papers
Publications (16)
cs.LG2017
Variational Lossy Autoencoder
Xi Chen, Diederik P. Kingma, Tim Salimans +5
cs.LG2019
Policy Gradient Search: Online Planning and Expert Iteration without Search Trees
Thomas Anthony, Robert Nishihara, Philipp Moritz +2
stat.CO2014
Fixed-Form Variational Posterior Approximation through Stochastic Linear Regression
Tim Salimans, David A. Knowles
astro-ph.IM2013
Observing Dark Worlds: A crowdsourcing experiment for dark matter mapping
David Harvey, Thomas D. Kitching, Joyce Noah-Vanhoucke +2
cs.LG2018
Improving GANs Using Optimal Transport
Tim Salimans, Han Zhang, Alec Radford +1
stat.ML2015
Variational Dropout and the Local Reparameterization Trick
Diederik P. Kingma, Tim Salimans, Max Welling
stat.ML2017
Evolution Strategies as a Scalable Alternative to Reinforcement Learning
Tim Salimans, Jonathan Ho, Xi Chen +2
cs.LG2017
Improving Variational Inference with Inverse Autoregressive Flow
Diederik P. Kingma, Tim Salimans, Rafal Jozefowicz +3
stat.CO2014
On Using Control Variates with Stochastic Approximation for Variational Bayes and its Connection to Stochastic Linear Regression
Tim Salimans, David A. Knowles
cs.LG2017
PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications
Tim Salimans, Andrej Karpathy, Xi Chen +1
cs.LG2018
Learning Montezuma's Revenge from a Single Demonstration
Tim Salimans, Richard Chen
cs.LG2016
Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks
Tim Salimans, Diederik P. Kingma
stat.ML2016
A Structured Variational Auto-encoder for Learning Deep Hierarchies of Sparse Features
Tim Salimans
stat.CO2015
Markov Chain Monte Carlo and Variational Inference: Bridging the Gap
Tim Salimans, Diederik P. Kingma, Max Welling
stat.CO2014
Implementing and Automating Fixed-Form Variational Posterior Approximation through Stochastic Linear Regression
Tim Salimans
cs.LG2016
Improved Techniques for Training GANs
Tim Salimans, Ian Goodfellow, Wojciech Zaremba +3