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cs.LG2025
Continuously Tempered Diffusion Samplers
Ezra Erives, Bowen Jing, Peter Holderrieth +1
Annealing-based neural samplers seek to amortize sampling from unnormalized distributions by training neural networks to transport a family of densities interpolating from source t…
cs.LG2025
An Introduction to Flow Matching and Diffusion Models
Peter Holderrieth, Ezra Erives
Diffusion and flow-based models have become the state of the art for generative AI across a wide range of data modalities, including images, videos, shapes, molecules, music, and m…
cs.LG2024
Verlet Flows: Exact-Likelihood Integrators for Flow-Based Generative Models
Ezra Erives, Bowen Jing, Tommi Jaakkola
Approximations in computing model likelihoods with continuous normalizing flows (CNFs) hinder the use of these models for importance sampling of Boltzmann distributions, where exac…