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 …

Vision transformer networks have shown superiority in many computer vision tasks. In this
paper, we take a step further by proposing a novel generative vision transformer with latent …

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 …

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 …

Text summarization aims to condense long documents and retain key information. Critical to
the success of a summarization model is the faithful inference of latent representations of …

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 …

It has been suggested that the brain employs probabilistic generative models to optimally
interpret sensory information. This hypothesis has been formalised in distinct frameworks …

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 …

Detecting differentially expressed genes is important for characterizing subpopulations of
cells. In scRNA-seq data, however, nuisance variation due to technical factors like …

This paper studies the fundamental learning problem of the energy-based model (EBM).
Learning the EBM can be achieved using the maximum likelihood estimation (MLE), which …