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English(EN) Why Heavy-Tailed Weight Spectra Predict Model Quality

新的统计框架解释深度神经网络质量预测

研究人员开发了一个统计框架,解释了分析深度神经网络的权重矩阵如何预测其质量。这项工作仅使用最后一层权重推导了 DNN 的边际似然,并在大规模卷积 DNN 和 LLM 中展示了其预测能力。该研究进一步探讨了重尾谱的作用,特别是高温 Marchenko-Pastur 分布,展示了最优的重度参数如何经验性地预测大规模 DNN 中的过拟合。 AI

影响 为理解和预测深度神经网络的性能提供了理论基础,可能导致更高效的模型开发。

排序理由 该条目是一篇发表在 arXiv 上的学术论文,详细介绍了一种预测模型质量的新统计框架。[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 →

新的统计框架解释深度神经网络质量预测

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该条目是一篇发表在 arXiv 上的学术论文,详细介绍了一种预测模型质量的新统计框架。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Joseph Wilson, Chris van der Heide, Liam Hodgkinson, Zhichao Wang, Fred Roosta, Michael W. Mahoney ·

    为什么重尾权重谱预测模型质量

    arXiv:2610.13123v1 Announce Type: new Abstract: A solid understanding of the predictive success of deep neural networks (DNNs) remains elusive. For example, while many data-dependent metrics exist that seek to predict model quality of DNNs, these metrics may be expensive to compu…