Equivariant Convolution Frameworks for Representation Learning on Non-Euclidean Domains

Geometric deep learning models leveraging symmetry group equivariance can effectively represent and process non-Euclidean data like graphs and manifolds, achieving improved statistical efficiency, interpretability, and generalization.