English(EN) Universal Approximation Theorem for Metaplectic Neural Operators

新定理证明了metaplectic神经网络算子的通用逼近性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-11 05:00

研究人员为metaplectic神经网络算子，特别是在有界Lipschitz域上，建立了一个通用逼近定理。该定理表明，只要使用光滑的、全局Lipschitz的、非多项式的激活函数，一个具有非局部分支的单层就可以在指定范数内逼近连续算子。该证明将平均神经网络算子理论与导数估计和精确的chirp共轭平均恒等式相结合，包括傅里叶和分数傅里叶乘子构造。 AI

影响为一类新的神经网络算子奠定了理论基础，有可能在AI模型中实现更强大的函数逼近能力。

排序理由详细介绍机器学习新理论成果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Ahmed Abdeljawad, Malek Yacine Rezoug · 2026-11-11 05:00

万能逼近定理用于模辛神经网络算子

arXiv:2611.13991v1 Announce Type: new Abstract: We prove universal approximation for metaplectic neural operators on nonempty bounded Lipschitz domains. The nonlocal branches are whole-space metaplectic multipliers with zero extension and restriction, associated with any fixed fr…

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万能逼近定理用于模辛神经网络算子

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