M. Spannowsky

Optimal Symmetries in Binary Classification

A theoretical foundation for designing group-equivariant neural networks that align the choice of symmetries with the underlying probability distributions of the data is developed and there can be performance gains when one utilises smaller permutation symmetries within the constituents.