Work

Graded Representation Theory of Equivariant Neural Networks

Year: 2026
Type: preprint
Abstract: Nonlinear activations can create equivariant interactions between irreducible representations that linear maps cannot. We use the Gaussian degree decomposition to extend ordinary polynomial degree to such nonlinear maps, and prove that for a fixed coordinatewise equivariant layer each degree factors into a polynomial determined by the linear maps and a scalar determined by the activation. This separates three distinct obstructions, coming from symmetry, coordinates, and activation.
Language: English
Open Access status: green
OpenAlex ID: W7214069747
Cites: 3
Cited by: 1
Related to: 10