Tim Salimans

A Spectral Energy Distance for Parallel Speech Synthesis

Speech synthesis is an important practical generative modeling problem t...
08/03/2020 ∙ by Alexey A. Gritsenko, et al. ∙ 3 ∙ share

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IDF++: Analyzing and Improving Integer Discrete Flows for Lossless Compression

In this paper we analyse and improve integer discrete flows for lossless...
06/22/2020 ∙ by Rianne van den Berg, et al. ∙ 12 ∙ share

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Milking CowMask for Semi-Supervised Image Classification

Consistency regularization is a technique for semi-supervised learning t...
03/26/2020 ∙ by Geoff French, et al. ∙ 4 ∙ share

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MetNet: A Neural Weather Model for Precipitation Forecasting

Weather forecasting is a long standing scientific challenge with direct ...
03/24/2020 ∙ by Casper Kaae Sønderby, et al. ∙ 0 ∙ share

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The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks

Variational Bayesian Inference is a popular methodology for approximatin...
02/07/2020 ∙ by Jakub Świątkowski, et al. ∙ 6 ∙ share

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How Good is the Bayes Posterior in Deep Neural Networks Really?

During the past five years the Bayesian deep learning community has deve...
02/06/2020 ∙ by Florian Wenzel, et al. ∙ 27 ∙ share

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Hydra: Preserving Ensemble Diversity for Model Distillation

Ensembles of models have been empirically shown to improve predictive pe...
01/14/2020 ∙ by Linh Tran, et al. ∙ 1 ∙ share

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Axial Attention in Multidimensional Transformers

We propose Axial Transformers, a self-attention-based autoregressive mod...
12/20/2019 ∙ by Jonathan Ho, et al. ∙ 0 ∙ share

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Dota 2 with Large Scale Deep Reinforcement Learning

On April 13th, 2019, OpenAI Five became the first AI system to defeat th...
12/13/2019 ∙ by OpenAI, et al. ∙ 13 ∙ share

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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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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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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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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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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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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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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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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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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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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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Tim Salimans

Featured Co-authors

Pieter Abbeel

Xi Chen

Max Welling

Jie Tang

Ian Goodfellow

Ilya Sutskever

Han Zhang

Stephan Mandt

Sebastian Nowozin

Wojciech Zaremba

Dimitris Metaxas

A Spectral Energy Distance for Parallel Speech Synthesis

IDF++: Analyzing and Improving Integer Discrete Flows for Lossless Compression

Milking CowMask for Semi-Supervised Image Classification

MetNet: A Neural Weather Model for Precipitation Forecasting

The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks

How Good is the Bayes Posterior in Deep Neural Networks Really?

Hydra: Preserving Ensemble Diversity for Model Distillation

Axial Attention in Multidimensional Transformers

Dota 2 with Large Scale Deep Reinforcement Learning

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

Learning Montezuma's Revenge from a Single Demonstration

Improving GANs Using Optimal Transport

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

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

Variational Lossy Autoencoder

Improving Variational Inference with Inverse Autoregressive Flow

Improved Techniques for Training GANs

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

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

Variational Dropout and the Local Reparameterization Trick

Markov Chain Monte Carlo and Variational Inference: Bridging the Gap

Fixed-Form Variational Posterior Approximation through Stochastic Linear Regression