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English(EN) $α$-TCAV: A Unified Framework for Testing with Concept Activation Vectors

新框架增强了深度学习概念可解释性的稳定性

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员推出$\alpha$-TCAV，一个旨在提高深度学习可解释性中概念激活向量（CAVs）的统计稳定性和实际效用的新框架。所提出的方法通过用一个平滑的、参数化的函数替换一个不连续的函数，解决了标准TCAV分数的一个基本缺陷，该缺陷可能导致结果不稳定。这种泛化统一了现有的TCAV变体，并为参数调优提供了原则性的指导，有可能以更低的计算成本实现更可靠的概念影响测量。 AI

影响 提高了可解释性方法的可靠性，可能带来更值得信赖的AI系统。

排序理由 发表了一篇学术论文，详细介绍了一种用于机器学习可解释性的新框架。

在 arXiv stat.ML 阅读 →

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新框架增强了深度学习概念可解释性的稳定性

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报道来源 [2]

  1. arXiv stat.ML TIER_1 English(EN) · Ekkehard Schnoor, Jawher Said, Malik Tiomoko, Wojciech Samek, Alexander Jung ·

    $\alpha$-TCAV：一种用于概念激活向量测试的统一框架

    arXiv:2605.15688v1 Announce Type: new Abstract: Concept Activation Vectors (CAVs) are a fundamental tool for concept-based explainability in deep learning, yet their practical utility is limited by statistical instability. We analyze the stochastic nature of CAVs and the Testing …

  2. arXiv stat.ML TIER_1 English(EN) · Alexander Jung ·

    $α$-TCAV：一种用于概念激活向量测试的统一框架

    Concept Activation Vectors (CAVs) are a fundamental tool for concept-based explainability in deep learning, yet their practical utility is limited by statistical instability. We analyze the stochastic nature of CAVs and the Testing with CAVs (TCAV) method, deriving the distributi…

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