2 papers
cs.LG2025
Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching
Aaron Havens, Benjamin Kurt Miller, Bing Yan +10
We introduce Adjoint Sampling, a highly scalable and efficient algorithm for learning diffusion processes that sample from unnormalized densities, or energy functions. It is the fi…
cs.LG2025
Wasserstein Flow Matching: Generative modeling over families of distributions
Doron Haviv, Aram-Alexandre Pooladian, Dana Pe'er +1
Generative modeling typically concerns transporting a single source distribution to a target distribution via simple probability flows. However, in fields like computer graphics an…