189 citations · 339 across the 5 of their papers we have counts for
7 papers · 1 filter
Variable Skipping for Autoregressive Range Density Estimation
Eric Liang, Zongheng Yang, Ion Stoica +3
Deep autoregressive models compute point likelihood estimates of individual data points. However, many applications (i.e., database cardinality estimation) require estimating range…
Evaluating Protein Transfer Learning with TAPE
Roshan Rao, Nicholas Bhattacharya, Neil Thomas +5
Protein modeling is an increasingly popular area of machine learning research. Semi-supervised learning has emerged as an important paradigm in protein modeling due to the high cos…
Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design
Jonathan Ho, Xi Chen, Aravind Srinivas +2
Flow-based generative models are powerful exact likelihood models with efficient sampling and inference. Despite their computational efficiency, flow-based models generally have mu…
Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines
Cathy Wu, Aravind Rajeswaran, Yan Duan +5
Policy gradient methods have enjoyed great success in deep reinforcement learning but suffer from high variance of gradient estimates. The high variance problem is particularly exa…
Adversarial Attacks on Neural Network Policies
Sandy Huang, Nicolas Papernot, Ian Goodfellow +2
Machine learning classifiers are known to be vulnerable to inputs maliciously constructed by adversaries to force misclassification. Such adversarial examples have been extensively…
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
Xi Chen, Yan Duan, Rein Houthooft +3
This paper describes InfoGAN, an information-theoretic extension to the Generative Adversarial Network that is able to learn disentangled representations in a completely unsupervis…