Gaussian Sheaf Neural Networks derive a sheaf Laplacian for Gaussian node features on graphs to preserve their geometric structure during message passing.
International Conference on Learning Representations (ICLR) , year=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
TopoNTK defines a new kernel for simplicial complexes via Hodge Laplacians that is sensitive to higher-order topology and exhibits topological spectral bias in learning.
citing papers explorer
-
Gaussian Sheaf Neural Networks
Gaussian Sheaf Neural Networks derive a sheaf Laplacian for Gaussian node features on graphs to preserve their geometric structure during message passing.
-
Topological Neural Tangent Kernel
TopoNTK defines a new kernel for simplicial complexes via Hodge Laplacians that is sensitive to higher-order topology and exhibits topological spectral bias in learning.