NeurIPS 2021oral231 citations
E(n) Equivariant Normalizing Flows
Victor Garcia Satorras, Emiel Hoogeboom, Fabian Bernd Fuchs, Ingmar Posner, Max Welling
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.
equivariancenormalizing flowsmolecule generationgenerative modelsgraph neural networks
BibTeX
@inproceedings{
satorras2021en,
title={E(n) Equivariant Normalizing Flows},
author={Victor Garcia Satorras and Emiel Hoogeboom and Fabian Bernd Fuchs and Ingmar Posner and Max Welling},
booktitle={Advances in Neural Information Processing Systems},
editor={A. Beygelzimer and Y. Dauphin and P. Liang and J. Wortman Vaughan},
year={2021},
url={https://openreview.net/forum?id=N5hQI_RowVA}
}