We study generative diffusion models in the regime where both the data dimension and the
sample size are large, and the score function is trained optimally. Using statistical physics …

In this work, we investigate an intriguing and prevalent phenomenon of diffusion models
which we term as" consistent model reproducibility'': given the same starting noise input and …

Due to their capacity to generate novel and high-quality samples, diffusion models have
attracted significant research interest in recent years. Notably, the typical training objective of …

Proteins are essential for almost all biological processes and derive their diverse functions
from complex 3D structures, which are in turn determined by their amino acid sequences. In …

Recent empirical studies have demonstrated that diffusion models can effectively learn the
image distribution and generate new samples. Remarkably, these models can achieve this …

Diffusion models, which convert noise into new data instances by learning to reverse a
diffusion process, have become a cornerstone in contemporary generative modeling. In this …

Denoising diffusions are state-of-the-art generative models exhibiting remarkable empirical
performance. They work by diffusing the data distribution into a Gaussian distribution and …

In this work, we study the generalizability of diffusion models by looking into the hidden
properties of the learned score functions, which are essentially a series of deep denoisers …

In recent years there has been increased interest in understanding the interplay between
deep generative models (DGMs) and the manifold hypothesis. Research in this area focuses …

Detecting change points in data is challenging because of the range of possible types of
change and types of behaviour of data when there is no change. Statistically efficient …