On learning higher-order cumulants in diffusion models

• URL: http://arxiv.org/abs/2410.21212v1
• Date: Mon, 28 Oct 2024 16:57:02 GMT
• Title: On learning higher-order cumulants in diffusion models
• Authors: Gert Aarts, Diaa E. Habibi, Lingxiao Wang, Kai Zhou,
• Abstract summary: We study the behaviour of higher-order cumulants, or connected n-point functions, under both the forward and backward process. We derive explicit expressions for the moment- and cumulant-generating functionals. We confirm our results in an exactly solvable toy model with nonzero cumulants and in scalar lattice field theory.
• Score: 6.610338540492242
• License:
• Abstract: To analyse how diffusion models learn correlations beyond Gaussian ones, we study the behaviour of higher-order cumulants, or connected n-point functions, under both the forward and backward process. We derive explicit expressions for the moment- and cumulant-generating functionals, in terms of the distribution of the initial data and properties of forward process. It is shown analytically that during the forward process higher-order cumulants are conserved in models without a drift, such as the variance-expanding scheme, and that therefore the endpoint of the forward process maintains nontrivial correlations. We demonstrate that since these correlations are encoded in the score function, higher-order cumulants are learnt in the backward process, also when starting from a normal prior. We confirm our analytical results in an exactly solvable toy model with nonzero cumulants and in scalar lattice field theory.

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