3 papers
cs.LG2024
A Diagrammatic Approach to Improve Computational Efficiency in Group Equivariant Neural Networks
Edward Pearce-Crump, William J. Knottenbelt
Group equivariant neural networks are growing in importance owing to their ability to generalise well in applications where the data has known underlying symmetries. Recent charact…
cs.LG2023
An Algorithm for Computing with Brauer's Group Equivariant Neural Network Layers
Edward Pearce-Crump
The learnable, linear neural network layers between tensor power spaces of that are equivariant to the orthogonal group, , the special orthogonal group, $SO(…
cs.LG2023
Categorification of Group Equivariant Neural Networks
Edward Pearce-Crump
We present a novel application of category theory for deep learning. We show how category theory can be used to understand and work with the linear layer functions of group equivar…