How Jellyfish Characterise Alternating Group Equivariant Neural Networks
Edward Pearce-Crump
1/24/2023
cs.LGmath.COmath.RT
No confident code implementation found for this paper.
Abstract
We provide a full characterisation of all of the possible alternating group ($A_n$) equivariant neural networks whose layers are some tensor power of $\mathbb{R}^{n}$. In particular, we find a basis of matrices for the learnable, linear, $A_n$-equivariant layer functions between such tensor power spaces in the standard basis of $\mathbb{R}^{n}$. We also describe how our approach generalises to the construction of neural networks that are equivariant to local symmetries.
