Accurate Sampling from Diffusion Models

Abstract:A new proposal called DM-SMC (Diffusion Model - Sequential Monte Carlo) is investigated, which samples ensembles defined in terms of an action, using diffusion models trained on samples from the ensemble. The SMC setup allows for accurate sampling in spite of an approximate diffusion model and the finite stepsize used in the numerical solution of the stochastic process. Improved update strategies are also investigated. Results are presented for a $Z_2$ symmetric scalar field theory in 2 dimensions near its 2nd order phase transition.

High Energy Physics - Lattice (hep-lat); Disordered Systems and Neural Networks (cond-mat.dis-nn); Machine Learning (cs.LG)

arXiv:2609.29902 [hep-lat]

(or arXiv:2609.29902v1 [hep-lat] for this version)

https://doi.org/10.48550/arXiv.2609.29902 Focus to learn more arXiv-issued DOI via DataCite

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