Contrastive divergence is a popular method of training energy-based models, but is known
to have difficulties with training stability. We propose an adaptation to improve contrastive …

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

We propose an energy-based model (EBM) in the latent space of a generator model, so that
the EBM serves as a prior model that stands on the top-down network of the generator …

This study investigates the effects of Markov chain Monte Carlo (MCMC) sampling in
unsupervised Maximum Likelihood (ML) learning. Our attention is restricted to the family of …

We show that the sum of the implicit generator log-density $\log p_g $ of a GAN with the logit
score of the discriminator defines an energy function which yields the true data density when …

Spatial-temporal reasoning is a challenging task in Artificial Intelligence (AI) due to its
demanding but unique nature: a theoretic requirement on representing and reasoning …

Variational autoencoders (VAEs) are one of the powerful likelihood-based generative
models with applications in many domains. However, they struggle to generate high-quality …

With the remarkable recent progress on learning deep generative models, it becomes
increasingly interesting to develop models for controllable image synthesis from …

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 …