Deep generative models are a class of techniques that train deep neural networks to model
the distribution of training samples. Research has fragmented into various interconnected …

Sampling from known probability distributions is a ubiquitous task in computational science,
underlying calculations in domains from linguistics to biology and physics. Generative …

Energy-based models (EBMs) have recently been successful in representing complex
distributions of small images. However, sampling from them requires expensive Markov …

We propose a generative model of unordered point sets, such as point clouds, in the forms
of an energy-based model, where the energy function is parameterized by an input …

This paper studies the cooperative learning of two generative flow models, in which the two
models are iteratively updated based on the jointly synthesized examples. The first flow …

Multi-task learning for molecular property prediction is becoming increasingly important in
drug discovery. However, in contrast to other domains, the performance of multi-task …

Given an inverse problem with a normalizing flow prior, we wish to estimate the distribution
of the underlying signal conditioned on the observations. We approach this problem as a …

We combine inverse autoregressive flows (IAF) and variational Bayesian inference
(variational Bayes) in the context of geophysical inversion parameterized with deep …

Training energy-based models (EBMs) with maximum likelihood estimation on high-
dimensional data can be both challenging and time-consuming. As a result, there a …

Energy-based latent variable models (EBLVMs) are more expressive than conventional
energy-based models. However, its potential on visual tasks are limited by its training …