continu control environments. finally, even method optim skill discovery, learn skill success solv difficult continu navig task spars rewards, standard skill discoveri approach effective.",
    "paper illustr local sensit hase (lsh) model identif remov nearli redund data text dataset. evalu differ models, creat artifici dataset data dedupl use english wikipedia articles. area-under-curv (auc) 0.9 observ models, best model reach 0.96. dedupl enabl effect model train prevent model learn distribut differ real one result repeat data.",
    "diabet retinopathi (dr) one major caus visual impair blind across world. usual found patient suffer diabet long period. major focu work deriv optim represent retin imag help improv perform dr recognit models. extract optim representation, featur extract multipl pre-train convnet model blend use propos multi-mod fusion module. final represent use train deep neural network (dnn) use dr identif sever level prediction. convnet extract differ features, fuse use 1d pool cross pool lead better represent use featur extract singl convnet. experiment studi benchmark kaggl apto 2019 contest dataset reveal model train propos blend featur represent superior exist methods. addition, notic cross averag pool base fusion featur xception vgg16 appropri dr recognition. propos model, achiev accuraci 97.41%, kappa statist 94.82 dr identif accuraci 81.7% kappa statist 71.1% sever level prediction. anoth interest observ dnn dropout input layer converg quickli train use blend features, compar model train use uni-mod deep features.",
    "effect learn electron health record (ehr) data predict clinic outcom often challeng featur record irregular timestep loss follow-up well compet event death diseas progression. end, propos gener time-to-ev model, survlat ode, adopt ordinari differenti equation-bas recurr neural network (ode-rnn) encod effect parameter latent represent irregularli sampl data. model util latent represent flexibl estim surviv time multipl compet event without specifi shape event-specif hazard function. demonstr competit perform model mimic-iii, freely-avail longitudin dataset collect critic care units, predict hospit mortal well data dana-farb cancer institut (dfci) predict onset deep vein thrombosi (dvt), life-threaten complic patient cancer, death compet event. survlat ode outperform current clinic standard khorana risk score stratifi dvt risk groups.",
    "safeti perspective, machin learn method embed real-world applic requir distinguish irregular situations. reason, grow interest anomali detect (ad) task. sinc cannot observ abnorm sampl cases, recent ad method attempt formul task classifi whether sampl normal not. however, potenti fail given normal sampl inherit divers semant labels. tackl problem, introduc latent class-condition-bas ad scenario. addition, propos confidence-bas self-label ad framework tailor propos scenario. sinc method leverag hidden class information, success avoid gener undesir loos decis region one-class method suffer. propos framework outperform recent one-class ad method latent multi-class scenarios.",
    "exist model cross-domain name entiti recognit (ner) reli numer unlabel corpu label ner train data target domains. however, collect data low-resourc target domain expens also time-consuming. hence, propos cross-domain ner model use extern resources. first introduc multi-task learn (mtl) ad new object function detect whether token name entiti not. introduc framework call mixtur entiti expert (moee) improv robust zero-resourc domain adaptation. finally, experiment result show model outperform strong unsupervis cross-domain sequenc label models, perform model close state-of-the-art model leverag extens resources.",
    "ecologist long suspect speci like interact trait match particular way. example, pollin interact may like proport bee' tongu fit plant' flower shape. empir estim import trait-match determin speci interactions, however, vari significantli among differ type ecolog networks. here, show ambigu among empir trait-match studi may arisen least part use overli simpl statist models. use simul real data, contrast convent gener linear model (glm) flexibl machin learn (ml) model (random forest, boost regress trees, deep neural networks, convolut neural networks, support vector machines, naiv bayes, k-nearest-neighbor), test abil predict speci interact base traits, infer trait combin causal respons speci interactions. find best ml model success predict speci interact plant-pollin networks, outperform glm substanti margin. result also demonstr ml model better identifi causal respons trait-match combin glms. two case studies, best ml model success predict speci interact global plant-pollin databas infer ecolog plausibl trait-match rule plant-hummingbird network, without prior assumptions. conclud flexibl ml model offer mani advantag tradit regress model understand interact networks. anticip result extrapol ecolog network types.",
    "generally, result highlight potenti machin learn artifici intellig infer ecology, beyond standard task imag pattern recognition.",
    "paper examin problem learn finit possibl larg set p base kernels. present theoret empir analysi approach address problem base ensembl kernel predictors. includ novel theoret guarante base rademach complex correspond hypothesi sets, introduct analysi learn algorithm base hypothesi sets, seri experi use ensembl kernel predictor sever data sets. convex combin kernel-bas hypothes gener lq-regular nonneg combin analyzed. theoretical, algorithmic, empir result compar achiev use learn kernel techniques, view anoth approach solv problem.",
    "consid problem determin class function test effici learned, distribution-fre sample-bas model correspond standard pac learn setting. main result show vc dimens alway provid tight bound number sampl requir test class function model, combin closely-rel variant call \"lower vc\" (or lvc) dimens obtain strong lower bound sampl complexity. use result obtain strong mani case nearli optim lower bound sampl complex test union intervals, halfspaces, intersect halfspaces, polynomi threshold functions, decis trees. conversely, show two natur class functions, junta monoton functions, test number sampl polynomi smaller number sampl requir pac learning. finally, also use connect vc dimens properti test establish new lower bound test radiu cluster test feasibl linear constraint systems.",
    "event-driven elast natur serverless runtim make effici cost-effect altern scale computations. far, mostli use stateless, data parallel ephemer computations. work, propos use serverless runtim solv generic, large-scal optim problems. specifically, build master-work setup use aw lambda sourc workers, implement parallel optim algorithm solv regular logist regress problem, show rel speedup 256 worker effici 70% 64 worker expected. also identifi possibl algorithm system-level bottlenecks, propos improvements, discuss limit challeng realiz improvements.",
    "recur problem face train neural network typic enough data maxim gener capabl deep neural networks(dnn). mani techniqu address this, includ data augmentation, dropout, transfer learning. paper, introduc addit method call smart augment show use increas accuraci reduc overfit target network. smart augment work creat network learn gener augment data train process target network way reduc network loss. allow us learn augment minim error network. smart augment shown potenti increas accuraci demonstr signific measur dataset tested. addition, shown potenti achiev similar improv perform level significantli smaller network size number test cases.",
    "highli increas interest artifici neural network (anns) result impress wide-rang improv structure. work, come idea instead static plugin current avail loss function are, default flexibl nature. flexibl loss function insight navig neural network lead higher converg rate therefor reach optimum accuraci quickly. insight help decid degre flexibl deriv complex anns, data distribution, select hyper-paramet on. wake this, introduc novel flexibl loss function neural networks. function shown character rang fundament uniqu properti which, much properti loss function subset vari flexibl paramet function allow emul loss curv learn behavior preval static loss functions. extens experiment perform loss function demonstr abl give state-of-the-art perform select data sets. thus, idea flexibl propos function built upon carri potenti open new interest chapter deep learn research.",
    "light-up puzzle, also known akari puzzle, never solv use modern artifici intellig (ai) methods. currently, wide use comput techniqu autonom develop solut involv evolut theori algorithms. project effort appli new ai techniqu solv light-up puzzl faster comput efficient. algorithm explor produc optim solut includ hill climbing, simul annealing, feed-forward neural network (fnn), convolut neural network (cnn). two algorithm develop hill climb simul anneal use 2 action (add remov light bulb) versu 3 actions(add, remove, move light-bulb differ cell). hill climb simul anneal algorithm show higher accuraci case 3 actions. simul anneal show significantli outperform hill climbing, fnn, cnn, evolutionari theori algorithm achiev 100% accuraci 30 uniqu board configurations. lastly, fnn cnn algorithm show low accuracies, comput time significantli faster compar remain algorithms. github repositori project found https://github.com/rperera12/akari-lightup-gamesolver-with-deepneuralnetworks-and-hillclimb-or-simulatedannealing.",
    "varieti control task invers kinemat (ik), trajectori optim (to), model predict control (mpc) commonli formul energi minim problems. numer solut problem well-established. however, often slow use directli real-tim applications. altern learn solut manifold control problem offlin stage. although distil process trivial formul behavior clone (bc) problem imit learn setting, experi highlight number signific shortcom aris due incompat local minima, interpol artifacts, insuffici coverag state space. paper, propos altern bc effici numer robust. formul learn solut manifold minim energi term control object integr space problem interest. minim energi integr novel method combin mont carlo-inspir adapt sampl strategi deriv use solv individu instanc control task. evalu perform formul seri robot control problem increas complexity, highlight benefit comparison tradit method behavior clone dataset aggreg (dagger).",
    "paper propos algorithm (rmda) train neural network (nns) regular term promot desir structures. rmda incur comput addit proxim sgd momentum, achiev varianc reduct without requir object function finite-sum form. tool manifold identif nonlinear optimization, prove finit number iterations, iter rmda possess desir structur ident induc regular stationari point asymptot convergence, even presenc engin trick like data augment dropout complic train process. experi train nn structur sparsiti confirm varianc reduct necessari identification, show rmda thu significantli outperform exist method task. unstructur sparsity, rmda also outperform state-of-the-art prune method, valid benefit train structur nn regularization.",
    "present shapeformer, transformer-bas network produc distribut object completions, condit incomplete, possibl noisy, point clouds. result distribut sampl gener like completions, exhibit plausibl shape detail faith input. facilit use transform 3d, introduc compact 3d representation, vector quantiz deep implicit function, util spatial sparsiti repres close approxim 3d shape short sequenc discret variables. experi demonstr shapeform outperform prior art shape complet ambigu partial input term complet qualiti diversity. also show approach effect handl varieti shape types, incomplet patterns, real-world scans.",
    "vanilla cnns, uncalibr classifiers, suffer classifi out-of-distribut (ood) sampl nearli confid in-distribut samples. tackl challenge, recent work demonstr gain leverag avail ood set train end-to-end calibr cnns. however, critic question remain unansw works: differenti ood set select effect one(s) induc train cnn high detect rate unseen ood sets? address pivot question, provid criterion base gener error augmented-cnn, vanilla cnn ad extra class employ rejection, in-distribut unseen ood sets. however, select effect ood set directli optim criterion incur huge comput cost. instead, propos three novel computationally-effici metric differenti ood set accord \"protection\" level in-distribut sub-manifolds. empir verifi protect ood set -- select accord metric -- lead a-cnn significantli lower gener error a-cnn train least protect ones. also empir show effect protect ood set train well-gener confidence-calibr vanilla cnns. result confirm 1) ood set equal effect train well-perform end-to-end model (i.e., a-cnn calibr cnns) ood detect task 2) protect level ood set viabl factor recogn effect one.",
    "finally, across imag classif tasks, exhibit a-cnn train protect ood set also detect black-box fg adversari exampl distanc (measur metrics) becom larger protect sub-manifolds.",
    "present villa, first known effort large-scal adversari train vision-and-languag (v+l) represent learning. villa consist two train stages: (i) task-agnost adversari pre-training; follow (ii) task-specif adversari finetuning. instead ad adversari perturb imag pixel textual tokens, propos perform adversari train embed space modality. enabl large-scal training, adopt \"free\" adversari train strategy, combin kl-divergence-bas regular promot higher invari embed space. appli villa current best-perform v+l models, achiev new state art wide rang tasks, includ visual question answering, visual commonsens reasoning, image-text retrieval, refer express comprehension, visual entailment, nlvr2.",
    "3d face reconstruct face align two fundament highli relat topic comput vision. recently, work start use deep learn model estim 3dmm coeffici reconstruct 3d face geometry. however, perform restrict due limit pre-defin face templates. address problem, end-to-end methods, complet bypass calcul 3dmm coefficients, propos attract much attention. report, introduc analys three state-of-the-art method 3d face reconstruct face alignment. potenti improv prn propos enhanc accuraci speed.",
    "paper, propos effect threshold method base order statistic, call thors, convert arbitrari scoring-typ classifier, induc continu cumul distribut function score, cost-sensit one. procedure, use order statist find optim threshold classification, requir almost knowledg classifi itself. unlik common data-driven methods, analyt show thor theoret guarante performance, theoret bound cost lower time complexity. coupl empir result sever real-world data sets, argu thor prefer cost-sensit technique.",
    "frame foundat linear oper use decomposit reconstruct signals, discret fourier transform, gabor, wavelets, curvelet transforms. emerg spars represent model shift emphasi frame theori toward spars l1-minim problems. paper, appli frame theori spars represent signal synthesi dictionari use frame analysi dictionari use dual frame. sought formul novel dual frame design spars vector obtain decomposit signal also spars solut repres signal base reconstruct frame. find demonstr type dual frame cannot construct over-complet frames, therebi preclud use linear analysi oper drive spars synthesi coeffici signal representation. nonetheless, best approxim spars synthesi solut deriv analysi coeffici use canon dual frame. study, develop novel dictionari learn algorithm (call parsev k-svd) learn tight-fram dictionary. leverag analysi synthesi perspect signal represent frame deriv optim formul problem pertain imag recovery. preliminary, result demonstr imag recov use approach correl frame bound dictionaries, therebi demonstr import use differ dictionari differ applications.",
    "introduc algorithm learn nonlinear dynam system form $x_{t+1}=\\sigma(\\theta^{\\star}x_t)+\\varepsilon_t$, $\\theta^{\\star}$ weight matrix, $\\sigma$ nonlinear link function, $\\varepsilon_t$ mean-zero nois process. give algorithm recov weight matrix $\\theta^{\\star}$ singl trajectori optim sampl complex linear run time. algorithm succe weaker statist assumpt previou work, particular i) requir bound spectral norm weight matrix $\\theta^{\\star}$ (rather, depend gener spectral radius) ii) enjoy guarante non-strictly-increas link function relu. analysi two key components: i) give gener recip wherebi global stabil nonlinear dynam system use certifi state-vector covari well-conditioned, ii) use tools, extend well-known algorithm effici learn gener linear model depend setting.",
    "diabet foot ulcer classif system use presenc wound infect (bacteria present within wound) ischaemia (restrict blood supply) vital clinic indic treatment predict wound healing. studi investig use autom computeris method classifi infect ischaemia within diabet foot wound limit due pauciti publicli avail dataset sever data imbal exist. diabet foot ulcer challeng 2021 provid particip substanti dataset compris total 15,683 diabet foot ulcer patches, 5,955 use training, 5,734 use test addit 3,994 unlabel patch promot develop semi-supervis weakly-supervis deep learn techniques. paper provid evalu method use diabet foot ulcer challeng 2021, summaris result obtain network. best perform network ensembl result top 3 models, macro-averag f1-score 0.6307.",
    "track multipl objects, often assum observ (measurement) origin one one object. however, may encount situat measur may may associ multipl object time step --spawning. therefore, associ measur multipl object crucial task perform order track multipl object birth death. paper, introduc novel bayesian nonparametr approach model scenario observ may drawn unknown number object provid tractabl markov chain mont carlo (mcmc) approach sampl posterior distribution. number object time step, itself, also assum unknown. we, then, show experi advantag nonparametr model scenario spawn events. experi result also demonstr advantag framework exist methods.",
    "principl maximum entropi broadli applic techniqu comput distribut least amount inform possibl constrain match empir data, instance, featur expectations. seek gener principl scenario empir featur expect cannot comput model variabl partial observed, introduc depend learn model. gener principl latent maximum entropy, introduc uncertain maximum entropi describ expectation-maxim base solut approxim solv problems. show techniqu addit gener principl maximum entropy. addit discuss use black box classifi technique, simplifi process util sparse, larg data sets.",
    "estim treatment effect pervas problem medicine. exist method estim treatment effect longitudin observ data assum hidden confounders, assumpt testabl practic and, hold, lead bias estimates. paper, develop time seri deconfounder, method leverag assign multipl treatment time enabl estim treatment effect presenc multi-caus hidden confounders. time seri deconfound use novel recurr neural network architectur multitask output build factor model time infer latent variabl render assign treatment condit independent; then, perform causal infer use latent variabl act substitut multi-caus unobserv confounders. provid theoret analysi obtain unbias causal effect time-vari exposur use time seri deconfounder. use simul real data show effect method deconfound estim treatment respons time.",
    "dedic acceler design address huge resourc requir deep neural network (dnn) applications. power, perform area (ppa) constraint limit number mac avail accelerators. convolut layer requir huge number mac often partit multipl iter sub-tasks. put huge pressur avail system resourc interconnect memori bandwidth. optim partit featur map sub-task reduc bandwidth requir substantially. acceler avoid off-chip interconnect transfer implement local memories; however, memori access still perform reduc bandwidth help save power architectures. paper, propos first order analyt method partit featur map optim bandwidth evalu impact partit bandwidth. bandwidth save design activ memori control perform basic arithmet operations. shown optim partit activ memori control achiev 40% bandwidth reduction.",
    "human-machin interact gain traction rehabilit tasks, control prosthet hand robot arms. gestur recognit exploit surfac electromyograph (semg) signal one promis approaches, given semg signal acquisit non-invas directli relat muscl contraction. however, analysi signal still present mani challeng sinc similar gestur result similar muscl contractions. thu result signal shape almost identical, lead low classif accuracy. tackl challenge, complex neural network employed, requir larg memori footprints, consum rel high energi limit maximum batteri life devic use classification. work address problem introduct bioformers. new famili ultra-smal attention-bas architectur approach state-of-the-art perform reduc number paramet oper 4.9x. additionally, introduc new inter-subject pre-training, improv accuraci best bioform 3.39%, match state-of-the-art accuraci without addit infer cost. deploy best perform bioform parallel, ultra-low power (pulp) microcontrol unit (mcu), greenwav gap8, achiev infer latenc energi 2.72 ms 0.14 mj, respectively, 8.0x lower previou state-of-the-art neural network, occupi 94.2 kb memory.",
    "reinforc learn nowaday popular framework solv differ decis make problem autom driving. however, still remain crucial challeng need address provid reliabl policies. paper, propos gener risk-awar dqn approach order learn high level action drive unsign occlud intersections. propos state represent provid lane base inform allow use multi-lan scenarios. moreover, propos risk base reward function punish riski situat instead collis failures. reward approach help incorpor risk predict deep q network learn reliabl polici safer challeng situations. effici propos approach compar dqn learn convent collis base reward scheme also rule-bas intersect navig policy. evalu result show propos approach outperform methods. provid safer action collision-awar dqn approach less overcauti rule-bas policy.",
    "introduct exist literatur onlin handwrit analysi support patholog diagnosi taken advantag in-air trajectories. similar situat occur biometr secur applic goal identifi verifi individu use signatur handwriting. studi consid distanc pen tip write surface. due fact current acquisit devic provid height formation. however, quit straightforward differenti movement two differ heights: a) short distance: height lower equal 1 cm surfac digitizer, digit provid x coordinates. b) long distance: height exceed 1 cm, inform avail time stamp indic time specif stroke spent long distance. although short distanc use sever papers, long distanc ignor investig paper. method paper, analyz larg set databas (biosecurid, emothaw, pahaw, oxygen-therapi salt), contain total amount 663 user 17951 files. specif studied: a) percentag time spent on-surface, in-air short distance, in-air long distanc differ user profil (patholog healthi users) differ tasks; b) potenti use signal improv classif rates.",
    "result conclus experiment result reveal long-dist movement repres small portion total execut time (0.5 % case signatur 10.4% uppercas word biosecur-id, largest database). addition, signific differ found comparison patholog versu control group letter l pahaw databas (p=0.0157) cross pentagon salt databas (p=0.0122)",
    "paper explor sever strategi forens voic comparison (fvc), aim improv perform lr use gener gaussian score-to-lr models. first, differ anchor strategi proposed, object adapt lr comput process case hand, alway respect proposit defin particular case. second, fully-bayesian gaussian model use tackl sparsiti train score often present propos anchor strategi used. experi perform use 2014 i-vector challeng set-up, present high variabl telephon speech context. result show propos fully-bayesian model clearli outperform common maximum-likelihood approach, lead high robust score train model becom sparse.",
    "post-hoc explan method import class approach help understand rational underli train model' decision. use end-us toward accomplish given task? vision paper, argu need benchmark facilit evalu util post-hoc explan methods. first step end, enumer desir properti benchmark possess task debug text classifiers. additionally, highlight benchmark facilit assess effect explan also efficiency.",
    "dbscan popular density-bas cluster algorithm. comput $\\epsilon$-neighborhood graph dataset use connect compon high-degre node decid clusters. however, full neighborhood graph may costli comput worst-cas complex $o(n^2)$. paper, propos simpl variant call sng-dbscan, cluster base subsampl $\\epsilon$-neighborhood graph, requir access similar queri pair point particular avoid complex data structur need embed data point themselves. runtim procedur $o(sn^2)$, $s$ sampl rate. show natur theoret assumpt $s \\approx \\log n/n$ suffici statist cluster recoveri guarante lead $o(n\\log n)$ complexity. provid extens experiment analysi show larg datasets, one subsampl littl $0.1\\%$ neighborhood graph, lead much 200x speedup 250x reduct ram consumpt compar scikit-learn' implement dbscan, still maintain competit cluster performance.",
    "contrast pattern mine (cpm) aim discov pattern whose support increas significantli background dataset compar target dataset. cpm particularli use characteris chang evolv systems, e.g., network traffic analysi detect unusu activity. exist techniqu focu extract either whole set contrast pattern (cps) minim sets, problem effici find relev subset cps, especi high dimension datasets, open challenge. paper, focu extract specif set cp discov signific chang two datasets. approach problem use close pattern substanti reduc redund patterns. experiment result sever real emul network traffic dataset demonstr propos unsupervis algorithm 100 time faster exist approach cpm network traffic data [2]. addition, applic cps, demonstr cpm highli effect method detect meaning chang network traffic.",
    "survey latest advanc machin learn deep neural network appli task radio modul recognition. result show radio modul recognit limit network depth work focu improv learn synchron equalization. advanc area like come novel architectur design task novel train methods.",
    "question answer (qa) task machin understand given document question find answer. despit impress progress nlp area, qa still challeng problem, especi non-english languag due lack annot datasets. paper, present japanes question answer dataset, jaquad, annot humans. jaquad consist 39,696 extract question-answ pair japanes wikipedia articles. finetun baselin model achiev 78.92% f1 score 63.38% em test set. dataset experi avail https://github.com/skelterlabsinc/jaquad.",
    "work consid method impos sparsiti bayesian regress applic nonlinear system identification. first review automat relev determin (ard) analyt demonstr need addit regular threshold achiev spars models. discuss two class methods, regular base threshold based, build ard learn parsimoni solut linear problems. case orthogon covariates, analyt demonstr favor perform regard learn small set activ term linear system spars solution. sever exampl problem present compar set propos method term advantag limit ard base hundr elements. aim paper analyz understand assumpt lead sever algorithm provid theoret empir result reader may gain insight make inform choic regard spars bayesian regression.",
    "\"benign overfitting\", classifi memor noisi train data yet still achiev good gener performance, drawn great attent machin learn community. explain surpris phenomenon, seri work provid theoret justif over-parameter linear regression, classification, kernel methods. however, clear benign overfit still occur presenc adversari examples, i.e., exampl tini intent perturb fool classifiers. paper, show benign overfit inde occur adversari training, principl approach defend adversari examples. detail, prove risk bound adversari train linear classifi mixtur sub-gaussian data $\\ell_p$ adversari perturbations. result suggest moder perturbations, adversari train linear classifi achiev near-optim standard adversari risks, despit overfit noisi train data. numer experi valid theoret findings.",
    "uncertainti structur inevitable, gener lead variat dynam respons predictions. complex structure, brute forc mont carlo simul respons variat analysi infeas sinc one singl run may alreadi comput costly. data driven meta-model approach thu explor facilit effici emul statist inference. perform meta-model hing upon qualiti quantiti train dataset. actual practice, however, high-fidel data acquir high-dimension finit element simul experi gener scarce, pose signific challeng meta-model establishment. research, take advantag multi-level respons predict opportun structur dynam analysis, i.e., acquir rapidli larg amount low-fidel data reduced-ord modeling, acquir accur small amount high-fidel data full-scal finit element analysis. specifically, formul composit neural network fusion approach fulli util multi-level, heterogen dataset obtained. implicitli identifi correl low- high-fidel datasets, yield improv accuraci compar state-of-the-art. comprehens investig use frequenc respons variat character case exampl carri demonstr performance.",
    "deep learning-bas model util achiev state-of-the-art perform recommend systems. key challeng model work million categor class tokens. standard approach learn end-to-end, dens latent represent embed token. result embed requir larg amount memori blow number tokens. train infer model creat storage, memori bandwidth bottleneck lead signific comput energi consumpt deploy practice. end, present problem \\textit{memori allocation} budget embed propos novel formul memori share embedding, memori share proport overlap semant information. formul admit practic effici random solut local sensit hash base memori alloc (lma). demonstr signific reduct memori footprint maintain performance. particular, lma embed achiev perform compar standard embed 16$\\times$ reduct memori footprint. moreover, lma achiev averag improv 0.003 auc across differ memori regim standard dlrm model criteo avazu dataset",
    "control time evolut interact spin system import approach implement quantum computing. differ approach compil circuit product multipl elementari gates, propos quantum circuit encapsul (qce), encapsul circuit differ parts, optim magnet field realiz unitari transform part time evolution. qce demonstr possess well-control error time cost, avoid error accumul aim find shortest path directli target unitary. test four differ encapsul way realiz multi-qubit quantum fourier transform control time evolut quantum ise chain. scale behavior time cost error number two-qubit control gate demonstrated. qce provid altern compil scheme translat circuit physically-execut form base quantum many-bodi dynamics, key issu becom encapsul way balanc effici flexibility.",
    "paper propos solv import problem recommend -- user cold start, base meta lean method. previou meta learn approach finetun paramet new user, comput storag expensive. contrast, divid model paramet fix adapt part develop two-stag meta learn algorithm learn separately. fix part, captur user invari features, share user learn offlin meta learn stage. adapt part, captur user specif features, learn onlin meta learn stage. decoupl user invari paramet user depend parameters, propos approach effici storag cheaper previou methods. also potenti deal catastroph forget continu adapt stream come users. experi product data demonstr propos method converg faster better perform baselin methods. meta-train without onlin meta model finetun increas auc 72.24% 74.72% (2.48% absolut improvement). onlin meta train achiev gain 2.46\\% absolut improv compar offlin meta training.",
    "relationship entiti dataset often multipl nature, like geograph distance, social relationships, common interest among peopl social network, example. inform natur model set weight undirect graph form global multilay graph, common vertex set repres entiti edg differ layer captur similar entiti term differ modalities. paper, address problem analyz multi-lay graph propos method cluster vertic effici merg inform provid multipl modalities. end, propos combin characterist individu graph layer use tool subspac analysi grassmann manifold. result combin view low dimension represent origin data preserv import inform divers relationship entities. use inform new cluster method test algorithm sever synthet real world dataset demonstr superior competit perform compar baselin state-of-the-art techniques. gener framework extend numer analysi learn problem involv differ type inform graphs.",
    "televis ever-evolv multi billion dollar industry. success televis show increasingli technolog societi vast multi-vari formula. art success someth happens, studied, replicated, applied. hollywood unpredict regard success, mani movi sitcom hype promis hit end box offic failur complet disappointments. current studies, linguist explor perform relationship televis seri target commun viewers. decis support system display sound predict result would need build confid invest new tv series. model present studi use data studi determin make sitcom successful. paper, use descript predict model techniqu assess continu success televis comedies: office, big bang theory, arrest development, scrubs, south park. factor test statist signific episod rate charact presence, director, writer. statist show charact inde crucial show themselves, creation direct show pose implic upon rate therefor success shows. use machin learn base forecast model accur predict success shows.",
    "model repres baselin understand success televis show produc increas success current televis show util data creation futur shows. due mani factor go series, empir analysi work show one-fits-al model forecast rate success televis show.",
    "deep neural network (dnn) call lanes, reorgan such. lane path network data-independ typic learn differ featur add resili network. given data-independence, lane amen parallel processing. multi-lan capsnet (mlcn) propos reorgan capsul network shown achiev better accuraci bring highly-parallel lanes. however, effici scalabl mlcn systemat examined. work, studi mlcn network multipl gpu find 2x effici origin capsnet use model-parallelism. further, present load balanc problem distribut heterogen lane homogen heterogen acceler show simpl greedi heurist almost 50% faster naiv random approach.",
    "kernel onlin convex optim (koco) framework combin express non-parametr kernel model regret guarante onlin learning. first-ord koco method function gradient descent requir $\\mathcal{o}(t)$ time space per iteration, and, inform loss convexity, achiev minimax optim $\\mathcal{o}(\\sqrt{t})$ regret. nonetheless, mani common loss kernel problems, squar loss, logist loss, squar hing loss poss stronger curvatur exploited. case, second-ord koco method achiev $\\mathcal{o}(\\log(\\text{det}(\\boldsymbol{k})))$ regret, show scale $\\mathcal{o}(d_{\\text{eff}}\\log t)$, $d_{\\text{eff}}$ effect dimens problem usual much smaller $\\mathcal{o}(\\sqrt{t})$. main drawback second-ord method much higher $\\mathcal{o}(t^2)$ space time complexity. paper, introduc kernel onlin newton step (kons), new second-ord koco method also achiev $\\mathcal{o}(d_{\\text{eff}}\\log t)$ regret. address comput complex second-ord methods, introduc new matrix sketch algorithm kernel matrix $\\boldsymbol{k}_t$, show chosen paramet $\\gamma \\leq 1$ sketched-kon reduc space time complex factor $\\gamma^2$ $\\mathcal{o}(t^2\\gamma^2)$ space time per iteration, incur $1/\\gamma$ time regret.",
    "propos metric -- project norm -- predict model' perform out-of-distribut (ood) data without access ground truth labels. project norm first use model predict pseudo-label test sampl train new model pseudo-labels. new model' paramet differ in-distribut model, greater predict ood error. empirically, approach outperform exist method imag text classif task across differ network architectures. theoretically, connect approach bound test error overparameter linear models. furthermore, find project norm approach achiev non-trivi detect perform adversari examples. code avail https://github.com/yaodongyu/projnorm.",
    "evolutionari algorithm (eas), larg class gener purpos optim algorithm inspir natur phenomena, wide use variou industri optim often show excel performance. paper present attempt toward reveal gener power statist view eas. summar larg rang ea sampling-and-learn framework, show framework directli admit gener analysi probable-absolute-approxim (paa) queri complexity. particularli focu framework learn subroutin restrict binari classification, result sampling-and-classif (sac) algorithms. help learn theory, obtain gener upper bound paa queri complex sac algorithms. compar sac algorithm uniform search differ situations. error-target independ condition, show sac algorithm achiev polynomi speedup uniform search, super-polynomi speedup. one-side-error condition, show super-polynomi speedup achieved. work touch surfac framework. power condit still open.",
    "studi problem identifi top $m$ arm multi-arm bandit game. propos solut reli new algorithm base success reject seemingli bad arms, success accept good ones. algorithm contribut allow tackl multipl identif set previous reach. particular show idea success accept reject appli multi-bandit best arm identif problem.",
    "new initi method hidden paramet neural network proposed. deriv integr represent neural network, nonparametr probabl distribut hidden paramet introduced. proposal, hidden paramet initi sampl drawn distribution, output paramet fit ordinari linear regression. numer experi show backpropag propos initi converg faster uniformli random initialization. also shown propos method achiev enough accuraci without backpropag cases.",
    "recent deep learn base recommend system activ explor solv cold-start problem use hybrid approach. however, major previou studi propos hybrid model collabor filter content-bas filter modul independ trained. end-to-end approach take differ modal data input jointli train model provid better optim fulli explor yet. work, propos deep content-us embed model, simpl intuit architectur combin user-item interact music audio content. evalu model music recommend music auto-tag tasks. result show propos model significantli outperform previou work. also discuss variou direct improv propos model further.",
    "understand heterogen multivari time seri data import mani applic rang smart home aviation. learn model heterogen multivari time seri also human-interpret challeng adequ address exist literature. propos grammar-bas decis tree (gbdts) algorithm learn them. gbdt extend decis tree grammar framework. logic express deriv context-fre grammar use branch place simpl threshold attributes. ad express enabl support wide rang data type retain interpret decis trees. particular, grammar base tempor logic used, show gbdt use interpret classi cation high-dimension heterogen time seri data. furthermore, show gbdt also use categorization, combin cluster gener interpret explan cluster. appli gbdt analyz classic australian sign languag dataset well data near mid-air collis (nmacs). nmac data come aircraft simul use develop next-gener airborn collis avoid system (aca x).",
    "machin learn field studi machin alter adapt behavior, improv action accord inform given. field subdivid multipl areas, among best known supervis learn (e.g. classif regression) unsupervis learn (e.g. cluster associ rules). within supervis learning, studi research focus well known standard tasks, binari classification, multiclass classif regress one depend variable. however, mani less known problems. gener call nonstandard supervis learn problems. literatur much sparse, studi direct specif task. therefore, definitions, relat applic kind learner hard find. goal paper provid reader broad view distinct variat nonstandard supervis problems. comprehens taxonomi summar trait proposed. review common approach follow accomplish main applic provid well.",
    "3d microscopi key investig divers biolog systems, ever increas avail larg dataset demand automat cell identif method accurate, also impli uncertainti predict inform potenti error henc confid conclus use them. convent deep learn method often yield determinist results, advanc deep bayesian learn allow accur predict probabilist interpret numer imag classif segment tasks. howev nontrivi extend bayesian method cell detection, requir special learn frameworks. particular, regress densiti map popular success approach extract cell coordin local peak postprocess step, hinder meaning probabilist output. herein propos deep learning-bas cell detect framework oper larg microscopi imag output desir probabilist predict (i) integr bayesian techniqu regress uncertainty-awar densiti maps, peak detect appli gener cell proposals, (ii) learn map numer propos probabilist space calibrated, i.e. accur repres chanc success prediction. util calibr predictions, propos probabilist spatial analysi monte-carlo sampling. demonstr revis exist descript distribut mesenchym stromal cell type within bone marrow, propos method allow us reveal spatial pattern otherwis undetectable.",
    "introduc probabilist analysi quantit microscopi pipelin allow report confid interv test biolog hypothes spatial distributions.",
    "present sound complet algorithm recov causal graph observed, non-intervent data, possibl presenc latent confound select bias. reli causal markov faith assumpt recov equival class underli causal graph perform seri condit independ (ci) test observ variables. propos singl step appli iteratively, independ causal relat entail result graph, iteration, correct becom inform success iteration. essentially, tie size ci condit set distanc test node result graph. iter refin skeleton orient perform ci test condit set larger preced iteration. iteration, condit set ci test construct node within specifi search distance, size condit set equal search distance. algorithm iter increas search distanc along condit set sizes. thus, iter refin graph, recov previou iter smaller condit set -- higher statist power. demonstr algorithm requir significantli fewer ci test smaller condit set compar fci algorithm. evid recov true underli graph use perfect ci oracle, accur estim graph use limit observ data.",
    "work aim address long-establish problem learn diversifi representations. end, combin information-theoret argument stochast competition-bas activations, name stochast local winner-takes-al (lwta) units. context, ditch convent deep architectur commonli use represent learning, reli non-linear activations; instead, replac set local stochast compet linear units. setting, network layer yield spars outputs, determin outcom competit unit organ block competitors. adopt stochast argument competit mechanism, perform posterior sampl determin winner block. endow consid network abil infer sub-part network essenti model data hand; impos appropri stick-break prior end. enrich inform emerg representations, resort information-theoret principles, name inform compet process (icp). then, compon tie togeth stochast variat bay framework inference. perform thorough experiment investig approach use benchmark dataset imag classification. experiment show, result network yield signific discrimin represent learn abilities. addition, introduc paradigm allow principl investig mechan emerg intermedi network representations.",
    "behavior synthet charact current militari simul limit sinc gener gener rule-bas reactiv comput model minim intelligence. comput model cannot adapt reflect experi characters, result brittl intellig even effect behavior model devis via costli labor-intens processes. observation-bas behavior model adapt leverag machin learn experi synthet entiti combin appropri prior knowledg address issu exist comput behavior model creat better train experi militari train simulations. paper, introduc framework aim creat autonom synthet charact perform coher sequenc believ behavior awar human traine need within train simulation. framework bring togeth three mutual complementari components. first compon unity-bas simul environ - rapid integr develop environ (ride) - support one world terrain (owt) model capabl run support machin learn experiments. second shiva, novel multi-ag reinforc imit learn framework interfac varieti simul environments, addit util varieti learn algorithms. final compon sigma cognit architectur augment behavior model symbol probabilist reason capabilities. success creat proof-of-concept behavior model leverag framework realist terrain essenti step toward bring machin learn militari simulations.",
    "studi problem learn similar function larg corpora use neural network embed models. model typic train use sgd sampl random observ unobserv pairs, number sampl grow quadrat corpu size, make expens scale larg corpora. propos new effici method train model without sampl unobserv pairs. inspir matrix factorization, approach reli ad global quadrat penalti pair exampl express term matrix-inner-product two gener gramians. show gradient term effici comput maintain estim gramians, develop varianc reduct scheme improv qualiti estimates. conduct large-scal experi show signific improv train time gener qualiti compar tradit sampl methods.",
    "proxim polici optim (ppo) yield state-of-the-art result polici search, subfield reinforc learning, one key point use surrog object function restrict step size polici update. although restrict helpful, algorithm still suffer perform instabl optim ineffici sudden flatten curve. address issu present ppo variant, name proxim polici optim smooth algorithm (ppos), critic improv use function clip method instead flat clip method. compar method ppo pporb, adopt rollback clip method, prove method conduct accur updat time step ppo methods. moreover, show outperform latest ppo variant perform stabil challeng continu control tasks.",
    "work propos novel portfolio manag technique, meta portfolio method (mpm), inspir success meta approach field bioinformat elsewhere. mpm use xgboost learn switch two risk-bas portfolio alloc strategies, hierarch risk pariti (hrp) classic na\\\"iv risk pariti (nrp). demonstr mpm abl success take advantag best characterist strategi (the nrp' fast growth market uptrends, hrp' protect drawdown market turmoil). result, mpm shown possess excel out-of-sampl risk-reward profile, measur sharp ratio, addit offer high degre interpret asset alloc decisions.",
    "parallel extens use reinforc learn (rl), quantit effect parallel explor well understood theoretically. studi benefit simpl parallel explor reward-fre rl linear markov decis process (mdps) two-play zero-sum markov game (mgs). contrast exist literatur focus approach encourag agent explor divers set policies, show use singl polici guid explor across agent suffici obtain almost-linear speedup case compar fulli sequenti counterpart. further, show simpl procedur minimax optim logarithm factor reward-fre set linear mdp two-play zero-sum mgs. practic perspective, paper show singl polici suffici provabl optim incorpor parallel explor phase.",
    "human migrat type human mobility, trip involv person move intent chang home location. predict human migrat accur possibl import citi plan applications, intern trade, spread infecti diseases, conserv planning, public polici development. tradit human mobil models, graviti model recent radiat model, predict human mobil flow base popul distanc featur only. model valid commut flows, differ type human mobility, mainli use model scenario larg amount prior ground truth mobil data available. one downsid model fix form therefor abl captur complic migrat dynamics. propos machin learn model abl incorpor number exogen features, predict origin/destin human migrat flows. machin learn model outperform tradit human mobil model varieti evalu metrics, task predict migrat us counti well intern migrations. general, predict machin learn model human migrat provid flexibl base model human migrat differ what-if conditions, potenti sea level rise popul growth scenarios.",
    "present converg algorithm tikhonov regular nonneg matrix factor (nmf). special choos regular known tikhonov regular least squar (ls) prefer form solv linear invers problem convent ls. nmf problem decompos ls subproblems, expect tikhonov regular nmf appropri approach solv nmf problems. algorithm deriv use addit updat rule shown converg guarantee. equip algorithm mechan automat determin regular paramet base l-curve, well-known concept invers problem community, rather unknown nmf research. introduct algorithm thu solv two inher problem tikhonov regular nmf algorithm research, i.e., converg guarante regular paramet determination.",
    "paper, propos deep learn approach smartphon user identif base analyz motion signal record acceleromet gyroscope, singl tap gestur perform user screen. transform discret 3-axi signal motion sensor gray-scal imag represent provid input convolut neural network (cnn) pre-train multi-class user classification. pre-train stage, benefit differ user multipl sampl per user. pre-training, use cnn featur extractor, gener embed associ singl tap screen. result embed use train support vector machin (svm) model few-shot user identif setting, i.e. requir 20 tap screen registr phase. compar identif system base cnn featur two baselin systems, one employ handcraft featur anoth employ recurr neural network (rnn) features. system base classifier, name svm. pre-train cnn rnn model multi-class user classification, use differ set user set use few-shot user identification, ensur realist scenario. empir result demonstr cnn model yield top accuraci 89.75% multi-class user classif top accuraci 96.72% few-shot user identification. conclusion, believ system readi practic use, better gener capac baselines.",
    "high dimension regression, featur cluster effect outcom often import featur selection. purpose, cluster lasso octagon shrinkag cluster algorithm regress (oscar) use make featur group automat pairwis $l_1$ norm pairwis $l_\\infty$ norm, respectively. paper propos effici path algorithm cluster lasso oscar construct solut path respect regular parameters. despit mani term exhaust pairwis regularization, comput cost reduc use symmetri terms. simpl equival condit check subgradi equat featur group deriv graph theories. propos algorithm shown effici exist algorithm numer experiments.",
    "objective: epilept seizur rel common critically-il children admit pediatr intens care unit (picu) thu serv import target identif treatment. seizur discern clinic manifest still signific impact morbid mortality. children deem risk seizur within picu monitor use continuous-electroencephalogram (ceeg). ceeg monitor cost consider number avail machin alway limited, clinician need resort triag patient accord perceiv risk order alloc resources. research aim develop comput aid tool improv seizur risk assess critically-il children, use ubiquit record signal picu, name electrocardiogram (ecg). approach: novel data-driven model develop patient-level approach, base featur extract first hour ecg record clinic data patient. main results: predict featur age patient, brain injuri coma etiolog qr area. patient without prior clinic data, use one hour ecg recording, classif perform random forest classifi reach area receiv oper characterist curv (auroc) score 0.84. combin ecg featur patient clinic history, auroc reach 0.87. significance: take real clinic scenario, estim clinic decis support triag tool improv posit predict valu 59% clinic standard.",
    "increas number machin learn problems, robust adversari variant exist algorithms, requir minim loss function defin maximum. carri loop stochast gradient ascent (sga) step (inner) maxim problem, follow sgd step (outer) minimization, known epoch stochast gradient \\textit{desc ascent} (esgda). success practice, theoret analysi esgda remain challenging, clear guidanc choic inner loop size interplay inner/out step sizes. propos rsgda (random sgda), variant esgda stochast loop size simpler theoret analysis. rsgda come first (among sgda algorithms) almost sure converg rate use nonconvex min/strongly-concav max settings. rsgda parameter use optim loop size guarante best converg rate known hold sgda. test rsgda toy larger scale problems, use distribut robust optim single-cel data match use optim transport testbed.",
    "stream engin (se) coarse-grain reconfigur array provid program flexibl high-perform energi efficiency. applic program execut se repres combin synchron data flow (sdf) graphs, everi instruct repres node. node need map right slot array se ensur correct execut program. creat optim problem vast spars search space find map manual impract requir expertis knowledg se micro-architecture. work propos reinforc learn framework global graph attent (gga) modul output mask invalid placement find optim instruct schedules. use proxim polici optim order train model place oper se tile base reward function model se devic constraints. gga modul consist graph neural network attent module. graph neural network creat embed sdf attent block use model sequenti oper placement. show result certain workload map se factor affect map quality. find addit gga, average, find 10% better instruct schedul term total clock cycl taken mask improv reward obtain 20%.",
    "non-determinist measur common real-world scenarios: perform stochast optim algorithm total reward reinforc learn agent chaotic environ two exampl unpredict outcom common. measur model random variabl compar among via expect valu sophist tool null hypothesi statist tests. paper, propos altern framework visual compar two sampl accord estim cumul distribut functions. first, introduc domin measur two random variabl quantifi proport cumul distribut function one random variabl scholast domin one. then, present graphic method decompos quantil i) propos domin measur ii) probabl one random variabl take lower valu other. illustr purposes, re-evalu experiment alreadi publish work propos methodolog show addit conclus (miss rest methods) inferred. additionally, softwar packag rvcompar creat conveni way appli experi propos framework.",
    "network larg recept field (rf) shown advanc fit abil recent years. work, util short-term residu learn method improv perform robust network imag denois tasks. here, choos multi-wavelet convolut neural network (mwcnn), one state-of-art network larg rf, backbone, insert residu dens block (rdbs) layer. call scheme multi-wavelet residu dens convolut neural network (mwrdcnn). compar rdb-base networks, extract featur object adjac layers, preserv larg rf, boost comput efficiency. meanwhile, approach also provid possibl absorb advantag multipl architectur singl network without conflicts. perform propos method demonstr extens experi comparison exist techniques.",
    "investig use discret continu version physics-inform neural network method learn unknown dynam constitut relat dynam system. case unknown dynamics, repres dynam deep neural network (dnn). dynam system known specif constitut relat (that depend state system), repres constitut relat dnn. discret version combin classic multistep discret method dynam system neural network base machin learn methods. hand, continu version util deep neural network minim residu function continu govern equations. use case fedbatch bioreactor system studi effect approach discuss condit applicability. result indic accuraci train neural network model much higher case learn constitut relat instead whole dynamics. find corrobor well-known fact scientif comput build much structur inform avail algorithm enhanc effici and/or accuracy.",
    "variat autoencod learn unsupervis data representations, model frequent converg minima fail preserv meaning semant information. example, variat autoencod autoregress decod often collaps autodecoders, learn ignor encod input. work, demonstr ad auxiliari decod regular latent space prevent collapse, success auxiliari decod task domain dependent. auxiliari decod increas amount semant inform encod latent space visibl reconstructions. semant inform variat autoencoder' represent weakli correl rate, distortion, evid lower bound. compar popular strategi modifi train objective, regular latent space gener increas semant inform content.",
    "consid sequenti optim expens evalu possibl non-convex object function $f$ noisi feedback, consid continuum-arm bandit problem. upper bound regret perform sever learn algorithm (gp-ucb, gp-ts, variants) known bayesian (when $f$ sampl gaussian process (gp)) frequentist (when $f$ live reproduc kernel hilbert space) setting. regret bound often reli maxim inform gain $\\gamma_t$ $t$ observ underli gp (surrogate) model. provid gener bound $\\gamma_t$ base decay rate eigenvalu gp kernel, whose specialis commonli use kernels, improv exist bound $\\gamma_t$, subsequ regret bound reli $\\gamma_t$ numer settings. mat\\'ern famili kernels, lower bound $\\gamma_t$, regret frequentist setting, known, result close huge polynomi $t$ gap upper lower bound (up logarithm $t$ factors).",
    "depress detect use vocal biomark highli research area. articulatori coordin featur (acfs) develop base chang neuromotor coordin due psychomotor slowing, key featur major depress disorder. howev find exist studi mostli valid singl databas limit generaliz results. variabl across differ depress databas advers affect result cross corpu evalu (cces). propos develop gener classifi depress detect use dilat convolut neural network train acf extract two depress databases. show acf deriv vocal tract variabl (tvs) show promis robust set featur depress detection. model achiev rel accuraci improv ~10% compar cce perform model train singl database. extend studi show fuse tv mel-frequ cepstral coeffici improv perform classifier.",
    "highli comparative, feature-bas approach time seri classif introduc use extens databas algorithm extract thousand interpret featur time series. featur deriv across scientif time-seri analysi literature, includ summari time seri term correl structure, distribution, entropy, stationarity, scale properties, fit rang time-seri models. comput thousand featur time seri train set, inform class structur select use greedi forward featur select linear classifier. result feature-bas classifi automat learn differ class use reduc number time-seri properties, circumv need calcul distanc time series. repres time seri way result order magnitud dimension reduction, allow method perform well larg dataset contain long time seri time seri differ lengths. mani dataset studied, classif perform exceed convent instance-bas classifiers, includ one nearest neighbor classifi use euclidean distanc dynam time warp and, importantly, featur select provid understand properti dataset, insight guid scientif investigation.",
    "propos novel unsupervis domain adapt framework base domain-specif batch normal deep neural networks. aim adapt domain special batch normal layer convolut neural network allow share model parameters, realiz two-stag algorithm. first stage, estim pseudo-label exampl target domain use extern unsupervis domain adapt algorithm---for example, mstn cpua---integr propos domain-specif batch normalization. second stage learn final model use multi-task classif loss sourc target domains. note two domain separ batch normal layer stages. framework easili incorpor domain adapt techniqu base deep neural network batch normal layers. also present approach extend problem multipl sourc domains. propos algorithm evalu multipl benchmark dataset achiev state-of-the-art accuraci standard set multi-sourc domain adapt scenario.",
    "novelti detect aim automat identifi out-of-distribut (ood) data, without prior knowledg them. critic step data monitoring, behavior analysi applications, help enabl continu learn field. convent method ood detect perform multi-vari analysi ensembl data features, usual resort supervis ood data improv accuracy. reality, supervis impract one cannot anticip anomal data. paper, propos novel, self-supervis approach reli pre-defin ood data: (1) new method evalu mahalanobi distanc gradient in-distribut ood data. (2) assist self-supervis binari classifi guid label select gener gradients, maxim mahalanobi distance. evalu multipl datasets, cifar-10, cifar-100, svhn tinyimagenet, propos approach consist outperform state-of-the-art supervis unsupervis method area receiv oper characterist (auroc) area precision-recal curv (aupr) metrics. demonstr detector abl accur learn one ood class continu learning.",
    "propos new continuous-tim formul first-ord stochast optim algorithm mini-batch gradient descent variance-reduc methods. exploit continuous-tim models, togeth simpl lyapunov analysi well tool stochast calculus, order deriv converg bound variou type non-convex functions. guid analysis, show lyapunov argument hold discrete-time, lead match rates. addition, use model ito calculu infer novel insight dynam sgd, prove decreas learn rate act time warp or, equivalently, landscap stretching."
]