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English(EN) Measuring Deep Learning Performance - An Empirical Study of Performance Distributions Across Architectures and Tasks

深度学习性能分布揭示了可信赖AI的尾部风险

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新发表在arXiv上的研究探讨了深度学习算法的非确定性本质，揭示了模型性能最好被理解为一种分布而非单一数值。该研究在各种架构和任务上进行了18,600次运行，发现性能分布通常是非高斯分布的，尤其是在时间序列预测方面。研究得出结论，仅凭平均性能不足以进行模型选择，强调了评估分布的离散度、对称性和尾部风险对于可信赖AI应用的重要性。 AI

影响 强调了在关键AI应用中进行可靠模型选择时，除了平均性能之外，还需要进行分布分析。

排序理由 分析深度学习性能分布的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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深度学习性能分布揭示了可信赖AI的尾部风险

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分析深度学习性能分布的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Kevin L Coakley, Odd Erik Gundersen ·

    衡量深度学习性能——跨架构和任务的性能分布实证研究

    arXiv:2610.38037v1 Announce Type: cross Abstract: Context: Non-determinism in deep learning algorithm design and implementation leads to performance variation, meaning model performance is not a single value, but rather a distribution. These model performance distributions are un…

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