HiSP is a sheaf-aware pooling framework using local spectral coarsening to project fine stalk features onto low-frequency modes of cluster sheaf Laplacians for hierarchical SNN representations.
Sheaf neural networks
5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.LG 5years
2026 5verdicts
UNVERDICTED 5representative citing papers
TNOs lift neural operators to topological cell complexes via Discrete Exterior Calculus for cross-dimensional coupling, subsuming prior NOs and showing accuracy gains on PDE benchmarks with irregular geometries.
GAUGE is a pretrainable Riemannian graph model with neural vector bundles and a Dirichlet loss that captures transferable intrinsic geometry, validated on zero-shot link prediction and graph isomorphism.
HilbNets define convolutions via Hilbert bundle connection Laplacians, prove that sampled Hilbert cellular sheaf Laplacians converge to the continuous operator, and show that discretized networks are consistent and transferable across samplings.
BrainDyn is a sheaf neural ODE model that encodes brain region activity history via LSTMs, projects states through restriction maps, and uses a sheaf Laplacian for message passing to generate continuous-time dynamics on brain graphs.
citing papers explorer
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Hierarchical Pooling for Sheaf Neural Networks
HiSP is a sheaf-aware pooling framework using local spectral coarsening to project fine stalk features onto low-frequency modes of cluster sheaf Laplacians for hierarchical SNN representations.
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Topological Neural Operators
TNOs lift neural operators to topological cell complexes via Discrete Exterior Calculus for cross-dimensional coupling, subsuming prior NOs and showing accuracy gains on PDE benchmarks with irregular geometries.
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Are Common Substructures Transferable? Riemannian Graph Foundation Model with Neural Vector Bundles
GAUGE is a pretrainable Riemannian graph model with neural vector bundles and a Dirichlet loss that captures transferable intrinsic geometry, validated on zero-shot link prediction and graph isomorphism.
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Consistent Geometric Deep Learning via Hilbert Bundles and Cellular Sheaves
HilbNets define convolutions via Hilbert bundle connection Laplacians, prove that sampled Hilbert cellular sheaf Laplacians converge to the continuous operator, and show that discretized networks are consistent and transferable across samplings.
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BrainDyn: A Sheaf Neural ODE for Generative Brain Dynamics
BrainDyn is a sheaf neural ODE model that encodes brain region activity history via LSTMs, projects states through restriction maps, and uses a sheaf Laplacian for message passing to generate continuous-time dynamics on brain graphs.