Papers
Bayesian Policy Optimization for Model Uncertainty
Gilwoo Lee, Brian Hou, Aditya Mandalika et al.
Gradient Descent Provably Optimizes Over-parameterized Neural Networks
Simon S. Du, Xiyu Zhai, Barnabas Poczos et al.
Bayesian Prediction of Future Street Scenes using Synthetic Likelihoods
Apratim Bhattacharyya, Mario Fritz, Bernt Schiele
Episodic Curiosity through Reachability
Nikolay Savinov, Anton Raichuk, Damien Vincent et al.
From Hard to Soft: Understanding Deep Network Nonlinearities via Vector Quantization and Statistical Inference
Randall Balestriero, Richard Baraniuk
A Unified Theory of Early Visual Representations from Retina to Cortex through Anatomically Constrained Deep CNNs
Jack Lindsey, Samuel A. Ocko, Surya Ganguli et al.
Learning-Based Frequency Estimation Algorithms
Chen-Yu Hsu, Piotr Indyk, Dina Katabi et al.
Deterministic PAC-Bayesian generalization bounds for deep networks via generalizing noise-resilience
Vaishnavh Nagarajan, Zico Kolter
Beyond Pixel Norm-Balls: Parametric Adversaries using an Analytically Differentiable Renderer
Hsueh-Ti Derek Liu, Michael Tao, Chun-Liang Li et al.
Dynamic Channel Pruning: Feature Boosting and Suppression
Xitong Gao, Yiren Zhao, Łukasz Dudziak et al.
Solving the Rubik's Cube with Approximate Policy Iteration
Stephen McAleer, Forest Agostinelli, Alexander Shmakov et al.
Diversity is All You Need: Learning Skills without a Reward Function
Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz et al.
Unsupervised Hyper-alignment for Multilingual Word Embeddings
Jean Alaux, Edouard Grave, Marco Cuturi et al.
Large-Scale Answerer in Questioner's Mind for Visual Dialog Question Generation
Sang-Woo Lee, Tong Gao, Sohee Yang et al.
Multi-class classification without multi-class labels
Yen-Chang Hsu, Zhaoyang Lv, Joel Schlosser et al.
Query-Efficient Hard-label Black-box Attack: An Optimization-based Approach
Minhao Cheng, Thong Le, Pin-Yu Chen et al.
Recurrent Experience Replay in Distributed Reinforcement Learning
Steven Kapturowski, Georg Ostrovski, John Quan et al.
Unsupervised Learning via Meta-Learning
Kyle Hsu, Sergey Levine, Chelsea Finn
Discriminator Rejection Sampling
Samaneh Azadi, Catherine Olsson, Trevor Darrell et al.
AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks
Bo Chang, Minmin Chen, Eldad Haber et al.
Excessive Invariance Causes Adversarial Vulnerability
Joern-Henrik Jacobsen, Jens Behrmann, Richard Zemel et al.
GENERATING HIGH FIDELITY IMAGES WITH SUBSCALE PIXEL NETWORKS AND MULTIDIMENSIONAL UPSCALING
Jacob Menick, Nal Kalchbrenner
Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning
Ilya Kostrikov, Kumar Krishna Agrawal, Debidatta Dwibedi et al.
On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length
Stanisław Jastrzębski, Zachary Kenton, Nicolas Ballas et al.