研究人员发表了一篇论文，详细介绍了深度ReLU神经网络如何高效地逼近和学习光滑函数。该研究将先前的发现扩展到了各向异性和混合光滑函数类别，并建立了新的逼近率。这些结果表明，深度ReLU网络可以为各种光滑函数类型实现接近最优的学习率。 AI
影响 为深度学习的逼近能力建立了理论界限，可能指导未来的模型架构和训练。
排序理由 该集群包含一篇在arXiv上发表的学术论文。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
深度ReLU网络高效学习光滑函数
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报道来源 [2]
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深度ReLU神经网络对各向异性和混合光滑函数的逼近与学习
arXiv:2605.31152v1 Announce Type: new Abstract: This paper studies how efficiently deep ReLU neural networks can approximate and learn smooth functions. When the error is measured in $L^p([0,1]^d)$ norm and the approximator is a network with width $W$ and depth $L$, recent works …
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深度ReLU神经网络对各向异性和混合光滑函数的逼近与学习
This paper studies how efficiently deep ReLU neural networks can approximate and learn smooth functions. When the error is measured in $L^p([0,1]^d)$ norm and the approximator is a network with width $W$ and depth $L$, recent works have proven the supper approximation rate $\math…