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arXiv Machine Learning By Riccardo Saporiti, Fabio Nobile

Neural Galerkin Normalizing Flow for Transition Probability Density Functions of Diffusion Models

Read the original on arXiv Machine Learning →

arXiv:2603. 18907v2 Announce Type: replace Abstract: We propose a new Neural Galerkin Normalizing Flow framework to approximate the transition probability density function of a diffusion process by solving the corresponding Fokker-Planck equation with an atomic initial distribution, parametrically with respect to the location of the initial mass.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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