Ce qui s'est passé: Riemannian Deep Learning:Modules, Networks, and Geometries

PhD thesis presenting unified framework for deep learning on curved geometric spaces, generalizing batch normalization and logistic regression to manifolds.

Pourquoi ça compte: Riemannian Deep Learning:Modules, Networks, and Geometries

Matters for engineers building neural networks on non-Euclidean data like graphs, covariance matrices, or hyperbolic embeddings needing stable geometric operations.

Dans l'actualité

Riemannian Deep Learning:Modules, Networks, and Geometries

arXiv cs.AI · Publié le 21 juillet 2026 · 3 min de lecture

En 30 secondes

Ce qui s'est passé: PhD thesis presenting unified framework for deep learning on curved geometric spaces, generalizing batch normalization and logistic regression to manifolds.
Pourquoi ça compte: Matters for engineers building neural networks on non-Euclidean data like graphs, covariance matrices, or hyperbolic embeddings needing stable geometric operations.
Vigilance: This is a thesis compilation of prior published work with typo corrections. Practical implementation complexity and computational overhead compared to Euclidean methods unclear.

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