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Every Layer Counts: An Exponential $L_2$ Depth Hierarchy for ReLU Networks

This story is from 2026-08-26. It is preserved in the archive; the latest stories are on the live feed.

arXiv:2608.23877v1 Announce Type: new Abstract: We prove a depth hierarchy for ReLU neural networks in which every additional ReLU layer can save exponentially many neurons. For every $\ell\geq 3$, a globally $[0,1]$-valued, $1$-Lipschitz function is realized by a depth-$\ell$ network of width $\ma…

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  1. 2026-08-26 04:00 · arXiv cs.LG
    Every Layer Counts: An Exponential $L_2$ Depth Hierarchy for ReLU Networks

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