High-throughput single-cell profiling provides an unprecedented ability to uncover the
molecular states of millions of cells. These technologies are, however, destructive to cells …

Molecular dynamics (MD) enables the study of physical systems with excellent
spatiotemporal resolution but suffers from severe timescale limitations. To address this …

Abstract We present Scalable Interpolant Transformers (SiT), a family of generative models
built on the backbone of Diffusion Transformers (DiT). The interpolant framework, which …

Solving transport problems, ie finding a map transporting one given distribution to another,
has numerous applications in machine learning. Novel mass transport methods motivated …

We provide the first polynomial-time convergence guarantees for the probabilistic flow ODE
implementation (together with a corrector step) of score-based generative modeling. Our …

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 …

Diffusion models with their powerful expressivity and high sample quality have achieved
State-Of-The-Art (SOTA) performance in the generative domain. The pioneering Vision …

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 …

The astonishing growth of generative tools in recent years has empowered many exciting
applications in text-to-image generation and text-to-video generation. The underlying …

Diffusion models are a powerful method for generating approximate samples from high-
dimensional data distributions. Several recent results have provided polynomial bounds on …