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English(EN) Neighborhood Smoothing for Calibration

新的邻域平滑方法提高了神经网络的校准性能

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员引入了一种名为“邻域平滑用于校准”（Neighborhood Smoothing for Calibration）的新方法，旨在解决现代神经网络中的过度自信问题。该方法利用学习表示中的邻域结构，鼓励相似样本具有相似的预测分布。该技术作为一种基于图的正则化器实现，会惩罚邻近预测分布之间的分歧，并在各种基准测试中显示出预测质量和校准方面的实证改进。 AI

影响 这项研究提供了一种新的训练时校准技术，有望带来更可靠、更值得信赖的神经网络预测。

排序理由 关于神经网络校准新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的邻域平滑方法提高了神经网络的校准性能

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关于神经网络校准新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Idan Horowitz, Avigdor Gal ·

    Neighborhood Smoothing for Calibration

    arXiv:2610.09020v1 Announce Type: new Abstract: Modern neural networks are often miscalibrated, with a tendency to overconfidence. Existing train-time calibration methods largely modify task losses or calibration penalties, leaving neighborhood structure in learned representation…

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