A Nonlinear Singular Value Theory for Neural Networks

arXiv:2605.06938v2 Announce Type: replace-cross Abstract: Recently Brown et al. [2025] established a singular value decomposition (SVD) for maps (especially nonlinear) satisfying certain norm conditions. We prove that most modern neural architectures admit this nonlinear SVD (NLSVD) representation—with no change in input–output behavior—and enumerate the classes…

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Source: cs.AI updates on arXiv.org

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