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Poster
E(n) Equivariant Normalizing Flows
Victor Garcia Satorras · Emiel Hoogeboom · Fabian Fuchs · Ingmar Posner · Max Welling

Thu Dec 09 12:30 AM -- 02:00 AM (PST) @
in Poster Session 5 »

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.

Keywords: Deep Learning · Generative Model · Graph Learning

Author Information

Victor Garcia Satorras (University of Amsterdam)
Emiel Hoogeboom (University of Amsterdam)
Fabian Fuchs (University of Oxford)
Fabian Fuchs

I am a Research Scientist at DeepMind and part of their Science team. After an undergrad in physics, I did my PhD at the Applied AI lab (A2I), supervised by Professor Ingmar Posner. In 2020, I did a research sabbatical at the BCAI collaborating with Max Welling’s lab at the University of Amsterdam.

Ingmar Posner (Oxford University)
Max Welling (University of Amsterdam / Qualcomm AI Research)

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