2.7k citations · 8.5k across the 41 of their papers we have counts for
4 papers · 1 filter
Flow Contrastive Estimation of Energy-Based Models
Ruiqi Gao, Erik Nijkamp, Diederik P. Kingma +3
This paper studies a training method to jointly estimate an energy-based model and a flow-based model, in which the two models are iteratively updated based on a shared adversarial…
MaskGAN: Better Text Generation via Filling in the______
William Fedus, Ian Goodfellow, Andrew M. Dai
Neural text generation models are often autoregressive language models or seq2seq models. These models generate text by sampling words sequentially, with each word conditioned on t…
Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step
William Fedus, Mihaela Rosca, Balaji Lakshminarayanan +3
Generative adversarial networks (GANs) are a family of generative models that do not minimize a single training criterion. Unlike other generative models, the data distribution is…
The supervised hierarchical Dirichlet process
Andrew M. Dai, Amos J. Storkey
We propose the supervised hierarchical Dirichlet process (sHDP), a nonparametric generative model for the joint distribution of a group of observations and a response variable dire…