Papers
Learning Discrete Weights Using the Local Reparameterization Trick
Oran Shayer, Dan Levi, Ethan Fetaya
Expressive power of recurrent neural networks
Valentin Khrulkov, Alexander Novikov, Ivan Oseledets
Progressive Reinforcement Learning with Distillation for Multi-Skilled Motion Control
Glen Berseth, Cheng Xie, Paul Cernek et al.
Learning a Generative Model for Validity in Complex Discrete Structures
Dave Janz, Jos van der Westhuizen, Brooks Paige et al.
DORA The Explorer: Directed Outreaching Reinforcement Action-Selection
Lior Fox, Leshem Choshen, Yonatan Loewenstein
The power of deeper networks for expressing natural functions
David Rolnick, Max Tegmark
Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling
Carlos Riquelme, George Tucker, Jasper Snoek
QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
Adams Wei Yu, David Dohan, Minh-Thang Luong et al.
Neural Speed Reading via Skim-RNN
Minjoon Seo, Sewon Min, Ali Farhadi et al.
Polar Transformer Networks
Carlos Esteves, Christine Allen-Blanchette, Xiaowei Zhou et al.
A Neural Representation of Sketch Drawings
David Ha, Douglas Eck
A Framework for the Quantitative Evaluation of Disentangled Representations
Cian Eastwood, Christopher K. I. Williams
Learning Latent Permutations with Gumbel-Sinkhorn Networks
Gonzalo Mena, David Belanger, Scott Linderman et al.
Understanding image motion with group representations
Andrew Jaegle, Stephen Phillips, Daphne Ippolito et al.
Ask the Right Questions: Active Question Reformulation with Reinforcement Learning
Christian Buck, Jannis Bulian, Massimiliano Ciaramita et al.
On the insufficiency of existing momentum schemes for Stochastic Optimization
Rahul Kidambi, Praneeth Netrapalli, Prateek Jain et al.
Emergence of Linguistic Communication from Referential Games with Symbolic and Pixel Input
Angeliki Lazaridou, Karl Moritz Hermann, Karl Tuyls et al.
TRUNCATED HORIZON POLICY SEARCH: COMBINING REINFORCEMENT LEARNING & IMITATION LEARNING
Wen Sun, J. Andrew Bagnell, Byron Boots
Auto-Encoding Sequential Monte Carlo
Tuan Anh Le, Maximilian Igl, Tom Rainforth et al.
Learning Differentially Private Recurrent Language Models
H. Brendan McMahan, Daniel Ramage, Kunal Talwar et al.
Automatically Inferring Data Quality for Spatiotemporal Forecasting
Sungyong Seo, Arash Mohegh, George Ban-Weiss et al.
Boosting Dilated Convolutional Networks with Mixed Tensor Decompositions
Nadav Cohen, Ronen Tamari, Amnon Shashua
Self-ensembling for visual domain adaptation
Geoff French, Michal Mackiewicz, Mark Fisher
When is a Convolutional Filter Easy to Learn?
Simon S. Du, Jason D. Lee, Yuandong Tian