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English(EN) Approximation Property of Dropout Neural Networks: Sobolev Rates and Confidence Bounds

Dropout神经网络：逼近性质与界限的探索

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员分析了dropout神经网络的逼近能力，特别关注ReLU网络，其中边以一定概率保留。该研究建立了在考虑深度、边保留概率和所需精度等因素的情况下，网络大小达到特定函数空间精确逼近的理论界限。研究结果揭示了网络复杂性与逼近性能之间的权衡，在某些条件下，一些界限匹配，而另一些则仍是悬而未决的问题。 AI

影响 为dropout神经网络的逼近能力和大小要求提供了理论见解。

排序理由 详细介绍神经网络性质理论分析的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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Dropout神经网络：逼近性质与界限的探索

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详细介绍神经网络性质理论分析的学术论文。[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) · Jia-He Yao ·

    Dropout神经网络的近似性质：Sobolev速率与置信界

    arXiv:2610.02253v1 Announce Type: new Abstract: The universal approximation property of dropout neural networks does not by itself describe the network size required for an accurate random realization. In this work, we study approximation of the unit ball of $W^{n,\infty}([0,1]^d…

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