Scattering network separation capacity on rectifiable sets is bounded by tangent-space rank and a second-moment matrix, yielding two filter design criteria: sufficient spectral coverage and well-conditioned frame-geometry coupling.
Testing the manifold hypothesis
3 Pith papers cite this work. Polarity classification is still indexing.
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Refines Cover's dichotomy counts and separation capacity for low-dimensional data by adjusting the general position assumption.
Invariant semantic features in language models are characterized as geometric subspaces in latent space, separated via contrastive discovery and used for model attribution.
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Separation Capacity of Scattering Networks on Low-Dimensional Datasets
Scattering network separation capacity on rectifiable sets is bounded by tangent-space rank and a second-moment matrix, yielding two filter design criteria: sufficient spectral coverage and well-conditioned frame-geometry coupling.
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Function-Counting Theory for Low-Dimensional Data Structures
Refines Cover's dichotomy counts and separation capacity for low-dimensional data by adjusting the general position assumption.
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Invariant Features in Language Models: Geometric Characterization and Model Attribution
Invariant semantic features in language models are characterized as geometric subspaces in latent space, separated via contrastive discovery and used for model attribution.