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New research estimates Lipschitz constants for deep random ReLU neural networks

By PulseAugur Editorial · [1 sources] ·

A new paper published on arXiv presents near-optimal estimates for the $\ell^p$-Lipschitz constants of deep random ReLU neural networks. The research focuses on networks with random parameters and a specific variant of He initialization, deriving high-probability upper and lower bounds for wide networks. The study highlights a significant difference in the behavior of the $\ell^p$-Lipschitz constant depending on whether $p$ is in the range $[1,2)$ or $[2,\infty]$. AI

IMPACT Provides theoretical insights into the properties of deep neural networks, potentially informing future model architectures and training strategies.

RANK_REASON The cluster contains a single academic paper published on arXiv detailing theoretical research on neural networks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New research estimates Lipschitz constants for deep random ReLU neural networks

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The cluster contains a single academic paper published on arXiv detailing theoretical research on neural networks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Sjoerd Dirksen, Patrick Finke, Paul Geuchen, Dominik St\"oger, Felix Voigtlaender ·

    Near-optimal estimates for the $\ell^p$-Lipschitz constants of deep random ReLU neural networks

    arXiv:2506.19695v2 Announce Type: replace Abstract: This paper studies the $\ell^p$-Lipschitz constants of ReLU neural networks $\Phi: \mathbb{R}^d \to \mathbb{R}$ with random parameters for $p \in [1,\infty]$. The distribution of the weights follows a variant of the He initializ…

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