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

Tue Dec 07 12:00 AM -- 12:15 AM (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.

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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