E(n) Equivariant Normalizing Flows

Victor Garcia Satorras · Emiel Hoogeboom · Fabian Fuchs · Ingmar Posner · Max Welling

Keywords: [ Deep Learning ] [ Graph Learning ] [ Generative Model ]

[ Abstract ]
[ OpenReview
Thu 9 Dec 12:30 a.m. PST — 2 a.m. PST
Oral presentation: Oral Session 1: Generative Modeling
Tue 7 Dec midnight PST — 1 a.m. PST


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 terms of log-likelihood. To the best of our knowledge, this is the first flow that jointly generates molecule features and positions in 3D.

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