Semi-Equivariant Conditional Normalizing Flows
Abstract
We study the problem of learning conditional distributions of the form $p(G | \hat{G})$, where $G$ and $\hat{G}$ are two 3D graphs, using continuous normalizing flows. We derive a semi-equivariance condition on the flow which ensures that conditional invariance to rigid motions holds. We demonstrate the effectiveness of the technique in the molecular setting of receptor-aware ligand generation.
Cite
Text
Rozenberg and Freedman. "Semi-Equivariant Conditional Normalizing Flows." ICLR 2023 Workshops: Physics4ML, 2023.Markdown
[Rozenberg and Freedman. "Semi-Equivariant Conditional Normalizing Flows." ICLR 2023 Workshops: Physics4ML, 2023.](https://mlanthology.org/iclrw/2023/rozenberg2023iclrw-semiequivariant/)BibTeX
@inproceedings{rozenberg2023iclrw-semiequivariant,
title = {{Semi-Equivariant Conditional Normalizing Flows}},
author = {Rozenberg, Eyal and Freedman, Daniel},
booktitle = {ICLR 2023 Workshops: Physics4ML},
year = {2023},
url = {https://mlanthology.org/iclrw/2023/rozenberg2023iclrw-semiequivariant/}
}