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新型激活函数使固定大小的神经网络能够实现任意精度

作者 PulseAugur 编辑部 · [3 个来源] ·

研究人员引入了新的激活函数，即基本通用激活函数 (EUAF) 和可微通用激活函数 (DUAF)，旨在使固定大小的神经网络能够实现任意精度的 Sobolev 近似。该研究表明，使用这些新型激活函数的网络可以在 $W^{s-1, ext{inf}}$-范数下以任意精度近似 $W^{s, ext{inf}}((a,b)^d)$ 中的函数。文章提供了网络宽度和深度的显式界限，并探讨了 DUAF 的 S 型变体。 AI

影响 引入了新型激活函数，有望增强固定大小神经网络的近似能力。

排序理由 该集群包含一篇详细介绍神经网络激活函数新理论贡献的学术论文。

在 arXiv cs.LG 阅读 →

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新型激活函数使固定大小的神经网络能够实现任意精度

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报道来源 [3]

  1. arXiv cs.LG TIER_1 English(EN) · Yahong Yang, Zecheng Zhang, Wei Zhu, Wenjing Liao, Hao Liu ·

    Sobolev空间中多输入神经算子学习的泛化保证

    arXiv:2606.17419v1 Announce Type: new Abstract: We develop approximation and generalization error estimates for multi-input neural operators, with the output error measured in Sobolev norms. In contrast to standard operator-learning settings with a single input function, our fram…

  2. arXiv cs.LG TIER_1 English(EN) · Baicheng Li, Haizhao Yang, Shijun Zhang ·

    Sobolev近似与任意精度固定大小神经网络

    arXiv:2606.16975v1 Announce Type: cross Abstract: In this work, we investigate new activation functions for achieving arbitrary-accuracy Sobolev approximation by fixed-size neural networks. We first show that any function in $W^{2,\infty}((a,b)^d)$ can be approximated with arbitr…

  3. arXiv stat.ML TIER_1 English(EN) · Shijun Zhang ·

    Sobolev近似与任意精度固定大小神经网络

    In this work, we investigate new activation functions for achieving arbitrary-accuracy Sobolev approximation by fixed-size neural networks. We first show that any function in $W^{2,\infty}((a,b)^d)$ can be approximated with arbitrary accuracy, measured in the $W^{1,\infty}$-norm,…

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