Neural Galerkin Normalizing Flow for Transition Probability Density Functions of Diffusion Models
Neural Galerkin Normalizing Flow for Transition Probability Density Functions of Diffusion Models explores A novel framework for approximating diffusion process transition densities by solving the Fokker-Planck equation using Neural Galerkin Normalizing Flows, enabling cost-effective online evaluation for many-query problems.. Commercial viability score: 3/10 in Diffusion Models.
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