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Combines queries with their corresponding documents to show the titles and abstracts of papers related to each query, which helps in understanding the relevance and context of the queries.

corpusid int64 110 268M	title stringlengths 0 8.56k	abstract stringlengths 0 18.4k	citations listlengths 0 142	full_paper stringlengths 0 635k
252,715,594	PHENAKI: VARIABLE LENGTH VIDEO GENERATION FROM OPEN DOMAIN TEXTUAL DESCRIPTIONS	We present Phenaki, a model capable of realistic video synthesis, given a sequence of textual prompts. Generating videos from text is particularly challenging due to the computational cost, limited quantities of high quality text-video data and variable length of videos. To address these issues, we introduce a new mode...	[ 6628106, 174802916, 238582653 ]	PHENAKI: VARIABLE LENGTH VIDEO GENERATION FROM OPEN DOMAIN TEXTUAL DESCRIPTIONS Ruben Villegas University of Michigan University College London Google Brain University of Michigan University College London Mohammad Babaeizadeh University of Michigan University College London Google Brain University of Michi...
13,002,849	MODE REGULARIZED GENERATIVE ADVERSARIAL NETWORKS	Although Generative Adversarial Networks achieve state-of-the-art results on a variety of generative tasks, they are regarded as highly unstable and prone to miss modes. We argue that these bad behaviors of GANs are due to the very particular functional shape of the trained discriminators in high dimensional spaces, wh...	[]	MODE REGULARIZED GENERATIVE ADVERSARIAL NETWORKS † Tong Montreal Institute for Learning Algorithms Université de Montréal H3T 1J4MontréalQCCanada Department of Computing School of Computer Science The Hong Kong Polytechnic University University Of WaterlooN2L 3G1Hong Kong, WaterlooONCanada Che Yanran Li Montreal...
239,998,253	What Do We Mean by Generalization in Federated Learning?	Federated learning data is drawn from a distribution of distributions: clients are drawn from a meta-distribution, and their data are drawn from local data distributions. Thus generalization studies in federated learning should separate performance gaps from unseen client data (out-of-sample gap) from performance gaps ...	[ 235613568, 231924480, 211678094, 195798643, 43964415 ]	What Do We Mean by Generalization in Federated Learning? Honglin Yuan Warren Morningstar Lin Ning Karan Singhal What Do We Mean by Generalization in Federated Learning? Federated learning data is drawn from a distribution of distributions: clients are drawn from a meta-distribution, and their data are drawn from...
62,841,605	SPREADING VECTORS FOR SIMILARITY SEARCH	Discretizing multi-dimensional data distributions is a fundamental step of modern indexing methods. State-of-the-art techniques learn parameters of quantizers on training data for optimal performance, thus adapting quantizers to the data. In this work, we propose to reverse this paradigm and adapt the data to the quant...	[]	SPREADING VECTORS FOR SIMILARITY SEARCH Alexandre Sablayrolles Facebook AI Research Inria Matthijs Douze Facebook AI Research Inria Cordelia Schmid Facebook AI Research Inria Hervé Jégou Facebook AI Research Inria SPREADING VECTORS FOR SIMILARITY SEARCH Published as a conference paper at ICLR 2019 Discret...
253,237,531	MACHINE UNLEARNING OF FEDERATED CLUSTERS	Federated clustering (FC) is an unsupervised learning problem that arises in a number of practical applications, including personalized recommender and healthcare systems. With the adoption of recent laws ensuring the "right to be forgotten", the problem of machine unlearning for FC methods has become of significant im...	[]	MACHINE UNLEARNING OF FEDERATED CLUSTERS Chao Pan chaopan2@illinois.edu Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign USA Jin Sima jsima@illinois.edu Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign USA Saurav Prakash sauravp2@il...
222,291,443	CONTRASTIVE EXPLANATIONS FOR REINFORCEMENT LEARNING VIA EMBEDDED SELF PREDICTIONS	We investigate a deep reinforcement learning (RL) architecture that supports explaining why a learned agent prefers one action over another. The key idea is to learn action-values that are directly represented via human-understandable properties of expected futures. This is realized via the embedded self-prediction (ES...	[]	CONTRASTIVE EXPLANATIONS FOR REINFORCEMENT LEARNING VIA EMBEDDED SELF PREDICTIONS Zhengxian Lin Department of EECS Department of EECS Department of EECS Oregon State University Oregon State University Oregon State University Kim-Ho Lam Department of EECS Department of EECS Department of EECS Oregon State Universi...
223,956,716	FOR SELF-SUPERVISED LEARNING, RATIONALITY IMPLIES GENERALIZATION, PROVABLY	We prove a new upper bound on the generalization gap of classifiers that are obtained by first using self-supervision to learn a representation r of the training data, and then fitting a simple (e.g., linear) classifier g to the labels. Specifically, we show that (under the assumptions described below) the generalizati...	[ 6212000, 67855429 ]	FOR SELF-SUPERVISED LEARNING, RATIONALITY IMPLIES GENERALIZATION, PROVABLY Yamini Bansal Harvard University Harvard University Harvard University Gal Kaplun Harvard University Harvard University Harvard University Boaz Barak Harvard University Harvard University Harvard University FOR SELF-SUPERVISED LEARNIN...
263,605,472	MULTI-TASK LEARNING WITH 3D-AWARE REGULARIZATION	Deep neural networks have become a standard building block for designing models that can perform multiple dense computer vision tasks such as depth estimation and semantic segmentation thanks to their ability to capture complex correlations in high dimensional feature space across tasks.However, the cross-task correlat...	[]	MULTI-TASK LEARNING WITH 3D-AWARE REGULARIZATION Wei-Hong Li University of Edinburgh Steven Mcdonagh University of Edinburgh Ales Leonardis University of Birmingham Hakan Bilen University of Edinburgh MULTI-TASK LEARNING WITH 3D-AWARE REGULARIZATION 3F68DE01DC7497B9DA7372BB37520280 Deep neural networks ha...
212,996,548	LITE TRANSFORMER WITH LONG-SHORT RANGE ATTENTION	Transformer has become ubiquitous in natural language processing (e.g., machine translation, question answering); however, it requires enormous amount of computations to achieve high performance, which makes it not suitable for mobile applications that are tightly constrained by the hardware resources and battery. In t...	[ 91184134, 6628106, 2134321, 59310641, 9545399, 52892477, 964287, 54438210, 3508167, 44131019, 159041867, 1998416, 21850704, 201645145, 12713052, 13747425, 52967399, 11212020, 3725815, 14337532, 224893 ]	LITE TRANSFORMER WITH LONG-SHORT RANGE ATTENTION Zhanghao Wu zhwu@mit.edu Massachusetts Institute of Technology Shanghai Jiao Tong University Zhijian Liu zhijian@mit.edu Massachusetts Institute of Technology Ji Lin Massachusetts Institute of Technology Yujun Lin Massachusetts Institute of Technology Son...
202,719,276	ROBUST LOCAL FEATURES FOR IMPROVING THE GENERALIZATION OF ADVERSARIAL TRAINING	Adversarial training has been demonstrated as one of the most effective methods for training robust models so as to defend against adversarial examples. However, adversarial training often lacks adversarially robust generalization on unseen data. Recent works show that adversarially trained models may be more biased to...	[ 67855552, 58006571, 3604396, 6706414, 3488815, 17707860, 54101493, 53483414, 52898972 ]	ROBUST LOCAL FEATURES FOR IMPROVING THE GENERALIZATION OF ADVERSARIAL TRAINING Chubiao Song cbsong@hust.edu.cn Kun He Jiadong Lin jdlin@hust.edu.cn Liwei Wang wanglw@pku.edu.cn John E Hopcroft School of Computer Science and Technology School of Electronics Engineering and Computer Sciences Huazhong University o...
220,665,539	Randomized Automatic Differentiation	The successes of deep learning, variational inference, and many other fields have been aided by specialized implementations of reverse-mode automatic differentiation (AD) to compute gradients of mega-dimensional objectives. The AD techniques underlying these tools were designed to compute exact gradients to numerical p...	[ 6628106, 209318411, 5834589 ]	Randomized Automatic Differentiation Deniz Oktay doktay@princeton.edu Princeton University Nick Mcgreivy mcgreivy@princeton.edu Princeton University Joshua Aduol jaduol@princeton.edu Princeton University Alex Beatson abeatson@princeton.edu Princeton University Ryan P Adams Princeton University Randomiz...
263,152,628	3D RECONSTRUCTION WITH GENERALIZABLE NEURAL FIELDS USING SCENE PRIORS	High-fidelity 3D scene reconstruction has been substantially advanced by recent progress in neural fields. However, most existing methods train a separate network from scratch for each individual scene. This is not scalable, inefficient, and unable to yield good results given limited views. While learning-based multi-v...	[]	3D RECONSTRUCTION WITH GENERALIZABLE NEURAL FIELDS USING SCENE PRIORS Yang Fu UC San Diego 2 NVIDIA Shalini De Mello UC San Diego 2 NVIDIA Xueting Li UC San Diego 2 NVIDIA Amey Kulkarni UC San Diego 2 NVIDIA Jan Kautz UC San Diego 2 NVIDIA Xiaolong Wang UC San Diego 2 NVIDIA Sifei Liu UC San Diego 2 NVID...
264,802,502	OFFLINE RL WITH OBSERVATION HISTORIES: ANALYZING AND IMPROVING SAMPLE COMPLEXITY	Offline reinforcement learning (RL) can in principle synthesize more optimal behavior from a dataset consisting only of suboptimal trials.One way that this can happen is by "stitching" together the best parts of otherwise suboptimal trajectories that overlap on similar states, to create new behaviors where each individ...	[ 28202810, 219792420, 249954054 ]	OFFLINE RL WITH OBSERVATION HISTORIES: ANALYZING AND IMPROVING SAMPLE COMPLEXITY 31 Oct 2023 Joey Hong joeyhong@berkeley.edu Anca Dragan Sergey Levine sergey.levine@berkeley.edu U C Berkeley OFFLINE RL WITH OBSERVATION HISTORIES: ANALYZING AND IMPROVING SAMPLE COMPLEXITY 31 Oct 20236855E0A03706E6EA2B4E63C8CCB2F900...
227,068,701	LEARNING ENERGY-BASED MODELS BY DIFFUSION RECOVERY LIKELIHOOD	While energy-based models (EBMs) exhibit a number of desirable properties, training and sampling on high-dimensional datasets remains challenging. Inspired by recent progress on diffusion probabilistic models, we present a diffusion recovery likelihood method to tractably learn and sample from a sequence of EBMs traine...	[]	LEARNING ENERGY-BASED MODELS BY DIFFUSION RECOVERY LIKELIHOOD Ruiqi Gao ruiqigao@ucla.edu UCLA Stanford University UCLA Yang Song yangsong@cs.stanford.edu UCLA Stanford University UCLA Ben Poole pooleb@google.com UCLA Stanford University UCLA Google Brain UCLA Stanford University UCLA Ying Nian Wu UCLA S...
251,732,759	ENERGY-INSPIRED SELF-SUPERVISED PRETRAINING FOR VISION MODELS	Motivated by the fact that forward and backward passes of a deep network naturally form symmetric mappings between input and output representations, we introduce a simple yet effective self-supervised vision model pretraining framework inspired by energy-based models (EBMs). In the proposed framework, we model energy e...	[ 52967399 ]	ENERGY-INSPIRED SELF-SUPERVISED PRETRAINING FOR VISION MODELS Ze Wang zewang@purdue.edu Purdue University Microsoft Corporation Jiang Wang jiangwang@microsoft.com Purdue University Microsoft Corporation Zicheng Liu zliu@microsoft.com Purdue University Microsoft Corporation Qiang Qiu qqiu@purdue.edu Purdue U...
253,523,474	CHARACTERIZING THE SPECTRUM OF THE NTK VIA A POWER SERIES EXPANSION	Under mild conditions on the network initialization we derive a power series expansion for the Neural Tangent Kernel (NTK) of arbitrarily deep feedforward networks in the infinite width limit. We provide expressions for the coefficients of this power series which depend on both the Hermite coefficients of the activatio...	[ 2780493, 221836662, 245906072, 3708505, 222066778 ]	CHARACTERIZING THE SPECTRUM OF THE NTK VIA A POWER SERIES EXPANSION March 2, 2023 A Preprint Department of Mathematics UCLA CAUSA Michael Murray [mmurray@math.ucla.edu Department of Mathematics UCLA CAUSA Hui Jin huijin@math.ucla.edu Department of Mathematics UCLA CAUSA Benjamin Bowman benbowman314@math.ucla.edu...
162,184,036	DURATION-OF-STAY STORAGE ASSIGNMENT UNDER UNCERTAINTY	Storage assignment, the act of choosing what goods are placed in what locations in a warehouse, is a central problem of supply chain logistics. Past literature has shown that the optimal method to assign pallets is to arrange them in increasing duration of stay (DoS) in the warehouse (the DoS method), but the methodolo...	[ 1957433 ]	DURATION-OF-STAY STORAGE ASSIGNMENT UNDER UNCERTAINTY Michael Lingzhi Li mlli@mit.edu Elliott Wolf ewolf@lineagelogistics.com Daniel Wintz dwintz@lineagelogistics.com Lineage Logistics San Francisco Operation Research Center Massachusetts Institute of Technology Cambridge 02139MACalifornia Lineage Logistics San...
264,555,396	IMPROVING INTRINSIC EXPLORATION BY CREATING STATIONARY OBJECTIVES	Exploration bonuses in reinforcement learning guide long-horizon exploration by defining custom intrinsic objectives.Count-based methods use the frequency of state visits to derive an exploration bonus.In this paper, we identify that any intrinsic reward function derived from count-based methods is non-stationary and h...	[ 28202810 ]	IMPROVING INTRINSIC EXPLORATION BY CREATING STATIONARY OBJECTIVES 27 Oct 2023 Roger Creus Castanyer roger.creus-castanyer@mila.quebec Mila Québec AI Institute Université de Montréal Joshua Romoff joshua.romoff@ubisoft.com Ubisoft LaForge Glen Berseth glen.berseth@mila.quebec Mila Québec AI Institute Université ...
251,341,969	DYNAMIC UPDATE-TO-DATA RATIO: MINIMIZING WORLD MODEL OVERFITTING	Early stopping based on the validation set performance is a popular approach to find the right balance between under-and overfitting in the context of supervised learning. However, in reinforcement learning, even for supervised sub-problems such as world model learning, early stopping is not applicable as the dataset i...	[ 208857488, 222163237 ]	DYNAMIC UPDATE-TO-DATA RATIO: MINIMIZING WORLD MODEL OVERFITTING Nicolai Dorka dorka@cs.uni-freiburg.de University of Freiburg Tim Welschehold University of Freiburg Wolfram Burgard University of Technology Nuremberg DYNAMIC UPDATE-TO-DATA RATIO: MINIMIZING WORLD MODEL OVERFITTING Published as a conference p...
255,340,742	Delving into Semantic Scale Imbalance	Model bias triggered by long-tailed data has been widely studied. However, measure based on the number of samples cannot explicate three phenomena simultaneously: (1) Given enough data, the classification performance gain is marginal with additional samples. (2) Classification performance decays precipitously as the nu...	[]	Delving into Semantic Scale Imbalance Yanbiao Ma Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education Xidian University Xi'an 33:1513-1524710071, 2020China Licheng Jiao lchjiao@mail.xidian.edu.cn Key Laboratory of Intelligent Perception and Image Understanding of the Minist...
53,467,348	FEATURE-WISE BIAS AMPLIFICATION	We study the phenomenon of bias amplification in classifiers, wherein a machine learning model learns to predict classes with a greater disparity than the underlying ground truth. We demonstrate that bias amplification can arise via an inductive bias in gradient descent methods that results in the overestimation of the...	[]	FEATURE-WISE BIAS AMPLIFICATION 21 Dec 2018 Klas Leino Carnegie Mellon University Matt Fredrikson Carnegie Mellon University Emily Black Carnegie Mellon University Shayak Sen Carnegie Mellon University Anupam Datta Carnegie Mellon University FEATURE-WISE BIAS AMPLIFICATION 21 Dec 2018Published as a conf...
253,801,963	POWDERWORLD: A PLATFORM FOR UNDERSTANDING GENERALIZATION VIA RICH TASK DISTRIBUTIONS	One of the grand challenges of reinforcement learning is the ability to generalize to new tasks.However, general agents require a set of rich, diverse tasks to train on.Designing a 'foundation environment' for such tasks is tricky -the ideal environment would support a range of emergent phenomena, an expressive task sp...	[]	POWDERWORLD: A PLATFORM FOR UNDERSTANDING GENERALIZATION VIA RICH TASK DISTRIBUTIONS 15 Oct 2023 Kevin Frans kvfrans@mit.edu Mit Csail Phillip Isola phillipi@mit.edu POWDERWORLD: A PLATFORM FOR UNDERSTANDING GENERALIZATION VIA RICH TASK DISTRIBUTIONS 15 Oct 20239289E06A3E9F34316A2F8ECA419C9B30arXiv:2211.13051v3[cs....
249,888,901	THE POWER OF REGULARIZATION IN SOLVING EXTENSIVE-FORM GAMES	In this paper, we investigate the power of regularization, a common technique in reinforcement learning and optimization, in solving extensive-form games (EFGs). We propose a series of new algorithms based on regularizing the payoff functions of the game, and establish a set of convergence results that strictly improve...	[]	THE POWER OF REGULARIZATION IN SOLVING EXTENSIVE-FORM GAMES Mingyang Liu Institute for Interdisciplinary Information Sciences Tsinghua University Asuman Ozdaglar LIDS EECS Massachusetts Institute of Technology Tiancheng Yu LIDS EECS Massachusetts Institute of Technology Kaiqing Zhang 3kaiqing@umd.edu Univer...
231,632,937	HIERARCHICAL REINFORCEMENT LEARNING BY DISCOVERING INTRINSIC OPTIONS	We propose a hierarchical reinforcement learning method, HIDIO, that can learn task-agnostic options in a self-supervised manner while jointly learning to utilize them to solve sparse-reward tasks. Unlike current hierarchical RL approaches that tend to formulate goal-reaching low-level tasks or pre-define ad hoc lowerl...	[ 52911937, 13022595, 16326763, 53792719, 3521071, 7774489, 28202810, 53841789 ]	HIERARCHICAL REINFORCEMENT LEARNING BY DISCOVERING INTRINSIC OPTIONS Jesse Zhang University of Southern California Haonan Yu Horizon Robotics Wei Xu Horizon Robotics HIERARCHICAL REINFORCEMENT LEARNING BY DISCOVERING INTRINSIC OPTIONS Published as a conference paper at ICLR 2021 We propose a hierarchical rei...
246,904,522	REVISITING OVER-SMOOTHING IN BERT FROM THE PERSPECTIVE OF GRAPH	Recently over-smoothing phenomenon of Transformer-based models is observed in both vision and language fields. However, no existing work has delved deeper to further investigate the main cause of this phenomenon. In this work, we make the attempt to analyze the over-smoothing problem from the perspective of graph, wher...	[ 208117506, 225039882, 1238927, 229376913, 990233, 3144218, 202888986, 3432876, 52019251, 202888772, 44131019, 5034059, 11816014, 201645145, 212859361, 52967399, 5590763, 47018994, 4421747, 16639476 ]	REVISITING OVER-SMOOTHING IN BERT FROM THE PERSPECTIVE OF GRAPH Han Shi Hong Kong University of Science and Technology Jiahui Gao The University of Hong Kong Hang Xu Huawei Noah's Ark Lab Xiaodan Liang xdliang328@gmail.com Sun Yat-sen University Zhenguo Li li.zhenguo@huawei.com Huawei Noah's Ark Lab Li...
252,846,609	Few-shot Backdoor Attacks via Neural Tangent Kernels	In a backdoor attack, an attacker injects corrupted examples into the training set. The goal of the attacker is to cause the final trained model to predict the attacker's desired target label when a predefined trigger is added to test inputs. Central to these attacks is the trade-off between the success rate of the att...	[ 226226438, 52920808, 6628106, 203736530, 219792787, 221836662, 3526391 ]	Few-shot Backdoor Attacks via Neural Tangent Kernels Jonathan Hayase jhayase@cs.washington.edu School of Computer Science and Engineering University of Washington Sewoong Oh sewoong@cs.washington.edu School of Computer Science and Engineering University of Washington Paul G Allen School of Computer Science and...
257,834,209	SEMI-PARAMETRIC INDUCING POINT NETWORKS AND NEURAL PROCESSES	We introduce semi-parametric inducing point networks (SPIN), a general-purpose architecture that can query the training set at inference time in a compute-efficient manner. Semi-parametric architectures are typically more compact than parametric models, but their computational complexity is often quadratic. In contrast...	[ 236924832, 3626819, 226226438, 184487062, 204512247, 222067132 ]	SEMI-PARAMETRIC INDUCING POINT NETWORKS AND NEURAL PROCESSES Richa Rastogi Yair Schiff Zhaozhi Li Ian Lee Mert R Sabuncu msabuncu@cornell.edu Volodymyr Kuleshov kuleshov@cornell.edu Alon Hacohen alonhacohen@campus.technion.ac.il Yuntian Deng dengyuntian@seas.harvard.edu Institute of Technology Cornell Univer...
52,980,218	EFFICIENT AUGMENTATION VIA DATA SUBSAMPLING	Data augmentation is commonly used to encode invariances in learning methods. However, this process is often performed in an inefficient manner, as artificial examples are created by applying a number of transformations to all points in the training set. The resulting explosion of the dataset size can be an issue in te...	[]	EFFICIENT AUGMENTATION VIA DATA SUBSAMPLING Michael Kuchnik mkuchnik@cmu.edu Carnegie Mellon University Virginia Smith smithv@cmu.edu Carnegie Mellon University EFFICIENT AUGMENTATION VIA DATA SUBSAMPLING Data augmentation is commonly used to encode invariances in learning methods. However, this process is oft...
232,257,804	IMPLICIT NORMALIZING FLOWS		[]	IMPLICIT NORMALIZING FLOWS Cheng Lu Jianfei Chen chris.jianfei.chen@gmail.com Chongxuan Li chongxuanli1991@gmail.com † Qiuhao Wang Center for Data Science Peking University 100871BeijingChina Jun Zhu Dept. of Comp. Sci. & Tech Institute for AI BNRist Center † Tsinghua-Bosch Joint ML Center THBI Lab Tsinghua U...
263,831,863	SELF-SUPERVISED DATASET DISTILLATION FOR TRANSFER LEARNING	Dataset distillation methods have achieved remarkable success in distilling a large dataset into a small set of representative samples.However, they are not designed to produce a distilled dataset that can be effectively used for facilitating selfsupervised pre-training.To this end, we propose a novel problem of distil...	[ 219558792, 14124313, 49411844, 226226438 ]	SELF-SUPERVISED DATASET DISTILLATION FOR TRANSFER LEARNING 16 Oct 2023 Dong Bok Lee National University of Singapore Seanie Lee National University of Singapore Joonho Ko joonho.ko@kaist.ac.kr National University of Singapore Kenji Kawaguchi National University of Singapore Juho Lee National University of...
5,763,832	A Differentiable Physics Engine for Deep Learning in Robotics	An important field in robotics is the optimization of controllers. Currently, robots are often treated as a black box in this optimization process, which is the reason why derivative-free optimization methods such as evolutionary algorithms or reinforcement learning are omnipresent. When gradient-based methods are used...	[ 6628106, 5687613 ]	A Differentiable Physics Engine for Deep Learning in Robotics published: 07 March 2019 Published: 07 March 2019 Florian Röhrbein Eiji Uchibe Keyan Ghazi-Zahedi Jose De InstitutoJesus Rubio Politécnico Nacional Mexico Jonas Degrave Jonas Degrave IDLab-AIRO Department of Electronics and Information Systems Ghen...
261,245,530	INSERTNERF: INSTILLING GENERALIZABILITY INTO NERF WITH HYPERNET MODULES	Generalizing Neural Radiance Fields (NeRF) to new scenes is a significant challenge that existing approaches struggle to address without extensive modifications to vanilla NeRF framework. We introduce InsertNeRF, a method for INStilling gEneRalizabiliTy into NeRF. By utilizing multiple plug-and-play HyperNet modules, I...	[]	INSERTNERF: INSTILLING GENERALIZABILITY INTO NERF WITH HYPERNET MODULES Yanqi Bao State Key Laboratory for Novel Software Technology Nanjing University NanjingChina Tianyu Ding Applied Sciences Group Microsoft Corporation RedmondUSA Jing Huo State Key Laboratory for Novel Software Technology Nanjing University N...
21,850,704	A Deep Reinforced Model for Abstractive Summarization	Attentional, RNN-based encoder-decoder models for abstractive summarization have achieved good performance on short input and output sequences. However, for longer documents and summaries, these models often include repetitive and incoherent phrases. We introduce a neural network model with intra-attention and a new tr...	[ 10151113, 14068874, 16992492, 3937849, 1957433, 1729177, 9751546, 964287 ]	A Deep Reinforced Model for Abstractive Summarization Romain Paulus rpaulus@salesforce.com Caiming Xiong cxiong@salesforce.com Richard Socher rsocher@salesforce.com A Deep Reinforced Model for Abstractive Summarization Attentional, RNN-based encoder-decoder models for abstractive summarization have achieved good ...
239,009,555	ON-POLICY MODEL ERRORS IN REINFORCEMENT LEARNING	Model-free reinforcement learning algorithms can compute policy gradients given sampled environment transitions, but require large amounts of data. In contrast, model-based methods can use the learned model to generate new data, but model errors and bias can render learning unstable or suboptimal. In this paper, we pre...	[ 6628106, 28202810, 213529244, 3536221, 49666783 ]	ON-POLICY MODEL ERRORS IN REINFORCEMENT LEARNING Lukas P Fröhlich lukasfro@ethz.ch Maksym Lefarov maksym.lefarov@de.bosch.com Melanie N Zeilinger mzeilinger@ethz.ch Felix Berkenkamp felix.berkenkamp@de.bosch.com Institute for Dynamic Systems and Control Bosch Center for Artificial Intelligence Institute for Dyna...
43,939,886	DEEP LEARNING GENERALIZES BECAUSE THE PARAMETER-FUNCTION MAP IS BIASED TOWARDS SIMPLE FUNCTIONS	Deep neural networks generalize remarkably well without explicit regularization even in the strongly over-parametrized regime. This success suggests that some form of implicit regularization must be at work. In this paper we argue that a strong intrinsic bias in the parameter-function map helps explain the success of d...	[]	DEEP LEARNING GENERALIZES BECAUSE THE PARAMETER-FUNCTION MAP IS BIASED TOWARDS SIMPLE FUNCTIONS Guillermo Valle Pérez guillermo.valle@dtc.ox.ac.uk University of Oxford University of Oxford University of Oxford Chico Q Camargo University of Oxford University of Oxford University of Oxford Ard A Louis ard.louis@p...
7,305,965	OPTIMAL BINARY AUTOENCODING WITH PAIRWISE CORRELATIONS	We formulate learning of a binary autoencoder as a biconvex optimization problem which learns from the pairwise correlations between encoded and decoded bits. Among all possible algorithms that use this information, ours finds the autoencoder that reconstructs its inputs with worst-case optimal loss. The optimal decode...	[]	OPTIMAL BINARY AUTOENCODING WITH PAIRWISE CORRELATIONS Akshay Balsubramani abalsubr@ucsd.edu OPTIMAL BINARY AUTOENCODING WITH PAIRWISE CORRELATIONS Under review as a conference paper at ICLR 2017 We formulate learning of a binary autoencoder as a biconvex optimization problem which learns from the pairwise correlati...
261,697,392	InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generation	Diffusion models have revolutionized text-to-image generation with its exceptional quality and creativity. However, its multi-step sampling process is known to be slow, often requiring tens of inference steps to obtain satisfactory results. Previous attempts to improve its sampling speed and reduce computational costs ...	[ 246016304, 252734897, 251252882, 222140788, 247011732, 221818900, 227209335, 245704504, 247292764 ]	InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generation Xingchao Liu xcliu@cs.utexas.edu Department of Computer Science University of Texas at Austin Xiwen Zhang Helixon Research Jianzhu Ma majianzhu@tsinghua.edu.cn Helixon Research Jian Peng jianpeng@illinois.edu Helixon Res...
3,536,139	EMERGENCE OF GRID-LIKE REPRESENTATIONS BY TRAINING RECURRENT NEURAL NETWORKS TO PERFORM SPATIAL LOCALIZATION	Decades of research on the neural code underlying spatial navigation have revealed a diverse set of neural response properties. The Entorhinal Cortex (EC) of the mammalian brain contains a rich set of spatial correlates, including grid cells which encode space using tessellating patterns. However, the mechanisms and fu...	[]	EMERGENCE OF GRID-LIKE REPRESENTATIONS BY TRAINING RECURRENT NEURAL NETWORKS TO PERFORM SPATIAL LOCALIZATION Christopher J Cueva ccueva@gmail.com Columbia University New York 10027NYUSA Xue-Xin Wei Columbia University New York 10027NYUSA EMERGENCE OF GRID-LIKE REPRESENTATIONS BY TRAINING RECURRENT NEURAL NETWORKS...
220,302,524	Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval	Conducting text retrieval in a dense learned representation space has many intriguing advantages over sparse retrieval. Yet the effectiveness of dense retrieval (DR) often requires combination with sparse retrieval. In this paper, we identify that the main bottleneck is in the training mechanisms, where the negative in...	[ 173990818, 3618568, 210063976, 26501419, 11816014, 6401679, 86611921, 195873973 ]	Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval Lee Xiong lexion@microsoft.com Microsoft Corporation Chenyan Xiong chenyan.xiong@microsoft.com Microsoft Corporation Ye Li Microsoft Corporation Kwok-Fung Tang kwokfung.tang@microsoft.com Microsoft Corporation Jialin Liu ...
252,596,001	COMPOSITIONAL SEMANTIC PARSING WITH LARGE LANGUAGE MODELS	Humans can reason compositionally when presented with new tasks. Previous research shows that appropriate prompting techniques enable large language models (LLMs) to solve artificial compositional generalization tasks such as SCAN.In this work, we identify additional challenges in more realistic semantic parsing tasks ...	[ 235097473, 249017865, 202542872, 235367710, 222290851, 235829155, 9337134, 221655744, 204907203, 128000127, 248986239, 203836888, 235367771, 245218525, 209439843, 225066984, 245131376, 222208634, 247595263 ]	COMPOSITIONAL SEMANTIC PARSING WITH LARGE LANGUAGE MODELS October 3, 2022 Andrew Drozdov Google Research UMass Amherst CICS Nathanael Schärli Google Research Ekin Akyürek Google Research MIT CSAIL * Equal contribution Nathan Scales Google Research Xinying Song Google Research Xinyun Chen Google Res...