Riemannian Deep Learning:Modules, Networks, and Geometries
Deep neural networks on manifold-valued representations have attracted growing interest, but many basic components remain tied to specific manifolds, rely on Euclidean approximations, or require costly and numerically fragile geometric operations. This thesis develops a unified framework for Riemannian deep learning from three complementary perspectives: reusable neural modules, manifold-specific network architectures, and the design of underlying geometries. It generalizes batch normalization from Euclidean spaces and individual manifolds to broad classes of Lie groups and gyrogroups, and ext
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- PossiblePossibly related (embedding) · 56%Hamiltonian Neural Networks from a Differential Geometry Perspective [D] →
- PossiblePossibly related (embedding) · 51%Context and average best linear mappings [D] →
- FuzzySimilar title/name (fuzzy) · 87%aymericdamien/TopDeepLearning →
“Fuzzy title match (0.94): “Riemannian Deep Learning:Modules, Networks, and Geometries” ≈ “aymericdamien/TopDeepLearning””
- LinkedLinked via arxiv author · 85%Chen Ziheng →
“Riemannian Deep Learning:Modules, Networks, and Geometries”