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 …

Deep learning has become a popular tool for medical image analysis, but the limited
availability of training data remains a major challenge, particularly in the medical field where …

We introduce SceneDiffuser, a conditional generative model for 3D scene understanding.
SceneDiffuser provides a unified model for solving scene-conditioned generation …

Human skeleton-based action recognition offers a valuable means to understand the
intricacies of human behavior because it can handle the complex relationships between …

Predicting trajectories of multiple agents in interactive driving scenarios such as
intersections, and roundabouts are challenging due to the high density of agents, varying …

Abstract Reasoning about human motion is an important prerequisite to safe and socially-
aware robotic navigation. As a result, multi-agent behavior prediction has become a core …

Deep approaches to anomaly detection have recently shown promising results over shallow
methods on large and complex datasets. Typically anomaly detection is treated as an …

Human organoids recapitulating the cell-type diversity and function of their target organ are
valuable for basic and translational research. We developed light-sensitive human retinal …

We decompose the evidence lower bound to show the existence of a term measuring the
total correlation between latent variables. We use this to motivate the beta-TCVAE (Total …

Learning useful representations with little or no supervision is a key challenge in artificial
intelligence. We provide an in-depth review of recent advances in representation learning …