iclr2018.md

ICLR2018 Paper List

论文	作者	摘要	代码	引用数
On the Convergence of Adam and Beyond	Sashank J. Reddi, Satyen Kale, Sanjiv Kumar		code	-1
Synthetic and Natural Noise Both Break Neural Machine Translation	Yonatan Belinkov, Yonatan Bisk		code	-1
Multi-Scale Dense Networks for Resource Efficient Image Classification	Gao Huang, Danlu Chen, Tianhong Li, Felix Wu, Laurens van der Maaten, Kilian Q. Weinberger		code	-1
Training and Inference with Integers in Deep Neural Networks	Shuang Wu, Guoqi Li, Feng Chen, Luping Shi		code	-1
Emergence of Linguistic Communication from Referential Games with Symbolic and Pixel Input	Angeliki Lazaridou, Karl Moritz Hermann, Karl Tuyls, Stephen Clark		code	-1
Spherical CNNs	Taco S. Cohen, Mario Geiger, Jonas Köhler, Max Welling		code	-1
Ask the Right Questions: Active Question Reformulation with Reinforcement Learning	Christian Buck, Jannis Bulian, Massimiliano Ciaramita, Wojciech Gajewski, Andrea Gesmundo, Neil Houlsby, Wei Wang		code	-1
On the insufficiency of existing momentum schemes for Stochastic Optimization	Rahul Kidambi, Praneeth Netrapalli, Prateek Jain, Sham M. Kakade		code	-1
Certifying Some Distributional Robustness with Principled Adversarial Training	Aman Sinha, Hongseok Namkoong, John C. Duchi		code	-1
Learning Deep Mean Field Games for Modeling Large Population Behavior	Jiachen Yang, Xiaojing Ye, Rakshit Trivedi, Huan Xu, Hongyuan Zha		code	-1
Wasserstein Auto-Encoders	Ilya O. Tolstikhin, Olivier Bousquet, Sylvain Gelly, Bernhard Schölkopf		code	-1
Spectral Normalization for Generative Adversarial Networks	Takeru Miyato, Toshiki Kataoka, Masanori Koyama, Yuichi Yoshida		code	-1
Learning to Represent Programs with Graphs	Miltiadis Allamanis, Marc Brockschmidt, Mahmoud Khademi		code	-1
Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality	Xingjun Ma, Bo Li, Yisen Wang, Sarah M. Erfani, Sudanthi N. R. Wijewickrema, Grant Schoenebeck, Dawn Song, Michael E. Houle, James Bailey		code	-1
Breaking the Softmax Bottleneck: A High-Rank RNN Language Model	Zhilin Yang, Zihang Dai, Ruslan Salakhutdinov, William W. Cohen		code	-1
Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments	Maruan AlShedivat, Trapit Bansal, Yura Burda, Ilya Sutskever, Igor Mordatch, Pieter Abbeel		code	-1
Boosting Dilated Convolutional Networks with Mixed Tensor Decompositions	Nadav Cohen, Ronen Tamari, Amnon Shashua		code	-1
Neural Sketch Learning for Conditional Program Generation	Vijayaraghavan Murali, Letao Qi, Swarat Chaudhuri, Chris Jermaine		code	-1
Progressive Growing of GANs for Improved Quality, Stability, and Variation	Tero Karras, Timo Aila, Samuli Laine, Jaakko Lehtinen		code	-1
Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines	Cathy Wu, Aravind Rajeswaran, Yan Duan, Vikash Kumar, Alexandre M. Bayen, Sham M. Kakade, Igor Mordatch, Pieter Abbeel		code	-1
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		code	-1
Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs	W. James Murdoch, Peter J. Liu, Bin Yu		code	-1
AmbientGAN: Generative models from lossy measurements	Ashish Bora, Eric Price, Alexandros G. Dimakis		code	-1
Minimal-Entropy Correlation Alignment for Unsupervised Deep Domain Adaptation	Pietro Morerio, Jacopo Cavazza, Vittorio Murino		code	-1
Large Scale Optimal Transport and Mapping Estimation	Vivien Seguy, Bharath Bhushan Damodaran, Rémi Flamary, Nicolas Courty, Antoine Rolet, Mathieu Blondel		code	-1
Truncated horizon Policy Search: Combining Reinforcement Learning & Imitation Learning	Wen Sun, J. Andrew Bagnell, Byron Boots		code	-1
Model-Ensemble Trust-Region Policy Optimization	Thanard Kurutach, Ignasi Clavera, Yan Duan, Aviv Tamar, Pieter Abbeel		code	-1
A Neural Representation of Sketch Drawings	David Ha, Douglas Eck		code	-1
Deep Learning with Logged Bandit Feedback	Thorsten Joachims, Adith Swaminathan, Maarten de Rijke		code	-1
Learning Latent Permutations with Gumbel-Sinkhorn Networks	Gonzalo E. Mena, David Belanger, Scott W. Linderman, Jasper Snoek		code	-1
Learning an Embedding Space for Transferable Robot Skills	Karol Hausman, Jost Tobias Springenberg, Ziyu Wang, Nicolas Heess, Martin A. Riedmiller		code	-1
Unsupervised Learning of Goal Spaces for Intrinsically Motivated Goal Exploration	Alexandre Péré, Sébastien Forestier, Olivier Sigaud, PierreYves Oudeyer		code	-1
Multi-View Data Generation Without View Supervision	Mickaël Chen, Ludovic Denoyer, Thierry Artières		code	-1
Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling	Carlos Riquelme, George Tucker, Jasper Snoek		code	-1
Semantic Interpolation in Implicit Models	Yannic Kilcher, Aurélien Lucchi, Thomas Hofmann		code	-1
Fidelity-Weighted Learning	Mostafa Dehghani, Arash Mehrjou, Stephan Gouws, Jaap Kamps, Bernhard Schölkopf		code	-1
Latent Space Oddity: on the Curvature of Deep Generative Models	Georgios Arvanitidis, Lars Kai Hansen, Søren Hauberg		code	-1
Imitation Learning from Visual Data with Multiple Intentions	Aviv Tamar, Khashayar Rohanimanesh, Yinlam Chow, Chris Vigorito, Ben Goodrich, Michael Kahane, Derik Pridmore		code	-1
Hyperparameter optimization: a spectral approach	Elad Hazan, Adam R. Klivans, Yang Yuan		code	-1
Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis	Rudy Bunel, Matthew J. Hausknecht, Jacob Devlin, Rishabh Singh, Pushmeet Kohli		code	-1
Efficient Sparse-Winograd Convolutional Neural Networks	Xingyu Liu, Jeff Pool, Song Han, William J. Dally		code	-1
Espresso: Efficient Forward Propagation for Binary Deep Neural Networks	Fabrizio Pedersoli, George Tzanetakis, Andrea Tagliasacchi		code	-1
Auto-Conditioned Recurrent Networks for Extended Complex Human Motion Synthesis	Yi Zhou, Zimo Li, Shuangjiu Xiao, Chong He, Zeng Huang, Hao Li		code	-1
Decoupling the Layers in Residual Networks	Ricky Fok, Aijun An, Zana Rashidi, Xiaogang Wang		code	-1
Polar Transformer Networks	Carlos Esteves, Christine AllenBlanchette, Xiaowei Zhou, Kostas Daniilidis		code	-1
Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks	Shiyu Liang, Yixuan Li, R. Srikant		code	-1
Stabilizing Adversarial Nets with Prediction Methods	Abhay Kumar Yadav, Sohil Shah, Zheng Xu, David W. Jacobs, Tom Goldstein		code	-1
Graph Attention Networks	Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, Yoshua Bengio		code	-1
Minimax Curriculum Learning: Machine Teaching with Desirable Difficulties and Scheduled Diversity	Tianyi Zhou, Jeff A. Bilmes		code	-1
Generalizing Hamiltonian Monte Carlo with Neural Networks	Daniel Levy, Matthew D. Hoffman, Jascha SohlDickstein		code	-1
An Online Learning Approach to Generative Adversarial Networks	Paulina Grnarova, Kfir Y. Levy, Aurélien Lucchi, Thomas Hofmann, Andreas Krause		code	-1
Improving GANs Using Optimal Transport	Tim Salimans, Han Zhang, Alec Radford, Dimitris N. Metaxas		code	-1
The Kanerva Machine: A Generative Distributed Memory	Yan Wu, Greg Wayne, Alex Graves, Timothy P. Lillicrap		code	-1
Mixed Precision Training	Paulius Micikevicius, Sharan Narang, Jonah Alben, Gregory F. Diamos, Erich Elsen, David García, Boris Ginsburg, Michael Houston, Oleksii Kuchaiev, Ganesh Venkatesh, Hao Wu		code	-1
Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models	Jesse H. Engel, Matthew D. Hoffman, Adam Roberts		code	-1
MaskGAN: Better Text Generation via Filling in the _______	William Fedus, Ian J. Goodfellow, Andrew M. Dai		code	-1
Divide and Conquer Networks	Alex Nowak, David Folqué, Joan Bruna		code	-1
Meta-Learning and Universality: Deep Representations and Gradient Descent can Approximate any Learning Algorithm	Chelsea Finn, Sergey Levine		code	-1
Maximum a Posteriori Policy Optimisation	Abbas Abdolmaleki, Jost Tobias Springenberg, Yuval Tassa, Rémi Munos, Nicolas Heess, Martin A. Riedmiller		code	-1
Meta Learning Shared Hierarchies	Kevin Frans, Jonathan Ho, Xi Chen, Pieter Abbeel, John Schulman		code	-1
Deep Neural Networks as Gaussian Processes	Jaehoon Lee, Yasaman Bahri, Roman Novak, Samuel S. Schoenholz, Jeffrey Pennington, Jascha SohlDickstein		code	-1
Syntax-Directed Variational Autoencoder for Structured Data	Hanjun Dai, Yingtao Tian, Bo Dai, Steven Skiena, Le Song		code	-1
Neural-Guided Deductive Search for Real-Time Program Synthesis from Examples	Ashwin Kalyan, Abhishek Mohta, Oleksandr Polozov, Dhruv Batra, Prateek Jain, Sumit Gulwani		code	-1
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		code	-1
WRPN: Wide Reduced-Precision Networks	Asit K. Mishra, Eriko Nurvitadhi, Jeffrey J. Cook, Debbie Marr		code	-1
MGAN: Training Generative Adversarial Nets with Multiple Generators	Quan Hoang, Tu Dinh Nguyen, Trung Le, Dinh Q. Phung		code	-1
The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning	Audrunas Gruslys, Will Dabney, Mohammad Gheshlaghi Azar, Bilal Piot, Marc G. Bellemare, Rémi Munos		code	-1
SEARNN: Training RNNs with global-local losses	Rémi Leblond, JeanBaptiste Alayrac, Anton Osokin, Simon LacosteJulien		code	-1
Distributed Distributional Deterministic Policy Gradients	Gabriel BarthMaron, Matthew W. Hoffman, David Budden, Will Dabney, Dan Horgan, Dhruva TB, Alistair Muldal, Nicolas Heess, Timothy P. Lillicrap		code	-1
Hierarchical Subtask Discovery with Non-Negative Matrix Factorization	Adam Christopher Earle, Andrew M. Saxe, Benjamin Rosman		code	-1
Parametrized Hierarchical Procedures for Neural Programming	Roy Fox, Richard Shin, Sanjay Krishnan, Ken Goldberg, Dawn Song, Ion Stoica		code	-1
Viterbi-based Pruning for Sparse Matrix with Fixed and High Index Compression Ratio	Dongsoo Lee, Daehyun Ahn, Taesu Kim, Pierce IJen Chuang, JaeJoon Kim		code	-1
cGANs with Projection Discriminator	Takeru Miyato, Masanori Koyama		code	-1
Unsupervised Representation Learning by Predicting Image Rotations	Spyros Gidaris, Praveer Singh, Nikos Komodakis		code	-1
Emergent Communication in a Multi-Modal, Multi-Step Referential Game	Katrina Evtimova, Andrew Drozdov, Douwe Kiela, Kyunghyun Cho		code	-1
FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling	Jie Chen, Tengfei Ma, Cao Xiao		code	-1
Emergent Translation in Multi-Agent Communication	Jason Lee, Kyunghyun Cho, Jason Weston, Douwe Kiela		code	-1
An efficient framework for learning sentence representations	Lajanugen Logeswaran, Honglak Lee		code	-1
NerveNet: Learning Structured Policy with Graph Neural Networks	Tingwu Wang, Renjie Liao, Jimmy Ba, Sanja Fidler		code	-1
Learning Latent Representations in Neural Networks for Clustering through Pseudo Supervision and Graph-based Activity Regularization	Ozsel Kilinc, Ismail Uysal		code	-1
Adversarial Dropout Regularization	Kuniaki Saito, Yoshitaka Ushiku, Tatsuya Harada, Kate Saenko		code	-1
Demystifying MMD GANs	Mikolaj Binkowski, Danica J. Sutherland, Michael Arbel, Arthur Gretton		code	-1
Smooth Loss Functions for Deep Top-k Classification	Leonard Berrada, Andrew Zisserman, M. Pawan Kumar		code	-1
Deep Learning as a Mixed Convex-Combinatorial Optimization Problem	Abram L. Friesen, Pedro M. Domingos		code	-1
Learning Approximate Inference Networks for Structured Prediction	Lifu Tu, Kevin Gimpel		code	-1
Learning to Share: simultaneous parameter tying and Sparsification in Deep Learning	Dejiao Zhang, Haozhu Wang, Mário A. T. Figueiredo, Laura Balzano		code	-1
Model compression via distillation and quantization	Antonio Polino, Razvan Pascanu, Dan Alistarh		code	-1
Variational Message Passing with Structured Inference Networks	Wu Lin, Nicolas Hubacher, Mohammad Emtiyaz Khan		code	-1
Action-dependent Control Variates for Policy Optimization via Stein Identity	Hao Liu, Yihao Feng, Yi Mao, Dengyong Zhou, Jian Peng, Qiang Liu		code	-1
Variational image compression with a scale hyperprior	Johannes Ballé, David Minnen, Saurabh Singh, Sung Jin Hwang, Nick Johnston		code	-1
Variational Inference of Disentangled Latent Concepts from Unlabeled Observations	Abhishek Kumar, Prasanna Sattigeri, Avinash Balakrishnan		code	-1
Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches	Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger B. Grosse		code	-1
Kernel Implicit Variational Inference	Jiaxin Shi, Shengyang Sun, Jun Zhu		code	-1
A Scalable Laplace Approximation for Neural Networks	Hippolyt Ritter, Aleksandar Botev, David Barber		code	-1
The High-Dimensional Geometry of Binary Neural Networks	Alexander G. Anderson, Cory P. Berg		code	-1
Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy	Asit K. Mishra, Debbie Marr		code	-1
Distributed Prioritized Experience Replay	Dan Horgan, John Quan, David Budden, Gabriel BarthMaron, Matteo Hessel, Hado van Hasselt, David Silver		code	-1
Learning from Between-class Examples for Deep Sound Recognition	Yuji Tokozume, Yoshitaka Ushiku, Tatsuya Harada		code	-1
Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples	Kimin Lee, Honglak Lee, Kibok Lee, Jinwoo Shin		code	-1
VoiceLoop: Voice Fitting and Synthesis via a Phonological Loop	Yaniv Taigman, Lior Wolf, Adam Polyak, Eliya Nachmani		code	-1
Large scale distributed neural network training through online distillation	Rohan Anil, Gabriel Pereyra, Alexandre Passos, Róbert Ormándi, George E. Dahl, Geoffrey E. Hinton		code	-1
Learning Differentially Private Recurrent Language Models	H. Brendan McMahan, Daniel Ramage, Kunal Talwar, Li Zhang		code	-1
Mastering the Dungeon: Grounded Language Learning by Mechanical Turker Descent	Zhilin Yang, Saizheng Zhang, Jack Urbanek, Will Feng, Alexander H. Miller, Arthur Szlam, Douwe Kiela, Jason Weston		code	-1
Generating Wikipedia by Summarizing Long Sequences	Peter J. Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, Noam Shazeer		code	-1
Unsupervised Machine Translation Using Monolingual Corpora Only	Guillaume Lample, Alexis Conneau, Ludovic Denoyer, Marc'Aurelio Ranzato		code	-1
A Deep Reinforced Model for Abstractive Summarization	Romain Paulus, Caiming Xiong, Richard Socher		code	-1
Compressing Word Embeddings via Deep Compositional Code Learning	Raphael Shu, Hideki Nakayama		code	-1
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training	Yujun Lin, Song Han, Huizi Mao, Yu Wang, Bill Dally		code	-1
QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension	Adams Wei Yu, David Dohan, MinhThang Luong, Rui Zhao, Kai Chen, Mohammad Norouzi, Quoc V. Le		code	-1
Unsupervised Neural Machine Translation	Mikel Artetxe, Gorka Labaka, Eneko Agirre, Kyunghyun Cho		code	-1
Learning One-hidden-layer Neural Networks with Landscape Design	Rong Ge, Jason D. Lee, Tengyu Ma		code	-1
Critical Points of Linear Neural Networks: Analytical Forms and Landscape Properties	Yi Zhou, Yingbin Liang		code	-1
Learning Parametric Closed-Loop Policies for Markov Potential Games	Sergio Valcarcel Macua, Javier Zazo, Santiago Zazo		code	-1
The power of deeper networks for expressing natural functions	David Rolnick, Max Tegmark		code	-1
Empirical Risk Landscape Analysis for Understanding Deep Neural Networks	Pan Zhou, Jiashi Feng		code	-1
On the Discrimination-Generalization Tradeoff in GANs	Pengchuan Zhang, Qiang Liu, Dengyong Zhou, Tao Xu, Xiaodong He		code	-1
Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models	Wieland Brendel, Jonas Rauber, Matthias Bethge		code	-1
Unbiased Online Recurrent Optimization	Corentin Tallec, Yann Ollivier		code	-1
Measuring the Intrinsic Dimension of Objective Landscapes	Chunyuan Li, Heerad Farkhoor, Rosanne Liu, Jason Yosinski		code	-1
Memorization Precedes Generation: Learning Unsupervised GANs with Memory Networks	Youngjin Kim, Minjung Kim, Gunhee Kim		code	-1
Stochastic Activation Pruning for Robust Adversarial Defense	Guneet S. Dhillon, Kamyar Azizzadenesheli, Zachary C. Lipton, Jeremy Bernstein, Jean Kossaifi, Aran Khanna, Animashree Anandkumar		code	-1
Sparse Persistent RNNs: Squeezing Large Recurrent Networks On-Chip	Feiwen Zhu, Jeff Pool, Michael Andersch, Jeremy Appleyard, Fung Xie		code	-1
GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets	Jinsung Yoon, James Jordon, Mihaela van der Schaar		code	-1
Thermometer Encoding: One Hot Way To Resist Adversarial Examples	Jacob Buckman, Aurko Roy, Colin Raffel, Ian J. Goodfellow		code	-1
Trust-PCL: An Off-Policy Trust Region Method for Continuous Control	Ofir Nachum, Mohammad Norouzi, Kelvin Xu, Dale Schuurmans		code	-1
Stochastic Variational Video Prediction	Mohammad Babaeizadeh, Chelsea Finn, Dumitru Erhan, Roy H. Campbell, Sergey Levine		code	-1
Towards Image Understanding from Deep Compression Without Decoding	Robert Torfason, Fabian Mentzer, Eirikur Agustsson, Michael Tschannen, Radu Timofte, Luc Van Gool		code	-1
Automatically Inferring Data Quality for Spatiotemporal Forecasting	Sungyong Seo, Arash Mohegh, George BanWeiss, Yan Liu		code	-1
Towards better understanding of gradient-based attribution methods for Deep Neural Networks	Marco Ancona, Enea Ceolini, Cengiz Öztireli, Markus Gross		code	-1
Countering Adversarial Images using Input Transformations	Chuan Guo, Mayank Rana, Moustapha Cissé, Laurens van der Maaten		code	-1
Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks	Víctor Campos, Brendan Jou, Xavier GiróiNieto, Jordi Torres, ShihFu Chang		code	-1
Modular Continual Learning in a Unified Visual Environment	Kevin T. Feigelis, Blue Sheffer, Daniel L. K. Yamins		code	-1
Twin Networks: Matching the Future for Sequence Generation	Dmitriy Serdyuk, Nan Rosemary Ke, Alessandro Sordoni, Adam Trischler, Chris Pal, Yoshua Bengio		code	-1
Interpretable Counting for Visual Question Answering	Alexander Trott, Caiming Xiong, Richard Socher		code	-1
Interactive Grounded Language Acquisition and Generalization in a 2D World	Haonan Yu, Haichao Zhang, Wei Xu		code	-1
Universal Agent for Disentangling Environments and Tasks	Jiayuan Mao, Honghua Dong, Joseph J. Lim		code	-1
Residual Connections Encourage Iterative Inference	Stanislaw Jastrzebski, Devansh Arpit, Nicolas Ballas, Vikas Verma, Tong Che, Yoshua Bengio		code	-1
Emergent Communication through Negotiation	Kris Cao, Angeliki Lazaridou, Marc Lanctot, Joel Z. Leibo, Karl Tuyls, Stephen Clark		code	-1
Semi-parametric topological memory for navigation	Nikolay Savinov, Alexey Dosovitskiy, Vladlen Koltun		code	-1
Learning to Count Objects in Natural Images for Visual Question Answering	Yan Zhang, Jonathon S. Hare, Adam PrügelBennett		code	-1
i-RevNet: Deep Invertible Networks	JörnHenrik Jacobsen, Arnold W. M. Smeulders, Edouard Oyallon		code	-1
Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach	TsuiWei Weng, Huan Zhang, PinYu Chen, Jinfeng Yi, Dong Su, Yupeng Gao, ChoJui Hsieh, Luca Daniel		code	-1
HexaConv	Emiel Hoogeboom, Jorn W. T. Peters, Taco S. Cohen, Max Welling		code	-1
Towards Deep Learning Models Resistant to Adversarial Attacks	Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, Adrian Vladu		code	-1
Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge	Emmanuel de Bézenac, Arthur Pajot, Patrick Gallinari		code	-1
Communication Algorithms via Deep Learning	Hyeji Kim, Yihan Jiang, Ranvir Rana, Sreeram Kannan, Sewoong Oh, Pramod Viswanath		code	-1
Simulating Action Dynamics with Neural Process Networks	Antoine Bosselut, Omer Levy, Ari Holtzman, Corin Ennis, Dieter Fox, Yejin Choi		code	-1
Unsupervised Cipher Cracking Using Discrete GANs	Aidan N. Gomez, Sicong Huang, Ivan Zhang, Bryan M. Li, Muhammad Osama, Lukasz Kaiser		code	-1
Neural Speed Reading via Skim-RNN	Min Joon Seo, Sewon Min, Ali Farhadi, Hannaneh Hajishirzi		code	-1
Multi-level Residual Networks from Dynamical Systems View	Bo Chang, Lili Meng, Eldad Haber, Frederick Tung, David Begert		code	-1
Towards Neural Phrase-based Machine Translation	PoSen Huang, Chong Wang, Sitao Huang, Dengyong Zhou, Li Deng		code	-1
On the State of the Art of Evaluation in Neural Language Models	Gábor Melis, Chris Dyer, Phil Blunsom		code	-1
Memory-based Parameter Adaptation	Pablo Sprechmann, Siddhant M. Jayakumar, Jack W. Rae, Alexander Pritzel, Adrià Puigdomènech Badia, Benigno Uria, Oriol Vinyals, Demis Hassabis, Razvan Pascanu, Charles Blundell		code	-1
Initialization matters: Orthogonal Predictive State Recurrent Neural Networks	Krzysztof Choromanski, Carlton Downey, Byron Boots		code	-1
PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples	Yang Song, Taesup Kim, Sebastian Nowozin, Stefano Ermon, Nate Kushman		code	-1
Certified Defenses against Adversarial Examples	Aditi Raghunathan, Jacob Steinhardt, Percy Liang		code	-1
Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models	Pouya Samangouei, Maya Kabkab, Rama Chellappa		code	-1
Ensemble Adversarial Training: Attacks and Defenses	Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian J. Goodfellow, Dan Boneh, Patrick D. McDaniel		code	-1
Fraternal Dropout	Konrad Zolna, Devansh Arpit, Dendi Suhubdy, Yoshua Bengio		code	-1
Can recurrent neural networks warp time?	Corentin Tallec, Yann Ollivier		code	-1
Parallelizing Linear Recurrent Neural Nets Over Sequence Length	Eric Martin, Chris Cundy		code	-1
Attacking Binarized Neural Networks	Angus Galloway, Graham W. Taylor, Medhat Moussa		code	-1
Depthwise Separable Convolutions for Neural Machine Translation	Lukasz Kaiser, Aidan N. Gomez, François Chollet		code	-1
Noisy Networks For Exploration	Meire Fortunato, Mohammad Gheshlaghi Azar, Bilal Piot, Jacob Menick, Matteo Hessel, Ian Osband, Alex Graves, Volodymyr Mnih, Rémi Munos, Demis Hassabis, Olivier Pietquin, Charles Blundell, Shane Legg		code	-1
A Hierarchical Model for Device Placement	Azalia Mirhoseini, Anna Goldie, Hieu Pham, Benoit Steiner, Quoc V. Le, Jeff Dean		code	-1
Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection	Bo Zong, Qi Song, Martin Renqiang Min, Wei Cheng, Cristian Lumezanu, Daeki Cho, Haifeng Chen		code	-1
Learning Discrete Weights Using the Local Reparameterization Trick	Oran Shayer, Dan Levi, Ethan Fetaya		code	-1
Deep Rewiring: Training very sparse deep networks	Guillaume Bellec, David Kappel, Wolfgang Maass, Robert Legenstein		code	-1
Quantitatively Evaluating GANs With Divergences Proposed for Training	Daniel Jiwoong Im, He Ma, Graham W. Taylor, Kristin Branson		code	-1
Improving GAN Training via Binarized Representation Entropy (BRE) Regularization	Yanshuai Cao, Gavin Weiguang Ding, Kry YikChau Lui, Ruitong Huang		code	-1
Generative networks as inverse problems with Scattering transforms	Tomás Angles, Stéphane Mallat		code	-1
Critical Percolation as a Framework to Analyze the Training of Deep Networks	Zohar Ringel, Rodrigo Andrade de Bem		code	-1
On the Expressive Power of Overlapping Architectures of Deep Learning	Or Sharir, Amnon Shashua		code	-1
Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers	Jianbo Ye, Xin Lu, Zhe Lin, James Z. Wang		code	-1
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting	Yaguang Li, Rose Yu, Cyrus Shahabi, Yan Liu		code	-1
Simulated+Unsupervised Learning With Adaptive Data Generation and Bidirectional Mappings	Kangwook Lee, Hoon Kim, Changho Suh		code	-1
Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions	Sjoerd van Steenkiste, Michael Chang, Klaus Greff, Jürgen Schmidhuber		code	-1
Generative Models of Visually Grounded Imagination	Ramakrishna Vedantam, Ian Fischer, Jonathan Huang, Kevin Murphy		code	-1
Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions	Scott E. Reed, Yutian Chen, Thomas Paine, Aäron van den Oord, S. M. Ali Eslami, Danilo J. Rezende, Oriol Vinyals, Nando de Freitas		code	-1
Compositional Obverter Communication Learning from Raw Visual Input	Edward Choi, Angeliki Lazaridou, Nando de Freitas		code	-1
SCAN: Learning Hierarchical Compositional Visual Concepts	Irina Higgins, Nicolas Sonnerat, Loic Matthey, Arka Pal, Christopher P. Burgess, Matko Bosnjak, Murray Shanahan, Matthew M. Botvinick, Demis Hassabis, Alexander Lerchner		code	-1
Hierarchical Density Order Embeddings	Ben Athiwaratkun, Andrew Gordon Wilson		code	-1
Identifying Analogies Across Domains	Yedid Hoshen, Lior Wolf		code	-1
Emergence of grid-like representations by training recurrent neural networks to perform spatial localization	Christopher J. Cueva, XueXin Wei		code	-1
Learning a neural response metric for retinal prosthesis	Nishal P. Shah, Sasidhar Madugula, E. J. Chichilnisky, Yoram Singer		code	-1
Few-Shot Learning with Graph Neural Networks	Victor Garcia Satorras, Joan Bruna Estrach		code	-1
Semantically Decomposing the Latent Spaces of Generative Adversarial Networks	Chris Donahue, Zachary C. Lipton, Akshay Balsubramani, Julian J. McAuley		code	-1
A Framework for the Quantitative Evaluation of Disentangled Representations	Cian Eastwood, Christopher K. I. Williams		code	-1
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		code	-1
A DIRT-T Approach to Unsupervised Domain Adaptation	Rui Shu, Hung H. Bui, Hirokazu Narui, Stefano Ermon		code	-1
Generalizing Across Domains via Cross-Gradient Training	Shiv Shankar, Vihari Piratla, Soumen Chakrabarti, Siddhartha Chaudhuri, Preethi Jyothi, Sunita Sarawagi		code	-1
Learning to cluster in order to transfer across domains and tasks	YenChang Hsu, Zhaoyang Lv, Zsolt Kira		code	-1
Deep Complex Networks	Chiheb Trabelsi, Olexa Bilaniuk, Ying Zhang, Dmitriy Serdyuk, Sandeep Subramanian, João Felipe Santos, Soroush Mehri, Negar Rostamzadeh, Yoshua Bengio, Christopher J. Pal		code	-1
Skip Connections Eliminate Singularities	A. Emin Orhan, Xaq Pitkow		code	-1
Bi-Directional Block Self-Attention for Fast and Memory-Efficient Sequence Modeling	Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang		code	-1
Routing Networks: Adaptive Selection of Non-Linear Functions for Multi-Task Learning	Clemens Rosenbaum, Tim Klinger, Matthew Riemer		code	-1
Wavelet Pooling for Convolutional Neural Networks	Travis L. Williams, Robert Li		code	-1
FearNet: Brain-Inspired Model for Incremental Learning	Ronald Kemker, Christopher Kanan		code	-1
Do GANs learn the distribution? Some Theory and Empirics	Sanjeev Arora, Andrej Risteski, Yi Zhang		code	-1
Towards Reverse-Engineering Black-Box Neural Networks	Seong Joon Oh, Max Augustin, Mario Fritz, Bernt Schiele		code	-1
Understanding Deep Neural Networks with Rectified Linear Units	Raman Arora, Amitabh Basu, Poorya Mianjy, Anirbit Mukherjee		code	-1
Training wide residual networks for deployment using a single bit for each weight	Mark D. McDonnell		code	-1
Learn to Pay Attention	Saumya Jetley, Nicholas A. Lord, Namhoon Lee, Philip H. S. Torr		code	-1
Monotonic Chunkwise Attention	ChungCheng Chiu, Colin Raffel		code	-1
Recasting Gradient-Based Meta-Learning as Hierarchical Bayes	Erin Grant, Chelsea Finn, Sergey Levine, Trevor Darrell, Thomas L. Griffiths		code	-1
Don't Decay the Learning Rate, Increase the Batch Size	Samuel L. Smith, PieterJan Kindermans, Chris Ying, Quoc V. Le		code	-1
Kronecker-factored Curvature Approximations for Recurrent Neural Networks	James Martens, Jimmy Ba, Matt Johnson		code	-1
Proximal Backpropagation	Thomas Frerix, Thomas Möllenhoff, Michael Möller, Daniel Cremers		code	-1
Neumann Optimizer: A Practical Optimization Algorithm for Deep Neural Networks	Shankar Krishnan, Ying Xiao, Rif A. Saurous		code	-1
SGD Learns Over-parameterized Networks that Provably Generalize on Linearly Separable Data	Alon Brutzkus, Amir Globerson, Eran Malach, Shai ShalevShwartz		code	-1
A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks	Behnam Neyshabur, Srinadh Bhojanapalli, Nathan Srebro		code	-1
On the importance of single directions for generalization	Ari S. Morcos, David G. T. Barrett, Neil C. Rabinowitz, Matthew M. Botvinick		code	-1
The Implicit Bias of Gradient Descent on Separable Data	Daniel Soudry, Elad Hoffer, Mor Shpigel Nacson, Nathan Srebro		code	-1
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 J. Goodfellow		code	-1
Adaptive Dropout with Rademacher Complexity Regularization	Ke Zhai, Huan Wang		code	-1
A Bayesian Perspective on Generalization and Stochastic Gradient Descent	Samuel L. Smith, Quoc V. Le		code	-1
Implicit Causal Models for Genome-wide Association Studies	Dustin Tran, David M. Blei		code	-1
Sensitivity and Generalization in Neural Networks: an Empirical Study	Roman Novak, Yasaman Bahri, Daniel A. Abolafia, Jeffrey Pennington, Jascha SohlDickstein		code	-1
Regularizing and Optimizing LSTM Language Models	Stephen Merity, Nitish Shirish Keskar, Richard Socher		code	-1
DCN+: Mixed Objective And Deep Residual Coattention for Question Answering	Caiming Xiong, Victor Zhong, Richard Socher		code	-1
Word translation without parallel data	Guillaume Lample, Alexis Conneau, Marc'Aurelio Ranzato, Ludovic Denoyer, Hervé Jégou		code	-1
All-but-the-Top: Simple and Effective Postprocessing for Word Representations	Jiaqi Mu, Pramod Viswanath		code	-1
Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning	Sandeep Subramanian, Adam Trischler, Yoshua Bengio, Christopher J. Pal		code	-1
Natural Language Inference over Interaction Space	Yichen Gong, Heng Luo, Jian Zhang		code	-1
Multi-Task Learning for Document Ranking and Query Suggestion	Wasi Uddin Ahmad, KaiWei Chang, Hongning Wang		code	-1
Distributed Fine-tuning of Language Models on Private Data	Vadim Popov, Mikhail A. Kudinov, Irina Piontkovskaya, Petr Vytovtov, Alex Nevidomsky		code	-1
Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Play	Sainbayar Sukhbaatar, Zeming Lin, Ilya Kostrikov, Gabriel Synnaeve, Arthur Szlam, Rob Fergus		code	-1
Reinforcement Learning Algorithm Selection	Romain Laroche, Raphaël Féraud		code	-1
Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning	Benjamin Eysenbach, Shixiang Gu, Julian Ibarz, Sergey Levine		code	-1
Consequentialist conditional cooperation in social dilemmas with imperfect information	Alexander Peysakhovich, Adam Lerer		code	-1
Can Neural Networks Understand Logical Entailment?	Richard Evans, David Saxton, David Amos, Pushmeet Kohli, Edward Grefenstette		code	-1
Cascade Adversarial Machine Learning Regularized with a Unified Embedding	Taesik Na, Jong Hwan Ko, Saibal Mukhopadhyay		code	-1
Mitigating Adversarial Effects Through Randomization	Cihang Xie, Jianyu Wang, Zhishuai Zhang, Zhou Ren, Alan L. Yuille		code	-1
Decision Boundary Analysis of Adversarial Examples	Warren He, Bo Li, Dawn Song		code	-1
Matrix capsules with EM routing	Geoffrey E. Hinton, Sara Sabour, Nicholas Frosst		code	-1
CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training	Murat Kocaoglu, Christopher Snyder, Alexandros G. Dimakis, Sriram Vishwanath		code	-1
Learning Wasserstein Embeddings	Nicolas Courty, Rémi Flamary, Mélanie Ducoffe		code	-1
Training Generative Adversarial Networks via Primal-Dual subgradient Methods: a Lagrangian Perspective on GaN	Xu Chen, Jiang Wang, Hao Ge		code	-1
Activation Maximization Generative Adversarial Nets	Zhiming Zhou, Han Cai, Shu Rong, Yuxuan Song, Kan Ren, Weinan Zhang, Jun Wang, Yong Yu		code	-1
Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields	Thomas Unterthiner, Bernhard Nessler, Calvin Seward, Günter Klambauer, Martin Heusel, Hubert Ramsauer, Sepp Hochreiter		code	-1
Improving the Improved Training of Wasserstein GANs: A Consistency Term and Its Dual Effect	Xiang Wei, Boqing Gong, Zixia Liu, Wei Lu, Liqiang Wang		code	-1
FusionNet: Fusing via Fully-aware Attention with Application to Machine Comprehension	HsinYuan Huang, Chenguang Zhu, Yelong Shen, Weizhu Chen		code	-1
Neural Language Modeling by Jointly Learning Syntax and Lexicon	Yikang Shen, Zhouhan Lin, ChinWei Huang, Aaron C. Courville		code	-1
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		code	-1
Deep Active Learning for Named Entity Recognition	Yanyao Shen, Hyokun Yun, Zachary C. Lipton, Yakov Kronrod, Animashree Anandkumar		code	-1
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		code	-1
Lifelong Learning with Dynamically Expandable Networks	Jaehong Yoon, Eunho Yang, Jeongtae Lee, Sung Ju Hwang		code	-1
The Role of Minimal Complexity Functions in Unsupervised Learning of Semantic Mappings	Tomer Galanti, Lior Wolf, Sagie Benaim		code	-1
Dynamic Neural Program Embeddings for Program Repair	Rishabh Singh, Zhendong Su		code	-1
Compositional Attention Networks for Machine Reasoning	Drew A. Hudson, Christopher D. Manning		code	-1
Beyond Shared Hierarchies: Deep Multitask Learning through Soft Layer Ordering	Elliot Meyerson, Risto Miikkulainen		code	-1
Hierarchical Representations for Efficient Architecture Search	Hanxiao Liu, Karen Simonyan, Oriol Vinyals, Chrisantha Fernando, Koray Kavukcuoglu		code	-1
Reinforcement Learning on Web Interfaces using Workflow-Guided Exploration	Evan Zheran Liu, Kelvin Guu, Panupong Pasupat, Tianlin Shi, Percy Liang		code	-1
Combining Symbolic Expressions and Black-box Function Evaluations in Neural Programs	Forough Arabshahi, Sameer Singh, Animashree Anandkumar		code	-1
Scalable Private Learning with PATE	Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, Úlfar Erlingsson		code	-1
Active Learning for Convolutional Neural Networks: A Core-Set Approach	Ozan Sener, Silvio Savarese		code	-1
Loss-aware Weight Quantization of Deep Networks	Lu Hou, James T. Kwok		code	-1
Global Optimality Conditions for Deep Neural Networks	Chulhee Yun, Suvrit Sra, Ali Jadbabaie		code	-1
SpectralNet: Spectral Clustering using Deep Neural Networks	Uri Shaham, Kelly P. Stanton, Henry Li, Ronen Basri, Boaz Nadler, Yuval Kluger		code	-1
Not-So-Random Features	Brian Bullins, Cyril Zhang, Yi Zhang		code	-1
Learning how to explain neural networks: PatternNet and PatternAttribution	PieterJan Kindermans, Kristof T. Schütt, Maximilian Alber, KlausRobert Müller, Dumitru Erhan, Been Kim, Sven Dähne		code	-1
Detecting Statistical Interactions from Neural Network Weights	Michael Tsang, Dehua Cheng, Yan Liu		code	-1
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking	Aleksandar Bojchevski, Stephan Günnemann		code	-1
Generating Natural Adversarial Examples	Zhengli Zhao, Dheeru Dua, Sameer Singh		code	-1
Spatially Transformed Adversarial Examples	Chaowei Xiao, JunYan Zhu, Bo Li, Warren He, Mingyan Liu, Dawn Song		code	-1
Predicting Floor-Level for 911 Calls with Neural Networks and Smartphone Sensor Data	William Falcon, Henning Schulzrinne		code	-1
Understanding image motion with group representations	Andrew Jaegle, Stephen Phillips, Daphne Ippolito, Kostas Daniilidis		code	-1
Learning Awareness Models	Brandon Amos, Laurent Dinh, Serkan Cabi, Thomas Rothörl, Sergio Gomez Colmenarejo, Alistair Muldal, Tom Erez, Yuval Tassa, Nando de Freitas, Misha Denil		code	-1
Backpropagation through the Void: Optimizing control variates for black-box gradient estimation	Will Grathwohl, Dami Choi, Yuhuai Wu, Geoffrey Roeder, David Duvenaud		code	-1
On Unifying Deep Generative Models	Zhiting Hu, Zichao Yang, Ruslan Salakhutdinov, Eric P. Xing		code	-1
Debiasing Evidence Approximations: On Importance-weighted Autoencoders and Jackknife Variational Inference	Sebastian Nowozin		code	-1
Learning a Generative Model for Validity in Complex Discrete Structures	David Janz, Jos van der Westhuizen, Brooks Paige, Matt J. Kusner, José Miguel HernándezLobato		code	-1
Boundary Seeking GANs	R. Devon Hjelm, Athul Paul Jacob, Adam Trischler, Gerry Che, Kyunghyun Cho, Yoshua Bengio		code	-1
Learning Sparse Latent Representations with the Deep Copula Information Bottleneck	Aleksander Wieczorek, Mario Wieser, Damian Murezzan, Volker Roth		code	-1
WHAI: Weibull Hybrid Autoencoding Inference for Deep Topic Modeling	Hao Zhang, Bo Chen, Dandan Guo, Mingyuan Zhou		code	-1
Understanding Short-Horizon Bias in Stochastic Meta-Optimization	Yuhuai Wu, Mengye Ren, Renjie Liao, Roger B. Grosse		code	-1
Self-ensembling for visual domain adaptation	Geoffrey French, Michal Mackiewicz, Mark Fisher		code	-1
Gradient Estimators for Implicit Models	Yingzhen Li, Richard E. Turner		code	-1
Learning to Multi-Task by Active Sampling	Sahil Sharma, Ashutosh Kumar Jha, Parikshit Hegde, Balaraman Ravindran		code	-1
Learning Robust Rewards with Adverserial Inverse Reinforcement Learning	Justin Fu, Katie Luo, Sergey Levine		code	-1
A Simple Neural Attentive Meta-Learner	Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, Pieter Abbeel		code	-1
Deep Learning and Quantum Entanglement: Fundamental Connections with Implications to Network Design	Yoav Levine, David Yakira, Nadav Cohen, Amnon Shashua		code	-1
Towards Synthesizing Complex Programs From Input-Output Examples	Xinyun Chen, Chang Liu, Dawn Song		code	-1
Expressive power of recurrent neural networks	Valentin Khrulkov, Alexander Novikov, Ivan V. Oseledets		code	-1
Improving the Universality and Learnability of Neural Programmer-Interpreters with Combinator Abstraction	Da Xiao, JoYu Liao, Xingyuan Yuan		code	-1
An image representation based convolutional network for DNA classification	Bojian Yin, Marleen Balvert, Davide Zambrano, Alexander Schönhuth, Sander M. Bohté		code	-1
SMASH: One-Shot Model Architecture Search through HyperNetworks	Andrew Brock, Theodore Lim, James M. Ritchie, Nick Weston		code	-1
Parameter Space Noise for Exploration	Matthias Plappert, Rein Houthooft, Prafulla Dhariwal, Szymon Sidor, Richard Y. Chen, Xi Chen, Tamim Asfour, Pieter Abbeel, Marcin Andrychowicz		code	-1
Synthesizing realistic neural population activity patterns using Generative Adversarial Networks	Manuel MolanoMazon, Arno Onken, Eugenio Piasini, Stefano Panzeri		code	-1
Auto-Encoding Sequential Monte Carlo	Tuan Anh Le, Maximilian Igl, Tom Rainforth, Tom Jin, Frank Wood		code	-1
Learning to Teach	Yang Fan, Fei Tian, Tao Qin, XiangYang Li, TieYan Liu		code	-1
PixelNN: Example-based Image Synthesis	Aayush Bansal, Yaser Sheikh, Deva Ramanan		code	-1
Non-Autoregressive Neural Machine Translation	Jiatao Gu, James Bradbury, Caiming Xiong, Victor O. K. Li, Richard Socher		code	-1
Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning	Wei Ping, Kainan Peng, Andrew Gibiansky, Sercan Ömer Arik, Ajay Kannan, Sharan Narang, Jonathan Raiman, John Miller		code	-1
mixup: Beyond Empirical Risk Minimization	Hongyi Zhang, Moustapha Cissé, Yann N. Dauphin, David LopezPaz		code	-1
TD or not TD: Analyzing the Role of Temporal Differencing in Deep Reinforcement Learning	Artemij Amiranashvili, Alexey Dosovitskiy, Vladlen Koltun, Thomas Brox		code	-1
DORA The Explorer: Directed Outreaching Reinforcement Action-Selection	Lior Fox, Leshem Choshen, Yonatan Loewenstein		code	-1
Temporal Difference Models: Model-Free Deep RL for Model-Based Control	Vitchyr Pong, Shixiang Gu, Murtaza Dalal, Sergey Levine		code	-1
TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning	Gregory Farquhar, Tim Rocktäschel, Maximilian Igl, Shimon Whiteson		code	-1
Alternating Multi-bit Quantization for Recurrent Neural Networks	Chen Xu, Jianqiang Yao, Zhouchen Lin, Wenwu Ou, Yuanbin Cao, Zhirong Wang, Hongbin Zha		code	-1
Residual Loss Prediction: Reinforcement Learning With No Incremental Feedback	Hal Daumé III, John Langford, Amr Sharaf		code	-1
Adaptive Quantization of Neural Networks	Soroosh Khoram, Jing Li		code	-1
Boosting the Actor with Dual Critic	Bo Dai, Albert E. Shaw, Niao He, Lihong Li, Le Song		code	-1
Guide Actor-Critic for Continuous Control	Voot Tangkaratt, Abbas Abdolmaleki, Masashi Sugiyama		code	-1
Policy Optimization by Genetic Distillation	Tanmay Gangwani, Jian Peng		code	-1
When is a Convolutional Filter Easy to Learn?	Simon S. Du, Jason D. Lee, Yuandong Tian		code	-1
Online Learning Rate Adaptation with Hypergradient Descent	Atilim Gunes Baydin, Robert Cornish, David MartínezRubio, Mark Schmidt, Frank Wood		code	-1
Stochastic gradient descent performs variational inference, converges to limit cycles for deep networks	Pratik Chaudhari, Stefano Soatto		code	-1
Robustness of Classifiers to Universal Perturbations: A Geometric Perspective	SeyedMohsen MoosaviDezfooli, Alhussein Fawzi, Omar Fawzi, Pascal Frossard, Stefano Soatto		code	-1
On the regularization of Wasserstein GANs	Henning Petzka, Asja Fischer, Denis Lukovnikov		code	-1
Eigenoption Discovery through the Deep Successor Representation	Marlos C. Machado, Clemens Rosenbaum, Xiaoxiao Guo, Miao Liu, Gerald Tesauro, Murray Campbell		code	-1
Neural Map: Structured Memory for Deep Reinforcement Learning	Emilio Parisotto, Ruslan Salakhutdinov		code	-1
Active Neural Localization	Devendra Singh Chaplot, Emilio Parisotto, Ruslan Salakhutdinov		code	-1
Overcoming Catastrophic Interference using Conceptor-Aided Backpropagation	Xu He, Herbert Jaeger		code	-1
Memory Augmented Control Networks	Arbaaz Khan, Clark Zhang, Nikolay Atanasov, Konstantinos Karydis, Vijay Kumar, Daniel D. Lee		code	-1
Progressive Reinforcement Learning with Distillation for Multi-Skilled Motion Control	Glen Berseth, Cheng Xie, Paul Cernek, Michiel van de Panne		code	-1
N2N learning: Network to Network Compression via Policy Gradient Reinforcement Learning	Anubhav Ashok, Nicholas Rhinehart, Fares Beainy, Kris M. Kitani		code	-1
Hierarchical and Interpretable Skill Acquisition in Multi-task Reinforcement Learning	Tianmin Shu, Caiming Xiong, Richard Socher		code	-1
Divide-and-Conquer Reinforcement Learning	Dibya Ghosh, Avi Singh, Aravind Rajeswaran, Vikash Kumar, Sergey Levine		code	-1
A Compressed Sensing View of Unsupervised Text Embeddings, Bag-of-n-Grams, and LSTMs	Sanjeev Arora, Mikhail Khodak, Nikunj Saunshi, Kiran Vodrahalli		code	-1
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		code	-1
Fix your classifier: the marginal value of training the last weight layer	Elad Hoffer, Itay Hubara, Daniel Soudry		code	-1
Multi-Mention Learning for Reading Comprehension with Neural Cascades	Swabha Swayamdipta, Ankur P. Parikh, Tom Kwiatkowski		code	-1
Deep Sensing: Active Sensing using Multi-directional Recurrent Neural Networks	Jinsung Yoon, William R. Zame, Mihaela van der Schaar		code	-1
Temporally Efficient Deep Learning with Spikes	Peter O'Connor, Efstratios Gavves, Matthias Reisser, Max Welling		code	-1
Variational Network Quantization	Jan Achterhold, Jan M. Köhler, Anke Schmeink, Tim Genewein		code	-1
Training GANs with Optimism	Constantinos Daskalakis, Andrew Ilyas, Vasilis Syrgkanis, Haoyang Zeng		code	-1
Sobolev GAN	Youssef Mroueh, ChunLiang Li, Tom Sercu, Anant Raj, Yu Cheng		code	-1
Learning From Noisy Singly-labeled Data	Ashish Khetan, Zachary C. Lipton, Animashree Anandkumar		code	-1
Learning Sparse Neural Networks through L_0 Regularization	Christos Louizos, Max Welling, Diederik P. Kingma		code	-1
Variational Continual Learning	Cuong V. Nguyen, Yingzhen Li, Thang D. Bui, Richard E. Turner		code	-1
Gaussian Process Behaviour in Wide Deep Neural Networks	Alexander G. de G. Matthews, Jiri Hron, Mark Rowland, Richard E. Turner, Zoubin Ghahramani		code	-1
Mixed Precision Training of Convolutional Neural Networks using Integer Operations	Dipankar Das, Naveen Mellempudi, Dheevatsa Mudigere, Dhiraj D. Kalamkar, Sasikanth Avancha, Kunal Banerjee, Srinivas Sridharan, Karthik Vaidyanathan, Bharat Kaul, Evangelos Georganas, Alexander Heinecke, Pradeep Dubey, Jesús Corbal, Nikita Shustrov, Roman Dubtsov, Evarist Fomenko, Vadim O. Pirogov		code	-1
Memory Architectures in Recurrent Neural Network Language Models	Dani Yogatama, Yishu Miao, Gábor Melis, Wang Ling, Adhiguna Kuncoro, Chris Dyer, Phil Blunsom		code	-1
On the Information Bottleneck Theory of Deep Learning	Andrew M. Saxe, Yamini Bansal, Joel Dapello, Madhu Advani, Artemy Kolchinsky, Brendan D. Tracey, David D. Cox		code	-1