Geometric Deep Learning

A research area and set of techniques that generalize deep learning methods to non-Euclidean domains (graphs, meshes, manifolds, point clouds) by building architectures and layers that respect underlying geometric and topological structure, such as permutation invariance, equivariance to group actions, and locality defined by the geometry.