Explicit formulas for intrinsic volumes of ℓ_p-balls via one-dimensional integrals with special function F_p, plus Maxwell-Poincaré-Borel limit laws for curvature measures in high dimensions.
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MA-GIG uses VAE latent space to align Integrated Gradients paths with the data manifold for more faithful feature attributions in deep neural networks.
Witness motifs in constrained geometric graphs saturate Weyl bounds on Laplacian perturbations under heavy-tailed noise, with new metrics SC and S3I to distinguish noise-driven spectral effects.
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Intrinsic volumes of $\ell_p$-balls and a continuum of Maxwell--Poincar\'e--Borel laws for their curvature measures
Explicit formulas for intrinsic volumes of ℓ_p-balls via one-dimensional integrals with special function F_p, plus Maxwell-Poincaré-Borel limit laws for curvature measures in high dimensions.
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Manifold-Aligned Guided Integrated Gradients for Reliable Feature Attribution
MA-GIG uses VAE latent space to align Integrated Gradients paths with the data manifold for more faithful feature attributions in deep neural networks.
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Spectral Effects Of Heavy-Tailed Vertex Noise In Geometric Graphs
Witness motifs in constrained geometric graphs saturate Weyl bounds on Laplacian perturbations under heavy-tailed noise, with new metrics SC and S3I to distinguish noise-driven spectral effects.