On learning higher-order cumulants in diffusion models

Aarts, Gert; Habibi, Diaa Eddin; Wang, Lingxiao; Zhou, Kai

doi:10.1088/2632-2153/adc53a

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On learning higher-order cumulants in diffusion models

Gert Aarts

, Diaa Eddin Habibi, Lingxiao Wang

, Kai Zhou

Machine Learning: Science and Technology, Volume: 6, Issue: 2, Start page: 025004

Swansea University Authors: Gert Aarts , Diaa Eddin Habibi

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DOI (Published version): 10.1088/2632-2153/adc53a

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...

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Published in:	Machine Learning: Science and Technology
ISSN:	2632-2153
Published:	IOP Publishing 2025
Online Access:	Check full text
URI:	https://cronfa.swan.ac.uk/Record/cronfa69229

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spelling	2025-04-30T13:34:31.4559271 v2 69229 2025-04-04 On learning higher-order cumulants in diffusion models 1ba0dad382dfe18348ec32fc65f3f3de 0000-0002-6038-3782 Gert Aarts Gert Aarts true false 5d736de7adfea5495e2e56a4dcb42524 Diaa Eddin Habibi Diaa Eddin Habibi true false 2025-04-04 BGPS 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. Journal Article Machine Learning: Science and Technology 6 2 025004 IOP Publishing 2632-2153 learning, cumulants, lattice field theory, diffusion models 3 4 2025 2025-04-03 10.1088/2632-2153/adc53a COLLEGE NANME Biosciences Geography and Physics School COLLEGE CODE BGPS Swansea University SU Library paid the OA fee (TA Institutional Deal) GAis supported by STFC Consolidated Grant ST/X000648/1. DEH is supported by the UKRI AIMLAC CDT EP/S023992/1. We thanks the DEEP-IN working group at RIKEN-iTHEMS for its support in the preparation of this paper. LW is also supported by the RIKEN TRIP initiative (RIKEN Quantum) and JST-BOOST Grant (No. 24036419). KZ is supported by the CUHK-Shenzhen university development fund under Grant Nos. UDF01003041 and UDF03003041, and Shenzhen Peacock fund under No. 2023TC0179. Weacknowledge the support of the Supercomputing Wales project, which is part-funded by the European Regional Development Fund (ERDF) via Welsh Government. 2025-04-30T13:34:31.4559271 2025-04-04T14:34:48.3174628 Faculty of Science and Engineering School of Biosciences, Geography and Physics - Physics Gert Aarts 0000-0002-6038-3782 1 Diaa Eddin Habibi 2 Lingxiao Wang 0000-0003-3757-3403 3 Kai Zhou 0000-0001-9859-1758 4 69229__33952__79d50f3460394df89449cc2754ce8d69.pdf Aarts_2025_Mach._Learn.__Sci._Technol._6_025004.pdf 2025-04-04T14:37:06.2560512 Output 1751324 application/pdf Version of Record true ©2025TheAuthor(s). Released under the terms of the Creative Commons Attribution 4.0 licence. true eng https://creativecommons.org/licenses/by/4.0/
title	On learning higher-order cumulants in diffusion models
spellingShingle	On learning higher-order cumulants in diffusion models Gert Aarts Diaa Eddin Habibi
title_short	On learning higher-order cumulants in diffusion models
title_full	On learning higher-order cumulants in diffusion models
title_fullStr	On learning higher-order cumulants in diffusion models
title_full_unstemmed	On learning higher-order cumulants in diffusion models
title_sort	On learning higher-order cumulants in diffusion models
author_id_str_mv	1ba0dad382dfe18348ec32fc65f3f3de 5d736de7adfea5495e2e56a4dcb42524
author_id_fullname_str_mv	1ba0dad382dfe18348ec32fc65f3f3de_*_Gert Aarts 5d736de7adfea5495e2e56a4dcb42524_*_Diaa Eddin Habibi
author	Gert Aarts Diaa Eddin Habibi
author2	Gert Aarts Diaa Eddin Habibi Lingxiao Wang Kai Zhou
format	Journal article
container_title	Machine Learning: Science and Technology
container_volume	6
container_issue	2
container_start_page	025004
publishDate	2025
institution	Swansea University
issn	2632-2153
doi_str_mv	10.1088/2632-2153/adc53a
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description	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.
published_date	2025-04-03T05:21:47Z
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On learning higher-order cumulants in diffusion models

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