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English(EN) ReliableNet: A Chance-Constrained Approach to Trustworthy Classification in Deep Learning

ReliableNet 解决深度学习中自信错误预测问题

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出了一种用于深度学习中可信分类的新方法 ReliableNet。该方法直接约束预测既自信又错误的概率，这是一种可能绕过人工审查的关键故障模式。ReliableNet 将此表述为机会约束经验风险最小化问题，确保联合自信错误（JCW）概率低于用户定义的风险预算。在各种数据集和不同类型的分布变化下，ReliableNet 证明了其对 JCW 预算的认证遵守以及具有竞争力的准确性，在选择性排序方面优于现有方法。 AI

影响 通过直接解决自信错误预测问题来增强模型可靠性，这对于安全关键型应用至关重要。

排序理由 该条目是一篇 arXiv 预印本，详细介绍了深度学习的新研究方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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ReliableNet 解决深度学习中自信错误预测问题

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该条目是一篇 arXiv 预印本，详细介绍了深度学习的新研究方法。[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) · Ange-Cl\'ement Akazan, Ineza Remy Mugenga, Abebe Geletu, Jean Medard Ngnotchouye, Issa Karambal ·

    ReliableNet：深度学习中可信分类的约束机会方法

    arXiv:2608.09768v1 Announce Type: new Abstract: A prediction that is both confident and wrong is a critical reliability failure because it can bypass abstention and human review precisely when the model is mistaken. Empirical risk minimization (ERM) controls average loss but not …

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