English(EN) How Width and Data Shape Generalization Scaling Laws in Quadratic Neural Networks

二次神经网络：关于泛化缩放定律的新研究

作者 PulseAugur 编辑部 · [2 个来源] · 2026-06-26 16:27

一篇新发表在arXiv上的研究论文探讨了二次神经网络中泛化缩放定律。该研究在有限样本和结构化数据的设定下，分析了可训练参数数量和数据集大小如何影响泛化误差。研究结果揭示了不同的缩放模式和依赖于数据的幂律，为理解这些模式之间的过渡及其对泛化的影响提供了见解。 AI

影响为模型大小和数据量如何影响神经网络性能提供了理论见解。

排序理由该集群包含一篇发表在arXiv上的研究论文，详细介绍了对神经网络泛化进行的理论分析。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

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Newsworthiness bucket: Research; 该集群包含一篇发表在arXiv上的研究论文，详细介绍了对神经网络泛化进行的理论分析。
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arXiv stat.ML TIER_1 English(EN) · Julius Girardin, Emanuele Troiani, Yizhou Xu, Vittorio Erba, Florent Krzakala, Lenka Zdeborov\'a · 2026-06-29 04:00

宽度和数据如何塑造二次神经网络中的泛化缩放定律

arXiv:2606.28242v1 Announce Type: cross Abstract: Understanding how performance scales jointly with model size and data is a central problem in modern machine learning. Existing theoretical works on scaling laws typically describe generalization as a function of data or compute, …
arXiv stat.ML TIER_1 English(EN) · Lenka Zdeborová · 2026-06-26 16:27

宽度和数据如何塑造二次神经网络中的泛化缩放定律

Understanding how performance scales jointly with model size and data is a central problem in modern machine learning. Existing theoretical works on scaling laws typically describe generalization as a function of data or compute, often in fixed-feature or infinite-width regimes a…