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arXiv · 2105.09016

E(n) Equivariant Normalizing Flows

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

Victor Garcia Satorras, Emiel Hoogeboom, Fabian B. Fuchs, Ingmar Posner, Max Welling. 2021-05-19. E(n) Equivariant Normalizing Flows. https://arxiv.org/abs/2105.09016

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