English(EN) Generalization in Neural Networks Through the Lens of Magnitude Potential

新的“幅度势能”指标有助于神经网络泛化分析

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-03 04:00

研究人员引入了一个名为“幅度势能”的新概念，该概念源自度量幅度理论，以更好地理解神经网络的泛化能力。该量在logit层计算，反映了一个点在数据集中的表示程度。实验表明，该幅度势能的比率与记忆分数相关，并且可以检测决策边界的结构变化，作为模块化算术任务中“grokking”的几何指标。即使在抑制神经网络坍塌的情况下，幅度势能也保留了关于几何结构的信息。 AI

影响引入了一个新的指标来分析神经网络的泛化能力和训练动态。

排序理由该集群包含一篇研究论文，详细介绍了用于分析神经网络泛化能力的新理论概念。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Sahel Torkamani, Henry Gouk, Rik Sarkar · 2026-10-03 04:00

通过幅度势能视角看神经网络的泛化能力

arXiv:2610.01633v1 Announce Type: new Abstract: Explaining generalization and training dynamics in neural networks remains a challenge, and various approaches have been developed to study different aspects of these phenomena. In this paper, we introduce the idea of {\em magnitude…

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通过幅度势能视角看神经网络的泛化能力

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