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

Human trajectory prediction is critical for autonomous platforms like self-driving cars or
social robots. We present a latent belief energy-based model (LB-EBM) for diverse human …

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 …

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 …

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

While energy-based models (EBMs) exhibit a number of desirable properties, training and
sampling on high-dimensional datasets remains challenging. Inspired by recent progress on …

The recently introduced introspective variational autoencoder (IntroVAE) exhibits
outstanding image generations, and allows for amortized inference using an image encoder …

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 …

This paper studies the fundamental problem of learning multi-layer generator models. The
multi-layer generator model builds multiple layers of latent variables as a prior model on top …

We present a new method of training energy-based models (EBMs) for anomaly detection
that leverages low-dimensional structures within data. The proposed algorithm, Manifold …