First Hitting Diffusion Models for Generating Manifold, Graph and Categorical DataDownload PDF

Published: 29 Nov 2022, Last Modified: 05 May 2023SBM 2022 PosterReaders: Everyone
Abstract: We propose a family of First Hitting Diffusion Models (FHDM), deep generative models that generate data with a diffusion process that terminates at a random first hitting time. This yields an extension of the standard fixed-time diffusion models that terminate at a pre-specified deterministic time. Although standard diffusion models are designed for continuous unconstrained data, FHDM is naturally designed to learn distributions on continuous as well as a range of discrete and structure domains.
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