
AgentCentric Representations for MultiAgent Reinforcement Learning
Objectcentric representations have recently enabled significant progres...
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Image SuperResolution via Iterative Refinement
We present SR3, an approach to image SuperResolution via Repeated Refin...
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A Spectral Energy Distance for Parallel Speech Synthesis
Speech synthesis is an important practical generative modeling problem t...
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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...
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Milking CowMask for SemiSupervised Image Classification
Consistency regularization is a technique for semisupervised learning t...
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MetNet: A Neural Weather Model for Precipitation Forecasting
Weather forecasting is a long standing scientific challenge with direct ...
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The ktied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks
Variational Bayesian Inference is a popular methodology for approximatin...
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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...
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Hydra: Preserving Ensemble Diversity for Model Distillation
Ensembles of models have been empirically shown to improve predictive pe...
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Axial Attention in Multidimensional Transformers
We propose Axial Transformers, a selfattentionbased autoregressive mod...
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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...
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Policy Gradient Search: Online Planning and Expert Iteration without Search Trees
Monte Carlo Tree Search (MCTS) algorithms perform simulationbased searc...
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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...
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Improving GANs Using Optimal Transport
We present Optimal Transport GAN (OTGAN), a variant of generative adver...
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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...
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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...
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Variational Lossy Autoencoder
Representation learning seeks to expose certain aspects of observed data...
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Improving Variational Inference with Inverse Autoregressive Flow
The framework of normalizing flows provides a general strategy for flexi...
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Improved Techniques for Training GANs
We present a variety of new architectural features and training procedur...
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A Structured Variational Autoencoder for Learning Deep Hierarchies of Sparse Features
In this note we present a generative model of natural images consisting ...
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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...
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Variational Dropout and the Local Reparameterization Trick
We investigate a local reparameterizaton technique for greatly reducing ...
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Markov Chain Monte Carlo and Variational Inference: Bridging the Gap
Recent advances in stochastic gradient variational inference have made i...
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FixedForm Variational Posterior Approximation through Stochastic Linear Regression
We propose a general algorithm for approximating nonstandard Bayesian po...
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Tim Salimans
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