English(EN) A Function-Space Stability Boundary for Generalization in Interpolating Learning Systems

关于学习系统泛化性的研究论文从arXiv撤回

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-02 05:00

一篇题为“用于插值学习系统的泛化函数空间稳定性边界”的研究论文被提交到arXiv，提出了一个新的框架来分析插值训练数据的学习系统的泛化能力。该论文引入了一个收缩传播条件和一个稳定性证书来衡量对单样本扰动的敏感性。虽然研究表明稳定性可以解释某些情况下的泛化能力，但也指出了其他机制必须负责成功的场景。该论文后来被作者Ronald Katende撤回。 AI

影响这项研究探讨了AI泛化的理论基础，但其撤回限制了直接影响。

排序理由该条目描述了一篇关于机器学习主题的已撤回的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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本文如何被排名

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Newsworthiness bucket: Tool; 该条目描述了一篇关于机器学习主题的已撤回的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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arXiv stat.ML TIER_1 English(EN) · Ronald Katende · 2026-11-02 05:00

A Function-Space Stability Boundary for Generalization in Interpolating Learning Systems

arXiv:2602.03514v3 Announce Type: replace-cross Abstract: Modern learning systems often interpolate training data while still generalizing well, yet it remains unclear when algorithmic stability explains this behavior. We model training as a function-space trajectory and measure …

本文如何被排名

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A Function-Space Stability Boundary for Generalization in Interpolating Learning Systems

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