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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…
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…
Neural Optimal Transport with Lagrangian Costs
Aram-Alexandre Pooladian, Carles Domingo-Enrich, Ricky T. Q. Chen +1
We investigate the optimal transport problem between probability measures when the underlying cost function is understood to satisfy a least action principle, also known as a Lagra…