Graded Representation Theory of Equivariant Neural Networks

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.

Representation Theory (math.RT); Machine Learning (cs.LG)

arXiv:2609.25776 [math.RT]

(or arXiv:2609.25776v1 [math.RT] for this version)

https://doi.org/10.48550/arXiv.2609.25776 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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