Index#

Demistifying E(3)-equivariant neural networks!#

Welcome to our blog post on demistifying E(3)-equivariant neural networks! This blog aim to introduces the following concepts, in order to understand the mathematical tools leading to learnable equivariant operations:

Poster#

poster

Disclaimer#

This post serves as an explanation of the paper by Geiger et al [1]. It was written as a final project for the Fall 2022 Final Project section of the 6.S898: Deep Learning course at the Massachusetts Institute of Technology. Needless to say that the intuition presented herein are heavily influenced by the explanations of Geiger et al.

References#

  1. Geiger, M. & Smidt, T. e3nn: Euclidean Neural Networks. Arxiv (2022).

Citation#

If used, please cite:

@software{killian_sheriff_2022_7430281,
  author       = {Killian Sheriff and
                  Yifan Cao},
  title        = {killiansheriff/blog\_e3nn: blog\_e3nn},
  month        = dec,
  year         = 2022,
  publisher    = {Zenodo},
  version      = {blog\_e3nn},
  doi          = {10.5281/zenodo.7430281},
  url          = {https://doi.org/10.5281/zenodo.7430281}
}