iclr2017.md

ICLR2017 Paper List

论文	作者	摘要	代码	引用数
Making Neural Programming Architectures Generalize via Recursion	Jonathon Cai, Richard Shin, Dawn Song		code	-1
End-to-end Optimized Image Compression	Johannes Ballé, Valero Laparra, Eero P. Simoncelli		code	-1
Optimization as a Model for Few-Shot Learning	Sachin Ravi, Hugo Larochelle		code	-1
Learning End-to-End Goal-Oriented Dialog	Antoine Bordes, YLan Boureau, Jason Weston		code	-1
Towards Principled Methods for Training Generative Adversarial Networks	Martín Arjovsky, Léon Bottou		code	-1
Reinforcement Learning with Unsupervised Auxiliary Tasks	Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, Tom Schaul, Joel Z. Leibo, David Silver, Koray Kavukcuoglu		code	-1
Multi-Agent Cooperation and the Emergence of (Natural) Language	Angeliki Lazaridou, Alexander Peysakhovich, Marco Baroni		code	-1
Understanding deep learning requires rethinking generalization	Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, Oriol Vinyals		code	-1
Neural Architecture Search with Reinforcement Learning	Barret Zoph, Quoc V. Le		code	-1
Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic	Shixiang Gu, Timothy P. Lillicrap, Zoubin Ghahramani, Richard E. Turner, Sergey Levine		code	-1
Learning to Act by Predicting the Future	Alexey Dosovitskiy, Vladlen Koltun		code	-1
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima	Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, Ping Tak Peter Tang		code	-1
Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data	Nicolas Papernot, Martín Abadi, Úlfar Erlingsson, Ian J. Goodfellow, Kunal Talwar		code	-1
Amortised MAP Inference for Image Super-resolution	Casper Kaae Sønderby, Jose Caballero, Lucas Theis, Wenzhe Shi, Ferenc Huszár		code	-1
Learning Graphical State Transitions	Daniel D. Johnson		code	-1
Maximum Entropy Flow Networks	Gabriel LoaizaGanem, Yuanjun Gao, John P. Cunningham		code	-1
Topology and Geometry of Half-Rectified Network Optimization	C. Daniel Freeman, Joan Bruna		code	-1
Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer	Sergey Zagoruyko, Nikos Komodakis		code	-1
Learning Visual Servoing with Deep Features and Fitted Q-Iteration	Alex X. Lee, Sergey Levine, Pieter Abbeel		code	-1
Stochastic Neural Networks for Hierarchical Reinforcement Learning	Carlos Florensa, Yan Duan, Pieter Abbeel		code	-1
Nonparametric Neural Networks	George Philipp, Jaime G. Carbonell		code	-1
Distributed Second-Order Optimization using Kronecker-Factored Approximations	Jimmy Ba, Roger B. Grosse, James Martens		code	-1
Pruning Filters for Efficient ConvNets	Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, Hans Peter Graf		code	-1
Learning to Generate Samples from Noise through Infusion Training	Florian Bordes, Sina Honari, Pascal Vincent		code	-1
Filter shaping for Convolutional Neural Networks	Xingyi Li, Fuxin Li, Xiaoli Z. Fern, Raviv Raich		code	-1
Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes	Mengye Ren, Renjie Liao, Raquel Urtasun, Fabian H. Sinz, Richard S. Zemel		code	-1
Multilayer Recurrent Network Models of Primate Retinal Ganglion Cell Responses	Eleanor Batty, Josh Merel, Nora Brackbill, Alexander Heitman, Alexander Sher, Alan M. Litke, E. J. Chichilnisky, Liam Paninski		code	-1
Improving Generative Adversarial Networks with Denoising Feature Matching	David WardeFarley, Yoshua Bengio		code	-1
Efficient Vector Representation for Documents through Corruption	Minmin Chen		code	-1
Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learning	Abhishek Gupta, Coline Devin, Yuxuan Liu, Pieter Abbeel, Sergey Levine		code	-1
Transfer of View-manifold Learning to Similarity Perception of Novel Objects	Xingyu Lin, Hao Wang, Zhihao Li, Yimeng Zhang, Alan L. Yuille, Tai Sing Lee		code	-1
What does it take to generate natural textures?	Ivan Ustyuzhaninov, Wieland Brendel, Leon A. Gatys, Matthias Bethge		code	-1
Emergence of foveal image sampling from learning to attend in visual scenes	Brian Cheung, Eric Weiss, Bruno A. Olshausen		code	-1
An Information-Theoretic Framework for Fast and Robust Unsupervised Learning via Neural Population Infomax	Wentao Huang, Kechen Zhang		code	-1
PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications	Tim Salimans, Andrej Karpathy, Xi Chen, Diederik P. Kingma		code	-1
Mode Regularized Generative Adversarial Networks	Tong Che, Yanran Li, Athul Paul Jacob, Yoshua Bengio, Wenjie Li		code	-1
Highway and Residual Networks learn Unrolled Iterative Estimation	Klaus Greff, Rupesh Kumar Srivastava, Jürgen Schmidhuber		code	-1
Improving Neural Language Models with a Continuous Cache	Edouard Grave, Armand Joulin, Nicolas Usunier		code	-1
Unsupervised Cross-Domain Image Generation	Yaniv Taigman, Adam Polyak, Lior Wolf		code	-1
Third Person Imitation Learning	Bradly C. Stadie, Pieter Abbeel, Ilya Sutskever		code	-1
Variational Recurrent Adversarial Deep Domain Adaptation	Sanjay Purushotham, Wilka Carvalho, Tanachat Nilanon, Yan Liu		code	-1
Program Synthesis for Character Level Language Modeling	Pavol Bielik, Veselin Raychev, Martin T. Vechev		code	-1
Episodic Exploration for Deep Deterministic Policies for StarCraft Micromanagement	Nicolas Usunier, Gabriel Synnaeve, Zeming Lin, Soumith Chintala		code	-1
Soft Weight-Sharing for Neural Network Compression	Karen Ullrich, Edward Meeds, Max Welling		code	-1
Neural Program Lattices	Chengtao Li, Daniel Tarlow, Alexander L. Gaunt, Marc Brockschmidt, Nate Kushman		code	-1
Tracking the World State with Recurrent Entity Networks	Mikael Henaff, Jason Weston, Arthur Szlam, Antoine Bordes, Yann LeCun		code	-1
Steerable CNNs	Taco S. Cohen, Max Welling		code	-1
Learning to Query, Reason, and Answer Questions On Ambiguous Texts	Xiaoxiao Guo, Tim Klinger, Clemens Rosenbaum, Joseph P. Bigus, Murray Campbell, Ban Kawas, Kartik Talamadupula, Gerry Tesauro, Satinder Singh		code	-1
Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning	William Lotter, Gabriel Kreiman, David D. Cox		code	-1
Diet Networks: Thin Parameters for Fat Genomics	Adriana Romero, Pierre Luc Carrier, Akram Erraqabi, Tristan Sylvain, Alex Auvolat, Etienne Dejoie, MarcAndré Legault, MariePierre Dubé, Julie G. Hussin, Yoshua Bengio		code	-1
Deep Biaffine Attention for Neural Dependency Parsing	Timothy Dozat, Christopher D. Manning		code	-1
PixelVAE: A Latent Variable Model for Natural Images	Ishaan Gulrajani, Kundan Kumar, Faruk Ahmed, Adrien Ali Taïga, Francesco Visin, David Vázquez, Aaron C. Courville		code	-1
Snapshot Ensembles: Train 1, Get M for Free	Gao Huang, Yixuan Li, Geoff Pleiss, Zhuang Liu, John E. Hopcroft, Kilian Q. Weinberger		code	-1
Training Agent for First-Person Shooter Game with Actor-Critic Curriculum Learning	Yuxin Wu, Yuandong Tian		code	-1
Neuro-Symbolic Program Synthesis	Emilio Parisotto, Abdelrahman Mohamed, Rishabh Singh, Lihong Li, Dengyong Zhou, Pushmeet Kohli		code	-1
Decomposing Motion and Content for Natural Video Sequence Prediction	Ruben Villegas, Jimei Yang, Seunghoon Hong, Xunyu Lin, Honglak Lee		code	-1
Towards a Neural Statistician	Harrison Edwards, Amos J. Storkey		code	-1
Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy	Danica J. Sutherland, HsiaoYu Tung, Heiko Strathmann, Soumyajit De, Aaditya Ramdas, Alexander J. Smola, Arthur Gretton		code	-1
Generalizing Skills with Semi-Supervised Reinforcement Learning	Chelsea Finn, Tianhe Yu, Justin Fu, Pieter Abbeel, Sergey Levine		code	-1
Learning Curve Prediction with Bayesian Neural Networks	Aaron Klein, Stefan Falkner, Jost Tobias Springenberg, Frank Hutter		code	-1
Learning to Optimize	Ke Li, Jitendra Malik		code	-1
A Compare-Aggregate Model for Matching Text Sequences	Shuohang Wang, Jing Jiang		code	-1
Data Noising as Smoothing in Neural Network Language Models	Ziang Xie, Sida I. Wang, Jiwei Li, Daniel Lévy, Aiming Nie, Dan Jurafsky, Andrew Y. Ng		code	-1
Training Compressed Fully-Connected Networks with a Density-Diversity Penalty	Shengjie Wang, Haoran Cai, Jeff A. Bilmes, William S. Noble		code	-1
Autoencoding Variational Inference For Topic Models	Akash Srivastava, Charles Sutton		code	-1
Optimal Binary Autoencoding with Pairwise Correlations	Akshay Balsubramani		code	-1
On the Quantitative Analysis of Decoder-Based Generative Models	Yuhuai Wu, Yuri Burda, Ruslan Salakhutdinov, Roger B. Grosse		code	-1
Trained Ternary Quantization	Chenzhuo Zhu, Song Han, Huizi Mao, William J. Dally		code	-1
DSD: Dense-Sparse-Dense Training for Deep Neural Networks	Song Han, Jeff Pool, Sharan Narang, Huizi Mao, Enhao Gong, Shijian Tang, Erich Elsen, Peter Vajda, Manohar Paluri, John Tran, Bryan Catanzaro, William J. Dally		code	-1
A Compositional Object-Based Approach to Learning Physical Dynamics	Michael Chang, Tomer D. Ullman, Antonio Torralba, Joshua B. Tenenbaum		code	-1
Learning to Remember Rare Events	Lukasz Kaiser, Ofir Nachum, Aurko Roy, Samy Bengio		code	-1
Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks	Zhilin Yang, Ruslan Salakhutdinov, William W. Cohen		code	-1
Words or Characters? Fine-grained Gating for Reading Comprehension	Zhilin Yang, Bhuwan Dhingra, Ye Yuan, Junjie Hu, William W. Cohen, Ruslan Salakhutdinov		code	-1
A Simple but Tough-to-Beat Baseline for Sentence Embeddings	Sanjeev Arora, Yingyu Liang, Tengyu Ma		code	-1
Capacity and Trainability in Recurrent Neural Networks	Jasmine Collins, Jascha SohlDickstein, David Sussillo		code	-1
Learning to Perform Physics Experiments via Deep Reinforcement Learning	Misha Denil, Pulkit Agrawal, Tejas D. Kulkarni, Tom Erez, Peter W. Battaglia, Nando de Freitas		code	-1
Improving Policy Gradient by Exploring Under-appreciated Rewards	Ofir Nachum, Mohammad Norouzi, Dale Schuurmans		code	-1
Deep Learning with Dynamic Computation Graphs	Moshe Looks, Marcello Herreshoff, DeLesley Hutchins, Peter Norvig		code	-1
Calibrating Energy-based Generative Adversarial Networks	Zihang Dai, Amjad Almahairi, Philip Bachman, Eduard H. Hovy, Aaron C. Courville		code	-1
Pruning Convolutional Neural Networks for Resource Efficient Inference	Pavlo Molchanov, Stephen Tyree, Tero Karras, Timo Aila, Jan Kautz		code	-1
Query-Reduction Networks for Question Answering	Min Joon Seo, Sewon Min, Ali Farhadi, Hannaneh Hajishirzi		code	-1
Designing Neural Network Architectures using Reinforcement Learning	Bowen Baker, Otkrist Gupta, Nikhil Naik, Ramesh Raskar		code	-1
Machine Comprehension Using Match-LSTM and Answer Pointer	Shuohang Wang, Jing Jiang		code	-1
DeepDSL: A Compilation-based Domain-Specific Language for Deep Learning	Tian Zhao, Xiaobing Huang, Yu Cao		code	-1
Bidirectional Attention Flow for Machine Comprehension	Min Joon Seo, Aniruddha Kembhavi, Ali Farhadi, Hannaneh Hajishirzi		code	-1
Incorporating long-range consistency in CNN-based texture generation	Guillaume Berger, Roland Memisevic		code	-1
Dynamic Coattention Networks For Question Answering	Caiming Xiong, Victor Zhong, Richard Socher		code	-1
SampleRNN: An Unconditional End-to-End Neural Audio Generation Model	Soroush Mehri, Kundan Kumar, Ishaan Gulrajani, Rithesh Kumar, Shubham Jain, Jose Sotelo, Aaron C. Courville, Yoshua Bengio		code	-1
Metacontrol for Adaptive Imagination-Based Optimization	Jessica B. Hamrick, Andrew J. Ballard, Razvan Pascanu, Oriol Vinyals, Nicolas Heess, Peter W. Battaglia		code	-1
Exploring Sparsity in Recurrent Neural Networks	Sharan Narang, Greg Diamos, Shubho Sengupta, Erich Elsen		code	-1
Lossy Image Compression with Compressive Autoencoders	Lucas Theis, Wenzhe Shi, Andrew Cunningham, Ferenc Huszár		code	-1
Structured Attention Networks	Yoon Kim, Carl Denton, Luong Hoang, Alexander M. Rush		code	-1
Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations	David Krueger, Tegan Maharaj, János Kramár, Mohammad Pezeshki, Nicolas Ballas, Nan Rosemary Ke, Anirudh Goyal, Yoshua Bengio, Aaron C. Courville, Christopher J. Pal		code	-1
Deep Probabilistic Programming	Dustin Tran, Matthew D. Hoffman, Rif A. Saurous, Eugene Brevdo, Kevin Murphy, David M. Blei		code	-1
LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation	Jianwei Yang, Anitha Kannan, Dhruv Batra, Devi Parikh		code	-1
Variational Lossy Autoencoder	Xi Chen, Diederik P. Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, Pieter Abbeel		code	-1
A recurrent neural network without chaos	Thomas Laurent, James von Brecht		code	-1
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer	Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc V. Le, Geoffrey E. Hinton, Jeff Dean		code	-1
Tree-structured decoding with doubly-recurrent neural networks	David AlvarezMelis, Tommi S. Jaakkola		code	-1
Introspection: Accelerating Neural Network Training By Learning Weight Evolution	Abhishek Sinha, Aahitagni Mukherjee, Mausoom Sarkar, Balaji Krishnamurthy		code	-1
Hyperband: Bandit-Based Configuration Evaluation for Hyperparameter Optimization	Lisha Li, Kevin G. Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, Ameet Talwalkar		code	-1
Lie-Access Neural Turing Machines	Greg Yang, Alexander M. Rush		code	-1
Quasi-Recurrent Neural Networks	James Bradbury, Stephen Merity, Caiming Xiong, Richard Socher		code	-1
Recurrent Environment Simulators	Silvia Chiappa, Sébastien Racanière, Daan Wierstra, Shakir Mohamed		code	-1
EPOpt: Learning Robust Neural Network Policies Using Model Ensembles	Aravind Rajeswaran, Sarvjeet Ghotra, Balaraman Ravindran, Sergey Levine		code	-1
Attend, Adapt and Transfer: Attentive Deep Architecture for Adaptive Transfer from multiple sources in the same domain	Janarthanan Rajendran, Aravind S. Lakshminarayanan, Mitesh M. Khapra, P. Prasanna, Balaraman Ravindran		code	-1
Multi-view Recurrent Neural Acoustic Word Embeddings	Wanjia He, Weiran Wang, Karen Livescu		code	-1
Learning Features of Music From Scratch	John Thickstun, Zaïd Harchaoui, Sham M. Kakade		code	-1
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks	Dan Hendrycks, Kevin Gimpel		code	-1
Learning to superoptimize programs	Rudy Bunel, Alban Desmaison, M. Pawan Kumar, Philip H. S. Torr, Pushmeet Kohli		code	-1
Trusting SVM for Piecewise Linear CNNs	Leonard Berrada, Andrew Zisserman, M. Pawan Kumar		code	-1
Sigma Delta Quantized Networks	Peter O'Connor, Max Welling		code	-1
A Structured Self-Attentive Sentence Embedding	Zhouhan Lin, Minwei Feng, Cícero Nogueira dos Santos, Mo Yu, Bing Xiang, Bowen Zhou, Yoshua Bengio		code	-1
Regularizing CNNs with Locally Constrained Decorrelations	Pau Rodríguez, Jordi Gonzàlez, Guillem Cucurull, Josep M. Gonfaus, F. Xavier Roca		code	-1
The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables	Chris J. Maddison, Andriy Mnih, Yee Whye Teh		code	-1
Unrolled Generative Adversarial Networks	Luke Metz, Ben Poole, David Pfau, Jascha SohlDickstein		code	-1
TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency	Adji B. Dieng, Chong Wang, Jianfeng Gao, John W. Paisley		code	-1
Frustratingly Short Attention Spans in Neural Language Modeling	Michal Daniluk, Tim Rocktäschel, Johannes Welbl, Sebastian Riedel		code	-1
Recurrent Hidden Semi-Markov Model	Hanjun Dai, Bo Dai, YanMing Zhang, Shuang Li, Le Song		code	-1
Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data	Maximilian Karl, Maximilian Soelch, Justin Bayer, Patrick van der Smagt		code	-1
Generative Multi-Adversarial Networks	Ishan P. Durugkar, Ian Gemp, Sridhar Mahadevan		code	-1
Mollifying Networks	Çaglar Gülçehre, Marcin Moczulski, Francesco Visin, Yoshua Bengio		code	-1
beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework	Irina Higgins, Loïc Matthey, Arka Pal, Christopher P. Burgess, Xavier Glorot, Matthew M. Botvinick, Shakir Mohamed, Alexander Lerchner		code	-1
Offline bilingual word vectors, orthogonal transformations and the inverted softmax	Samuel L. Smith, David H. P. Turban, Steven Hamblin, Nils Y. Hammerla		code	-1
Visualizing Deep Neural Network Decisions: Prediction Difference Analysis	Luisa M. Zintgraf, Taco S. Cohen, Tameem Adel, Max Welling		code	-1
Categorical Reparameterization with Gumbel-Softmax	Eric Jang, Shixiang Gu, Ben Poole		code	-1
Online Bayesian Transfer Learning for Sequential Data Modeling	Priyank Jaini, Zhitang Chen, Pablo Carbajal, Edith Law, Laura Middleton, Kayla Regan, Mike Schaekermann, George Trimponias, James Tung, Pascal Poupart		code	-1
Latent Sequence Decompositions	William Chan, Yu Zhang, Quoc V. Le, Navdeep Jaitly		code	-1
Paleo: A Performance Model for Deep Neural Networks	Hang Qi, Evan R. Sparks, Ameet Talwalkar		code	-1
Combining policy gradient and Q-learning	Brendan O'Donoghue, Rémi Munos, Koray Kavukcuoglu, Volodymyr Mnih		code	-1
Density estimation using Real NVP	Laurent Dinh, Jascha SohlDickstein, Samy Bengio		code	-1
Recurrent Batch Normalization	Tim Cooijmans, Nicolas Ballas, César Laurent, Çaglar Gülçehre, Aaron C. Courville		code	-1
SGDR: Stochastic Gradient Descent with Warm Restarts	Ilya Loshchilov, Frank Hutter		code	-1
Learning a Natural Language Interface with Neural Programmer	Arvind Neelakantan, Quoc V. Le, Martín Abadi, Andrew McCallum, Dario Amodei		code	-1
Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU	Mohammad Babaeizadeh, Iuri Frosio, Stephen Tyree, Jason Clemons, Jan Kautz		code	-1
Learning to Navigate in Complex Environments	Piotr Mirowski, Razvan Pascanu, Fabio Viola, Hubert Soyer, Andy Ballard, Andrea Banino, Misha Denil, Ross Goroshin, Laurent Sifre, Koray Kavukcuoglu, Dharshan Kumaran, Raia Hadsell		code	-1
DeepCoder: Learning to Write Programs	Matej Balog, Alexander L. Gaunt, Marc Brockschmidt, Sebastian Nowozin, Daniel Tarlow		code	-1
Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks	Stefan Depeweg, José Miguel HernándezLobato, Finale DoshiVelez, Steffen Udluft		code	-1
Variable Computation in Recurrent Neural Networks	Yacine Jernite, Edouard Grave, Armand Joulin, Tomás Mikolov		code	-1
Deep Variational Information Bottleneck	Alexander A. Alemi, Ian Fischer, Joshua V. Dillon, Kevin Murphy		code	-1
The Neural Noisy Channel	Lei Yu, Phil Blunsom, Chris Dyer, Edward Grefenstette, Tomás Kociský		code	-1
Automatic Rule Extraction from Long Short Term Memory Networks	W. James Murdoch, Arthur Szlam		code	-1
Dialogue Learning With Human-in-the-Loop	Jiwei Li, Alexander H. Miller, Sumit Chopra, Marc'Aurelio Ranzato, Jason Weston		code	-1
Adversarially Learned Inference	Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Alex Lamb, Martín Arjovsky, Olivier Mastropietro, Aaron C. Courville		code	-1
Learning through Dialogue Interactions by Asking Questions	Jiwei Li, Alexander H. Miller, Sumit Chopra, Marc'Aurelio Ranzato, Jason Weston		code	-1
Deep Information Propagation	Samuel S. Schoenholz, Justin Gilmer, Surya Ganguli, Jascha SohlDickstein		code	-1
FractalNet: Ultra-Deep Neural Networks without Residuals	Gustav Larsson, Michael Maire, Gregory Shakhnarovich		code	-1
Revisiting Classifier Two-Sample Tests	David LopezPaz, Maxime Oquab		code	-1
Learning to Repeat: Fine Grained Action Repetition for Deep Reinforcement Learning	Sahil Sharma, Aravind S. Lakshminarayanan, Balaraman Ravindran		code	-1
Loss-aware Binarization of Deep Networks	Lu Hou, Quanming Yao, James T. Kwok		code	-1
Learning to Play in a Day: Faster Deep Reinforcement Learning by Optimality Tightening	Frank S. He, Yang Liu, Alexander G. Schwing, Jian Peng		code	-1
Energy-based Generative Adversarial Networks	Junbo Jake Zhao, Michaël Mathieu, Yann LeCun		code	-1
Central Moment Discrepancy (CMD) for Domain-Invariant Representation Learning	Werner Zellinger, Thomas Grubinger, Edwin Lughofer, Thomas Natschläger, Susanne SamingerPlatz		code	-1
Incremental Network Quantization: Towards Lossless CNNs with Low-precision Weights	Aojun Zhou, Anbang Yao, Yiwen Guo, Lin Xu, Yurong Chen		code	-1
Entropy-SGD: Biasing Gradient Descent Into Wide Valleys	Pratik Chaudhari, Anna Choromanska, Stefano Soatto, Yann LeCun, Carlo Baldassi, Christian Borgs, Jennifer T. Chayes, Levent Sagun, Riccardo Zecchina		code	-1
Deep Multi-task Representation Learning: A Tensor Factorisation Approach	Yongxin Yang, Timothy M. Hospedales		code	-1
Sample Efficient Actor-Critic with Experience Replay	Ziyu Wang, Victor Bapst, Nicolas Heess, Volodymyr Mnih, Rémi Munos, Koray Kavukcuoglu, Nando de Freitas		code	-1
Temporal Ensembling for Semi-Supervised Learning	Samuli Laine, Timo Aila		code	-1
On Detecting Adversarial Perturbations	Jan Hendrik Metzen, Tim Genewein, Volker Fischer, Bastian Bischoff		code	-1
Training deep neural-networks using a noise adaptation layer	Jacob Goldberger, Ehud BenReuven		code	-1
Learning to Compose Words into Sentences with Reinforcement Learning	Dani Yogatama, Phil Blunsom, Chris Dyer, Edward Grefenstette, Wang Ling		code	-1
Delving into Transferable Adversarial Examples and Black-box Attacks	Yanpei Liu, Xinyun Chen, Chang Liu, Dawn Song		code	-1
Identity Matters in Deep Learning	Moritz Hardt, Tengyu Ma		code	-1
Adversarial Feature Learning	Jeff Donahue, Philipp Krähenbühl, Trevor Darrell		code	-1
Towards the Limit of Network Quantization	Yoojin Choi, Mostafa ElKhamy, Jungwon Lee		code	-1
Faster CNNs with Direct Sparse Convolutions and Guided Pruning	Jongsoo Park, Sheng R. Li, Wei Wen, Ping Tak Peter Tang, Hai Li, Yiran Chen, Pradeep Dubey		code	-1
Stick-Breaking Variational Autoencoders	Eric T. Nalisnick, Padhraic Smyth		code	-1
Batch Policy Gradient Methods for Improving Neural Conversation Models	Kirthevasan Kandasamy, Yoram Bachrach, Ryota Tomioka, Daniel Tarlow, David Carter		code	-1
Support Regularized Sparse Coding and Its Fast Encoder	Yingzhen Yang, Jiahui Yu, Pushmeet Kohli, Jianchao Yang, Thomas S. Huang		code	-1
Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling	Hakan Inan, Khashayar Khosravi, Richard Socher		code	-1
Towards Deep Interpretability (MUS-ROVER II): Learning Hierarchical Representations of Tonal Music	Haizi Yu, Lav R. Varshney		code	-1
Discrete Variational Autoencoders	Jason Tyler Rolfe		code	-1
Do Deep Convolutional Nets Really Need to be Deep and Convolutional?	Gregor Urban, Krzysztof J. Geras, Samira Ebrahimi Kahou, Özlem Aslan, Shengjie Wang, Abdelrahman Mohamed, Matthai Philipose, Matthew Richardson, Rich Caruana		code	-1
Geometry of Polysemy	Jiaqi Mu, Suma Bhat, Pramod Viswanath		code	-1
Learning Invariant Representations Of Planar Curves	Gautam Pai, Aaron Wetzler, Ron Kimmel		code	-1
Reasoning with Memory Augmented Neural Networks for Language Comprehension	Tsendsuren Munkhdalai, Hong Yu		code	-1
Learning Recurrent Representations for Hierarchical Behavior Modeling	Eyrun Eyjolfsdottir, Kristin Branson, Yisong Yue, Pietro Perona		code	-1
Adversarial Machine Learning at Scale	Alexey Kurakin, Ian J. Goodfellow, Samy Bengio		code	-1
Predicting Medications from Diagnostic Codes with Recurrent Neural Networks	Jacek M. Bajor, Thomas A. Lasko		code	-1
Recurrent Mixture Density Network for Spatiotemporal Visual Attention	Loris Bazzani, Hugo Larochelle, Lorenzo Torresani		code	-1
Inductive Bias of Deep Convolutional Networks through Pooling Geometry	Nadav Cohen, Amnon Shashua		code	-1
Efficient Representation of Low-Dimensional Manifolds using Deep Networks	Ronen Basri, David W. Jacobs		code	-1
Semi-Supervised Classification with Graph Convolutional Networks	Thomas N. Kipf, Max Welling		code	-1
Sparsely-Connected Neural Networks: Towards Efficient VLSI Implementation of Deep Neural Networks	Arash Ardakani, Carlo Condo, Warren J. Gross		code	-1
Adversarial Training Methods for Semi-Supervised Text Classification	Takeru Miyato, Andrew M. Dai, Ian J. Goodfellow		code	-1
Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks	Yossi Adi, Einat Kermany, Yonatan Belinkov, Ofer Lavi, Yoav Goldberg		code	-1
Pointer Sentinel Mixture Models	Stephen Merity, Caiming Xiong, James Bradbury, Richard Socher		code	-1
An Actor-Critic Algorithm for Sequence Prediction	Dzmitry Bahdanau, Philemon Brakel, Kelvin Xu, Anirudh Goyal, Ryan Lowe, Joelle Pineau, Aaron C. Courville, Yoshua Bengio		code	-1
Understanding Trainable Sparse Coding with Matrix Factorization	Thomas Moreau, Joan Bruna		code	-1
Tighter bounds lead to improved classifiers	Nicolas Le Roux		code	-1
HolStep: A Machine Learning Dataset for Higher-order Logic Theorem Proving	Cezary Kaliszyk, François Chollet, Christian Szegedy		code	-1
Why Deep Neural Networks for Function Approximation?	Shiyu Liang, R. Srikant		code	-1
Hierarchical Multiscale Recurrent Neural Networks	Junyoung Chung, Sungjin Ahn, Yoshua Bengio		code	-1
Neural Photo Editing with Introspective Adversarial Networks	Andrew Brock, Theodore Lim, James M. Ritchie, Nick Weston		code	-1
Dropout with Expectation-linear Regularization	Xuezhe Ma, Yingkai Gao, Zhiting Hu, Yaoliang Yu, Yuntian Deng, Eduard H. Hovy		code	-1
HyperNetworks	David Ha, Andrew M. Dai, Quoc V. Le		code	-1
A Learned Representation For Artistic Style	Vincent Dumoulin, Manjunath Kudlur		code	-1
Hadamard Product for Low-rank Bilinear Pooling	JinHwa Kim, Kyoung Woon On, Woosang Lim, Jeonghee Kim, JungWoo Ha, ByoungTak Zhang		code	-1