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New method improves Gaussian approximation for wide neural networks

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a method to improve the convergence rate of Gaussian approximations for wide neural networks. This new technique lifts finite-sample bounds to functional bounds, achieving a convergence rate of m^{-1/2} in the supremum norm. The approach measures error per sample, making the constant uniform and thus independent of the sample set size, which addresses limitations of previous methods where constants grew with sample refinement. AI

IMPACT This research offers a theoretical advancement in understanding neural network behavior, potentially leading to more stable and predictable models.

RANK_REASON Academic paper published on arXiv detailing a new theoretical method for neural network approximation. [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 method improves Gaussian approximation for wide neural networks

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Academic paper published on arXiv detailing a new theoretical method for neural network approximation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Luis Wiederhold, Nil Ayday, Debarghya Ghoshdastidar ·

    Lifting Finite-Sample Gaussian Approximation of Wide Neural Networks to Functional Convergence

    arXiv:2610.18166v1 Announce Type: new Abstract: Randomly initialised neural networks of width $m$ converge to Gaussian processes as $m \to \infty$, with two types of quantitative bounds characterising this convergence. Finite-sample bounds compare the network with its Gaussian li…

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