46 citations · 82 across the 6 of their papers we have counts for
13 papers
Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation
Xiaohui Chen, Xu Han, Jiajing Hu +2
A graph generative model defines a distribution over graphs. One type of generative model is constructed by autoregressive neural networks, which sequentially add nodes and edges t…
VarGrad: A Low-Variance Gradient Estimator for Variational Inference
Lorenz Richter, Ayman Boustati, Nikolas Nüsken +2
We analyse the properties of an unbiased gradient estimator of the ELBO for variational inference, based on the score function method with leave-one-out control variates. We show t…
Prescribed Generative Adversarial Networks
Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei +1
Generative adversarial networks (GANs) are a powerful approach to unsupervised learning. They have achieved state-of-the-art performance in the image domain. However, GANs are limi…
Topic Modeling in Embedding Spaces
Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei
Topic modeling analyzes documents to learn meaningful patterns of words. However, existing topic models fail to learn interpretable topics when working with large and heavy-tailed…
The Dynamic Embedded Topic Model
Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei
Topic modeling analyzes documents to learn meaningful patterns of words. For documents collected in sequence, dynamic topic models capture how these patterns vary over time. We dev…
A Contrastive Divergence for Combining Variational Inference and MCMC
Francisco J. R. Ruiz, Michalis K. Titsias
We develop a method to combine Markov chain Monte Carlo (MCMC) and variational inference (VI), leveraging the advantages of both inference approaches. Specifically, we improve the…