Neural point-forms are introduced as permutation-invariant neural layers that output learned form-comparison matrices for point clouds, with a claimed consistency proof under sampling and manifold assumptions and competitive results on synthetic and biological data.
Simplicial representation learning with neuralk-forms
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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Tutorial on TSP foundations via the combinatorial Hodge Laplacian with an illustrative application to edge signals in brain imaging data.
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Neural Point-Forms
Neural point-forms are introduced as permutation-invariant neural layers that output learned form-comparison matrices for point clouds, with a claimed consistency proof under sampling and manifold assumptions and competitive results on synthetic and biological data.
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Topological Signal Processing: An Application-Oriented Tutorial
Tutorial on TSP foundations via the combinatorial Hodge Laplacian with an illustrative application to edge signals in brain imaging data.