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English(EN) The Rules-and-Facts Model for Simultaneous Generalization and Memorization in Neural Networks

新研究探讨神经网络泛化理论极限 · 4篇论文

作者 PulseAugur 编辑部 · [4 个来源] ·

四篇新研究论文深入探讨了神经网络泛化的理论基础。其中一篇论文建立了可证明的组合泛化必要且充分的条件，侧重于结构对齐和无歧义的最小化表示，并在Lean 4中进行了验证。另一篇论文分析了带权重衰减的梯度下降下的泛化动力学，分解了总体误差并界定了预测变化。第三篇论文推导了泛化差距的微分方程，适用于深度网络和光滑损失函数，并在数值实验中显示了其准确性。最后一篇论文引入了一个“规则与事实”模型，用于表征神经网络如何同时学习底层规则和记忆特定例外，并探讨了过参数化和正则化的作用。 AI

影响 这些理论上的进步可能带来更强大、更可预测的神经网络架构和训练方法。

排序理由 该集群包含多篇关于机器学习泛化理论方面的学术论文。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 4 个来源。 我们如何撰写摘要 →

新研究探讨神经网络泛化理论极限 · 4篇论文

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该集群包含多篇关于机器学习泛化理论方面的学术论文。
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4 independent sources
Strong cross-source corroboration — multiple independent publishers covered this within the clustering window.
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paper, model release
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High
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报道来源 [4]

  1. arXiv cs.AI TIER_1 English(EN) · Yuanpeng Li ·

    神经网络可证明组合泛化理论分析：充要条件

    arXiv:2505.02627v2 Announce Type: replace-cross Abstract: Compositional generalization$\unicode{x2013}$the ability to systematically process novel combinations of known components$\unicode{x2013}$is a hallmark of human intelligence; however, its theoretical foundation in neural n…

  2. arXiv cs.LG TIER_1 English(EN) · Yuqing Wang, Ioannis G. Kevrekidis, Mikhail Belkin ·

    梯度下降与权重衰减下神经网络泛化动力学的理论分析

    arXiv:2609.07755v1 Announce Type: new Abstract: Understanding generalization remains a central challenge in machine learning because it requires jointly considering data, architecture, and training dynamics. In this paper, we develop a theoretical framework that characterizes how…

  3. arXiv cs.LG TIER_1 English(EN) · Rubing Yang, Pratik Chaudhari ·

    深度学习中泛化的动态

    arXiv:2504.16450v4 Announce Type: replace Abstract: We derive a differential equation that governs the evolution of the generalization gap when a model is trained by gradient descent-based methods. This differential equation is driven by two key quantities, a contraction factor t…

  4. arXiv cs.LG TIER_1 English(EN) · Gabriele Farn\'e, Fabrizio Boncoraglio, Lenka Zdeborov\'a ·

    神经网络的规则-事实模型用于同步泛化和记忆

    arXiv:2603.25579v2 Announce Type: replace-cross Abstract: A key capability of modern neural networks is their capacity to simultaneously learn underlying rules and memorize specific facts or exceptions. Yet, theoretical understanding of this dual capability remains limited. We in…

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