Diffusion models have revolutionized text-to-image generation with its exceptional quality
and creativity. However, its multi-step sampling process is known to be slow, often requiring …

Recent studies have demonstrated the strong empirical performance of diffusion models on
discrete sequences across domains from natural language to biological sequence …

In light of the remarkable success of in-context learning in large language models, its
potential extension to the vision domain, particularly with visual foundation models like …

Diffusion-based text-to-image generation models trained on extensive text-image pairs have
shown the capacity to generate photorealistic images consistent with textual descriptions …

The recently proposed Bayesian Flow Networks~(BFNs) show great potential in modeling
parameter spaces, offering a unified strategy for handling continuous, discretized, and …

The machine learning community is increasingly recognizing the importance of fostering
trust and safety in modern generative AI (GenAI) models. We posit machine unlearning (MU) …

Diffusion models have excelled in generating natural images and are now being adapted to
a variety of data types, including graphs. However, conventional models often rely on …

Marked event data captures events by recording their continuous-valued occurrence
timestamps along with their corresponding discrete-valued types. They have appeared in …

The beta distribution serves as a canonical tool for modeling probabilities and is extensively
used in statistics and machine learning, especially in the field of Bayesian nonparametrics …