3 citations · 4 across the 5 of their papers we have counts for
5 papers
Discrete Adjoint Matching
Oswin So, Brian Karrer, Chuchu Fan +2
Computation methods for solving entropy-regularized reward optimization -- a class of problems widely used for fine-tuning generative models -- have advanced rapidly. Among those,…
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…
Adjoint Matching: Fine-tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control
Carles Domingo-Enrich, Michal Drozdzal, Brian Karrer +1
Dynamical generative models that produce samples through an iterative process, such as Flow Matching and denoising diffusion models, have seen widespread use, but there have not be…
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…
Stochastic Optimal Control Matching
Carles Domingo-Enrich, Jiequn Han, Brandon Amos +2
Stochastic optimal control, which has the goal of driving the behavior of noisy systems, is broadly applicable in science, engineering and artificial intelligence. Our work introdu…