ICLR 2018

337 papers

Neural mAP: Structured Memory for Deep Reinforcement Learning Emilio Parisotto, Ruslan Salakhutdinov

A Bayesian Perspective on Generalization and Stochastic Gradient Descent Samuel L. Smith and Quoc V. Le

A Compressed Sensing View of Unsupervised Text Embeddings, Bag-of-N-Grams, and LSTMs Sanjeev Arora, Mikhail Khodak, Nikunj Saunshi, Kiran Vodrahalli

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A Deep Reinforced Model for Abstractive Summarization Romain Paulus, Caiming Xiong, Richard Socher

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A DIRT-T Approach to Unsupervised Domain Adaptation Rui Shu, Hung Bui, Hirokazu Narui, Stefano Ermon

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A Framework for the Quantitative Evaluation of Disentangled Representations Cian Eastwood, Christopher K. I. Williams

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A Hierarchical Model for Device Placement Azalia Mirhoseini, Anna Goldie, Hieu Pham, Benoit Steiner, Quoc V. Le, Jeff Dean

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A Neural Representation of Sketch Drawings David Ha, Douglas Eck

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A New Method of Region Embedding for Text Classification Chao Qiao, Bo Huang, Guocheng Niu, Daren Li, Daxiang Dong, Wei He, Dianhai Yu, Hua Wu

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A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks Behnam Neyshabur, Srinadh Bhojanapalli, Nathan Srebro

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A Scalable Laplace Approximation for Neural Networks Hippolyt Ritter, Aleksandar Botev, David Barber

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A Simple Neural Attentive Meta-Learner Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, Pieter Abbeel

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Action-Dependent Control Variates for Policy Optimization via Stein Identity Hao Liu, Yihao Feng, Yi Mao, Dengyong Zhou, Jian Peng, Qiang Liu

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Activation Maximization Generative Adversarial Nets Zhiming Zhou, Han Cai, Shu Rong, Yuxuan Song, Kan Ren, Weinan Zhang, Jun Wang, Yong Yu

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Active Learning for Convolutional Neural Networks: A Core-Set Approach Ozan Sener, Silvio Savarese

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Active Neural Localization Devendra Singh Chaplot, Emilio Parisotto, Ruslan Salakhutdinov

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Adaptive Dropout with Rademacher Complexity Regularization Ke Zhai, Huan Wang

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Adaptive Quantization of Neural Networks Soroosh Khoram, Jing Li

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Adversarial Dropout Regularization Kuniaki Saito, Yoshitaka Ushiku, Tatsuya Harada, Kate Saenko

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All-but-the-Top: Simple and Effective Postprocessing for Word Representations Jiaqi Mu, Pramod Viswanath

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Alternating Multi-Bit Quantization for Recurrent Neural Networks Chen Xu, Jianqiang Yao, Zhouchen Lin, Wenwu Ou, Yuanbin Cao, Zhirong Wang, Hongbin Zha

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AmbientGAN: Generative Models from Lossy Measurements Ashish Bora, Eric Price, Alexandros G. Dimakis

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An Efficient Framework for Learning Sentence Representations Lajanugen Logeswaran, Honglak Lee

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An Image Representation Based Convolutional Network for DNA Classification Bojian Yin, Marleen Balvert, Davide Zambrano, Alexander Schoenhuth, Sander Bohte

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An Online Learning Approach to Generative Adversarial Networks Paulina Grnarova, Kfir Y Levy, Aurelien Lucchi, Thomas Hofmann, Andreas Krause

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Apprentice: Using Knowledge Distillation Techniques to Improve Low-Precision Network Accuracy Asit Mishra, Debbie Marr

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Ask the Right Questions: Active Question Reformulation with Reinforcement Learning Christian Buck, Jannis Bulian, Massimiliano Ciaramita, Wojciech Gajewski, Andrea Gesmundo, Neil Houlsby, Wei Wang.

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Attacking Binarized Neural Networks Angus Galloway, Graham W. Taylor, Medhat Moussa

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Auto-Conditioned Recurrent Networks for Extended Complex Human Motion Synthesis Yi Zhou, Zimo Li, Shuangjiu Xiao, Chong He, Zeng Huang, Hao Li

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Auto-Encoding Sequential Monte Carlo Tuan Anh Le, Maximilian Igl, Tom Rainforth, Tom Jin, Frank Wood

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Automatically Inferring Data Quality for Spatiotemporal Forecasting Sungyong Seo, Arash Mohegh, George Ban-Weiss, Yan Liu

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Backpropagation Through the Void: Optimizing Control Variates for Black-Box Gradient Estimation Will Grathwohl, Dami Choi, Yuhuai Wu, Geoff Roeder, David Duvenaud

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Beyond Shared Hierarchies: Deep Multitask Learning Through Soft Layer Ordering Elliot Meyerson, Risto Miikkulainen

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Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs W. James Murdoch, Peter J. Liu, Bin Yu

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Bi-Directional Block Self-Attention for Fast and Memory-Efficient Sequence Modeling Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang

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Boosting Dilated Convolutional Networks with Mixed Tensor Decompositions Nadav Cohen, Ronen Tamari, Amnon Shashua

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Boosting the Actor with Dual Critic Bo Dai, Albert Shaw, Niao He, Lihong Li, Le Song

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Boundary Seeking GANs R Devon Hjelm, Athul Paul Jacob, Adam Trischler, Gerry Che, Kyunghyun Cho, Yoshua Bengio

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Breaking the SoftMax Bottleneck: A High-Rank RNN Language Model Zhilin Yang, Zihang Dai, Ruslan Salakhutdinov, William W. Cohen

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Can Neural Networks Understand Logical Entailment? Richard Evans, David Saxton, David Amos, Pushmeet Kohli, Edward Grefenstette

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Can Recurrent Neural Networks Warp Time? Corentin Tallec, Yann Ollivier

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Cascade Adversarial Machine Learning Regularized with a Unified Embedding Taesik Na, Jong Hwan Ko, Saibal Mukhopadhyay

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CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training Murat Kocaoglu, Christopher Snyder, Alexandros G. Dimakis, Sriram Vishwanath

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Certified Defenses Against Adversarial Examples Aditi Raghunathan, Jacob Steinhardt, Percy Liang

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Certifying Some Distributional Robustness with Principled Adversarial Training Aman Sinha, Hongseok Namkoong, John Duchi

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cGANs with Projection Discriminator Takeru Miyato, Masanori Koyama

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Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality Xingjun Ma, Bo Li, Yisen Wang, Sarah M. Erfani, Sudanthi Wijewickrema, Grant Schoenebeck, Dawn Song, Michael E. Houle, James Bailey

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Combining Symbolic Expressions and Black-Box Function Evaluations in Neural Programs Forough Arabshahi, Sameer Singh, Animashree Anandkumar

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Communication Algorithms via Deep Learning Hyeji Kim, Yihan Jiang, Ranvir B. Rana, Sreeram Kannan, Sewoong Oh, Pramod Viswanath

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Compositional Attention Networks for Machine Reasoning Drew A. Hudson, Christopher D. Manning

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Compositional Obverter Communication Learning from Raw Visual Input Edward Choi, Angeliki Lazaridou, Nando de Freitas

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Compressing Word Embeddings via Deep Compositional Code Learning Raphael Shu, Hideki Nakayama

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Consequentialist Conditional Cooperation in Social Dilemmas with Imperfect Information Alexander Peysakhovich, Adam Lerer

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Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments Maruan Al-Shedivat, Trapit Bansal, Yura Burda, Ilya Sutskever, Igor Mordatch, Pieter Abbeel

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Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields Thomas Unterthiner, Bernhard Nessler, Calvin Seward, Günter Klambauer, Martin Heusel, Hubert Ramsauer, Sepp Hochreiter

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Countering Adversarial Images Using Input Transformations Chuan Guo, Mayank Rana, Moustapha Cisse, Laurens van der Maaten

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Critical Percolation as a Framework to Analyze the Training of Deep Networks Zohar Ringel, Rodrigo Andrade de Bem

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Critical Points of Linear Neural Networks: Analytical Forms and Landscape Properties Yi Zhou, Yingbin Liang

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DCN+: Mixed Objective and Deep Residual Coattention for Question Answering Caiming Xiong, Victor Zhong, Richard Socher

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Debiasing Evidence Approximations: On Importance-Weighted Autoencoders and Jackknife Variational Inference Sebastian Nowozin

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Decision Boundary Analysis of Adversarial Examples Warren He, Bo Li, Dawn Song

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Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models Wieland Brendel, Jonas Rauber, Matthias Bethge

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Decoupling the Layers in Residual Networks Ricky Fok, Aijun An, Zana Rashidi, Xiaogang Wang

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Deep Active Learning for Named Entity Recognition Yanyao Shen, Hyokun Yun, Zachary C. Lipton, Yakov Kronrod, Animashree Anandkumar

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Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection Bo Zong, Qi Song, Martin Renqiang Min, Wei Cheng, Cristian Lumezanu, Daeki Cho, Haifeng Chen

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Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling Carlos Riquelme, George Tucker, Jasper Snoek

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Deep Complex Networks Chiheb Trabelsi, Olexa Bilaniuk, Ying Zhang, Dmitriy Serdyuk, Sandeep Subramanian, Joao Felipe Santos, Soroush Mehri, Negar Rostamzadeh, Yoshua Bengio, Christopher J Pal

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Deep Contextualized Word Representations Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, Luke Zettlemoyer

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Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking Aleksandar Bojchevski, Stephan Günnemann

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Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training Yujun Lin, Song Han, Huizi Mao, Yu Wang, Bill Dally

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Deep Learning and Quantum Entanglement: Fundamental Connections with Implications to Network Design Yoav Levine, David Yakira, Nadav Cohen, Amnon Shashua

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Deep Learning as a Mixed Convex-Combinatorial Optimization Problem Abram L. Friesen, Pedro Domingos

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Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge Emmanuel de Bezenac, Arthur Pajot, Patrick Gallinari

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Deep Learning with Logged Bandit Feedback Thorsten Joachims, Adith Swaminathan, Maarten de Rijke

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Deep Neural Networks as Gaussian Processes Jaehoon Lee, Yasaman Bahri, Roman Novak, Samuel S. Schoenholz, Jeffrey Pennington, Jascha Sohl-Dickstein

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Deep Rewiring: Training Very Sparse Deep Networks Guillaume Bellec, David Kappel, Wolfgang Maass, Robert Legenstein

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Deep Sensing: Active Sensing Using Multi-Directional Recurrent Neural Networks Jinsung Yoon, William R. Zame, Mihaela van der Schaar

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Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning Wei Ping, Kainan Peng, Andrew Gibiansky, Sercan O. Arik, Ajay Kannan, Sharan Narang, Jonathan Raiman, John Miller

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Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models Pouya Samangouei, Maya Kabkab, Rama Chellappa

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Demystifying MMD GANs Mikołaj Bińkowski, Danica J. Sutherland, Michael Arbel, Arthur Gretton

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Depthwise Separable Convolutions for Neural Machine Translation Lukasz Kaiser, Aidan N. Gomez, Francois Chollet

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Detecting Statistical Interactions from Neural Network Weights Michael Tsang, Dehua Cheng, Yan Liu

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Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting Yaguang Li, Rose Yu, Cyrus Shahabi, Yan Liu

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Distributed Distributional Deterministic Policy Gradients Gabriel Barth-Maron, Matthew W. Hoffman, David Budden, Will Dabney, Dan Horgan, Dhruva Tb, Alistair Muldal, Nicolas Heess, Timothy Lillicrap

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Distributed Fine-Tuning of Language Models on Private Data Vadim Popov, Mikhail Kudinov, Irina Piontkovskaya, Petr Vytovtov, Alex Nevidomsky

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Distributed Prioritized Experience Replay Dan Horgan, John Quan, David Budden, Gabriel Barth-Maron, Matteo Hessel, Hado van Hasselt, David Silver

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Divide and Conquer Networks Alex Nowak, David Folqué, Joan Bruna

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Divide-and-Conquer Reinforcement Learning Dibya Ghosh, Avi Singh, Aravind Rajeswaran, Vikash Kumar, Sergey Levine

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Do GANs Learn the Distribution? Some Theory and Empirics Sanjeev Arora, Andrej Risteski, Yi Zhang

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Don't Decay the Learning Rate, Increase the Batch Size Samuel L. Smith, Pieter-Jan Kindermans, Chris Ying, Quoc V. Le

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DORA the Explorer: Directed Outreaching Reinforcement Action-Selection Lior Fox, Leshem Choshen, Yonatan Loewenstein

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Dynamic Neural Program Embeddings for Program Repair Ke Wang, Rishabh Singh, Zhendong Su

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Efficient Sparse-Winograd Convolutional Neural Networks Xingyu Liu, Jeff Pool, Song Han, William J. Dally

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Eigenoption Discovery Through the Deep Successor Representation Marlos C. Machado, Clemens Rosenbaum, Xiaoxiao Guo, Miao Liu, Gerald Tesauro, Murray Campbell

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Emergence of Grid-like Representations by Training Recurrent Neural Networks to Perform Spatial Localization Christopher J. Cueva, Xue-Xin Wei

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Emergence of Linguistic Communication from Referential Games with Symbolic and Pixel Input Angeliki Lazaridou, Karl Moritz Hermann, Karl Tuyls, Stephen Clark

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Emergent Communication in a Multi-Modal, Multi-Step Referential Game Katrina Evtimova, Andrew Drozdov, Douwe Kiela, Kyunghyun Cho

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Emergent Communication Through Negotiation Kris Cao, Angeliki Lazaridou, Marc Lanctot, Joel Z Leibo, Karl Tuyls, Stephen Clark

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Emergent Complexity via Multi-Agent Competition Trapit Bansal, Jakub Pachocki, Szymon Sidor, Ilya Sutskever, Igor Mordatch

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Emergent Translation in Multi-Agent Communication Jason Lee, Kyunghyun Cho, Jason Weston, Douwe Kiela

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Empirical Risk Landscape Analysis for Understanding Deep Neural Networks Pan Zhou, Jiashi Feng

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Enhancing the Reliability of Out-of-Distribution Image Detection in Neural Networks Shiyu Liang, Yixuan Li, R. Srikant

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Ensemble Adversarial Training: Attacks and Defenses Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, Patrick McDaniel

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Espresso: Efficient Forward Propagation for Binary Deep Neural Networks Fabrizio Pedersoli, George Tzanetakis, Andrea Tagliasacchi

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Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach Tsui-Wei Weng, Huan Zhang, Pin-Yu Chen, Jinfeng Yi, Dong Su, Yupeng Gao, Cho-Jui Hsieh, Luca Daniel

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Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering Shuohang Wang, Mo Yu, Jing Jiang, Wei Zhang, Xiaoxiao Guo, Shiyu Chang, Zhiguo Wang, Tim Klinger, Gerald Tesauro, Murray Campbell

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Expressive Power of Recurrent Neural Networks Valentin Khrulkov, Alexander Novikov, Ivan Oseledets

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FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling Jie Chen, Tengfei Ma, Cao Xiao

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FearNet: Brain-Inspired Model for Incremental Learning Ronald Kemker, Christopher Kanan

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Few-Shot Autoregressive Density Estimation: Towards Learning to Learn Distributions Scott Reed, Yutian Chen, Thomas Paine, Aäron van den Oord, S. M. Ali Eslami, Danilo Rezende, Oriol Vinyals, Nando de Freitas

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Few-Shot Learning with Graph Neural Networks Victor Garcia Satorras, Joan Bruna Estrach

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Fidelity-Weighted Learning Mostafa Dehghani, Arash Mehrjou, Stephan Gouws, Jaap Kamps, Bernhard Schölkopf

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Fix Your Classifier: The Marginal Value of Training the Last Weight Layer Elad Hoffer, Itay Hubara, Daniel Soudry

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Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger Grosse

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Fraternal Dropout Konrad Zolna, Devansh Arpit, Dendi Suhubdy, Yoshua Bengio

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FusionNet: Fusing via Fully-Aware Attention with Application to Machine Comprehension Hsin-Yuan Huang, Chenguang Zhu, Yelong Shen, Weizhu Chen

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GANITE: Estimation of Individualized Treatment Effects Using Generative Adversarial Nets Jinsung Yoon, James Jordon, Mihaela van der Schaar

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Gaussian Process Behaviour in Wide Deep Neural Networks Alexander G. de G. Matthews, Jiri Hron, Mark Rowland, Richard E. Turner, Zoubin Ghahramani

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Generalizing Across Domains via Cross-Gradient Training Shiv Shankar, Vihari Piratla, Soumen Chakrabarti, Siddhartha Chaudhuri, Preethi Jyothi, Sunita Sarawagi

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Generalizing Hamiltonian Monte Carlo with Neural Networks Daniel Levy, Matt D. Hoffman, Jascha Sohl-Dickstein

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Generating Natural Adversarial Examples Zhengli Zhao, Dheeru Dua, Sameer Singh

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Generating Wikipedia by Summarizing Long Sequences Peter J. Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, Noam Shazeer

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Generative Models of Visually Grounded Imagination Ramakrishna Vedantam, Ian Fischer, Jonathan Huang, Kevin Murphy

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Generative Networks as Inverse Problems with Scattering Transforms Tomás Angles, Stéphane Mallat

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Global Optimality Conditions for Deep Neural Networks Chulhee Yun, Suvrit Sra, Ali Jadbabaie

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Go for a Walk and Arrive at the Answer: Reasoning over Paths in Knowledge Bases Using Reinforcement Learning Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Luke Vilnis, Ishan Durugkar, Akshay Krishnamurthy, Alex Smola, Andrew McCallum

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Gradient Estimators for Implicit Models Yingzhen Li, Richard E. Turner

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Graph Attention Networks Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, Yoshua Bengio

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Guide Actor-Critic for Continuous Control Voot Tangkaratt, Abbas Abdolmaleki, Masashi Sugiyama

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HexaConv Emiel Hoogeboom, Jorn W.T. Peters, Taco S. Cohen, Max Welling

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Hierarchical and Interpretable Skill Acquisition in Multi-Task Reinforcement Learning Tianmin Shu, Caiming Xiong, Richard Socher

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Hierarchical Density Order Embeddings Ben Athiwaratkun, Andrew Gordon Wilson

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Hierarchical Representations for Efficient Architecture Search Hanxiao Liu, Karen Simonyan, Oriol Vinyals, Chrisantha Fernando, Koray Kavukcuoglu

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Hierarchical Subtask Discovery with Non-Negative Matrix Factorization Adam C. Earle, Andrew M. Saxe, Benjamin Rosman

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Hyperparameter Optimization: A Spectral Approach Elad Hazan, Adam Klivans, Yang Yuan

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I-RevNet: Deep Invertible Networks Jörn-Henrik Jacobsen, Arnold W.M. Smeulders, Edouard Oyallon

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Identifying Analogies Across Domains Yedid Hoshen, Lior Wolf

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Imitation Learning from Visual Data with Multiple Intentions Aviv Tamar, Khashayar Rohanimanesh, Yinlam Chow, Chris Vigorito, Ben Goodrich, Michael Kahane, Derik Pridmore

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Implicit Causal Models for Genome-Wide Association Studies Dustin Tran, David M. Blei

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Improving GAN Training via Binarized Representation Entropy (BRE) Regularization Yanshuai Cao, Gavin Weiguang Ding, Kry Yik-Chau Lui, Ruitong Huang

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Improving GANs Using Optimal Transport Tim Salimans, Han Zhang, Alec Radford, Dimitris Metaxas

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Improving the Improved Training of Wasserstein GANs: A Consistency Term and Its Dual Effect Xiang Wei, Boqing Gong, Zixia Liu, Wei Lu, Liqiang Wang

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Improving the Universality and Learnability of Neural Programmer-Interpreters with Combinator Abstraction Da Xiao, Jo-Yu Liao, Xingyuan Yuan

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Initialization Matters: Orthogonal Predictive State Recurrent Neural Networks Krzysztof Choromanski, Carlton Downey, Byron Boots

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Interactive Grounded Language Acquisition and Generalization in a 2D World Haonan Yu, Haichao Zhang, Wei Xu

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Interpretable Counting for Visual Question Answering Alexander Trott, Caiming Xiong, Richard Socher

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Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Play Sainbayar Sukhbaatar, Zeming Lin, Ilya Kostrikov, Gabriel Synnaeve, Arthur Szlam, Rob Fergus

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Kernel Implicit Variational Inference Jiaxin Shi, Shengyang Sun, Jun Zhu

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Kronecker-Factored Curvature Approximations for Recurrent Neural Networks James Martens, Jimmy Ba, Matt Johnson

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Large Scale Distributed Neural Network Training Through Online Distillation Rohan Anil, Gabriel Pereyra, Alexandre Passos, Robert Ormandi, George E. Dahl, Geoffrey E. Hinton

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Large Scale Optimal Transport and Mapping Estimation Vivien Seguy, Bharath Bhushan Damodaran, Remi Flamary, Nicolas Courty, Antoine Rolet, Mathieu Blondel

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Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models Jesse Engel, Matthew Hoffman, Adam Roberts

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Latent Space Oddity: On the Curvature of Deep Generative Models Georgios Arvanitidis, Lars Kai Hansen, Søren Hauberg

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Learn to Pay Attention Saumya Jetley, Nicholas A. Lord, Namhoon Lee, Philip H. S. Torr

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Learning a Generative Model for Validity in Complex Discrete Structures Dave Janz, Jos van der Westhuizen, Brooks Paige, Matt Kusner, José Miguel Hernández-Lobato

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Learning a Neural Response Metric for Retinal Prosthesis Nishal P Shah, Sasidhar Madugula, Ej Chichilnisky, Yoram Singer, Jonathon Shlens

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Learning an Embedding Space for Transferable Robot Skills Karol Hausman, Jost Tobias Springenberg, Ziyu Wang, Nicolas Heess, Martin Riedmiller

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Learning Approximate Inference Networks for Structured Prediction Lifu Tu, Kevin Gimpel

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Learning Awareness Models Brandon Amos, Laurent Dinh, Serkan Cabi, Thomas Rothörl, Sergio Gómez Colmenarejo, Alistair Muldal, Tom Erez, Yuval Tassa, Nando de Freitas, Misha Denil

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Learning Deep Mean Field Games for Modeling Large Population Behavior Jiachen Yang, Xiaojing Ye, Rakshit Trivedi, Huan Xu, Hongyuan Zha

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Learning Differentially Private Recurrent Language Models H. Brendan McMahan, Daniel Ramage, Kunal Talwar, Li Zhang

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Learning Discrete Weights Using the Local Reparameterization Trick Oran Shayer, Dan Levi, Ethan Fetaya

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Learning from Between-Class Examples for Deep Sound Recognition Yuji Tokozume, Yoshitaka Ushiku, Tatsuya Harada

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Learning from Noisy Singly-Labeled Data Ashish Khetan, Zachary C. Lipton, Animashree Anandkumar

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Learning General Purpose Distributed Sentence Representations via Large Scale Multi-Task Learning Sandeep Subramanian, Adam Trischler, Yoshua Bengio, Christopher J Pal

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Learning How to Explain Neural Networks: PatternNet and PatternAttribution Pieter-Jan Kindermans, Kristof T. Schütt, Maximilian Alber, Klaus-Robert Müller, Dumitru Erhan, Been Kim, Sven Dähne

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Learning Intrinsic Sparse Structures Within Long Short-Term Memory Wei Wen, Yuxiong He, Samyam Rajbhandari, Minjia Zhang, Wenhan Wang, Fang Liu, Bin Hu, Yiran Chen, Hai Li

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Learning Latent Permutations with Gumbel-Sinkhorn Networks Gonzalo Mena, David Belanger, Scott Linderman, Jasper Snoek

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Learning Latent Representations in Neural Networks for Clustering Through Pseudo Supervision and Graph-Based Activity Regularization Ozsel Kilinc, Ismail Uysal

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Learning One-Hidden-Layer Neural Networks with Landscape Design Rong Ge, Jason D. Lee, Tengyu Ma

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Learning Parametric Closed-Loop Policies for Markov Potential Games Sergio Valcarcel Macua, Javier Zazo, Santiago Zazo

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Learning Robust Rewards with Adverserial Inverse Reinforcement Learning Justin Fu, Katie Luo, Sergey Levine

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Learning Sparse Latent Representations with the Deep Copula Information Bottleneck Aleksander Wieczorek, Mario Wieser, Damian Murezzan, Volker Roth

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Learning Sparse Neural Networks Through L_0 Regularization Christos Louizos, Max Welling, Diederik P. Kingma

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Learning to Cluster in Order to Transfer Across Domains and Tasks Yen-Chang Hsu, Zhaoyang Lv, Zsolt Kira

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Learning to Count Objects in Natural Images for Visual Question Answering Yan Zhang, Jonathon Hare, Adam Prügel-Bennett

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Learning to Multi-Task by Active Sampling Sahil Sharma, Ashutosh Kumar Jha, Parikshit S Hegde, Balaraman Ravindran

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Learning to Represent Programs with Graphs Miltiadis Allamanis, Marc Brockschmidt, Mahmoud Khademi

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Learning to Share: Simultaneous Parameter Tying and Sparsification in Deep Learning Dejiao Zhang, Haozhu Wang, Mario Figueiredo, Laura Balzano

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Learning to Teach Yang Fan, Fei Tian, Tao Qin, Xiang-Yang Li, Tie-Yan Liu

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Learning Wasserstein Embeddings Nicolas Courty, Rémi Flamary, Mélanie Ducoffe

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Leave No Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning Benjamin Eysenbach, Shixiang Gu, Julian Ibarz, Sergey Levine

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Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis Rudy Bunel, Matthew Hausknecht, Jacob Devlin, Rishabh Singh, Pushmeet Kohli

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Lifelong Learning with Dynamically Expandable Networks Jaehong Yoon, Eunho Yang, Jeongtae Lee, Sung Ju Hwang

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Loss-Aware Weight Quantization of Deep Networks Lu Hou, James T. Kwok

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Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence at Every Step William Fedus, Mihaela Rosca, Balaji Lakshminarayanan, Andrew M. Dai, Shakir Mohamed, Ian Goodfellow

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MaskGAN: Better Text Generation via Filling in the _______ William Fedus, Ian Goodfellow, Andrew M. Dai

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Mastering the Dungeon: Grounded Language Learning by Mechanical Turker Descent Zhilin Yang, Saizheng Zhang, Jack Urbanek, Will Feng, Alexander Miller, Arthur Szlam, Douwe Kiela, Jason Weston

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Matrix Capsules with EM Routing Geoffrey E Hinton, Sara Sabour, Nicholas Frosst

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Maximum a Posteriori Policy Optimisation Abbas Abdolmaleki, Jost Tobias Springenberg, Yuval Tassa, Remi Munos, Nicolas Heess, Martin Riedmiller

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Measuring the Intrinsic Dimension of Objective Landscapes Chunyuan Li, Heerad Farkhoor, Rosanne Liu, Jason Yosinski

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Memorization Precedes Generation: Learning Unsupervised GANs with Memory Networks Youngjin Kim, Minjung Kim, Gunhee Kim

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Memory Architectures in Recurrent Neural Network Language Models Dani Yogatama, Yishu Miao, Gabor Melis, Wang Ling, Adhiguna Kuncoro, Chris Dyer, Phil Blunsom

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Memory Augmented Control Networks Arbaaz Khan, Clark Zhang, Nikolay Atanasov, Konstantinos Karydis, Vijay Kumar, Daniel D. Lee

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Memory-Based Parameter Adaptation Pablo Sprechmann, Siddhant M. Jayakumar, Jack W. Rae, Alexander Pritzel, Adria Puigdomenech Badia, Benigno Uria, Oriol Vinyals, Demis Hassabis, Razvan Pascanu, Charles Blundell

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Meta Learning Shared Hierarchies Kevin Frans, Jonathan Ho, Xi Chen, Pieter Abbeel, John Schulman

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Meta-Learning and Universality: Deep Representations and Gradient Descent Can Approximate Any Learning Algorithm Chelsea Finn, Sergey Levine

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Meta-Learning for Semi-Supervised Few-Shot Classification Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Joshua B. Tenenbaum, Hugo Larochelle, Richard S. Zemel

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MGAN: Training Generative Adversarial Nets with Multiple Generators Quan Hoang, Tu Dinh Nguyen, Trung Le, Dinh Phung

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Minimal-Entropy Correlation Alignment for Unsupervised Deep Domain Adaptation Pietro Morerio, Jacopo Cavazza, Vittorio Murino

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Minimax Curriculum Learning: Machine Teaching with Desirable Difficulties and Scheduled Diversity Tianyi Zhou, Jeff Bilmes

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Mitigating Adversarial Effects Through Randomization Cihang Xie, Jianyu Wang, Zhishuai Zhang, Zhou Ren, Alan Yuille

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Mixed Precision Training Paulius Micikevicius, Sharan Narang, Jonah Alben, Gregory Diamos, Erich Elsen, David Garcia, Boris Ginsburg, Michael Houston, Oleksii Kuchaiev, Ganesh Venkatesh, Hao Wu

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Mixed Precision Training of Convolutional Neural Networks Using Integer Operations Dipankar Das, Naveen Mellempudi, Dheevatsa Mudigere, Dhiraj Kalamkar, Sasikanth Avancha, Kunal Banerjee, Srinivas Sridharan, Karthik Vaidyanathan, Bharat Kaul, Evangelos Georganas, Alexander Heinecke, Pradeep Dubey, Jesus Corbal, Nikita Shustrov, Roma Dubtsov, Evarist Fomenko, Vadim Pirogov

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Mixup: Beyond Empirical Risk Minimization Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz

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Model Compression via Distillation and Quantization Antonio Polino, Razvan Pascanu, Dan Alistarh

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Model-Ensemble Trust-Region Policy Optimization Thanard Kurutach, Ignasi Clavera, Yan Duan, Aviv Tamar, Pieter Abbeel

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Modular Continual Learning in a Unified Visual Environment Kevin T. Feigelis, Blue Sheffer, Daniel L. K. Yamins

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Monotonic Chunkwise Attention Chung-Cheng Chiu, Colin Raffel

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Multi-Level Residual Networks from Dynamical Systems View Bo Chang, Lili Meng, Eldad Haber, Frederick Tung, David Begert

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Multi-Mention Learning for Reading Comprehension with Neural Cascades Swabha Swayamdipta, Ankur P. Parikh, Tom Kwiatkowski

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Multi-Scale Dense Networks for Resource Efficient Image Classification Gao Huang, Danlu Chen, Tianhong Li, Felix Wu, Laurens van der Maaten, Kilian Weinberger

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Multi-Task Learning for Document Ranking and Query Suggestion Wasi Uddin Ahmad, Kai-Wei Chang, Hongning Wang

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Multi-View Data Generation Without View Supervision Mickael Chen, Ludovic Denoyer, Thierry Artières

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N2N Learning: Network to Network Compression via Policy Gradient Reinforcement Learning Anubhav Ashok, Nicholas Rhinehart, Fares Beainy, Kris M. Kitani

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Natural Language Inference over Interaction Space Yichen Gong, Heng Luo, Jian Zhang

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NerveNet: Learning Structured Policy with Graph Neural Networks Tingwu Wang, Renjie Liao, Jimmy Ba, Sanja Fidler

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Neumann Optimizer: A Practical Optimization Algorithm for Deep Neural Networks Shankar Krishnan, Ying Xiao, Rif. A. Saurous

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Neural Language Modeling by Jointly Learning Syntax and Lexicon Yikang Shen, Zhouhan Lin, Chin-wei Huang, Aaron Courville

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Neural Sketch Learning for Conditional Program Generation Vijayaraghavan Murali, Letao Qi, Swarat Chaudhuri, Chris Jermaine

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Neural Speed Reading via Skim-RNN Minjoon Seo, Sewon Min, Ali Farhadi, Hannaneh Hajishirzi

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Neural-Guided Deductive Search for Real-Time Program Synthesis from Examples Ashwin Kalyan, Abhishek Mohta, Oleksandr Polozov, Dhruv Batra, Prateek Jain, Sumit Gulwani

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Noisy Networks for Exploration Meire Fortunato, Mohammad Gheshlaghi Azar, Bilal Piot, Jacob Menick, Matteo Hessel, Ian Osband, Alex Graves, Volodymyr Mnih, Remi Munos, Demis Hassabis, Olivier Pietquin, Charles Blundell, Shane Legg

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Non-Autoregressive Neural Machine Translation Jiatao Gu, James Bradbury, Caiming Xiong, Victor O.K. Li, Richard Socher

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Not-so-Random Features Brian Bullins, Cyril Zhang, Yi Zhang

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On the Convergence of Adam and Beyond Sashank J. Reddi, Satyen Kale, Sanjiv Kumar

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On the Discrimination-Generalization Tradeoff in GANs Pengchuan Zhang, Qiang Liu, Dengyong Zhou, Tao Xu, Xiaodong He

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On the Expressive Power of Overlapping Architectures of Deep Learning Or Sharir, Amnon Shashua

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On the Importance of Single Directions for Generalization Ari S. Morcos, David G.T. Barrett, Neil C. Rabinowitz, Matthew Botvinick

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On the Information Bottleneck Theory of Deep Learning Andrew Michael Saxe, Yamini Bansal, Joel Dapello, Madhu Advani, Artemy Kolchinsky, Brendan Daniel Tracey, David Daniel Cox

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On the Insufficiency of Existing Momentum Schemes for Stochastic Optimization Rahul Kidambi, Praneeth Netrapalli, Prateek Jain, Sham M. Kakade

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On the Regularization of Wasserstein GANs Henning Petzka, Asja Fischer, Denis Lukovnikov

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On the State of the Art of Evaluation in Neural Language Models Gábor Melis, Chris Dyer, Phil Blunsom

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On Unifying Deep Generative Models Zhiting Hu, Zichao Yang, Ruslan Salakhutdinov, Eric P. Xing

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Online Learning Rate Adaptation with Hypergradient Descent Atilim Gunes Baydin, Robert Cornish, David Martinez Rubio, Mark Schmidt, Frank Wood

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Overcoming Catastrophic Interference Using Conceptor-Aided Backpropagation Xu He, Herbert Jaeger

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Parallelizing Linear Recurrent Neural Nets over Sequence Length Eric Martin, Chris Cundy

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Parameter Space Noise for Exploration Matthias Plappert, Rein Houthooft, Prafulla Dhariwal, Szymon Sidor, Richard Y. Chen, Xi Chen, Tamim Asfour, Pieter Abbeel, Marcin Andrychowicz

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Parametrized Hierarchical Procedures for Neural Programming Roy Fox, Richard Shin, Sanjay Krishnan, Ken Goldberg, Dawn Song, Ion Stoica

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PixelDefend: Leveraging Generative Models to Understand and Defend Against Adversarial Examples Yang Song, Taesup Kim, Sebastian Nowozin, Stefano Ermon, Nate Kushman

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PixelNN: Example-Based Image Synthesis Aayush Bansal, Yaser Sheikh, Deva Ramanan

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Polar Transformer Networks Carlos Esteves, Christine Allen-Blanchette, Xiaowei Zhou, Kostas Daniilidis

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Policy Optimization by Genetic Distillation Tanmay Gangwani, Jian Peng

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Predicting Floor-Level for 911 Calls with Neural Networks and Smartphone Sensor Data William Falcon, Henning Schulzrinne

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Progressive Growing of GANs for Improved Quality, Stability, and Variation Tero Karras, Timo Aila, Samuli Laine, Jaakko Lehtinen

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Progressive Reinforcement Learning with Distillation for Multi-Skilled Motion Control Glen Berseth, Cheng Xie, Paul Cernek, Michiel Van de Panne

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Proximal Backpropagation Thomas Frerix, Thomas Möllenhoff, Michael Moeller, Daniel Cremers

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QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension Adams Wei Yu, David Dohan, Minh-Thang Luong, Rui Zhao, Kai Chen, Mohammad Norouzi, Quoc V. Le

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Quantitatively Evaluating GANs with Divergences Proposed for Training Daniel Jiwoong Im, He Ma, Graham W. Taylor, Kristin Branson

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Recasting Gradient-Based Meta-Learning as Hierarchical Bayes Erin Grant, Chelsea Finn, Sergey Levine, Trevor Darrell, Thomas Griffiths

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Regularizing and Optimizing LSTM Language Models Stephen Merity, Nitish Shirish Keskar, Richard Socher

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Reinforcement Learning Algorithm Selection Romain Laroche, Raphael Feraud

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Reinforcement Learning on Web Interfaces Using Workflow-Guided Exploration Evan Zheran Liu, Kelvin Guu, Panupong Pasupat, Tianlin Shi, Percy Liang

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Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and Their Interactions Sjoerd van Steenkiste, Michael Chang, Klaus Greff, Jürgen Schmidhuber

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Residual Connections Encourage Iterative Inference Stanisław Jastrzebski, Devansh Arpit, Nicolas Ballas, Vikas Verma, Tong Che, Yoshua Bengio

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Residual Loss Prediction: Reinforcement Learning with No Incremental Feedback Hal Daumé Iii, John Langford, Amr Sharaf

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Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers Jianbo Ye, Xin Lu, Zhe Lin, James Z. Wang

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Robustness of Classifiers to Universal Perturbations: A Geometric Perspective Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, Pascal Frossard, Stefano Soatto

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Routing Networks: Adaptive Selection of Non-Linear Functions for Multi-Task Learning Clemens Rosenbaum, Tim Klinger, Matthew Riemer

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Scalable Private Learning with PATE Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, Ulfar Erlingsson

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SCAN: Learning Hierarchical Compositional Visual Concepts Irina Higgins, Nicolas Sonnerat, Loic Matthey, Arka Pal, Christopher P Burgess, Matko Bošnjak, Murray Shanahan, Matthew Botvinick, Demis Hassabis, Alexander Lerchner

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SEARNN: Training RNNs with Global-Local Losses Rémi Leblond, Jean-Baptiste Alayrac, Anton Osokin, Simon Lacoste-Julien

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Self-Ensembling for Visual Domain Adaptation Geoff French, Michal Mackiewicz, Mark Fisher

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Semantic Interpolation in Implicit Models Yannic Kilcher, Aurelien Lucchi, Thomas Hofmann

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Semantically Decomposing the Latent Spaces of Generative Adversarial Networks Chris Donahue, Zachary C. Lipton, Akshay Balsubramani, Julian McAuley

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Semi-Parametric Topological Memory for Navigation Nikolay Savinov, Alexey Dosovitskiy, Vladlen Koltun

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Sensitivity and Generalization in Neural Networks: An Empirical Study Roman Novak, Yasaman Bahri, Daniel A. Abolafia, Jeffrey Pennington, Jascha Sohl-Dickstein

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SGD Learns Over-Parameterized Networks That Provably Generalize on Linearly Separable Data Alon Brutzkus, Amir Globerson, Eran Malach, Shai Shalev-Shwartz

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Simulated+Unsupervised Learning with Adaptive Data Generation and Bidirectional Mappings Kangwook Lee, Hoon Kim, Changho Suh

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Simulating Action Dynamics with Neural Process Networks Antoine Bosselut, Omer Levy, Ari Holtzman, Corin Ennis, Dieter Fox, Yejin Choi

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Skip Connections Eliminate Singularities Emin Orhan, Xaq Pitkow

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Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks Víctor Campos, Brendan Jou, Xavier Giró-i-Nieto, Jordi Torres, Shih-Fu Chang

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SMASH: One-Shot Model Architecture Search Through HyperNetworks Andrew Brock, Theo Lim, J.M. Ritchie, Nick Weston

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Smooth Loss Functions for Deep Top-K Classification Leonard Berrada, Andrew Zisserman, M. Pawan Kumar

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Sobolev GAN Youssef Mroueh, Chun-Liang Li, Tom Sercu, Anant Raj, Yu Cheng

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Sparse Persistent RNNs: Squeezing Large Recurrent Networks On-Chip Feiwen Zhu, Jeff Pool, Michael Andersch, Jeremy Appleyard, Fung Xie

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Spatially Transformed Adversarial Examples Chaowei Xiao, Jun-Yan Zhu, Bo Li, Warren He, Mingyan Liu, Dawn Song

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Spectral Normalization for Generative Adversarial Networks Takeru Miyato, Toshiki Kataoka, Masanori Koyama, Yuichi Yoshida

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SpectralNet: Spectral Clustering Using Deep Neural Networks Uri Shaham, Kelly Stanton, Henry Li, Ronen Basri, Boaz Nadler, Yuval Kluger

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Spherical CNNs Taco S. Cohen, Mario Geiger, Jonas Köhler, Max Welling

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Stabilizing Adversarial Nets with Prediction Methods Abhay Yadav, Sohil Shah, Zheng Xu, David Jacobs, Tom Goldstein

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Stochastic Activation Pruning for Robust Adversarial Defense Guneet S. Dhillon, Kamyar Azizzadenesheli, Zachary C. Lipton, Jeremy D. Bernstein, Jean Kossaifi, Aran Khanna, Animashree Anandkumar

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Stochastic Gradient Descent Performs Variational Inference, Converges to Limit Cycles for Deep Networks Pratik Chaudhari, Stefano Soatto

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Stochastic Variational Video Prediction Mohammad Babaeizadeh, Chelsea Finn, Dumitru Erhan, Roy H. Campbell, Sergey Levine

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Syntax-Directed Variational Autoencoder for Structured Data Hanjun Dai, Yingtao Tian, Bo Dai, Steven Skiena, Le Song

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Synthesizing Realistic Neural Population Activity Patterns Using Generative Adversarial Networks Manuel Molano-Mazon, Arno Onken, Eugenio Piasini, Stefano Panzeri

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Synthetic and Natural Noise Both Break Neural Machine Translation Yonatan Belinkov, Yonatan Bisk

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TD or Not TD: Analyzing the Role of Temporal Differencing in Deep Reinforcement Learning Artemij Amiranashvili, Alexey Dosovitskiy, Vladlen Koltun, Thomas Brox

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Temporal Difference Models: Model-Free Deep RL for Model-Based Control Vitchyr Pong, Shixiang Gu, Murtaza Dalal, Sergey Levine

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Temporally Efficient Deep Learning with Spikes Peter O'Connor, Efstratios Gavves, Matthias Reisser, Max Welling

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The High-Dimensional Geometry of Binary Neural Networks Alexander G. Anderson, Cory P. Berg

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The Implicit Bias of Gradient Descent on Separable Data Daniel Soudry, Elad Hoffer, Mor Shpigel Nacson, Nathan Srebro

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The Kanerva Machine: A Generative Distributed Memory Yan Wu, Greg Wayne, Alex Graves, Timothy Lillicrap

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The Power of Deeper Networks for Expressing Natural Functions David Rolnick, Max Tegmark

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The Reactor: A Fast and Sample-Efficient Actor-Critic Agent for Reinforcement Learning Audrunas Gruslys, Will Dabney, Mohammad Gheshlaghi Azar, Bilal Piot, Marc Bellemare, Remi Munos

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The Role of Minimal Complexity Functions in Unsupervised Learning of Semantic Mappings Tomer Galanti, Lior Wolf, Sagie Benaim

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Thermometer Encoding: One Hot Way to Resist Adversarial Examples Jacob Buckman, Aurko Roy, Colin Raffel, Ian Goodfellow

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Towards Better Understanding of Gradient-Based Attribution Methods for Deep Neural Networks Marco Ancona, Enea Ceolini, Cengiz Öztireli, Markus Gross

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Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, Adrian Vladu

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Towards Image Understanding from Deep Compression Without Decoding Robert Torfason, Fabian Mentzer, Eirikur Agustsson, Michael Tschannen, Radu Timofte, Luc Van Gool

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Towards Neural Phrase-Based Machine Translation Po-Sen Huang, Chong Wang, Sitao Huang, Dengyong Zhou, Li Deng

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Towards Reverse-Engineering Black-Box Neural Networks Seong Joon Oh, Max Augustin, Mario Fritz, Bernt Schiele

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Towards Synthesizing Complex Programs from Input-Output Examples Xinyun Chen, Chang Liu, Dawn Song

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Training and Inference with Integers in Deep Neural Networks Shuang Wu, Guoqi Li, Feng Chen, Luping Shi

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Training Confidence-Calibrated Classifiers for Detecting Out-of-Distribution Samples Kimin Lee, Honglak Lee, Kibok Lee, Jinwoo Shin

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Training GANs with Optimism Constantinos Daskalakis, Andrew Ilyas, Vasilis Syrgkanis, Haoyang Zeng

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Training Generative Adversarial Networks via Primal-Dual Subgradient Methods: A Lagrangian Perspective on GAN Xu Chen, Jiang Wang, Hao Ge

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Training Wide Residual Networks for Deployment Using a Single Bit for Each Weight Mark D. McDonnell

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TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning Gregory Farquhar, Tim Rocktäschel, Maximilian Igl, Shimon Whiteson

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Truncated Horizon Policy Search: Combining Reinforcement Learning & Imitation Learning Wen Sun, J. Andrew Bagnell, Byron Boots

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Trust-PCL: An Off-Policy Trust Region Method for Continuous Control Ofir Nachum, Mohammad Norouzi, Kelvin Xu, Dale Schuurmans

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Twin Networks: Matching the Future for Sequence Generation Dmitriy Serdyuk, Nan Rosemary Ke, Alessandro Sordoni, Adam Trischler, Chris Pal, Yoshua Bengio

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Unbiased Online Recurrent Optimization Corentin Tallec, Yann Ollivier

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Understanding Deep Neural Networks with Rectified Linear Units Raman Arora, Amitabh Basu, Poorya Mianjy, Anirbit Mukherjee

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Understanding Image Motion with Group Representations Andrew Jaegle, Stephen Phillips, Daphne Ippolito, Kostas Daniilidis

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Understanding Short-Horizon Bias in Stochastic Meta-Optimization Yuhuai Wu, Mengye Ren, Renjie Liao, Roger Grosse.

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Universal Agent for Disentangling Environments and Tasks Jiayuan Mao, Honghua Dong, Joseph J. Lim

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Unsupervised Cipher Cracking Using Discrete GANs Aidan N. Gomez, Sicong Huang, Ivan Zhang, Bryan M. Li, Muhammad Osama, Lukasz Kaiser

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Unsupervised Learning of Goal Spaces for Intrinsically Motivated Goal Exploration Alexandre Péré, Sébastien Forestier, Olivier Sigaud, Pierre-Yves Oudeyer

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Unsupervised Machine Translation Using Monolingual Corpora Only Guillaume Lample, Alexis Conneau, Ludovic Denoyer, Marc'Aurelio Ranzato

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Unsupervised Neural Machine Translation Mikel Artetxe, Gorka Labaka, Eneko Agirre, Kyunghyun Cho

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Unsupervised Representation Learning by Predicting Image Rotations Spyros Gidaris, Praveer Singh, Nikos Komodakis

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Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines Cathy Wu, Aravind Rajeswaran, Yan Duan, Vikash Kumar, Alexandre M Bayen, Sham Kakade, Igor Mordatch, Pieter Abbeel

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Variational Continual Learning Cuong V. Nguyen, Yingzhen Li, Thang D. Bui, Richard E. Turner

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Variational Image Compression with a Scale Hyperprior Johannes Ballé, David Minnen, Saurabh Singh, Sung Jin Hwang, Nick Johnston

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Variational Inference of Disentangled Latent Concepts from Unlabeled Observations Abhishek Kumar, Prasanna Sattigeri, Avinash Balakrishnan

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Variational Message Passing with Structured Inference Networks Wu Lin, Nicolas Hubacher, Mohammad Emtiyaz Khan

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Variational Network Quantization Jan Achterhold, Jan Mathias Koehler, Anke Schmeink, Tim Genewein

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Viterbi-Based Pruning for Sparse Matrix with Fixed and High Index Compression Ratio Dongsoo Lee, Daehyun Ahn, Taesu Kim, Pierce I. Chuang, Jae-Joon Kim

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VoiceLoop: Voice Fitting and Synthesis via a Phonological Loop Yaniv Taigman, Lior Wolf, Adam Polyak, Eliya Nachmani

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Wasserstein Auto-Encoders Ilya Tolstikhin, Olivier Bousquet, Sylvain Gelly, Bernhard Schoelkopf

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Wavelet Pooling for Convolutional Neural Networks Travis Williams, Robert Li

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WHAI: Weibull Hybrid Autoencoding Inference for Deep Topic Modeling Hao Zhang, Bo Chen, Dandan Guo, Mingyuan Zhou

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When Is a Convolutional Filter Easy to Learn? Simon S. Du, Jason D. Lee, Yuandong Tian

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Word Translation Without Parallel Data Guillaume Lample, Alexis Conneau, Marc'Aurelio Ranzato, Ludovic Denoyer, Hervé Jégou

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WRPN: Wide Reduced-Precision Networks Asit Mishra, Eriko Nurvitadhi, Jeffrey J Cook, Debbie Marr

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Zero-Shot Visual Imitation Deepak Pathak, Parsa Mahmoudieh, Guanghao Luo, Pulkit Agrawal, Dian Chen, Yide Shentu, Evan Shelhamer, Jitendra Malik, Alexei A. Efros, Trevor Darrell

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