English(EN) Lifting Finite-Sample Gaussian Approximation of Wide Neural Networks to Functional Convergence

新方法改进了宽神经网络的高斯近似

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-14 04:00

研究人员开发了一种方法，以提高宽神经网络高斯近似的收敛速度。这项新技术将有限样本界限提升到泛函界限，在上确界范数中实现了 m^{-1/2} 的收敛速度。该方法按样本测量误差，使常数均匀且独立于样本集大小，从而解决了先前方法中常数随样本精化而增长的局限性。 AI

影响这项研究在理解神经网络行为方面提供了理论上的进步，有可能带来更稳定和可预测的模型。

排序理由学术论文发表在 arXiv 上，详细介绍了一种新的神经网络近似理论方法。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv stat.ML TIER_1 English(EN) · Luis Wiederhold, Nil Ayday, Debarghya Ghoshdastidar · 2026-10-14 04:00

宽神经网络有限样本高斯近似的函数收敛性提升

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…