A Diagrammatic Approach to Improve Computational Efficiency in Group
Equivariant Neural Networks
A Diagrammatic Approach to Improve Computational Efficiency in Group
Equivariant Neural Networks
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 characterisations of a class of these networks that use high-order tensor power spaces as their layers suggest that they have significant potential; however, their implementation …