
Glow: Generative Flow with Invertible 1x1 Convolutions
Flowbased generative models (Dinh et al., 2014) are conceptually attrac...
07/09/2018 ∙ by Diederik P. Kingma, et al. ∙ 2 ∙ shareread it

Variational Autoencoders and Nonlinear ICA: A Unifying Framework
The framework of variational autoencoders allows us to efficiently learn...
07/10/2019 ∙ by Ilyes Khemakhem, et al. ∙ 2 ∙ shareread it

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 ∙ shareread it

Variational Lossy Autoencoder
Representation learning seeks to expose certain aspects of observed data...
11/08/2016 ∙ by Xi Chen, et al. ∙ 0 ∙ shareread it

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 ∙ shareread it

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 ∙ shareread it

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 ∙ shareread it

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 ∙ shareread it

Efficient GradientBased Inference through Transformations between Bayes Nets and Neural Nets
Hierarchical Bayesian networks and neural networks with stochastic hidde...
02/03/2014 ∙ by Diederik P. Kingma, et al. ∙ 0 ∙ shareread it

AutoEncoding Variational Bayes
How can we perform efficient inference and learning in directed probabil...
12/20/2013 ∙ by Diederik P. Kingma, et al. ∙ 0 ∙ shareread it

Fast GradientBased Inference with Continuous Latent Variable Models in Auxiliary Form
We propose a technique for increasing the efficiency of gradientbased i...
06/04/2013 ∙ by Diederik P. Kingma, et al. ∙ 0 ∙ shareread it

Learning Sparse Neural Networks through L_0 Regularization
We propose a practical method for L_0 norm regularization for neural net...
12/04/2017 ∙ by Christos Louizos, et al. ∙ 0 ∙ shareread it

An Introduction to Variational Autoencoders
Variational autoencoders provide a principled framework for learning dee...
06/06/2019 ∙ by Diederik P. Kingma, et al. ∙ 0 ∙ shareread it