9 citations · 17 across the 4 of their papers we have counts for
4 papers
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…
Flow Matching with General Discrete Paths: A Kinetic-Optimal Perspective
Neta Shaul, Itai Gat, Marton Havasi +6
The design space of discrete-space diffusion or flow generative models are significantly less well-understood than their continuous-space counterparts, with many works focusing onl…
FlowLLM: Flow Matching for Material Generation with Large Language Models as Base Distributions
Anuroop Sriram, Benjamin Kurt Miller, Ricky T. Q. Chen +1
Material discovery is a critical area of research with the potential to revolutionize various fields, including carbon capture, renewable energy, and electronics. However, the imme…
FlowMM: Generating Materials with Riemannian Flow Matching
Benjamin Kurt Miller, Ricky T. Q. Chen, Anuroop Sriram +1
Crystalline materials are a fundamental component in next-generation technologies, yet modeling their distribution presents unique computational challenges. Of the plausible arrang…