English(EN) High Probability Derivative Bounds for Random tanh Neural Networks on a Hypercube

新研究详细介绍了随机tanh神经网络的导数界限

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-28 04:00

研究人员已经为使用双曲正切（tanh）激活函数的宽随机神经网络的混合输入导数建立了高概率界限。这项研究侧重于具有Xavier初始化的网络，表明通过分离特定项和控制切线方向，可以显著改善足够宽的高斯网络的导数界限。这项工作将导数估计与拟蒙特卡洛积分联系起来，暗示了在QMC训练分析中的潜在应用。 AI

影响为理解神经网络行为和改进训练方法提供了理论基础。

排序理由学术论文，详细介绍了神经网络导数方面的理论进展。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Josef Dick, Michael Feischl, Fabian Zehetgruber · 2026-08-28 04:00

超立方体上随机 tanh 神经网络的高概率导数界限

arXiv:2608.26526v1 Announce Type: new Abstract: We establish high-probability bounds for mixed input derivatives of wide random neural networks whose activation derivatives satisfy a factorial growth bound. Our main result specializes these estimates to $\tanh$ networks with Xavi…