English(EN) When is early stopping beneficial under label noise? A phase diagram for kernel gradient flow

研究发现，在标签噪声下，提前停止不一定总是有益的

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-27 04:00

一篇新研究论文探讨了在面对噪声标签时，机器学习模型中提前停止的有效性。该研究使用了一个合成噪声分类任务并在 CIFAR-10 上进行了实验，表明在某些情况下，特别是对于更深的卷积网络，插值（训练超过所有标签都被分类的点）可以比提前停止获得更好的泛化能力。该研究引入了一个相图来说明学习共享关系和拟合噪声之间的平衡如何影响不同核类型和训练时间的泛化误差。 AI

影响这项研究可能会影响处理不完美数据集的模型的训练策略，从而在标签噪声普遍存在的实际应用中提高性能。

排序理由关于理论机器学习概念的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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arXiv stat.ML TIER_1 English(EN) · Fabrizio Boncoraglio, Lenka Zdeborov\'a · 2026-10-27 04:00

何时采用早停法有益于标签噪声？核梯度流的相图

arXiv:2610.33846v1 Announce Type: cross Abstract: Early stopping is commonly used to prevent fitting label noise from degrading generalization. Does this mean that, when a positive fraction of the training labels is intrinsically unpredictable, the best test error must occur befo…

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何时采用早停法有益于标签噪声？核梯度流的相图

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