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ORCID5th ICLR 2017: Toulon, France
- 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. OpenReview.net 2017
Paper decision: Accept (Oral)
- Jonathon Cai, Richard Shin, Dawn Song:
Making Neural Programming Architectures Generalize via Recursion. - Johannes Ballé, Valero Laparra, Eero P. Simoncelli:
End-to-end Optimized Image Compression. - Sachin Ravi, Hugo Larochelle:
Optimization as a Model for Few-Shot Learning. - Antoine Bordes, Y-Lan Boureau, Jason Weston:
Learning End-to-End Goal-Oriented Dialog. - Martín Arjovsky, Léon Bottou:
Towards Principled Methods for Training Generative Adversarial Networks. - Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, Tom Schaul, Joel Z. Leibo
, David Silver, Koray Kavukcuoglu:
Reinforcement Learning with Unsupervised Auxiliary Tasks. - Angeliki Lazaridou, Alexander Peysakhovich, Marco Baroni:
Multi-Agent Cooperation and the Emergence of (Natural) Language. - Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, Oriol Vinyals:
Understanding deep learning requires rethinking generalization. - Barret Zoph, Quoc V. Le:
Neural Architecture Search with Reinforcement Learning. - Shixiang Gu, Timothy P. Lillicrap, Zoubin Ghahramani, Richard E. Turner, Sergey Levine:
Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic. - Alexey Dosovitskiy, Vladlen Koltun:
Learning to Act by Predicting the Future. - Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, Ping Tak Peter Tang:
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima. - Nicolas Papernot, Martín Abadi, Úlfar Erlingsson, Ian J. Goodfellow, Kunal Talwar:
Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data. - Casper Kaae Sønderby, Jose Caballero, Lucas Theis, Wenzhe Shi, Ferenc Huszár:
Amortised MAP Inference for Image Super-resolution. - Daniel D. Johnson:
Learning Graphical State Transitions.
Paper decision: Accept (Poster)
- Gabriel Loaiza-Ganem, Yuanjun Gao, John P. Cunningham:
Maximum Entropy Flow Networks. - C. Daniel Freeman, Joan Bruna:
Topology and Geometry of Half-Rectified Network Optimization. - Sergey Zagoruyko, Nikos Komodakis:
Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer. - Alex X. Lee, Sergey Levine, Pieter Abbeel:
Learning Visual Servoing with Deep Features and Fitted Q-Iteration. - Carlos Florensa, Yan Duan, Pieter Abbeel:
Stochastic Neural Networks for Hierarchical Reinforcement Learning. - George Philipp, Jaime G. Carbonell:
Nonparametric Neural Networks. - Jimmy Ba, Roger B. Grosse, James Martens:
Distributed Second-Order Optimization using Kronecker-Factored Approximations. - Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, Hans Peter Graf:
Pruning Filters for Efficient ConvNets. - Florian Bordes, Sina Honari, Pascal Vincent:
Learning to Generate Samples from Noise through Infusion Training. - Xingyi Li, Fuxin Li, Xiaoli Z. Fern, Raviv Raich:
Filter shaping for Convolutional Neural Networks. - Mengye Ren, Renjie Liao, Raquel Urtasun, Fabian H. Sinz, Richard S. Zemel:
Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes. - Eleanor Batty, Josh Merel, Nora Brackbill, Alexander Heitman, Alexander Sher, Alan M. Litke, E. J. Chichilnisky, Liam Paninski:
Multilayer Recurrent Network Models of Primate Retinal Ganglion Cell Responses. - David Warde-Farley, Yoshua Bengio:
Improving Generative Adversarial Networks with Denoising Feature Matching. - Minmin Chen:
Efficient Vector Representation for Documents through Corruption. - Abhishek Gupta, Coline Devin, Yuxuan Liu, Pieter Abbeel, Sergey Levine:
Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learning. - Xingyu Lin, Hao Wang, Zhihao Li, Yimeng Zhang, Alan L. Yuille, Tai Sing Lee:
Transfer of View-manifold Learning to Similarity Perception of Novel Objects. - Ivan Ustyuzhaninov, Wieland Brendel, Leon A. Gatys, Matthias Bethge:
What does it take to generate natural textures? - Brian Cheung, Eric Weiss, Bruno A. Olshausen:
Emergence of foveal image sampling from learning to attend in visual scenes. - Wentao Huang, Kechen Zhang:
An Information-Theoretic Framework for Fast and Robust Unsupervised Learning via Neural Population Infomax. - Tim Salimans, Andrej Karpathy, Xi Chen, Diederik P. Kingma:
PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications. - Tong Che, Yanran Li, Athul Paul Jacob, Yoshua Bengio, Wenjie Li:
Mode Regularized Generative Adversarial Networks. - Klaus Greff, Rupesh Kumar Srivastava, Jürgen Schmidhuber:
Highway and Residual Networks learn Unrolled Iterative Estimation. - Edouard Grave, Armand Joulin, Nicolas Usunier:
Improving Neural Language Models with a Continuous Cache. - Yaniv Taigman, Adam Polyak, Lior Wolf:
Unsupervised Cross-Domain Image Generation. - Bradly C. Stadie, Pieter Abbeel, Ilya Sutskever:
Third Person Imitation Learning. - Sanjay Purushotham, Wilka Carvalho, Tanachat Nilanon, Yan Liu:
Variational Recurrent Adversarial Deep Domain Adaptation. - Pavol Bielik, Veselin Raychev, Martin T. Vechev:
Program Synthesis for Character Level Language Modeling. - Nicolas Usunier, Gabriel Synnaeve, Zeming Lin, Soumith Chintala:
Episodic Exploration for Deep Deterministic Policies for StarCraft Micromanagement. - Karen Ullrich, Edward Meeds, Max Welling:
Soft Weight-Sharing for Neural Network Compression. - Chengtao Li, Daniel Tarlow, Alexander L. Gaunt, Marc Brockschmidt, Nate Kushman:
Neural Program Lattices. - Mikael Henaff, Jason Weston, Arthur Szlam, Antoine Bordes, Yann LeCun:
Tracking the World State with Recurrent Entity Networks. - Taco S. Cohen, Max Welling:
Steerable CNNs. - Xiaoxiao Guo, Tim Klinger, Clemens Rosenbaum, Joseph P. Bigus, Murray Campbell, Ban Kawas, Kartik Talamadupula, Gerry Tesauro, Satinder Singh:
Learning to Query, Reason, and Answer Questions On Ambiguous Texts. - William Lotter, Gabriel Kreiman, David D. Cox:
Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning. - Adriana Romero, Pierre Luc Carrier, Akram Erraqabi, Tristan Sylvain, Alex Auvolat, Etienne Dejoie, Marc-André Legault, Marie-Pierre Dubé, Julie G. Hussin, Yoshua Bengio:
Diet Networks: Thin Parameters for Fat Genomics. - Timothy Dozat, Christopher D. Manning:
Deep Biaffine Attention for Neural Dependency Parsing. - Ishaan Gulrajani, Kundan Kumar, Faruk Ahmed, Adrien Ali Taïga, Francesco Visin, David Vázquez, Aaron C. Courville:
PixelVAE: A Latent Variable Model for Natural Images. - Gao Huang, Yixuan Li, Geoff Pleiss, Zhuang Liu, John E. Hopcroft, Kilian Q. Weinberger:
Snapshot Ensembles: Train 1, Get M for Free. - Yuxin Wu, Yuandong Tian:
Training Agent for First-Person Shooter Game with Actor-Critic Curriculum Learning. - Emilio Parisotto, Abdel-rahman Mohamed, Rishabh Singh, Lihong Li, Dengyong Zhou, Pushmeet Kohli:
Neuro-Symbolic Program Synthesis. - Ruben Villegas, Jimei Yang, Seunghoon Hong, Xunyu Lin, Honglak Lee:
Decomposing Motion and Content for Natural Video Sequence Prediction. - Harrison Edwards, Amos J. Storkey:
Towards a Neural Statistician. - Danica J. Sutherland, Hsiao-Yu Tung, Heiko Strathmann, Soumyajit De, Aaditya Ramdas, Alexander J. Smola, Arthur Gretton:
Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy. - Chelsea Finn, Tianhe Yu, Justin Fu, Pieter Abbeel, Sergey Levine:
Generalizing Skills with Semi-Supervised Reinforcement Learning. - Aaron Klein, Stefan Falkner, Jost Tobias Springenberg, Frank Hutter:
Learning Curve Prediction with Bayesian Neural Networks. - Ke Li, Jitendra Malik:
Learning to Optimize. - Shuohang Wang, Jing Jiang:
A Compare-Aggregate Model for Matching Text Sequences. - Ziang Xie, Sida I. Wang, Jiwei Li, Daniel Lévy, Aiming Nie, Dan Jurafsky, Andrew Y. Ng:
Data Noising as Smoothing in Neural Network Language Models. - Shengjie Wang, Haoran Cai, Jeff A. Bilmes, William S. Noble:
Training Compressed Fully-Connected Networks with a Density-Diversity Penalty. - Akash Srivastava, Charles Sutton:
Autoencoding Variational Inference For Topic Models. - Akshay Balsubramani:
Optimal Binary Autoencoding with Pairwise Correlations. - Yuhuai Wu, Yuri Burda, Ruslan Salakhutdinov, Roger B. Grosse:
On the Quantitative Analysis of Decoder-Based Generative Models. - Chenzhuo Zhu, Song Han, Huizi Mao, William J. Dally:
Trained Ternary Quantization. - 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:
DSD: Dense-Sparse-Dense Training for Deep Neural Networks. - Michael Chang, Tomer D. Ullman, Antonio Torralba, Joshua B. Tenenbaum:
A Compositional Object-Based Approach to Learning Physical Dynamics. - Lukasz Kaiser, Ofir Nachum, Aurko Roy, Samy Bengio:
Learning to Remember Rare Events. - Zhilin Yang, Ruslan Salakhutdinov, William W. Cohen:
Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks. - Zhilin Yang, Bhuwan Dhingra, Ye Yuan, Junjie Hu, William W. Cohen, Ruslan Salakhutdinov:
Words or Characters? Fine-grained Gating for Reading Comprehension. - Sanjeev Arora, Yingyu Liang, Tengyu Ma:
A Simple but Tough-to-Beat Baseline for Sentence Embeddings. - Jasmine Collins, Jascha Sohl-Dickstein, David Sussillo:
Capacity and Trainability in Recurrent Neural Networks. - Misha Denil, Pulkit Agrawal, Tejas D. Kulkarni, Tom Erez, Peter W. Battaglia, Nando de Freitas:
Learning to Perform Physics Experiments via Deep Reinforcement Learning. - Ofir Nachum, Mohammad Norouzi, Dale Schuurmans:
Improving Policy Gradient by Exploring Under-appreciated Rewards. - Moshe Looks, Marcello Herreshoff, DeLesley Hutchins, Peter Norvig:
Deep Learning with Dynamic Computation Graphs. - Zihang Dai, Amjad Almahairi, Philip Bachman, Eduard H. Hovy, Aaron C. Courville:
Calibrating Energy-based Generative Adversarial Networks. - Pavlo Molchanov, Stephen Tyree, Tero Karras, Timo Aila, Jan Kautz:
Pruning Convolutional Neural Networks for Resource Efficient Inference. - Min Joon Seo, Sewon Min, Ali Farhadi, Hannaneh Hajishirzi:
Query-Reduction Networks for Question Answering. - Bowen Baker, Otkrist Gupta, Nikhil Naik, Ramesh Raskar:
Designing Neural Network Architectures using Reinforcement Learning. - Shuohang Wang, Jing Jiang:
Machine Comprehension Using Match-LSTM and Answer Pointer. - Tian Zhao, Xiaobing Huang, Yu Cao:
DeepDSL: A Compilation-based Domain-Specific Language for Deep Learning. - Min Joon Seo, Aniruddha Kembhavi, Ali Farhadi, Hannaneh Hajishirzi:
Bidirectional Attention Flow for Machine Comprehension. - Guillaume Berger, Roland Memisevic:
Incorporating long-range consistency in CNN-based texture generation. - Caiming Xiong, Victor Zhong, Richard Socher:
Dynamic Coattention Networks For Question Answering. - Soroush Mehri, Kundan Kumar, Ishaan Gulrajani, Rithesh Kumar, Shubham Jain, Jose Sotelo, Aaron C. Courville, Yoshua Bengio:
SampleRNN: An Unconditional End-to-End Neural Audio Generation Model. - Jessica B. Hamrick, Andrew J. Ballard, Razvan Pascanu, Oriol Vinyals, Nicolas Heess, Peter W. Battaglia:
Metacontrol for Adaptive Imagination-Based Optimization. - Sharan Narang, Greg Diamos, Shubho Sengupta, Erich Elsen:
Exploring Sparsity in Recurrent Neural Networks. - Lucas Theis, Wenzhe Shi, Andrew Cunningham, Ferenc Huszár:
Lossy Image Compression with Compressive Autoencoders. - Yoon Kim, Carl Denton, Luong Hoang, Alexander M. Rush:
Structured Attention Networks. - David Krueger, Tegan Maharaj, János Kramár, Mohammad Pezeshki, Nicolas Ballas, Nan Rosemary Ke, Anirudh Goyal, Yoshua Bengio, Aaron C. Courville, Christopher J. Pal:
Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations. - Dustin Tran, Matthew D. Hoffman, Rif A. Saurous, Eugene Brevdo, Kevin Murphy, David M. Blei:
Deep Probabilistic Programming. - Jianwei Yang, Anitha Kannan, Dhruv Batra, Devi Parikh:
LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation. - Xi Chen, Diederik P. Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, Pieter Abbeel:
Variational Lossy Autoencoder. - Thomas Laurent, James von Brecht:
A recurrent neural network without chaos. - Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc V. Le, Geoffrey E. Hinton, Jeff Dean:
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer. - David Alvarez-Melis, Tommi S. Jaakkola:
Tree-structured decoding with doubly-recurrent neural networks. - Abhishek Sinha, Aahitagni Mukherjee, Mausoom Sarkar, Balaji Krishnamurthy:
Introspection: Accelerating Neural Network Training By Learning Weight Evolution. - Lisha Li, Kevin G. Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, Ameet Talwalkar:
Hyperband: Bandit-Based Configuration Evaluation for Hyperparameter Optimization. - Greg Yang, Alexander M. Rush:
Lie-Access Neural Turing Machines. - James Bradbury, Stephen Merity, Caiming Xiong, Richard Socher:
Quasi-Recurrent Neural Networks. - Silvia Chiappa, Sébastien Racanière, Daan Wierstra, Shakir Mohamed:
Recurrent Environment Simulators. - Aravind Rajeswaran, Sarvjeet Ghotra, Balaraman Ravindran, Sergey Levine:
EPOpt: Learning Robust Neural Network Policies Using Model Ensembles. - Janarthanan Rajendran, Aravind S. Lakshminarayanan, Mitesh M. Khapra, P. Prasanna, Balaraman Ravindran:
Attend, Adapt and Transfer: Attentive Deep Architecture for Adaptive Transfer from multiple sources in the same domain. - Wanjia He, Weiran Wang, Karen Livescu:
Multi-view Recurrent Neural Acoustic Word Embeddings. - John Thickstun, Zaïd Harchaoui, Sham M. Kakade:
Learning Features of Music From Scratch. - Dan Hendrycks, Kevin Gimpel:
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks. - Rudy Bunel, Alban Desmaison, M. Pawan Kumar, Philip H. S. Torr, Pushmeet Kohli:
Learning to superoptimize programs. - Leonard Berrada, Andrew Zisserman, M. Pawan Kumar:
Trusting SVM for Piecewise Linear CNNs. - Peter O'Connor, Max Welling:
Sigma Delta Quantized Networks. - Zhouhan Lin, Minwei Feng, Cícero Nogueira dos Santos, Mo Yu, Bing Xiang, Bowen Zhou, Yoshua Bengio:
A Structured Self-Attentive Sentence Embedding. - Pau Rodríguez, Jordi Gonzàlez, Guillem Cucurull, Josep M. Gonfaus, F. Xavier Roca:
Regularizing CNNs with Locally Constrained Decorrelations. - Chris J. Maddison, Andriy Mnih, Yee Whye Teh:
The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables. - Luke Metz, Ben Poole, David Pfau, Jascha Sohl-Dickstein:
Unrolled Generative Adversarial Networks. - Adji B. Dieng, Chong Wang, Jianfeng Gao, John W. Paisley:
TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency. - Michal Daniluk, Tim Rocktäschel, Johannes Welbl, Sebastian Riedel:
Frustratingly Short Attention Spans in Neural Language Modeling. - Hanjun Dai, Bo Dai, Yan-Ming Zhang, Shuang Li, Le Song:
Recurrent Hidden Semi-Markov Model. - Maximilian Karl, Maximilian Soelch, Justin Bayer, Patrick van der Smagt:
Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data. - Ishan P. Durugkar, Ian Gemp, Sridhar Mahadevan:
Generative Multi-Adversarial Networks. - Çaglar Gülçehre, Marcin Moczulski, Francesco Visin, Yoshua Bengio:
Mollifying Networks. - Irina Higgins, Loïc Matthey, Arka Pal, Christopher P. Burgess, Xavier Glorot, Matthew M. Botvinick, Shakir Mohamed, Alexander Lerchner:
beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework. - Samuel L. Smith, David H. P. Turban, Steven Hamblin, Nils Y. Hammerla:
Offline bilingual word vectors, orthogonal transformations and the inverted softmax. - Luisa M. Zintgraf, Taco S. Cohen, Tameem Adel, Max Welling:
Visualizing Deep Neural Network Decisions: Prediction Difference Analysis. - Eric Jang, Shixiang Gu, Ben Poole:
Categorical Reparameterization with Gumbel-Softmax. - Priyank Jaini, Zhitang Chen, Pablo Carbajal, Edith Law, Laura Middleton, Kayla Regan, Mike Schaekermann, George Trimponias, James Tung, Pascal Poupart:
Online Bayesian Transfer Learning for Sequential Data Modeling. - William Chan, Yu Zhang, Quoc V. Le, Navdeep Jaitly:
Latent Sequence Decompositions. - Hang Qi, Evan Randall Sparks, Ameet Talwalkar:
Paleo: A Performance Model for Deep Neural Networks. - Brendan O'Donoghue, Rémi Munos, Koray Kavukcuoglu, Volodymyr Mnih:
Combining policy gradient and Q-learning. - Laurent Dinh, Jascha Sohl-Dickstein, Samy Bengio:
Density estimation using Real NVP. - Tim Cooijmans, Nicolas Ballas, César Laurent, Çaglar Gülçehre, Aaron C. Courville:
Recurrent Batch Normalization. - Ilya Loshchilov, Frank Hutter:
SGDR: Stochastic Gradient Descent with Warm Restarts. - Arvind Neelakantan, Quoc V. Le, Martín Abadi, Andrew McCallum, Dario Amodei:
Learning a Natural Language Interface with Neural Programmer. - Mohammad Babaeizadeh, Iuri Frosio, Stephen Tyree, Jason Clemons, Jan Kautz:
Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU. - Piotr Mirowski, Razvan Pascanu, Fabio Viola, Hubert Soyer, Andy Ballard, Andrea Banino, Misha Denil, Ross Goroshin, Laurent Sifre, Koray Kavukcuoglu, Dharshan Kumaran, Raia Hadsell:
Learning to Navigate in Complex Environments. - Matej Balog, Alexander L. Gaunt, Marc Brockschmidt, Sebastian Nowozin, Daniel Tarlow:
DeepCoder: Learning to Write Programs. - Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, Steffen Udluft:
Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks. - Yacine Jernite, Edouard Grave, Armand Joulin, Tomás Mikolov:
Variable Computation in Recurrent Neural Networks. - Alexander A. Alemi, Ian Fischer, Joshua V. Dillon, Kevin Murphy:
Deep Variational Information Bottleneck. - Lei Yu, Phil Blunsom, Chris Dyer, Edward Grefenstette, Tomás Kociský:
The Neural Noisy Channel. - W. James Murdoch, Arthur Szlam:
Automatic Rule Extraction from Long Short Term Memory Networks. - Jiwei Li, Alexander H. Miller, Sumit Chopra, Marc'Aurelio Ranzato, Jason Weston:
Dialogue Learning With Human-in-the-Loop. - Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Alex Lamb, Martín Arjovsky, Olivier Mastropietro, Aaron C. Courville:
Adversarially Learned Inference. - Jiwei Li, Alexander H. Miller, Sumit Chopra, Marc'Aurelio Ranzato, Jason Weston:
Learning through Dialogue Interactions by Asking Questions. - Samuel S. Schoenholz, Justin Gilmer, Surya Ganguli, Jascha Sohl-Dickstein:
Deep Information Propagation. - Gustav Larsson, Michael Maire, Gregory Shakhnarovich:
FractalNet: Ultra-Deep Neural Networks without Residuals. - David Lopez-Paz, Maxime Oquab:
Revisiting Classifier Two-Sample Tests. - Sahil Sharma, Aravind S. Lakshminarayanan, Balaraman Ravindran:
Learning to Repeat: Fine Grained Action Repetition for Deep Reinforcement Learning. - Lu Hou, Quanming Yao, James T. Kwok:
Loss-aware Binarization of Deep Networks. - Frank S. He, Yang Liu, Alexander G. Schwing, Jian Peng:
Learning to Play in a Day: Faster Deep Reinforcement Learning by Optimality Tightening. - Junbo Jake Zhao, Michaël Mathieu, Yann LeCun:
Energy-based Generative Adversarial Networks. - Werner Zellinger, Thomas Grubinger, Edwin Lughofer, Thomas Natschläger, Susanne Saminger-Platz:
Central Moment Discrepancy (CMD) for Domain-Invariant Representation Learning. - Aojun Zhou, Anbang Yao, Yiwen Guo, Lin Xu, Yurong Chen:
Incremental Network Quantization: Towards Lossless CNNs with Low-precision Weights. - Pratik Chaudhari, Anna Choromanska, Stefano Soatto, Yann LeCun, Carlo Baldassi, Christian Borgs, Jennifer T. Chayes, Levent Sagun, Riccardo Zecchina:
Entropy-SGD: Biasing Gradient Descent Into Wide Valleys. - Yongxin Yang, Timothy M. Hospedales:
Deep Multi-task Representation Learning: A Tensor Factorisation Approach. - Ziyu Wang, Victor Bapst, Nicolas Heess, Volodymyr Mnih, Rémi Munos, Koray Kavukcuoglu, Nando de Freitas:
Sample Efficient Actor-Critic with Experience Replay. - Samuli Laine, Timo Aila:
Temporal Ensembling for Semi-Supervised Learning. - Jan Hendrik Metzen, Tim Genewein, Volker Fischer, Bastian Bischoff:
On Detecting Adversarial Perturbations. - Jacob Goldberger, Ehud Ben-Reuven:
Training deep neural-networks using a noise adaptation layer. - Dani Yogatama, Phil Blunsom, Chris Dyer, Edward Grefenstette, Wang Ling:
Learning to Compose Words into Sentences with Reinforcement Learning. - Yanpei Liu, Xinyun Chen, Chang Liu, Dawn Song:
Delving into Transferable Adversarial Examples and Black-box Attacks. - Moritz Hardt, Tengyu Ma:
Identity Matters in Deep Learning. - Jeff Donahue, Philipp Krähenbühl, Trevor Darrell:
Adversarial Feature Learning. - Yoojin Choi, Mostafa El-Khamy, Jungwon Lee:
Towards the Limit of Network Quantization. - Jongsoo Park, Sheng R. Li, Wei Wen, Ping Tak Peter Tang, Hai Li, Yiran Chen, Pradeep Dubey:
Faster CNNs with Direct Sparse Convolutions and Guided Pruning. - Eric T. Nalisnick, Padhraic Smyth:
Stick-Breaking Variational Autoencoders. - Kirthevasan Kandasamy, Yoram Bachrach, Ryota Tomioka, Daniel Tarlow, David Carter:
Batch Policy Gradient Methods for Improving Neural Conversation Models. - Yingzhen Yang, Jiahui Yu, Pushmeet Kohli, Jianchao Yang, Thomas S. Huang:
Support Regularized Sparse Coding and Its Fast Encoder. - Hakan Inan, Khashayar Khosravi, Richard Socher:
Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling. - Haizi Yu, Lav R. Varshney:
Towards Deep Interpretability (MUS-ROVER II): Learning Hierarchical Representations of Tonal Music. - Jason Tyler Rolfe:
Discrete Variational Autoencoders. - Gregor Urban, Krzysztof J. Geras, Samira Ebrahimi Kahou, Özlem Aslan, Shengjie Wang, Abdelrahman Mohamed, Matthai Philipose, Matthew Richardson, Rich Caruana:
Do Deep Convolutional Nets Really Need to be Deep and Convolutional? - Jiaqi Mu, Suma Bhat, Pramod Viswanath:
Geometry of Polysemy. - Gautam Pai, Aaron Wetzler, Ron Kimmel:
Learning Invariant Representations Of Planar Curves. - Tsendsuren Munkhdalai, Hong Yu:
Reasoning with Memory Augmented Neural Networks for Language Comprehension. - Eyrun Eyjolfsdottir, Kristin Branson, Yisong Yue, Pietro Perona:
Learning Recurrent Representations for Hierarchical Behavior Modeling. - Alexey Kurakin, Ian J. Goodfellow, Samy Bengio:
Adversarial Machine Learning at Scale. - Jacek M. Bajor, Thomas A. Lasko:
Predicting Medications from Diagnostic Codes with Recurrent Neural Networks. - Loris Bazzani, Hugo Larochelle, Lorenzo Torresani:
Recurrent Mixture Density Network for Spatiotemporal Visual Attention. - Nadav Cohen, Amnon Shashua:
Inductive Bias of Deep Convolutional Networks through Pooling Geometry. - Ronen Basri, David W. Jacobs:
Efficient Representation of Low-Dimensional Manifolds using Deep Networks. - Thomas N. Kipf, Max Welling:
Semi-Supervised Classification with Graph Convolutional Networks. - Arash Ardakani, Carlo Condo, Warren J. Gross:
Sparsely-Connected Neural Networks: Towards Efficient VLSI Implementation of Deep Neural Networks. - Takeru Miyato, Andrew M. Dai, Ian J. Goodfellow:
Adversarial Training Methods for Semi-Supervised Text Classification. - Yossi Adi, Einat Kermany, Yonatan Belinkov, Ofer Lavi, Yoav Goldberg:
Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks. - Stephen Merity, Caiming Xiong, James Bradbury, Richard Socher:
Pointer Sentinel Mixture Models. - Dzmitry Bahdanau, Philemon Brakel, Kelvin Xu, Anirudh Goyal, Ryan Lowe, Joelle Pineau, Aaron C. Courville, Yoshua Bengio:
An Actor-Critic Algorithm for Sequence Prediction. - Thomas Moreau, Joan Bruna:
Understanding Trainable Sparse Coding with Matrix Factorization. - Nicolas Le Roux:
Tighter bounds lead to improved classifiers. - Cezary Kaliszyk, François Chollet, Christian Szegedy:
HolStep: A Machine Learning Dataset for Higher-order Logic Theorem Proving. - Shiyu Liang, R. Srikant:
Why Deep Neural Networks for Function Approximation? - Junyoung Chung, Sungjin Ahn, Yoshua Bengio:
Hierarchical Multiscale Recurrent Neural Networks. - Andrew Brock, Theodore Lim, James M. Ritchie, Nick Weston:
Neural Photo Editing with Introspective Adversarial Networks. - Xuezhe Ma, Yingkai Gao, Zhiting Hu, Yaoliang Yu, Yuntian Deng, Eduard H. Hovy:
Dropout with Expectation-linear Regularization. - David Ha, Andrew M. Dai, Quoc V. Le:
HyperNetworks. - Vincent Dumoulin, Jonathon Shlens, Manjunath Kudlur:
A Learned Representation For Artistic Style. - Jin-Hwa Kim, Kyoung Woon On, Woosang Lim, Jeonghee Kim, Jung-Woo Ha, Byoung-Tak Zhang:
Hadamard Product for Low-rank Bilinear Pooling.
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