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 …

While the choice of prior is one of the most critical parts of the Bayesian inference workflow,
recent Bayesian deep learning models have often fallen back on vague priors, such as …

Score-based generative models (SGMs) have recently demonstrated impressive results in
terms of both sample quality and distribution coverage. However, they are usually applied …

Many applications of text generation require incorporating different constraints to control the
semantics or style of generated text. These constraints can be hard (eg, ensuring certain …

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 …

Latent space Energy-Based Models (EBMs), also known as energy-based priors, have
drawn growing interests in generative modeling. Fueled by its flexibility in the formulation …

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 …

Controllable generation is one of the key requirements for successful adoption of deep
generative models in real-world applications, but it still remains as a great challenge. In …

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

In this paper, we propose a new method to perform data augmentation in a reliable way in
the High Dimensional Low Sample Size (HDLSS) setting using a geometry-based …