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English(EN) The Mathematical Theory of Double Descent as Shrinkage, not Overfitting: Matching Bounds for Path-Regularized Multilayer Neural Networks

新理论将双下降解释为收缩现象

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新论文探讨了多层神经网络中双下降现象背后的数学理论，提出该现象是由收缩而非过拟合驱动的。该研究为具有路径正则化的全连接网络提供了非渐近泛化理论，适用于各种损失函数和网络架构，且不受宽度或深度限制。研究发现，随着网络宽度的增长，误差先增加后减少，其上升归因于正则化引起的收缩，而非过拟合产生的方差。 AI

影响 提供了对神经网络行为更深入的理论理解，可能为未来的模型设计和训练策略提供信息。

排序理由 关于神经网络理论方面的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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新理论将双下降解释为收缩现象

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关于神经网络理论方面的学术论文。[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) · Hao Yu ·

    The Mathematical Theory of Double Descent as Shrinkage, not Overfitting: Matching Bounds for Path-Regularized Multilayer Neural Networks

    arXiv:2503.02129v4 Announce Type: replace Abstract: We give a nonasymptotic generalization theory for multilayer fully connected neural networks trained with path regularization, for general Lipschitz losses and without assuming the loss bounded, the architecture sparse, or any w…

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