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Math paper proves neural network identifiability via polynomial composition independence

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This paper proves the weak composition-independence conjectures of Kohn, Marchetti, Massarenti, and Mella using deformations of rational curves and a linearized equation kernel. The proof leverages Riemann-Roch theorems to derive a contradiction from tangent fields with controlled poles. By resolving these conjectures, the authors demonstrate that a neural network employing a general polynomial activation function of a sufficiently high degree is identifiable. AI

RANK_REASON The item is an academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=0.4]

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Math paper proves neural network identifiability via polynomial composition independence

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The item is an academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Xiaotian Chang, Yacoub Hendi ·

    Linear Independence of Polynomial Compositions with Bounded Input Degree

    arXiv:2610.20825v1 Announce Type: cross Abstract: We prove the weak composition-independence conjectures of Kohn, Marchetti, Massarenti and Mella. The proof uses deformations of rational curves and a single kernel of the linearized equation. Riemann-Roch gives a contradiction fro…

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