Tim Salimans

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Erasmus University Rotterdam ∙ OpenAI

Policy Gradient Search: Online Planning and Expert Iteration without Search Trees

Monte Carlo Tree Search (MCTS) algorithms perform simulation-based searc...

04/07/2019 ∙ by Thomas Anthony, et al. ∙ 4 ∙ share

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Evolution Strategies as a Scalable Alternative to Reinforcement Learning

We explore the use of Evolution Strategies (ES), a class of black box op...

03/10/2017 ∙ by Tim Salimans, et al. ∙ 0 ∙ share

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PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications

PixelCNNs are a recently proposed class of powerful generative models wi...

01/19/2017 ∙ by Tim Salimans, et al. ∙ 0 ∙ share

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Variational Lossy Autoencoder

Representation learning seeks to expose certain aspects of observed data...

11/08/2016 ∙ by Xi Chen, et al. ∙ 0 ∙ share

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Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks

We present weight normalization: a reparameterization of the weight vect...

02/25/2016 ∙ by Tim Salimans, et al. ∙ 0 ∙ share

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Improving Variational Inference with Inverse Autoregressive Flow

The framework of normalizing flows provides a general strategy for flexi...

06/15/2016 ∙ by Diederik P. Kingma, et al. ∙ 0 ∙ share

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A Structured Variational Auto-encoder for Learning Deep Hierarchies of Sparse Features

In this note we present a generative model of natural images consisting ...

02/28/2016 ∙ by Tim Salimans, et al. ∙ 0 ∙ share

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Variational Dropout and the Local Reparameterization Trick

We investigate a local reparameterizaton technique for greatly reducing ...

06/08/2015 ∙ by Diederik P. Kingma, et al. ∙ 0 ∙ share

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Improved Techniques for Training GANs

We present a variety of new architectural features and training procedur...

06/10/2016 ∙ by Tim Salimans, et al. ∙ 0 ∙ share

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Markov Chain Monte Carlo and Variational Inference: Bridging the Gap

Recent advances in stochastic gradient variational inference have made i...

10/23/2014 ∙ by Tim Salimans, et al. ∙ 0 ∙ share

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Fixed-Form Variational Posterior Approximation through Stochastic Linear Regression

We propose a general algorithm for approximating nonstandard Bayesian po...

06/28/2012 ∙ by Tim Salimans, et al. ∙ 0 ∙ share

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Improving GANs Using Optimal Transport

We present Optimal Transport GAN (OT-GAN), a variant of generative adver...

03/15/2018 ∙ by Tim Salimans, et al. ∙ 0 ∙ share

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Learning Montezuma's Revenge from a Single Demonstration

We propose a new method for learning from a single demonstration to solv...

12/08/2018 ∙ by Tim Salimans, et al. ∙ 0 ∙ share

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Tim Salimans

Featured Co-authors

Pieter Abbeel

Xi Chen

Max Welling

Ian Goodfellow

Ilya Sutskever

Han Zhang

Wojciech Zaremba

Dimitris Metaxas

John Schulman

Diederik P. Kingma

Yan Duan

Policy Gradient Search: Online Planning and Expert Iteration without Search Trees

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications

Variational Lossy Autoencoder

Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks

Improving Variational Inference with Inverse Autoregressive Flow

A Structured Variational Auto-encoder for Learning Deep Hierarchies of Sparse Features

Variational Dropout and the Local Reparameterization Trick

Improved Techniques for Training GANs

Markov Chain Monte Carlo and Variational Inference: Bridging the Gap

Fixed-Form Variational Posterior Approximation through Stochastic Linear Regression

Improving GANs Using Optimal Transport

Learning Montezuma's Revenge from a Single Demonstration