研究人员发表了一篇论文，详细介绍了神经网络的广义通用逼近定理。这个新定理扩展了先前的工作，不仅能够逼近函数，还能逼近它们的导数。这些发现适用于无限维流形上的可微映射，并对逼近非预期泛函和路径空间泛函具有意义。 AI
影响 扩展了对神经网络能力的理论理解，可能能够进行更复杂的函数和导数逼近。
排序理由 该集群包含一篇详细介绍机器学习新理论成果的学术论文。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
新定理使神经网络能够逼近函数导数
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报道来源 [2]
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无限维流形上可微映射的加权通用逼近
arXiv:2606.09820v1 Announce Type: cross Abstract: We generalize the universal approximation theorem for functional input neural networks (FNN) to differentiable maps by including the approximation of the derivatives. A FNN maps the input from a possibly infinite-dimensional weigh…
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无限维流形上可微映射的加权通用逼近
We generalize the universal approximation theorem for functional input neural networks (FNN) to differentiable maps by including the approximation of the derivatives. A FNN maps the input from a possibly infinite-dimensional weighted manifold to the real-valued hidden layer, on w…