English(EN) Grokking through the Lens of Minimum-Norm Interpolation

新理论解释机器学习模型中的“Grokking”现象

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-01 04:00

研究人员开发了一种统计理论来解释“Grokking”现象，即模型在与训练数据拟合的阶段之外学习底层信号。该理论描述了正则化几何和信号稀疏性如何影响插值附近的泛化能力，尤其是在高维回归中。在对角线性网络和Transformer上的实验证明了该理论的预测，揭示了最小范数插值中存在的统计不稳定性，其中正则化强度的小变化可能导致泛化能力显著不同，同时保持较低的训练误差。 AI

影响为理解模型泛化提供了理论基础，可能为未来的模型设计和训练策略提供信息。

排序理由该条目是发表在arXiv上的学术论文，详细介绍了一种新的机器学习现象的统计理论。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该条目是发表在arXiv上的学术论文，详细介绍了一种新的机器学习现象的统计理论。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, model release; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: 7 days old; Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv stat.ML TIER_1 English(EN) · Gil Kur, Ileana Rugina, Cl\'ementine Carla Juliette Domin\'e, Marco Mondelli · 2026-10-01 04:00

Grokking: 从最小范数插值视角切入

arXiv:2609.38453v1 Announce Type: cross Abstract: Grokking shows that fitting the training data and learning the underlying signal can occur at very different stages. However, existing theories offer limited quantitative insight into how this delayed generalization depends on ind…

本文如何被排名

报道来源 [1]

Grokking: 从最小范数插值视角切入

相关实体

相关话题