357 citations · 992 across the 28 of their papers we have counts for
5 papers · 1 filter
A-NICE-MC: Adversarial Training for MCMC
Jiaming Song, Shengjia Zhao, Stefano Ermon
Existing Markov Chain Monte Carlo (MCMC) methods are either based on general-purpose and domain-agnostic schemes which can lead to slow convergence, or hand-crafting of problem-spe…
InfoVAE: Information Maximizing Variational Autoencoders
Shengjia Zhao, Jiaming Song, Stefano Ermon
A key advance in learning generative models is the use of amortized inference distributions that are jointly trained with the models. We find that existing training objectives for…
On the Limits of Learning Representations with Label-Based Supervision
Jiaming Song, Russell Stewart, Shengjia Zhao +1
Advances in neural network based classifiers have transformed automatic feature learning from a pipe dream of stronger AI to a routine and expected property of practical systems. S…
Towards Deeper Understanding of Variational Autoencoding Models
Shengjia Zhao, Jiaming Song, Stefano Ermon
We propose a new family of optimization criteria for variational auto-encoding models, generalizing the standard evidence lower bound. We provide conditions under which they recove…
Learning Hierarchical Features from Generative Models
Shengjia Zhao, Jiaming Song, Stefano Ermon
Deep neural networks have been shown to be very successful at learning feature hierarchies in supervised learning tasks. Generative models, on the other hand, have benefited less f…