Papers
Demystifying MMD GANs
Mikołaj Bińkowski, Danica J. Sutherland, Michael Arbel et al.
Communication Algorithms via Deep Learning
Hyeji Kim, Yihan Jiang, Ranvir B. Rana et al.
Deep Rewiring: Training very sparse deep networks
Guillaume Bellec, David Kappel, Wolfgang Maass et al.
All-but-the-Top: Simple and Effective Postprocessing for Word Representations
Jiaqi Mu, Pramod Viswanath
DCN+: Mixed Objective And Deep Residual Coattention for Question Answering
Caiming Xiong, Victor Zhong, Richard Socher
Ensemble Adversarial Training: Attacks and Defenses
Florian Tramèr, Alexey Kurakin, Nicolas Papernot et al.
PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples
Yang Song, Taesup Kim, Sebastian Nowozin et al.
META LEARNING SHARED HIERARCHIES
Kevin Frans, Jonathan Ho, Xi Chen et al.
Deep Neural Networks as Gaussian Processes
Jaehoon Lee, Yasaman Bahri, Roman Novak et al.
Policy Optimization by Genetic Distillation
Tanmay Gangwani, Jian Peng
Mastering the Dungeon: Grounded Language Learning by Mechanical Turker Descent
Zhilin Yang, Saizheng Zhang, Jack Urbanek et al.
Boosting the Actor with Dual Critic
Bo Dai, Albert Shaw, Niao He et al.
Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning
Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer et al.
Towards Reverse-Engineering Black-Box Neural Networks
Seong Joon Oh, Max Augustin, Mario Fritz et al.
Breaking the Softmax Bottleneck: A High-Rank RNN Language Model
Zhilin Yang, Zihang Dai, Ruslan Salakhutdinov et al.
Distributed Distributional Deterministic Policy Gradients
Gabriel Barth-Maron, Matthew W. Hoffman, David Budden et al.
Residual Loss Prediction: Reinforcement Learning With No Incremental Feedback
Hal Daumé III, John Langford, Amr Sharaf
Kernel Implicit Variational Inference
Jiaxin Shi, Shengyang Sun, Jun Zhu
Synthesizing realistic neural population activity patterns using Generative Adversarial Networks
Manuel Molano-Mazon, Arno Onken, Eugenio Piasini* et al.
Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples
Kimin Lee, Honglak Lee, Kibok Lee et al.
On the Convergence of Adam and Beyond
Sashank J. Reddi, Satyen Kale, Sanjiv Kumar
Quantitatively Evaluating GANs With Divergences Proposed for Training
Daniel Jiwoong Im, He Ma, Graham W. Taylor et al.
Learning Wasserstein Embeddings
Nicolas Courty, Rémi Flamary, Mélanie Ducoffe
Unbiased Online Recurrent Optimization
Corentin Tallec, Yann Ollivier