arxiv
PublishedApril 24, 2026 at 4:00 AM
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Sheaf Neural Networks on SPD Manifolds: Second-Order Geometric Representation Learning
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arXiv:2604.20308v1 Announce Type: new Abstract: Graph neural networks face two fundamental challenges rooted in the linear structure of Euclidean vector spaces: (1) Current architectures represent geometry through vectors (directions, gradients), yet many tasks require matrix-valued representations
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