ICLR 2019

502 papers

The Relativistic Discriminator: A Key Element Missing from Standard GAN Alexia Jolicoeur-Martineau

A Closer Look at Deep Learning Heuristics: Learning Rate Restarts, Warmup and Distillation Akhilesh Gotmare, Nitish Shirish Keskar, Caiming Xiong, Richard Socher

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A Closer Look at Few-Shot Classification Wei-Yu Chen, Yen-Cheng Liu, Zsolt Kira, Yu-Chiang Frank Wang, Jia-Bin Huang

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A Comprehensive, Application-Oriented Study of Catastrophic Forgetting in DNNs B. Pfülb, A. Gepperth

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A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks Sanjeev Arora, Nadav Cohen, Noah Golowich, Wei Hu

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A Data-Driven and Distributed Approach to Sparse Signal Representation and Recovery Ali Mousavi, Gautam Dasarathy, Richard G. Baraniuk

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A Direct Approach to Robust Deep Learning Using Adversarial Networks Huaxia Wang, Chun-Nam Yu

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A Generative Model for Electron Paths John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato

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A Kernel Random Matrix-Based Approach for Sparse PCA Mohamed El Amine Seddik, Mohamed Tamaazousti, Romain Couillet

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A Max-Affine Spline Perspective of Recurrent Neural Networks Zichao Wang, Randall Balestriero, Richard Baraniuk

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A Mean Field Theory of Batch Normalization Greg Yang, Jeffrey Pennington, Vinay Rao, Jascha Sohl-Dickstein, Samuel S. Schoenholz

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A New Dog Learns Old Tricks: RL Finds Classic Optimization Algorithms Weiwei Kong, Christopher Liaw, Aranyak Mehta, D. Sivakumar

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A Rotation-Equivariant Convolutional Neural Network Model of Primary Visual Cortex Alexander S. Ecker, Fabian H. Sinz, Emmanouil Froudarakis, Paul G. Fahey, Santiago A. Cadena, Edgar Y. Walker, Erick Cobos, Jacob Reimer, Andreas S. Tolias, Matthias Bethge

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A Statistical Approach to Assessing Neural Network Robustness Stefan Webb, Tom Rainforth, Yee Whye Teh, M. Pawan Kumar

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A Unified Theory of Early Visual Representations from Retina to Cortex Through Anatomically Constrained Deep CNNs Jack Lindsey, Samuel A. Ocko, Surya Ganguli, Stephane Deny

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A Universal Music Translation Network Noam Mor, Lior Wolf, Adam Polyak, Yaniv Taigman

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A Variational Inequality Perspective on Generative Adversarial Networks Gauthier Gidel, Hugo Berard, Gaëtan Vignoud, Pascal Vincent, Simon Lacoste-Julien

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A2BCD: Asynchronous Acceleration with Optimal Complexity Robert Hannah, Fei Feng, Wotao Yin

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Accelerating Nonconvex Learning via Replica Exchange Langevin Diffusion Yi Chen, Jinglin Chen, Jing Dong, Jian Peng, Zhaoran Wang

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Accumulation Bit-Width Scaling for Ultra-Low Precision Training of Deep Networks Charbel Sakr, Naigang Wang, Chia-Yu Chen, Jungwook Choi, Ankur Agrawal, Naresh Shanbhag, Kailash Gopalakrishnan

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Active Learning with Partial Feedback Peiyun Hu, Zachary C. Lipton, Anima Anandkumar, Deva Ramanan

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AD-VAT: An Asymmetric Dueling Mechanism for Learning Visual Active Tracking Fangwei Zhong, Peng Sun, Wenhan Luo, Tingyun Yan, Yizhou Wang

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Adaptive Estimators Show Information Compression in Deep Neural Networks Ivan Chelombiev, Conor Houghton, Cian O'Donnell

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Adaptive Gradient Methods with Dynamic Bound of Learning Rate Liangchen Luo, Yuanhao Xiong, Yan Liu, Xu Sun

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Adaptive Input Representations for Neural Language Modeling Alexei Baevski, Michael Auli

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Adaptive Posterior Learning: Few-Shot Learning with a Surprise-Based Memory Module Tiago Ramalho, Marta Garnelo

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Adaptivity of Deep ReLU Network for Learning in Besov and Mixed Smooth Besov Spaces: Optimal Rate and Curse of Dimensionality Taiji Suzuki

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AdaShift: Decorrelation and Convergence of Adaptive Learning Rate Methods Zhiming Zhou, Qingru Zhang, Guansong Lu, Hongwei Wang, Weinan Zhang, Yong Yu

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ADef: An Iterative Algorithm to Construct Adversarial Deformations Rima Alaifari, Giovanni S. Alberti, Tandri Gauksson

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Adv-BNN: Improved Adversarial Defense Through Robust Bayesian Neural Network Xuanqing Liu, Yao Li, Chongruo Wu, Cho-Jui Hsieh

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Adversarial Attacks on Graph Neural Networks via Meta Learning Daniel Zügner, Stephan Günnemann

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Adversarial Audio Synthesis Chris Donahue, Julian McAuley, Miller Puckette

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Adversarial Domain Adaptation for Stable Brain-Machine Interfaces Ali Farshchian, Juan A. Gallego, Joseph P. Cohen, Yoshua Bengio, Lee E. Miller, Sara A. Solla

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Adversarial Imitation via Variational Inverse Reinforcement Learning Ahmed H. Qureshi, Byron Boots, Michael C. Yip

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Adversarial Reprogramming of Neural Networks Gamaleldin F. Elsayed, Ian Goodfellow, Jascha Sohl-Dickstein

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Aggregated Momentum: Stability Through Passive Damping James Lucas, Shengyang Sun, Richard Zemel, Roger Grosse

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Algorithmic Framework for Model-Based Deep Reinforcement Learning with Theoretical Guarantees Yuping Luo, Huazhe Xu, Yuanzhi Li, Yuandong Tian, Trevor Darrell, Tengyu Ma

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ALISTA: Analytic Weights Are as Good as Learned Weights in LISTA Jialin Liu, Xiaohan Chen, Zhangyang Wang, Wotao Yin

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Amortized Bayesian Meta-Learning Sachin Ravi, Alex Beatson

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An Analytic Theory of Generalization Dynamics and Transfer Learning in Deep Linear Networks Andrew K. Lampinen, Surya Ganguli

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An Empirical Study of Binary Neural Networks' Optimisation Milad Alizadeh, Javier Fernández-Marqués, Nicholas D. Lane, Yarin Gal

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An Empirical Study of Example Forgetting During Deep Neural Network Learning Mariya Toneva, Alessandro Sordoni, Remi Tachet des Combes, Adam Trischler, Yoshua Bengio, Geoffrey J. Gordon

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Analysing Mathematical Reasoning Abilities of Neural Models David Saxton, Edward Grefenstette, Felix Hill, Pushmeet Kohli

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Analysis of Quantized Models Lu Hou, Ruiliang Zhang, James T. Kwok

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Analyzing Inverse Problems with Invertible Neural Networks Lynton Ardizzone, Jakob Kruse, Carsten Rother, Ullrich Köthe

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AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks Bo Chang, Minmin Chen, Eldad Haber, Ed H. Chi

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Anytime Minibatch: Exploiting Stragglers in Online Distributed Optimization Nuwan Ferdinand, Haider Al-Lawati, Stark Draper, Matthew Nokleby

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Approximability of Discriminators Implies Diversity in GANs Yu Bai, Tengyu Ma, Andrej Risteski

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Approximating CNNs with Bag-of-Local-Features Models Works Surprisingly Well on ImageNet Wieland Brendel, Matthias Bethge

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Are Adversarial Examples Inevitable? Ali Shafahi, W. Ronny Huang, Christoph Studer, Soheil Feizi, Tom Goldstein

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ARM: Augment-REINFORCE-Merge Gradient for Stochastic Binary Networks Mingzhang Yin, Mingyuan Zhou

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Attention, Learn to Solve Routing Problems! Wouter Kool, Herke van Hoof, Max Welling

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Attentive Neural Processes Hyunjik Kim, Andriy Mnih, Jonathan Schwarz, Marta Garnelo, Ali Eslami, Dan Rosenbaum, Oriol Vinyals, Yee Whye Teh

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Augmented Cyclic Adversarial Learning for Low Resource Domain Adaptation Ehsan Hosseini-Asl, Yingbo Zhou, Caiming Xiong, Richard Socher

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AutoLoss: Learning Discrete Schedule for Alternate Optimization Haowen Xu, Hao Zhang, Zhiting Hu, Xiaodan Liang, Ruslan Salakhutdinov, Eric Xing

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Automatically Composing Representation Transformations as a Means for Generalization Michael Chang, Abhishek Gupta, Sergey Levine, Thomas L. Griffiths

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Auxiliary Variational MCMC Raza Habib, David Barber

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BA-Net: Dense Bundle Adjustment Networks Chengzhou Tang, Ping Tan

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BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning Maxime Chevalier-Boisvert, Dzmitry Bahdanau, Salem Lahlou, Lucas Willems, Chitwan Saharia, Thien Huu Nguyen, Yoshua Bengio

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Backpropamine: Training Self-Modifying Neural Networks with Differentiable Neuromodulated Plasticity Thomas Miconi, Aditya Rawal, Jeff Clune, Kenneth O. Stanley

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Bayesian Deep Convolutional Networks with Many Channels Are Gaussian Processes Roman Novak, Lechao Xiao, Yasaman Bahri, Jaehoon Lee, Greg Yang, Jiri Hron, Daniel A. Abolafia, Jeffrey Pennington, Jascha Sohl-dickstein

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Bayesian Policy Optimization for Model Uncertainty Gilwoo Lee, Brian Hou, Aditya Mandalika, Jeongseok Lee, Sanjiban Choudhury, Siddhartha S. Srinivasa

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Bayesian Prediction of Future Street Scenes Using Synthetic Likelihoods Apratim Bhattacharyya, Mario Fritz, Bernt Schiele

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Benchmarking Neural Network Robustness to Common Corruptions and Perturbations Dan Hendrycks, Thomas Dietterich

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Beyond Greedy Ranking: Slate Optimization via List-CVAE Ray Jiang, Sven Gowal, Yuqiu Qian, Timothy Mann, Danilo J. Rezende

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Beyond Pixel Norm-Balls: Parametric Adversaries Using an Analytically Differentiable Renderer Hsueh-Ti Derek Liu, Michael Tao, Chun-Liang Li, Derek Nowrouzezahrai, Alec Jacobson

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Bias-Reduced Uncertainty Estimation for Deep Neural Classifiers Yonatan Geifman, Guy Uziel, Ran El-Yaniv

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Big-Little Net: An Efficient Multi-Scale Feature Representation for Visual and Speech Recognition Chun-Fu Chen, Quanfu Fan, Neil Mallinar, Tom Sercu, Rogerio Feris

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Biologically-Plausible Learning Algorithms Can Scale to Large Datasets Will Xiao, Honglin Chen, Qianli Liao, Tomaso Poggio

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Boosting Robustness Certification of Neural Networks Gagandeep Singh, Timon Gehr, Markus Püschel, Martin Vechev

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Bounce and Learn: Modeling Scene Dynamics with Real-World Bounces Senthil Purushwalkam, Abhinav Gupta, Danny Kaufman, Bryan Russell

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Building Dynamic Knowledge Graphs from Text Using Machine Reading Comprehension Rajarshi Das, Tsendsuren Munkhdalai, Xingdi Yuan, Adam Trischler, Andrew McCallum

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CAMOU: Learning Physical Vehicle Camouflages to Adversarially Attack Detectors in the Wild Yang Zhang, Hassan Foroosh, Philip David, Boqing Gong

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Capsule Graph Neural Network Zhang Xinyi, Lihui Chen

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Caveats for Information Bottleneck in Deterministic Scenarios Artemy Kolchinsky, Brendan D. Tracey, Steven Van Kuyk

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CBOW Is Not All You Need: Combining CBOW with the Compositional Matrix Space Model Florian Mai, Lukas Galke, Ansgar Scherp

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CEM-RL: Combining Evolutionary and Gradient-Based Methods for Policy Search Pourchot, Sigaud

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Characterizing Audio Adversarial Examples Using Temporal Dependency Zhuolin Yang, Bo Li, Pin-Yu Chen, Dawn Song

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ClariNet: Parallel Wave Generation in End-to-End Text-to-Speech Wei Ping, Kainan Peng, Jitong Chen

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Coarse-Grain Fine-Grain Coattention Network for Multi-Evidence Question Answering Victor Zhong, Caiming Xiong, Nitish Shirish Keskar, Richard Socher

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Code2seq: Generating Sequences from Structured Representations of Code Uri Alon, Shaked Brody, Omer Levy, Eran Yahav

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Combinatorial Attacks on Binarized Neural Networks Elias B Khalil, Amrita Gupta, Bistra Dilkina

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Competitive Experience Replay Hao Liu, Alexander Trott, Richard Socher, Caiming Xiong

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Complement Objective Training Hao-Yun Chen, Pei-Hsin Wang, Chun-Hao Liu, Shih-Chieh Chang, Jia-Yu Pan, Yu-Ting Chen, Wei Wei, Da-Cheng Juan

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Composing Complex Skills by Learning Transition Policies Youngwoon Lee, Shao-Hua Sun, Sriram Somasundaram, Edward S. Hu, Joseph J. Lim

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Conditional Network Embeddings Bo Kang, Jefrey Lijffijt, Tijl De Bie

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Context-Adaptive Entropy Model for End-to-End Optimized Image Compression Jooyoung Lee, Seunghyun Cho, Seung-Kwon Beack

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Contingency-Aware Exploration in Reinforcement Learning Jongwook Choi, Yijie Guo, Marcin Moczulski, Junhyuk Oh, Neal Wu, Mohammad Norouzi, Honglak Lee

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Convolutional Neural Networks on Non-Uniform Geometrical Signals Using Euclidean Spectral Transformation Chiyu Max Jiang, Dequan Wang, Jingwei Huang, Philip Marcus, Matthias Niessner

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Cost-Sensitive Robustness Against Adversarial Examples Xiao Zhang, David Evans

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Critical Learning Periods in Deep Networks Alessandro Achille, Matteo Rovere, Stefano Soatto

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DARTS: Differentiable Architecture Search Hanxiao Liu, Karen Simonyan, Yiming Yang

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Data-Dependent Coresets for Compressing Neural Networks with Applications to Generalization Bounds Cenk Baykal, Lucas Liebenwein, Igor Gilitschenski, Dan Feldman, Daniela Rus

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Decoupled Weight Decay Regularization Ilya Loshchilov, Frank Hutter

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Deep Anomaly Detection with Outlier Exposure Dan Hendrycks, Mantas Mazeika, Thomas Dietterich

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Deep Convolutional Networks as Shallow Gaussian Processes Adrià Garriga-Alonso, Carl Edward Rasmussen, Laurence Aitchison

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Deep Decoder: Concise Image Representations from Untrained Non-Convolutional Networks Reinhard Heckel, Paul Hand

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Deep Frank-Wolfe for Neural Network Optimization Leonard Berrada, Andrew Zisserman, M. Pawan Kumar

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Deep Graph Infomax Petar Veličković, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, R Devon Hjelm

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Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning Michael Lutter, Christian Ritter, Jan Peters

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Deep Layers as Stochastic Solvers Adel Bibi, Bernard Ghanem, Vladlen Koltun, Rene Ranftl

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Deep Learning 3D Shapes Using Alt-Az Anisotropic 2-Sphere Convolution Min Liu, Fupin Yao, Chiho Choi, Ayan Sinha, Karthik Ramani

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Deep Learning Generalizes Because the Parameter-Function mAP Is Biased Towards Simple Functions Guillermo Valle-Perez, Chico Q. Camargo, Ard A. Louis

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Deep Online Learning via Meta-Learning: Continual Adaptation for Model-Based RL Anusha Nagabandi, Chelsea Finn, Sergey Levine

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Deep Reinforcement Learning with Relational Inductive Biases Vinicius Zambaldi, David Raposo, Adam Santoro, Victor Bapst, Yujia Li, Igor Babuschkin, Karl Tuyls, David Reichert, Timothy Lillicrap, Edward Lockhart, Murray Shanahan, Victoria Langston, Razvan Pascanu, Matthew Botvinick, Oriol Vinyals, Peter Battaglia

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Deep, Skinny Neural Networks Are Not Universal Approximators Jesse Johnson

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DeepOBS: A Deep Learning Optimizer Benchmark Suite Frank Schneider, Lukas Balles, Philipp Hennig

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Defensive Quantization: When Efficiency Meets Robustness Ji Lin, Chuang Gan, Song Han

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Delta: Deep Learning Transfer Using Feature mAP with Attention for Convolutional Networks Xingjian Li, Haoyi Xiong, Hanchao Wang, Yuxuan Rao, Liping Liu, Jun Huan

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Detecting Egregious Responses in Neural Sequence-to-Sequence Models Tianxing He, James Glass

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Deterministic PAC-Bayesian Generalization Bounds for Deep Networks via Generalizing Noise-Resilience Vaishnavh Nagarajan, Zico Kolter

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Deterministic Variational Inference for Robust Bayesian Neural Networks Anqi Wu, Sebastian Nowozin, Edward Meeds, Richard E. Turner, José Miguel Hernández-Lobato, Alexander L. Gaunt

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DHER: Hindsight Experience Replay for Dynamic Goals Meng Fang, Cheng Zhou, Bei Shi, Boqing Gong, Jia Xu, Tong Zhang

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Diagnosing and Enhancing VAE Models Bin Dai, David Wipf

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DialogWAE: Multimodal Response Generation with Conditional Wasserstein Auto-Encoder Xiaodong Gu, Kyunghyun Cho, Jung-Woo Ha, Sunghun Kim

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Differentiable Learning-to-Normalize via Switchable Normalization Ping Luo, Jiamin Ren, Zhanglin Peng, Ruimao Zhang, Jingyu Li

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Differentiable Perturb-and-Parse: Semi-Supervised Parsing with a Structured Variational Autoencoder Caio Corro, Ivan Titov

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Diffusion Scattering Transforms on Graphs Fernando Gama, Alejandro Ribeiro, Joan Bruna

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Dimensionality Reduction for Representing the Knowledge of Probabilistic Models Marc T Law, Jake Snell, Amir-massoud Farahmand, Raquel Urtasun, Richard S Zemel

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Directed-Info GAIL: Learning Hierarchical Policies from Unsegmented Demonstrations Using Directed Information Mohit Sharma, Arjun Sharma, Nicholas Rhinehart, Kris M. Kitani

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Discovery of Natural Language Concepts in Individual Units of CNNs Seil Na, Yo Joong Choe, Dong-Hyun Lee, Gunhee Kim

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Discriminator Rejection Sampling Samaneh Azadi, Catherine Olsson, Trevor Darrell, Ian Goodfellow, Augustus Odena

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Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning Ilya Kostrikov, Kumar Krishna Agrawal, Debidatta Dwibedi, Sergey Levine, Jonathan Tompson

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Disjoint Mapping Network for Cross-Modal Matching of Voices and Faces Yandong Wen, Mahmoud Al Ismail, Weiyang Liu, Bhiksha Raj, Rita Singh

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Distribution-Interpolation Trade Off in Generative Models Damian Leśniak, Igor Sieradzki, Igor Podolak

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Distributional Concavity Regularization for GANs Shoichiro Yamaguchi, Masanori Koyama

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Diversity and Depth in Per-Example Routing Models Prajit Ramachandran, Quoc V. Le

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Diversity Is All You Need: Learning Skills Without a Reward Function Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, Sergey Levine

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Diversity-Sensitive Conditional Generative Adversarial Networks Dingdong Yang, Seunghoon Hong, Yunseok Jang, Tianchen Zhao, Honglak Lee

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Do Deep Generative Models Know What They Don't Know? Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, Balaji Lakshminarayanan

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DOM-Q-NET: Grounded RL on Structured Language Sheng Jia, Jamie Ryan Kiros, Jimmy Ba

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Don't Let Your Discriminator Be Fooled Brady Zhou, Philipp Krähenbühl

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Don't Settle for Average, Go for the Max: Fuzzy Sets and Max-Pooled Word Vectors Vitalii Zhelezniak, Aleksandar Savkov, April Shen, Francesco Moramarco, Jack Flann, Nils Y. Hammerla

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Double Viterbi: Weight Encoding for High Compression Ratio and Fast On-Chip Reconstruction for Deep Neural Network Daehyun Ahn, Dongsoo Lee, Taesu Kim, Jae-Joon Kim

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Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives George Tucker, Dieterich Lawson, Shixiang Gu, Chris J. Maddison

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DPSNet: End-to-End Deep Plane Sweep Stereo Sunghoon Im, Hae-Gon Jeon, Stephen Lin, In So Kweon

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Dynamic Channel Pruning: Feature Boosting and Suppression Xitong Gao, Yiren Zhao, Łukasz Dudziak, Robert Mullins, Cheng-zhong Xu

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Dynamic Sparse Graph for Efficient Deep Learning Liu Liu, Lei Deng, Xing Hu, Maohua Zhu, Guoqi Li, Yufei Ding, Yuan Xie

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Dynamically Unfolding Recurrent Restorer: A Moving Endpoint Control Method for Image Restoration Xiaoshuai Zhang, Yiping Lu, Jiaying Liu, Bin Dong

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DyRep: Learning Representations over Dynamic Graphs Rakshit Trivedi, Mehrdad Farajtabar, Prasenjeet Biswal, Hongyuan Zha

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Efficient Augmentation via Data Subsampling Michael Kuchnik, Virginia Smith

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Efficient Lifelong Learning with A-GEM Arslan Chaudhry, Marc’Aurelio Ranzato, Marcus Rohrbach, Mohamed Elhoseiny

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Efficient Multi-Objective Neural Architecture Search via Lamarckian Evolution Thomas Elsken, Jan Hendrik Metzen, Frank Hutter

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Efficient Training on Very Large Corpora via Gramian Estimation Walid Krichene, Nicolas Mayoraz, Steffen Rendle, Li Zhang, Xinyang Yi, Lichan Hong, Ed Chi, John Anderson

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Efficiently Testing Local Optimality and Escaping Saddles for ReLU Networks Chulhee Yun, Suvrit Sra, Ali Jadbabaie

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Eidetic 3D LSTM: A Model for Video Prediction and Beyond Yunbo Wang, Lu Jiang, Ming-Hsuan Yang, Li-Jia Li, Mingsheng Long, Li Fei-Fei

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Emergent Coordination Through Competition Siqi Liu, Guy Lever, Josh Merel, Saran Tunyasuvunakool, Nicolas Heess, Thore Graepel

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Emerging Disentanglement in Auto-Encoder Based Unsupervised Image Content Transfer Ori Press, Tomer Galanti, Sagie Benaim, Lior Wolf

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Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset Curtis Hawthorne, Andriy Stasyuk, Adam Roberts, Ian Simon, Cheng-Zhi Anna Huang, Sander Dieleman, Erich Elsen, Jesse Engel, Douglas Eck

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Energy-Constrained Compression for Deep Neural Networks via Weighted Sparse Projection and Layer Input Masking Haichuan Yang, Yuhao Zhu, Ji Liu

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Environment Probing Interaction Policies Wenxuan Zhou, Lerrel Pinto, Abhinav Gupta

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Episodic Curiosity Through Reachability Nikolay Savinov, Anton Raichuk, Damien Vincent, Raphael Marinier, Marc Pollefeys, Timothy Lillicrap, Sylvain Gelly

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Equi-Normalization of Neural Networks Pierre Stock, Benjamin Graham, Rémi Gribonval, Hervé Jégou

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Evaluating Robustness of Neural Networks with Mixed Integer Programming Vincent Tjeng, Kai Y. Xiao, Russ Tedrake

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Excessive Invariance Causes Adversarial Vulnerability Joern-Henrik Jacobsen, Jens Behrmann, Richard Zemel, Matthias Bethge

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Execution-Guided Neural Program Synthesis Xinyun Chen, Chang Liu, Dawn Song

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Exemplar Guided Unsupervised Image-to-Image Translation with Semantic Consistency Liqian Ma, Xu Jia, Stamatios Georgoulis, Tinne Tuytelaars, Luc Van Gool

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Explaining Image Classifiers by Counterfactual Generation Chun-Hao Chang, Elliot Creager, Anna Goldenberg, David Duvenaud

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Exploration by Random Network Distillation Yuri Burda, Harrison Edwards, Amos Storkey, Oleg Klimov

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Feature Intertwiner for Object Detection Hongyang Li, Bo Dai, Shaoshuai Shi, Wanli Ouyang, Xiaogang Wang

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Feature-Wise Bias Amplification Klas Leino, Emily Black, Matt Fredrikson, Shayak Sen, Anupam Datta

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Feed-Forward Propagation in Probabilistic Neural Networks with Categorical and Max Layers Alexander Shekhovtsov, Boris Flach

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FFJORD: Free-Form Continuous Dynamics for Scalable Reversible Generative Models Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever, David Duvenaud

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Fixup Initialization: Residual Learning Without Normalization Hongyi Zhang, Yann N. Dauphin, Tengyu Ma

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FlowQA: Grasping Flow in History for Conversational Machine Comprehension Hsin-Yuan Huang, Eunsol Choi, Wen-tau Yih

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Fluctuation-Dissipation Relations for Stochastic Gradient Descent Sho Yaida

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From Hard to Soft: Understanding Deep Network Nonlinearities via Vector Quantization and Statistical Inference Randall Balestriero, Richard Baraniuk

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From Language to Goals: Inverse Reinforcement Learning for Vision-Based Instruction Following Justin Fu, Anoop Korattikara, Sergey Levine, Sergio Guadarrama

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Function Space Particle Optimization for Bayesian Neural Networks Ziyu Wang, Tongzheng Ren, Jun Zhu, Bo Zhang

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Functional Variational Bayesian Neural Networks Shengyang Sun, Guodong Zhang, Jiaxin Shi, Roger Grosse

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G-SGD: Optimizing ReLU Neural Networks in Its Positively Scale-Invariant Space Qi Meng, Shuxin Zheng, Huishuai Zhang, Wei Chen, Qiwei Ye, Zhi-Ming Ma, Nenghai Yu, Tie-Yan Liu

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GamePad: A Learning Environment for Theorem Proving Daniel Huang, Prafulla Dhariwal, Dawn Song, Ilya Sutskever

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GAN Dissection: Visualizing and Understanding Generative Adversarial Networks David Bau, Jun-Yan Zhu, Hendrik Strobelt, Bolei Zhou, Joshua B. Tenenbaum, William T. Freeman, Antonio Torralba

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GANSynth: Adversarial Neural Audio Synthesis Jesse Engel, Kumar Krishna Agrawal, Shuo Chen, Ishaan Gulrajani, Chris Donahue, Adam Roberts

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Generalizable Adversarial Training via Spectral Normalization Farzan Farnia, Jesse Zhang, David Tse

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Generalized Tensor Models for Recurrent Neural Networks Valentin Khrulkov, Oleksii Hrinchuk, Ivan Oseledets

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Generating High Fidelity Images with Subscale Pixel Networks and Multidimensional Upscaling Jacob Menick, Nal Kalchbrenner

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Generating Liquid Simulations with Deformation-Aware Neural Networks Lukas Prantl, Boris Bonev, Nils Thuerey

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Generating Multi-Agent Trajectories Using Programmatic Weak Supervision Eric Zhan, Stephan Zheng, Yisong Yue, Long Sha, Patrick Lucey

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Generating Multiple Objects at Spatially Distinct Locations Tobias Hinz, Stefan Heinrich, Stefan Wermter

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Generative Code Modeling with Graphs Marc Brockschmidt, Miltiadis Allamanis, Alexander L. Gaunt, Oleksandr Polozov

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Generative Predecessor Models for Sample-Efficient Imitation Learning Yannick Schroecker, Mel Vecerik, Jon Scholz

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Generative Question Answering: Learning to Answer the Whole Question Mike Lewis, Angela Fan

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Global-to-Local Memory Pointer Networks for Task-Oriented Dialogue Chien-Sheng Wu, Richard Socher, Caiming Xiong

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GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, Samuel R. Bowman

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GO Gradient for Expectation-Based Objectives Yulai Cong, Miaoyun Zhao, Ke Bai, Lawrence Carin

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Gradient Descent Aligns the Layers of Deep Linear Networks Ziwei Ji, Matus Telgarsky

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Gradient Descent Provably Optimizes Over-Parameterized Neural Networks Simon S. Du, Xiyu Zhai, Barnabas Poczos, Aarti Singh

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Graph HyperNetworks for Neural Architecture Search Chris Zhang, Mengye Ren, Raquel Urtasun

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Graph Wavelet Neural Network Bingbing Xu, Huawei Shen, Qi Cao, Yunqi Qiu, Xueqi Cheng

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Guiding Policies with Language via Meta-Learning John D. Co-Reyes, Abhishek Gupta, Suvansh Sanjeev, Nick Altieri, Jacob Andreas, John DeNero, Pieter Abbeel, Sergey Levine

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H-Detach: Modifying the LSTM Gradient Towards Better Optimization Bhargav Kanuparthi, Devansh Arpit, Giancarlo Kerg, Nan Rosemary Ke, Ioannis Mitliagkas, Yoshua Bengio

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Harmonic Unpaired Image-to-Image Translation Rui Zhang, Tomas Pfister, Jia Li

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Harmonizing Maximum Likelihood with GANs for Multimodal Conditional Generation Soochan Lee, Junsoo Ha, Gunhee Kim

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Hierarchical Generative Modeling for Controllable Speech Synthesis Wei-Ning Hsu, Yu Zhang, Ron J. Weiss, Heiga Zen, Yonghui Wu, Yuxuan Wang, Yuan Cao, Ye Jia, Zhifeng Chen, Jonathan Shen, Patrick Nguyen, Ruoming Pang

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Hierarchical Interpretations for Neural Network Predictions Chandan Singh, W. James Murdoch, Bin Yu

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Hierarchical Reinforcement Learning via Advantage-Weighted Information Maximization Takayuki Osa, Voot Tangkaratt, Masashi Sugiyama

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Hierarchical RL Using an Ensemble of Proprioceptive Periodic Policies Kenneth Marino, Abhinav Gupta, Rob Fergus, Arthur Szlam

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Hierarchical Visuomotor Control of Humanoids Josh Merel, Arun Ahuja, Vu Pham, Saran Tunyasuvunakool, Siqi Liu, Dhruva Tirumala, Nicolas Heess, Greg Wayne

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Hindsight Policy Gradients Paulo Rauber, Avinash Ummadisingu, Filipe Mutz, Jürgen Schmidhuber

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How Important Is a Neuron Kedar Dhamdhere, Mukund Sundararajan, Qiqi Yan

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How Powerful Are Graph Neural Networks? Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka

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How to Train Your MAML Antreas Antoniou, Harrison Edwards, Amos Storkey

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Human-Level Protein Localization with Convolutional Neural Networks Elisabeth Rumetshofer, Markus Hofmarcher, Clemens Röhrl, Sepp Hochreiter, Günter Klambauer

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Hyperbolic Attention Networks Caglar Gulcehre, Misha Denil, Mateusz Malinowski, Ali Razavi, Razvan Pascanu, Karl Moritz Hermann, Peter Battaglia, Victor Bapst, David Raposo, Adam Santoro, Nando de Freitas

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Identifying and Controlling Important Neurons in Neural Machine Translation Anthony Bau, Yonatan Belinkov, Hassan Sajjad, Nadir Durrani, Fahim Dalvi, James Glass

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ImageNet-Trained CNNs Are Biased Towards Texture; Increasing Shape Bias Improves Accuracy and Robustness Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A. Wichmann, Wieland Brendel

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Imposing Category Trees onto Word-Embeddings Using a Geometric Construction Tiansi Dong, Chrisitan Bauckhage, Hailong Jin, Juanzi Li, Olaf Cremers, Daniel Speicher, Armin B. Cremers, Joerg Zimmermann

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Improving Differentiable Neural Computers Through Memory Masking, De-Allocation, and Link Distribution Sharpness Control Robert Csordas, Juergen Schmidhuber

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Improving Generalization and Stability of Generative Adversarial Networks Hoang Thanh-Tung, Truyen Tran, Svetha Venkatesh

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Improving MMD-GAN Training with Repulsive Loss Function Wei Wang, Yuan Sun, Saman Halgamuge

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Improving Sequence-to-Sequence Learning via Optimal Transport Liqun Chen, Yizhe Zhang, Ruiyi Zhang, Chenyang Tao, Zhe Gan, Haichao Zhang, Bai Li, Dinghan Shen, Changyou Chen, Lawrence Carin

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Improving the Generalization of Adversarial Training with Domain Adaptation Chuanbiao Song, Kun He, Liwei Wang, John E. Hopcroft

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InfoBot: Transfer and Exploration via the Information Bottleneck Anirudh Goyal, Riashat Islam, Dj Strouse, Zafarali Ahmed, Hugo Larochelle, Matthew Botvinick, Yoshua Bengio, Sergey Levine

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Information Asymmetry in KL-Regularized RL Alexandre Galashov, Siddhant M. Jayakumar, Leonard Hasenclever, Dhruva Tirumala, Jonathan Schwarz, Guillaume Desjardins, Wojciech M. Czarnecki, Yee Whye Teh, Razvan Pascanu, Nicolas Heess

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Information Theoretic Lower Bounds on Negative Log Likelihood Luis A. Lastras-Montaño

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Information-Directed Exploration for Deep Reinforcement Learning Nikolay Nikolov, Johannes Kirschner, Felix Berkenkamp, Andreas Krause

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Initialized Equilibrium Propagation for Backprop-Free Training Peter O'Connor, Efstratios Gavves, Max Welling

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InstaGAN: Instance-Aware Image-to-Image Translation Sangwoo Mo, Minsu Cho, Jinwoo Shin

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Integer Networks for Data Compression with Latent-Variable Models Johannes Ballé, Nick Johnston, David Minnen

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Interpolation-Prediction Networks for Irregularly Sampled Time Series Satya Narayan Shukla, Benjamin Marlin

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Invariant and Equivariant Graph Networks Haggai Maron, Heli Ben-Hamu, Nadav Shamir, Yaron Lipman

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INVASE: Instance-Wise Variable Selection Using Neural Networks Jinsung Yoon, James Jordon, Mihaela van der Schaar

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Janossy Pooling: Learning Deep Permutation-Invariant Functions for Variable-Size Inputs Ryan L. Murphy, Balasubramaniam Srinivasan, Vinayak Rao, Bruno Ribeiro

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K for the Price of 1: Parameter-Efficient Multi-Task and Transfer Learning Pramod Kaushik Mudrakarta, Mark Sandler, Andrey Zhmoginov, Andrew Howard

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Kernel Change-Point Detection with Auxiliary Deep Generative Models Wei-Cheng Chang, Chun-Liang Li, Yiming Yang, Barnabás Póczos

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Kernel RNN Learning (KeRNL) Christopher Roth, Ingmar Kanitscheider, Ila Fiete

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KnockoffGAN: Generating Knockoffs for Feature Selection Using Generative Adversarial Networks James Jordon, Jinsung Yoon, Mihaela van der Schaar

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Knowledge Flow: Improve upon Your Teachers Iou-Jen Liu, Jian Peng, Alexander Schwing

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L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data Jianbo Chen, Le Song, Martin J. Wainwright, Michael I. Jordan

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L2-Nonexpansive Neural Networks Haifeng Qian, Mark N. Wegman

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Label Super-Resolution Networks Kolya Malkin, Caleb Robinson, Le Hou, Rachel Soobitsky, Jacob Czawlytko, Dimitris Samaras, Joel Saltz, Lucas Joppa, Nebojsa Jojic

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Lagging Inference Networks and Posterior Collapse in Variational Autoencoders Junxian He, Daniel Spokoyny, Graham Neubig, Taylor Berg-Kirkpatrick

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LanczosNet: Multi-Scale Deep Graph Convolutional Networks Renjie Liao, Zhizhen Zhao, Raquel Urtasun, Richard Zemel

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Large Scale GAN Training for High Fidelity Natural Image Synthesis Andrew Brock, Jeff Donahue, Karen Simonyan

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Large Scale Graph Learning from Smooth Signals Vassilis Kalofolias, Nathanaël Perraudin

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Large-Scale Answerer in Questioner's Mind for Visual Dialog Question Generation Sang-Woo Lee, Tong Gao, Sohee Yang, Jaejun Yoo, Jung-Woo Ha

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Large-Scale Study of Curiosity-Driven Learning Yuri Burda, Harri Edwards, Deepak Pathak, Amos Storkey, Trevor Darrell, Alexei A. Efros

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Latent Convolutional Models ShahRukh Athar, Evgeny Burnaev, Victor Lempitsky

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LayoutGAN: Generating Graphic Layouts with Wireframe Discriminators Jianan Li, Jimei Yang, Aaron Hertzmann, Jianming Zhang, Tingfa Xu

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Learnable Embedding Space for Efficient Neural Architecture Compression Shengcao Cao, Xiaofang Wang, Kris M. Kitani

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Learning a Meta-Solver for Syntax-Guided Program Synthesis Xujie Si, Yuan Yang, Hanjun Dai, Mayur Naik, Le Song

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Learning a SAT Solver from Single-Bit Supervision Daniel Selsam, Matthew Lamm, Benedikt B\"{u}nz, Percy Liang, Leonardo de Moura, David L. Dill

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Learning Actionable Representations with Goal Conditioned Policies Dibya Ghosh, Abhishek Gupta, Sergey Levine

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Learning Concise Representations for Regression by Evolving Networks of Trees William La Cava, Tilak Raj Singh, James Taggart, Srinivas Suri, Jason H. Moore

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Learning Deep Representations by Mutual Information Estimation and Maximization R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, Yoshua Bengio

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Learning Embeddings into Entropic Wasserstein Spaces Charlie Frogner, Farzaneh Mirzazadeh, Justin Solomon

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Learning Exploration Policies for Navigation Tao Chen, Saurabh Gupta, Abhinav Gupta

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Learning Factorized Multimodal Representations Yao-Hung Hubert Tsai, Paul Pu Liang, Amir Zadeh, Louis-Philippe Morency, Ruslan Salakhutdinov

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Learning Factorized Representations for Open-Set Domain Adaptation Mahsa Baktashmotlagh, Masoud Faraki, Tom Drummond, Mathieu Salzmann

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Learning Finite State Representations of Recurrent Policy Networks Anurag Koul, Alan Fern, Sam Greydanus

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Learning from Positive and Unlabeled Data with a Selection Bias Masahiro Kato, Takeshi Teshima, Junya Honda

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Learning Grid Cells as Vector Representation of Self-Position Coupled with Matrix Representation of Self-Motion Ruiqi Gao, Jianwen Xie, Song-Chun Zhu, Ying Nian Wu

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Learning Implicitly Recurrent CNNs Through Parameter Sharing Pedro Savarese, Michael Maire

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Learning Latent Superstructures in Variational Autoencoders for Deep Multidimensional Clustering Xiaopeng Li, Zhourong Chen, Leonard K. M. Poon, Nevin L. Zhang

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Learning Localized Generative Models for 3D Point Clouds via Graph Convolution Diego Valsesia, Giulia Fracastoro, Enrico Magli

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Learning Mixed-Curvature Representations in Product Spaces Albert Gu, Frederic Sala, Beliz Gunel, Christopher Ré

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Learning Multi-Level Hierarchies with Hindsight Andrew Levy, George Konidaris, Robert Platt, Kate Saenko

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Learning Multimodal Graph-to-Graph Translation for Molecule Optimization Wengong Jin, Kevin Yang, Regina Barzilay, Tommi Jaakkola

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Learning Neural PDE Solvers with Convergence Guarantees Jun-Ting Hsieh, Shengjia Zhao, Stephan Eismann, Lucia Mirabella, Stefano Ermon

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Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids Yunzhu Li, Jiajun Wu, Russ Tedrake, Joshua B. Tenenbaum, Antonio Torralba

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Learning Procedural Abstractions and Evaluating Discrete Latent Temporal Structure Karan Goel, Emma Brunskill

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Learning Programmatically Structured Representations with Perceptor Gradients Svetlin Penkov, Subramanian Ramamoorthy

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Learning Protein Sequence Embeddings Using Information from Structure Tristan Bepler, Bonnie Berger

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Learning Protein Structure with a Differentiable Simulator John Ingraham, Adam Riesselman, Chris Sander, Debora Marks

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Learning Recurrent Binary/Ternary Weights Arash Ardakani, Zhengyun Ji, Sean C. Smithson, Brett H. Meyer, Warren J. Gross

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Learning Representations of Sets Through Optimized Permutations Yan Zhang, Jonathon Hare, Adam Prügel-Bennett

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Learning Robust Representations by Projecting Superficial Statistics Out Haohan Wang, Zexue He, Zachary C. Lipton, Eric P. Xing

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Learning Self-Imitating Diverse Policies Tanmay Gangwani, Qiang Liu, Jian Peng

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Learning Sparse Relational Transition Models Victoria Xia, Zi Wang, Kelsey Allen, Tom Silver, Leslie Pack Kaelbling

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Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning Anusha Nagabandi, Ignasi Clavera, Simin Liu, Ronald S. Fearing, Pieter Abbeel, Sergey Levine, Chelsea Finn

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Learning to Describe Scenes with Programs Yunchao Liu, Zheng Wu, Daniel Ritchie, William T. Freeman, Joshua B. Tenenbaum, Jiajun Wu

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Learning to Design RNA Frederic Runge, Danny Stoll, Stefan Falkner, Frank Hutter

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Learning to Infer and Execute 3D Shape Programs Yonglong Tian, Andrew Luo, Xingyuan Sun, Kevin Ellis, William T. Freeman, Joshua B. Tenenbaum, Jiajun Wu

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Learning to Learn with Conditional Class Dependencies Xiang Jiang, Mohammad Havaei, Farshid Varno, Gabriel Chartrand, Nicolas Chapados, Stan Matwin

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Learning to Learn Without Forgetting by Maximizing Transfer and Minimizing Interference Matthew Riemer, Ignacio Cases, Robert Ajemian, Miao Liu, Irina Rish, Yuhai Tu, and Gerald Tesauro

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Learning to Make Analogies by Contrasting Abstract Relational Structure Felix Hill, Adam Santoro, David Barrett, Ari Morcos, Timothy Lillicrap

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Learning to Navigate the Web Izzeddin Gur, Ulrich Rueckert, Aleksandra Faust, Dilek Hakkani-Tur

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Learning to Propagate Labels: Transductive Propagation Network for Few-Shot Learning Yanbin Liu, Juho Lee, Minseop Park, Saehoon Kim, Eunho Yang, Sung Ju Hwang, Yi Yang

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Learning to Remember More with Less Memorization Hung Le, Truyen Tran, Svetha Venkatesh

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Learning to Represent Edits Pengcheng Yin, Graham Neubig, Miltiadis Allamanis, Marc Brockschmidt, Alexander L. Gaunt

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Learning to Schedule Communication in Multi-Agent Reinforcement Learning Daewoo Kim, Sangwoo Moon, David Hostallero, Wan Ju Kang, Taeyoung Lee, Kyunghwan Son, Yung Yi

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Learning to Screen for Fast SoftMax Inference on Large Vocabulary Neural Networks Patrick Chen, Si Si, Sanjiv Kumar, Yang Li, Cho-Jui Hsieh

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Learning to Simulate Nataniel Ruiz, Samuel Schulter, Manmohan Chandraker

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Learning to Solve Circuit-SAT: An Unsupervised Differentiable Approach Saeed Amizadeh, Sergiy Matusevych, Markus Weimer

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Learning to Understand Goal Specifications by Modelling Reward Dzmitry Bahdanau, Felix Hill, Jan Leike, Edward Hughes, Arian Hosseini, Pushmeet Kohli, Edward Grefenstette

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Learning Two-Layer Neural Networks with Symmetric Inputs Rong Ge, Rohith Kuditipudi, Zhize Li, Xiang Wang

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Learning What and Where to Attend Drew Linsley, Dan Shiebler, Sven Eberhardt, Thomas Serre

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Learning What You Can Do Before Doing Anything Oleh Rybkin, Karl Pertsch, Konstantinos G. Derpanis, Kostas Daniilidis, Andrew Jaegle

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Learning When to Communicate at Scale in Multiagent Cooperative and Competitive Tasks Amanpreet Singh, Tushar Jain, Sainbayar Sukhbaatar

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Learning-Based Frequency Estimation Algorithms Chen-Yu Hsu, Piotr Indyk, Dina Katabi, Ali Vakilian

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LeMoNADe: Learned Motif and Neuronal Assembly Detection in Calcium Imaging Videos Elke Kirschbaum, Manuel Haußmann, Steffen Wolf, Hannah Sonntag, Justus Schneider, Shehabeldin Elzoheiry, Oliver Kann, Daniel Durstewitz, Fred A Hamprecht

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Local SGD Converges Fast and Communicates Little Sebastian U. Stich

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M^3RL: Mind-Aware Multi-Agent Management Reinforcement Learning Tianmin Shu, Yuandong Tian

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MAE: Mutual Posterior-Divergence Regularization for Variational AutoEncoders Xuezhe Ma, Chunting Zhou, Eduard Hovy

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Marginal Policy Gradients: A Unified Family of Estimators for Bounded Action Spaces with Applications Carson Eisenach, Haichuan Yang, Ji Liu, Han Liu

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Marginalized Average Attentional Network for Weakly-Supervised Learning Yuan Yuan, Yueming Lyu, Xi Shen, Ivor W. Tsang, Dit-Yan Yeung

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Max-MIG: An Information Theoretic Approach for Joint Learning from Crowds Peng Cao, Yilun Xu, Yuqing Kong, Yizhou Wang

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Maximal Divergence Sequential Autoencoder for Binary Software Vulnerability Detection Tue Le, Tuan Nguyen, Trung Le, Dinh Phung, Paul Montague, Olivier De Vel, Lizhen Qu

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Measuring and Regularizing Networks in Function Space Ari Benjamin, David Rolnick, Konrad Kording

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Measuring Compositionality in Representation Learning Jacob Andreas

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Meta-Learning for Stochastic Gradient MCMC Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato

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Meta-Learning Probabilistic Inference for Prediction Jonathan Gordon, John Bronskill, Matthias Bauer, Sebastian Nowozin, Richard Turner

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Meta-Learning Update Rules for Unsupervised Representation Learning Luke Metz, Niru Maheswaranathan, Brian Cheung, Jascha Sohl-Dickstein

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Meta-Learning with Differentiable Closed-Form Solvers Luca Bertinetto, Joao F. Henriques, Philip Torr, Andrea Vedaldi

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Meta-Learning with Latent Embedding Optimization Andrei A. Rusu, Dushyant Rao, Jakub Sygnowski, Oriol Vinyals, Razvan Pascanu, Simon Osindero, Raia Hadsell

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Minimal Images in Deep Neural Networks: Fragile Object Recognition in Natural Images Sanjana Srivastava, Guy Ben-Yosef, Xavier Boix

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Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters Marton Havasi, Robert Peharz, José Miguel Hernández-Lobato

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Minimum Divergence vs. Maximum Margin: An Empirical Comparison on Seq2Seq Models Huan Zhang, Hai Zhao

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MisGAN: Learning from Incomplete Data with Generative Adversarial Networks Steven Cheng-Xian Li, Bo Jiang, Benjamin Marlin

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Mode Normalization Lucas Deecke, Iain Murray, Hakan Bilen

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Model-Predictive Policy Learning with Uncertainty Regularization for Driving in Dense Traffic Mikael Henaff, Alfredo Canziani, Yann LeCun

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Modeling the Long Term Future in Model-Based Reinforcement Learning Nan Rosemary Ke, Amanpreet Singh, Ahmed Touati, Anirudh Goyal, Yoshua Bengio, Devi Parikh, Dhruv Batra

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Modeling Uncertainty with Hedged Instance Embeddings Seong Joon Oh, Kevin P. Murphy, Jiyan Pan, Joseph Roth, Florian Schroff, Andrew C. Gallagher

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Multi-Agent Dual Learning Yiren Wang, Yingce Xia, Tianyu He, Fei Tian, Tao Qin, ChengXiang Zhai, Tie-Yan Liu

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Multi-Class Classification Without Multi-Class Labels Yen-Chang Hsu, Zhaoyang Lv, Joel Schlosser, Phillip Odom, Zsolt Kira

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Multi-Domain Adversarial Learning Alice Schoenauer-Sebag, Louise Heinrich, Marc Schoenauer, Michele Sebag, Lani F. Wu, Steve J. Altschuler

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Multi-Step Retriever-Reader Interaction for Scalable Open-Domain Question Answering Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Andrew McCallum

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Multilingual Neural Machine Translation with Knowledge Distillation Xu Tan, Yi Ren, Di He, Tao Qin, Zhou Zhao, Tie-Yan Liu

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Multilingual Neural Machine Translation with Soft Decoupled Encoding Xinyi Wang, Hieu Pham, Philip Arthur, Graham Neubig

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Multiple-Attribute Text Rewriting Guillaume Lample, Sandeep Subramanian, Eric Smith, Ludovic Denoyer, Marc'Aurelio Ranzato, Y-Lan Boureau

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Music Transformer: Generating Music with Long-Term Structure Cheng-Zhi Anna Huang, Ashish Vaswani, Jakob Uszkoreit, Ian Simon, Curtis Hawthorne, Noam Shazeer, Andrew M. Dai, Matthew D. Hoffman, Monica Dinculescu, Douglas Eck

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NADPEx: An On-Policy Temporally Consistent Exploration Method for Deep Reinforcement Learning Sirui Xie, Junning Huang, Lanxin Lei, Chunxiao Liu, Zheng Ma, Wei Zhang, Liang Lin

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Near-Optimal Representation Learning for Hierarchical Reinforcement Learning Ofir Nachum, Shixiang Gu, Honglak Lee, Sergey Levine

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Neural Graph Evolution: Towards Efficient Automatic Robot Design Tingwu Wang, Yuhao Zhou, Sanja Fidler, Jimmy Ba

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Neural Logic Machines Honghua Dong, Jiayuan Mao, Tian Lin, Chong Wang, Lihong Li, Denny Zhou

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Neural Network Gradient-Based Learning of Black-Box Function Interfaces Alon Jacovi, Guy Hadash, Einat Kermany, Boaz Carmeli, Ofer Lavi, George Kour, Jonathan Berant

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Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology Bastian Rieck, Matteo Togninalli, Christian Bock, Michael Moor, Max Horn, Thomas Gumbsch, Karsten Borgwardt

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Neural Probabilistic Motor Primitives for Humanoid Control Josh Merel, Leonard Hasenclever, Alexandre Galashov, Arun Ahuja, Vu Pham, Greg Wayne, Yee Whye Teh, Nicolas Heess

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Neural Program Repair by Jointly Learning to Localize and Repair Marko Vasic, Aditya Kanade, Petros Maniatis, David Bieber, Rishabh Singh

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Neural Speed Reading with Structural-Jump-LSTM Christian Hansen, Casper Hansen, Stephen Alstrup, Jakob Grue Simonsen, Christina Lioma

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Neural TTS Stylization with Adversarial and Collaborative Games Shuang Ma, Daniel Mcduff, Yale Song

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No Training Required: Exploring Random Encoders for Sentence Classification John Wieting, Douwe Kiela

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Non-Vacuous Generalization Bounds at the ImageNet Scale: A PAC-Bayesian Compression Approach Wenda Zhou, Victor Veitch, Morgane Austern, Ryan P. Adams, Peter Orbanz

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NOODL: Provable Online Dictionary Learning and Sparse Coding Sirisha Rambhatla, Xingguo Li, Jarvis Haupt

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Off-Policy Evaluation and Learning from Logged Bandit Feedback: Error Reduction via Surrogate Policy Yuan Xie, Boyi Liu, Qiang Liu, Zhaoran Wang, Yuan Zhou, Jian Peng

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On Computation and Generalization of Generative Adversarial Networks Under Spectrum Control Haoming Jiang, Zhehui Chen, Minshuo Chen, Feng Liu, Dingding Wang, Tuo Zhao

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On Random Deep Weight-Tied Autoencoders: Exact Asymptotic Analysis, Phase Transitions, and Implications to Training Ping Li, Phan-Minh Nguyen

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On Self Modulation for Generative Adversarial Networks Ting Chen, Mario Lucic, Neil Houlsby, Sylvain Gelly

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On the Convergence of a Class of Adam-Type Algorithms for Non-Convex Optimization Xiangyi Chen, Sijia Liu, Ruoyu Sun, Mingyi Hong

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On the Loss Landscape of a Class of Deep Neural Networks with No Bad Local Valleys Quynh Nguyen, Mahesh Chandra Mukkamala, Matthias Hein

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On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data Nan Lu, Gang Niu, Aditya Krishna Menon, Masashi Sugiyama

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On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length Stanisław Jastrzębski, Zachary Kenton, Nicolas Ballas, Asja Fischer, Yoshua Bengio, Amos Storkey

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On the Sensitivity of Adversarial Robustness to Input Data Distributions Gavin Weiguang Ding, Kry Yik Chau Lui, Xiaomeng Jin, Luyu Wang, Ruitong Huang

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On the Turing Completeness of Modern Neural Network Architectures Jorge Pérez, Javier Marinković, Pablo Barceló

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On the Universal Approximability and Complexity Bounds of Quantized ReLU Neural Networks Yukun Ding, Jinglan Liu, Jinjun Xiong, Yiyu Shi

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Opportunistic Learning: Budgeted Cost-Sensitive Learning from Data Streams Mohammad Kachuee, Orpaz Goldstein, Kimmo Kärkkäinen, Sajad Darabi, Majid Sarrafzadeh

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Optimal Completion Distillation for Sequence Learning Sara Sabour, William Chan, Mohammad Norouzi

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Optimal Control via Neural Networks: A Convex Approach Yize Chen, Yuanyuan Shi, Baosen Zhang

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Optimal Transport Maps for Distribution Preserving Operations on Latent Spaces of Generative Models Eirikur Agustsson, Alexander Sage, Radu Timofte, Luc Van Gool

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Optimistic Mirror Descent in Saddle-Point Problems: Going the Extra (gradient) Mile Panayotis Mertikopoulos, Bruno Lecouat, Houssam Zenati, Chuan-Sheng Foo, Vijay Chandrasekhar, Georgios Piliouras

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Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks Yikang Shen, Shawn Tan, Alessandro Sordoni, Aaron Courville

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Overcoming Catastrophic Forgetting for Continual Learning via Model Adaptation Wenpeng Hu, Zhou Lin, Bing Liu, Chongyang Tao, Zhengwei Tao, Jinwen Ma, Dongyan Zhao, Rui Yan

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Overcoming the Disentanglement vs Reconstruction Trade-Off via Jacobian Supervision José Lezama

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PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees James Jordon, Jinsung Yoon, Mihaela van der Schaar

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Pay Less Attention with Lightweight and Dynamic Convolutions Felix Wu, Angela Fan, Alexei Baevski, Yann Dauphin, Michael Auli

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PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks Jan Svoboda, Jonathan Masci, Federico Monti, Michael Bronstein, Leonidas Guibas

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Per-Tensor Fixed-Point Quantization of the Back-Propagation Algorithm Charbel Sakr, Naresh Shanbhag

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Phase-Aware Speech Enhancement with Deep Complex U-Net Hyeong-Seok Choi, Jang-Hyun Kim, Jaesung Huh, Adrian Kim, Jung-Woo Ha, Kyogu Lee

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Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control Kendall Lowrey, Aravind Rajeswaran, Sham Kakade, Emanuel Todorov, Igor Mordatch

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Poincare Glove: Hyperbolic Word Embeddings Alexandru Tifrea, Gary Becigneul, Octavian-Eugen Ganea

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Policy Transfer with Strategy Optimization Wenhao Yu, C. Karen Liu, Greg Turk

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Post Selection Inference with Incomplete Maximum Mean Discrepancy Estimator Makoto Yamada, Denny Wu, Yao-Hung Hubert Tsai, Hirofumi Ohta, Ruslan Salakhutdinov, Ichiro Takeuchi, Kenji Fukumizu

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Posterior Attention Models for Sequence to Sequence Learning Shiv Shankar, Sunita Sarawagi

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Practical Lossless Compression with Latent Variables Using Bits Back Coding James Townsend, Thomas Bird, David Barber

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Preconditioner on Matrix Lie Group for SGD Xi-Lin Li

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Predict Then Propagate: Graph Neural Networks Meet Personalized PageRank Johannes Gasteiger, Aleksandar Bojchevski, Stephan Günnemann

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Predicting the Generalization Gap in Deep Networks with Margin Distributions Yiding Jiang, Dilip Krishnan, Hossein Mobahi, Samy Bengio

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Preferences Implicit in the State of the World Rohin Shah, Dmitrii Krasheninnikov, Jordan Alexander, Pieter Abbeel, Anca Dragan

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Preventing Posterior Collapse with Delta-VAEs Ali Razavi, Aaron van den Oord, Ben Poole, Oriol Vinyals

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Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors Andrew Ilyas, Logan Engstrom, Aleksander Madry

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Probabilistic Planning with Sequential Monte Carlo Methods Alexandre Piche, Valentin Thomas, Cyril Ibrahim, Yoshua Bengio, Chris Pal

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Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning Ying Wen, Yaodong Yang, Rui Luo, Jun Wang, Wei Pan

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ProbGAN: Towards Probabilistic GAN with Theoretical Guarantees Hao He, Hao Wang, Guang-He Lee, Yonglong Tian

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ProMP: Proximal Meta-Policy Search Jonas Rothfuss, Dennis Lee, Ignasi Clavera, Tamim Asfour, Pieter Abbeel

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ProxQuant: Quantized Neural Networks via Proximal Operators Yu Bai, Yu-Xiang Wang, Edo Liberty

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ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware Han Cai, Ligeng Zhu, Song Han

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Quasi-Hyperbolic Momentum and Adam for Deep Learning Jerry Ma, Denis Yarats

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Quaternion Recurrent Neural Networks Titouan Parcollet, Mirco Ravanelli, Mohamed Morchid, Georges Linarès, Chiheb Trabelsi, Renato De Mori, Yoshua Bengio

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Query-Efficient Hard-Label Black-Box Attack: An Optimization-Based Approach Minhao Cheng, Thong Le, Pin-Yu Chen, Huan Zhang, JinFeng Yi, Cho-Jui Hsieh

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Random Mesh Projectors for Inverse Problems Konik Kothari, Sidharth Gupta, Maarten v. de Hoop, Ivan Dokmanic

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Reasoning About Physical Interactions with Object-Oriented Prediction and Planning Michael Janner, Sergey Levine, William T. Freeman, Joshua B. Tenenbaum, Chelsea Finn, Jiajun Wu

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Recall Traces: Backtracking Models for Efficient Reinforcement Learning Anirudh Goyal, Philemon Brakel, William Fedus, Soumye Singhal, Timothy Lillicrap, Sergey Levine, Hugo Larochelle, Yoshua Bengio

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Recurrent Experience Replay in Distributed Reinforcement Learning Steven Kapturowski, Georg Ostrovski, John Quan, Remi Munos, Will Dabney

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Regularized Learning for Domain Adaptation Under Label Shifts Kamyar Azizzadenesheli, Anqi Liu, Fanny Yang, Animashree Anandkumar

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Relational Forward Models for Multi-Agent Learning Andrea Tacchetti, H. Francis Song, Pedro A. M. Mediano, Vinicius Zambaldi, János Kramár, Neil C. Rabinowitz, Thore Graepel, Matthew Botvinick, Peter W. Battaglia

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Relaxed Quantization for Discretized Neural Networks Christos Louizos, Matthias Reisser, Tijmen Blankevoort, Efstratios Gavves, Max Welling

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RelGAN: Relational Generative Adversarial Networks for Text Generation Weili Nie, Nina Narodytska, Ankit Patel

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Representation Degeneration Problem in Training Natural Language Generation Models Jun Gao, Di He, Xu Tan, Tao Qin, Liwei Wang, Tieyan Liu

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Representing Formal Languages: A Comparison Between Finite Automata and Recurrent Neural Networks Joshua J. Michalenko, Ameesh Shah, Abhinav Verma, Richard G. Baraniuk, Swarat Chaudhuri, Ankit B. Patel

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Residual Non-Local Attention Networks for Image Restoration Yulun Zhang, Kunpeng Li, Kai Li, Bineng Zhong, Yun Fu

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Rethinking the Value of Network Pruning Zhuang Liu, Mingjie Sun, Tinghui Zhou, Gao Huang, Trevor Darrell

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Revealing Interpretable Object Representations from Human Behavior Charles Y. Zheng, Francisco Pereira, Chris I. Baker, Martin N. Hebart

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Reward Constrained Policy Optimization Chen Tessler, Daniel J. Mankowitz, Shie Mannor

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Riemannian Adaptive Optimization Methods Gary Becigneul, Octavian-Eugen Ganea

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Rigorous Agent Evaluation: An Adversarial Approach to Uncover Catastrophic Failures Jonathan Uesato, Ananya Kumar, Csaba Szepesvari, Tom Erez, Avraham Ruderman, Keith Anderson, Krishnamurthy Dvijotham, Nicolas Heess, Pushmeet Kohli

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RNNs Implicitly Implement Tensor-Product Representations R. Thomas McCoy, Tal Linzen, Ewan Dunbar, Paul Smolensky

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Robust Conditional Generative Adversarial Networks Grigorios G. Chrysos, Jean Kossaifi, Stefanos Zafeiriou

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Robust Estimation via Generative Adversarial Networks Chao Gao, Jiyi Liu, Yuan Yao, Weizhi Zhu

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Robustness May Be at Odds with Accuracy Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, Aleksander Madry

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RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, Jian Tang

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RotDCF: Decomposition of Convolutional Filters for Rotation-Equivariant Deep Networks Xiuyuan Cheng, Qiang Qiu, Robert Calderbank, Guillermo Sapiro

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Sample Efficient Adaptive Text-to-Speech Yutian Chen, Yannis Assael, Brendan Shillingford, David Budden, Scott Reed, Heiga Zen, Quan Wang, Luis C. Cobo, Andrew Trask, Ben Laurie, Caglar Gulcehre, Aäron van den Oord, Oriol Vinyals, Nando de Freitas

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Sample Efficient Imitation Learning for Continuous Control Fumihiro Sasaki, Tetsuya Yohira, Atsuo Kawaguchi

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Scalable Unbalanced Optimal Transport Using Generative Adversarial Networks Karren D. Yang, Caroline Uhler

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Self-Monitoring Navigation Agent via Auxiliary Progress Estimation Chih-Yao Ma, Jiasen Lu, Zuxuan Wu, Ghassan AlRegib, Zsolt Kira, Richard Socher, Caiming Xiong

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Self-Tuning Networks: Bilevel Optimization of Hyperparameters Using Structured Best-Response Functions Matthew Mackay, Paul Vicol, Jonathan Lorraine, David Duvenaud, Roger Grosse

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Selfless Sequential Learning Rahaf Aljundi, Marcus Rohrbach, Tinne Tuytelaars

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SGD Converges to Global Minimum in Deep Learning via Star-Convex Path Yi Zhou, Junjie Yang, Huishuai Zhang, Yingbin Liang, Vahid Tarokh

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signSGD via Zeroth-Order Oracle Sijia Liu, Pin-Yu Chen, Xiangyi Chen, Mingyi Hong

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signSGD with Majority Vote Is Communication Efficient and Fault Tolerant Jeremy Bernstein, Jiawei Zhao, Kamyar Azizzadenesheli, Anima Anandkumar

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Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware Florian Tramer, Dan Boneh

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Sliced Wasserstein Auto-Encoders Soheil Kolouri, Phillip E. Pope, Charles E. Martin, Gustavo K. Rohde

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Slimmable Neural Networks Jiahui Yu, Linjie Yang, Ning Xu, Jianchao Yang, Thomas Huang

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Small Nonlinearities in Activation Functions Create Bad Local Minima in Neural Networks Chulhee Yun, Suvrit Sra, Ali Jadbabaie

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Smoothing the Geometry of Probabilistic Box Embeddings Xiang Li, Luke Vilnis, Dongxu Zhang, Michael Boratko, Andrew McCallum

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SNAS: Stochastic Neural Architecture Search Sirui Xie, Hehui Zheng, Chunxiao Liu, Liang Lin

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Snip: Single-Shot Network Pruning Based on Connection Sensitivity Namhoon Lee, Thalaiyasingam Ajanthan, Philip Torr

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Soft Q-Learning with Mutual-Information Regularization Jordi Grau-Moya, Felix Leibfried, Peter Vrancx

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Solving the Rubik's Cube with Approximate Policy Iteration Stephen McAleer, Forest Agostinelli, Alexander Shmakov, Pierre Baldi

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SOM-VAE: Interpretable Discrete Representation Learning on Time Series Vincent Fortuin, Matthias Hüser, Francesco Locatello, Heiko Strathmann, Gunnar Rätsch

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Sparse Dictionary Learning by Dynamical Neural Networks Tsung-Han Lin, Ping Tak Peter Tang

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Spectral Inference Networks: Unifying Deep and Spectral Learning David Pfau, Stig Petersen, Ashish Agarwal, David G. T. Barrett, Kimberly L. Stachenfeld

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Spherical CNNs on Unstructured Grids Chiyu Max Jiang, Jingwei Huang, Karthik Kashinath, Prabhat, Philip Marcus, Matthias Niessner

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SPIGAN: Privileged Adversarial Learning from Simulation Kuan-Hui Lee, German Ros, Jie Li, Adrien Gaidon

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Spreading Vectors for Similarity Search Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid, Hervé Jégou

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Stable Opponent Shaping in Differentiable Games Alistair Letcher, Jakob Foerster, David Balduzzi, Tim Rocktäschel, Shimon Whiteson

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Stable Recurrent Models John Miller, Moritz Hardt

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STCN: Stochastic Temporal Convolutional Networks Emre Aksan, Otmar Hilliges

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Stochastic Gradient/Mirror Descent: Minimax Optimality and Implicit Regularization Navid Azizan, Babak Hassibi

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Stochastic Optimization of Sorting Networks via Continuous Relaxations Aditya Grover, Eric Wang, Aaron Zweig, Stefano Ermon

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Stochastic Prediction of Multi-Agent Interactions from Partial Observations Chen Sun, Per Karlsson, Jiajun Wu, Joshua B Tenenbaum, Kevin Murphy

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StrokeNet: A Neural Painting Environment Ningyuan Zheng, Yifan Jiang, Dingjiang Huang

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Structured Adversarial Attack: Towards General Implementation and Better Interpretability Kaidi Xu, Sijia Liu, Pu Zhao, Pin-Yu Chen, Huan Zhang, Quanfu Fan, Deniz Erdogmus, Yanzhi Wang, Xue Lin

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Structured Neural Summarization Patrick Fernandes, Miltiadis Allamanis, Marc Brockschmidt

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Subgradient Descent Learns Orthogonal Dictionaries Yu Bai, Qijia Jiang, Ju Sun

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Supervised Community Detection with Line Graph Neural Networks Zhengdao Chen, Lisha Li, Joan Bruna

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Supervised Policy Update for Deep Reinforcement Learning Quan Vuong, Yiming Zhang, Keith W. Ross

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Synthetic Datasets for Neural Program Synthesis Richard Shin, Neel Kant, Kavi Gupta, Chris Bender, Brandon Trabucco, Rishabh Singh, Dawn Song

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Systematic Generalization: What Is Required and Can It Be Learned? Dzmitry Bahdanau, Shikhar Murty, Michael Noukhovitch, Thien Huu Nguyen, Harm de Vries, Aaron Courville

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Temporal Difference Variational Auto-Encoder Karol Gregor, George Papamakarios, Frederic Besse, Lars Buesing, Theophane Weber

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textTOvec: DEEP CONTEXTUALIZED NEURAL AUTOREGRESSIVE TOPIC MODELS of LANGUAGE with DISTRIBUTED COMPOSITIONAL PRIOR Pankaj Gupta, Yatin Chaudhary, Florian Buettner, Hinrich Schuetze

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The Comparative Power of ReLU Networks and Polynomial Kernels in the Presence of Sparse Latent Structure Frederic Koehler, Andrej Risteski

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The Deep Weight Prior Andrei Atanov, Arsenii Ashukha, Kirill Struminsky, Dmitriy Vetrov, Max Welling

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The Laplacian in RL: Learning Representations with Efficient Approximations Yifan Wu, George Tucker, Ofir Nachum

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The Limitations of Adversarial Training and the Blind-Spot Attack Huan Zhang, Hongge Chen, Zhao Song, Duane Boning, Inderjit S. Dhillon, Cho-Jui Hsieh

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The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks Jonathan Frankle, Michael Carbin

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The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences from Natural Supervision Jiayuan Mao, Chuang Gan, Pushmeet Kohli, Joshua B. Tenenbaum, Jiajun Wu

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The Role of Over-Parametrization in Generalization of Neural Networks Behnam Neyshabur, Zhiyuan Li, Srinadh Bhojanapalli, Yann LeCun, Nathan Srebro

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The Singular Values of Convolutional Layers Hanie Sedghi, Vineet Gupta, Philip M. Long

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The Unusual Effectiveness of Averaging in GAN Training Yasin, Chuan-Sheng Foo, Stefan Winkler, Kim-Hui Yap, Georgios Piliouras, Vijay Chandrasekhar

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Theoretical Analysis of Auto Rate-Tuning by Batch Normalization Sanjeev Arora, Zhiyuan Li, Kaifeng Lyu

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There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average Ben Athiwaratkun, Marc Finzi, Pavel Izmailov, Andrew Gordon Wilson

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Three Mechanisms of Weight Decay Regularization Guodong Zhang, Chaoqi Wang, Bowen Xu, Roger Grosse

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TimbreTron: A WaveNet(CycleGAN(CQT(Audio))) Pipeline for Musical Timbre Transfer Sicong Huang, Qiyang Li, Cem Anil, Xuchan Bao, Sageev Oore, Roger B. Grosse

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Time-Agnostic Prediction: Predicting Predictable Video Frames Dinesh Jayaraman, Frederik Ebert, Alexei Efros, Sergey Levine

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Top-Down Neural Model for Formulae Karel Chvalovský

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Toward Understanding the Impact of Staleness in Distributed Machine Learning Wei Dai, Yi Zhou, Nanqing Dong, Hao Zhang, Eric Xing

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Towards GAN Benchmarks Which Require Generalization Ishaan Gulrajani, Colin Raffel, Luke Metz

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Towards Metamerism via Foveated Style Transfer Arturo Deza, Aditya Jonnalagadda, Miguel P. Eckstein

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Towards Robust, Locally Linear Deep Networks Guang-He Lee, David Alvarez-Melis, Tommi S. Jaakkola

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Towards the First Adversarially Robust Neural Network Model on MNIST Lukas Schott, Jonas Rauber, Matthias Bethge, Wieland Brendel

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Towards Understanding Regularization in Batch Normalization Ping Luo, Xinjiang Wang, Wenqi Shao, Zhanglin Peng

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Training for Faster Adversarial Robustness Verification via Inducing ReLU Stability Kai Y. Xiao, Vincent Tjeng, Nur Muhammad Shafiullah, Aleksander Madry

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Transfer Learning for Sequences via Learning to Collocate Wanyun Cui, Guangyu Zheng, Zhiqiang Shen, Sihang Jiang, Wei Wang

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Transferring Knowledge Across Learning Processes Sebastian Flennerhag, Pablo G. Moreno, Neil D. Lawrence, Andreas Damianou

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Tree-Structured Recurrent Switching Linear Dynamical Systems for Multi-Scale Modeling Josue Nassar, Scott Linderman, Monica Bugallo, Il Memming Park

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Trellis Networks for Sequence Modeling Shaojie Bai, J. Zico Kolter, Vladlen Koltun

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Two-Timescale Networks for Nonlinear Value Function Approximation Wesley Chung, Somjit Nath, Ajin Joseph, Martha White

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Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizer David Berthelot, Colin Raffel, Aurko Roy, Ian Goodfellow

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Understanding Composition of Word Embeddings via Tensor Decomposition Abraham Frandsen, Rong Ge

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Understanding Straight-Through Estimator in Training Activation Quantized Neural Nets Penghang Yin, Jiancheng Lyu, Shuai Zhang, Stanley Osher, Yingyong Qi, Jack Xin

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Universal Stagewise Learning for Non-Convex Problems with Convergence on Averaged Solutions Zaiyi Chen, Zhuoning Yuan, Jinfeng Yi, Bowen Zhou, Enhong Chen, Tianbao Yang

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Universal Successor Features Approximators Diana Borsa, Andre Barreto, John Quan, Daniel J. Mankowitz, Hado van Hasselt, Remi Munos, David Silver, Tom Schaul

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Universal Transformers Mostafa Dehghani, Stephan Gouws, Oriol Vinyals, Jakob Uszkoreit, Lukasz Kaiser

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Unsupervised Adversarial Image Reconstruction Arthur Pajot, Emmanuel de Bezenac, Patrick Gallinari

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Unsupervised Control Through Non-Parametric Discriminative Rewards David Warde-Farley, Tom Van de Wiele, Tejas Kulkarni, Catalin Ionescu, Steven Hansen, Volodymyr Mnih

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Unsupervised Discovery of Parts, Structure, and Dynamics Zhenjia Xu, Zhijian Liu, Chen Sun, Kevin Murphy, William T. Freeman, Joshua B. Tenenbaum, Jiajun Wu

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Unsupervised Domain Adaptation for Distance Metric Learning Kihyuk Sohn, Wenling Shang, Xiang Yu, Manmohan Chandraker

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Unsupervised Hyper-Alignment for Multilingual Word Embeddings Jean Alaux, Edouard Grave, Marco Cuturi, Armand Joulin

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Unsupervised Learning of the Set of Local Maxima Lior Wolf, Sagie Benaim, Tomer Galanti

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Unsupervised Learning via Meta-Learning Kyle Hsu, Sergey Levine, Chelsea Finn

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Unsupervised Speech Recognition via Segmental Empirical Output Distribution Matching Chih-Kuan Yeh, Jianshu Chen, Chengzhu Yu, Dong Yu

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Value Propagation Networks Nantas Nardelli, Gabriel Synnaeve, Zeming Lin, Pushmeet Kohli, Philip H. S. Torr, Nicolas Usunier

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Variance Networks: When Expectation Does Not Meet Your Expectations Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, Dmitry Vetrov

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Variance Reduction for Reinforcement Learning in Input-Driven Environments Hongzi Mao, Shaileshh Bojja Venkatakrishnan, Malte Schwarzkopf, Mohammad Alizadeh

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Variational Autoencoder with Arbitrary Conditioning Oleg Ivanov, Michael Figurnov, Dmitry Vetrov

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Variational Autoencoders with Jointly Optimized Latent Dependency Structure Jiawei He, Yu Gong, Joseph Marino, Greg Mori, Andreas Lehrmann

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Variational Bayesian Phylogenetic Inference Cheng Zhang, Frederick A. Matsen Iv

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Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow Xue Bin Peng, Angjoo Kanazawa, Sam Toyer, Pieter Abbeel, Sergey Levine

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Variational Smoothing in Recurrent Neural Network Language Models Lingpeng Kong, Gabor Melis, Wang Ling, Lei Yu, Dani Yogatama

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Verification of Non-Linear Specifications for Neural Networks Chongli Qin, Krishnamurthy Dvijotham, Brendan O'Donoghue, Rudy Bunel, Robert Stanforth, Sven Gowal, Jonathan Uesato, Grzegorz Swirszcz, Pushmeet Kohli

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Visceral Machines: Risk-Aversion in Reinforcement Learning with Intrinsic Physiological Rewards Daniel McDuff, Ashish Kapoor

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Visual Explanation by Interpretation: Improving Visual Feedback Capabilities of Deep Neural Networks Jose Oramas, Kaili Wang, Tinne Tuytelaars

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Visual Reasoning by Progressive Module Networks Seung Wook Kim, Makarand Tapaswi, Sanja Fidler

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Visual Semantic Navigation Using Scene Priors Wei Yang, Xiaolong Wang, Ali Farhadi, Abhinav Gupta, Roozbeh Mottaghi

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Von Mises-Fisher Loss for Training Sequence to Sequence Models with Continuous Outputs Sachin Kumar, Yulia Tsvetkov

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Wasserstein Barycenter Model Ensembling Pierre Dognin, Igor Melnyk, Youssef Mroueh, Jarret Ross, Cicero Dos Santos, Tom Sercu

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What Do You Learn from Context? Probing for Sentence Structure in Contextualized Word Representations Ian Tenney, Patrick Xia, Berlin Chen, Alex Wang, Adam Poliak, R Thomas McCoy, Najoung Kim, Benjamin Van Durme, Samuel R. Bowman, Dipanjan Das, Ellie Pavlick

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Whitening and Coloring Batch Transform for GANs Aliaksandr Siarohin, Enver Sangineto, Nicu Sebe

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Wizard of Wikipedia: Knowledge-Powered Conversational Agents Emily Dinan, Stephen Roller, Kurt Shuster, Angela Fan, Michael Auli, Jason Weston

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Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search Lars Buesing, Theophane Weber, Yori Zwols, Nicolas Heess, Sebastien Racaniere, Arthur Guez, Jean-Baptiste Lespiau

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