research
∙
07/10/2020
Variable Skipping for Autoregressive Range Density Estimation
Deep autoregressive models compute point likelihood estimates of individ...
share
research
∙
06/15/2020
NeuroCard: One Cardinality Estimator for All Tables
Query optimizers rely on accurate cardinality estimates to produce good ...
research
∙
06/19/2019
Evaluating Protein Transfer Learning with TAPE
Protein modeling is an increasingly popular area of machine learning res...
research
∙
05/10/2019
Selectivity Estimation with Deep Likelihood Models
Selectivity estimation has long been grounded in statistical tools for d...
research
∙
02/01/2019
Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design
Flow-based generative models are powerful exact likelihood models with e...
research
∙
03/20/2018
Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines
Policy gradient methods have enjoyed great success in deep reinforcement...
research
∙
03/03/2018
Some Considerations on Learning to Explore via Meta-Reinforcement Learning
We consider the problem of exploration in meta reinforcement learning. T...
research
∙
02/28/2018
Model-Ensemble Trust-Region Policy Optimization
Model-free reinforcement learning (RL) methods are succeeding in a growi...
research
∙
04/10/2017
Stochastic Neural Networks for Hierarchical Reinforcement Learning
Deep reinforcement learning has achieved many impressive results in rece...
research
∙
03/21/2017
One-Shot Imitation Learning
Imitation learning has been commonly applied to solve different tasks in...
research
∙
02/08/2017
Adversarial Attacks on Neural Network Policies
Machine learning classifiers are known to be vulnerable to inputs malici...
research
∙
11/15/2016
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning
Count-based exploration algorithms are known to perform near-optimally w...
research
∙
11/09/2016
RL^2: Fast Reinforcement Learning via Slow Reinforcement Learning
Deep reinforcement learning (deep RL) has been successful in learning so...
research
∙
11/08/2016
Variational Lossy Autoencoder
Representation learning seeks to expose certain aspects of observed data...
research
∙
06/12/2016
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
This paper describes InfoGAN, an information-theoretic extension to the ...
research
∙
05/31/2016
