Lorentz Group Equivariant Autoencoders
Abstract
We develop the Lorentz group autoencoder (LGAE), an autoencoder that is equivariant with respect to the proper, orthochronous Lorentz group $\mathrm{SO}^+(3,1)$, with a latent space living in the representations of the group. We present our architecture and several experimental results on data at the Large Hadron Collider and find it outperforms a graph neural network baseline model on several compression, reconstruction, and anomaly detection tasks. The PyTorch code for our models is provided in Hao et al. (2022a).
Cite
Text
Hao et al. "Lorentz Group Equivariant Autoencoders." ICLR 2023 Workshops: Physics4ML, 2023.Markdown
[Hao et al. "Lorentz Group Equivariant Autoencoders." ICLR 2023 Workshops: Physics4ML, 2023.](https://mlanthology.org/iclrw/2023/hao2023iclrw-lorentz/)BibTeX
@inproceedings{hao2023iclrw-lorentz,
title = {{Lorentz Group Equivariant Autoencoders}},
author = {Hao, Zichun and Kansal, Raghav and Duarte, Javier and Chernyavskaya, Nadya},
booktitle = {ICLR 2023 Workshops: Physics4ML},
year = {2023},
url = {https://mlanthology.org/iclrw/2023/hao2023iclrw-lorentz/}
}