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Research PaperResearchia:202610.20072

Two hidden layer suffice: the caterpillar net

Jakob Paul Zimmermann

Abstract

Every continuous piecewise linear (CPWL) function is a finite signed sum of maxima of affine functions, making maximum representation a central problem in the study of the expressiveness of ReLU networks. A now-disproved conjecture predicted logarithmic depth for maxima. Inspired by the proof by Brunck et al. (2026) that two hidden layers suffice for general CPWL functions, we give the Caterpillar Net, a nonrecursive construction for the maximum of $N$ inputs with two hidden layers of widths $O(...

Submitted: October 20, 2026Subjects: Machine Learning; Data Science

Description / Details

Every continuous piecewise linear (CPWL) function is a finite signed sum of maxima of affine functions, making maximum representation a central problem in the study of the expressiveness of ReLU networks. A now-disproved conjecture predicted logarithmic depth for maxima. Inspired by the proof by Brunck et al. (2026) that two hidden layers suffice for general CPWL functions, we give the Caterpillar Net, a nonrecursive construction for the maximum of NN inputs with two hidden layers of widths O(N2)O(N^2) and 2O(NlogโกN)2^{O(N\log N)}. The construction has a direct geometric interpretation.


Source: arXiv:2610.26795v1 - http://arxiv.org/abs/2610.26795v1 PDF: https://arxiv.org/pdf/2610.26795v1 Original Link: http://arxiv.org/abs/2610.26795v1

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Date:
Oct 20, 2026
Topic:
Data Science
Area:
Machine Learning
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