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E(n) Equivariant Normalizing Flows

Abstract:

This paper introduces a generative model equivariant to Euclidean symmetries: E(n) Equivariant Normalizing Flows (E-NFs). To construct E-NFs, we take the discriminative E(n) graph neural networks and integrate them as a differential equation to obtain an invertible equivariant function: a continuous-time normalizing flow. We demonstrate that E-NFs considerably outperform baselines and existing methods from the literature on particle systems such as DW4 and LJ13, and on molecules from QM9 in t...

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Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
Pembroke College
Role:
Author
ORCID:
0000-0001-6270-700X
Publisher:
Curran Associates
Host title:
Advances in Neural Information Processing Systems 34 (NeurIPS 2021)
Volume:
34
Pages:
4181-4192
Publication date:
2022-05-31
Acceptance date:
2021-09-28
Event title:
35th Conference on Neural Information Processing Systems (NeurIPS 2021)
Event location:
Virtual event
Event website:
https://nips.cc/Conferences/2021/
Event start date:
2021-12-06
Event end date:
2021-12-14
ISSN:
1049-5258
ISBN:
9781713845393
Language:
English
Keywords:
Pubs id:
1264397
Local pid:
pubs:1264397
Deposit date:
2023-03-10

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