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English(EN) Tight Asymptotic Bounds on the Maximum Number of Linear Regions of Piecewise-Linear Kolmogorov--Arnold networks

新界限揭示Kolmogorov-Arnold网络在几何复杂度上的优势

研究人员为连续分段线性Kolmogorov-Arnold网络(KANs)建立了线性区域最大数量的紧渐近界。这些界限包括上限和下限,确定了每条边上仿射段的增长阶数。研究还将CPWL KANs与ReLU网络的表达能力进行了比较,证明了在某些条件下KANs具有多项式优势,这为了解其复杂性并指导架构选择提供了基础。 AI

影响 为KANs与ReLU网络相比的表达能力提供了理论见解,可能指导未来的架构设计。

排序理由 学术论文,详细介绍了模型复杂度的理论界限。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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新界限揭示Kolmogorov-Arnold网络在几何复杂度上的优势

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学术论文,详细介绍了模型复杂度的理论界限。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Ye Su, Yong Liu ·

    Piecewise-Linear Kolmogorov--Arnold网络最大线性区域数量的紧渐近界

    arXiv:2611.07973v1 Announce Type: new Abstract: Schoots et al.~\citep{schoots2025relating} (AISTATS 2025) established a generally loose upper bound on the number of linear regions of continuous piecewise-linear (CPWL) Kolmogorov--Arnold networks (KANs), without providing a lower …