5 papers
Permutation Equivariant Neural Networks for Symmetric Tensors
Edward Pearce-Crump
Incorporating permutation equivariance into neural networks has proven to be useful in ensuring that models respect symmetries that exist in data. Symmetric tensors, which naturall…
Compact Matrix Quantum Group Equivariant Neural Networks
Edward Pearce-Crump
Group equivariant neural networks have proven effective in modelling a wide range of tasks where the data lives in a classical geometric space and exhibits well-defined group symme…
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…
Connecting Permutation Equivariant Neural Networks and Partition Diagrams
Edward Pearce-Crump
Permutation equivariant neural networks are often constructed using tensor powers of as their layer spaces. We show that all of the weight matrices that appear in…
Graph Automorphism Group Equivariant Neural Networks
Edward Pearce-Crump, William J. Knottenbelt
Permutation equivariant neural networks are typically used to learn from data that lives on a graph. However, for any graph that has vertices, using the symmetric group $S_…