2.1k citations · 9.7k across the 40 of their papers we have counts for
5 papers · 1 filter
Variational Lossy Autoencoder
Xi Chen, Diederik P. Kingma, Tim Salimans +5
Representation learning seeks to expose certain aspects of observed data in a learned representation that's amenable to downstream tasks like classification. For instance, a good r…
Improved Techniques for Training GANs
Tim Salimans, Ian Goodfellow, Wojciech Zaremba +3
We present a variety of new architectural features and training procedures that we apply to the generative adversarial networks (GANs) framework. We focus on two applications of GA…
Improving Variational Inference with Inverse Autoregressive Flow
Diederik P. Kingma, Tim Salimans, Rafal Jozefowicz +3
The framework of normalizing flows provides a general strategy for flexible variational inference of posteriors over latent variables. We propose a new type of normalizing flow, in…
A Structured Variational Auto-encoder for Learning Deep Hierarchies of Sparse Features
Tim Salimans
In this note we present a generative model of natural images consisting of a deep hierarchy of layers of latent random variables, each of which follows a new type of distribution t…
Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks
Tim Salimans, Diederik P. Kingma
We present weight normalization: a reparameterization of the weight vectors in a neural network that decouples the length of those weight vectors from their direction. By reparamet…