English(EN) Quantifying the Memorization-to-Generalization Transition: Scaling Laws and Phase Structure in Grokking

神经网络中的 Grokking 转换已量化，数据复杂度是关键

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-11 04:00

研究人员量化了神经网络中从记忆到泛化的转换现象，即 grokking。他们发现 grokking 发生时间存在幂律标度关系，表明数据复杂度比模型容量更重要。研究还确定了与权重衰减相关的清晰相边界，并观察到转换过程中权重范数的单调压缩，这表明隐式正则化倾向于更简单的解决方案。 AI

影响为预测和控制过参数化网络中的泛化转换提供了量化框架。

排序理由学术论文，详细介绍了对机器学习现象的新量化分析。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Anish Kataria · 2026-09-11 04:00

量化记忆到泛化转变：Grokking 中的尺度定律和相结构

arXiv:2609.10657v1 Announce Type: cross Abstract: Neural networks trained past memorization frequently undergo a delayed transition to generalization, a phenomenon known as grokking. Despite theoretical progress on \emph{why} this transition occurs, the quantitative structure of …