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English(EN) Kolmogorov--Arnold against bounded translations

探索 Kolmogorov-Arnold 网络对对抗性平移的鲁棒性

一篇新论文探讨了当 Kolmogorov-Arnold 表示定理 (KART) 应用于神经网络(特别是 Kolmogorov-Arnold 网络 (KANs))时的鲁棒性。该研究为使用固定的分段线性内函数进行近似表示提供了构造性证明。一项关键发现是开发了一个单一的外函数,该函数在所有被加数中保持不变,并且独立于对抗性平移,前提是事先知道其最大界限。 AI

影响 这项研究通过解决对抗性扰动问题,有望带来更稳定、更鲁棒的神经网络架构。

排序理由 该集群包含一篇详细介绍神经网络理论研究的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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探索 Kolmogorov-Arnold 网络对对抗性平移的鲁棒性

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该集群包含一篇详细介绍神经网络理论研究的学术论文。[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) · Sviatoslav V. Dzhenzher ·

    Kolmogorov--Arnold 反对有界翻译

    arXiv:2608.30710v1 Announce Type: new Abstract: Historically originating from Hilbert's 13th problem, the Kolmogorov-Arnold representation theorem (KART) has recently experienced a major revitalisation through its applications to neural networks, specifically Kolmogorov-Arnold Ne…