Connecting Permutation Equivariant Neural Networks and Partition Diagrams

Abstract

Permutation equivariant neural networks are often constructed using tensor powers of ${\mathbb{R}}^n$ as their layer spaces. We show that all of the weight matrices that appear in these neural networks can be obtained from Schur-Weyl duality between the symmetric group and the partition algebra. In particular, we adapt Schur-Weyl duality to derive a simple, diagrammatic method for calculating the weight matrices themselves.

arXiv commentECAI 2024; 16 pages