Skip to yearly menu bar Skip to main content

Workshop: Machine Learning and the Physical Sciences

SE(3)-equivariant self-attention via invariant features

Nan Chen · Soledad Villar


In this work, we use classical invariant theory to construct a self-attention module equivariant to 3D rotations and translations. The parameterization is based on the characterization of SE(3)-equivariant functions via the invariants ---scalar products of vectors and certain subdeterminants. This parameterization can be seen as a natural extension to a (more straightforward) E(3) equivariant attention based on invariants ---scalar products or pairwise distances of vectors. We evaluate our model using a toy N-body particle simulation dataset and a real-world dataset of molecular properties. Our model is easy to implement and it exhibits comparable performance and running time to state-of-the-art methods.

Chat is not available.