Diffusion models have emerged as a powerful new family of deep generative models with
record-breaking performance in many applications, including image synthesis, video …

As ChatGPT goes viral, generative AI (AIGC, aka AI-generated content) has made headlines
everywhere because of its ability to analyze and create text, images, and beyond. With such …

Deep generative models have unlocked another profound realm of human creativity. By
capturing and generalizing patterns within data, we have entered the epoch of all …

Guided image synthesis enables everyday users to create and edit photo-realistic images
with minimum effort. The key challenge is balancing faithfulness to the user input (eg, hand …

Diffusion models have shown incredible capabilities as generative models; indeed, they
power the current state-of-the-art models on text-conditioned image generation such as …

Large-scale computational molecular models provide scientists a means to investigate the
effect of microscopic details on emergent mesoscopic behavior. Elucidating the relationship …

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 …

Molecular graph representation learning is a fundamental problem in modern drug and
material discovery. Molecular graphs are typically modeled by their 2D topological …

Score-based diffusion models provide a powerful way to model images using the gradient of
the data distribution. Leveraging the learned score function as a prior, here we introduce a …

We find that across a wide range of robot policy learning scenarios, treating supervised
policy learning with an implicit model generally performs better, on average, than commonly …