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English(EN) Activation-Space Order-Swap Geometry: A Site-Asymmetry Audit

新审计方法阐明神经网络交互分析

研究人员开发了一种名为“位点不对称审计”的新方法,以更准确地解释神经网络中的激活统计数据。该审计有助于区分真实的交互效应与由网络中干预位置引起的效应。研究发现,在各种语言模型中,单一干预解释了绝大多数激活依赖性统计数据,这表明复杂交互的普遍性不如之前认为的。所提出的方法为分析神经网络中的表示几何提供了可重用的标准。 AI

影响 为理解神经网络行为提供了更严谨的框架,有望带来更可靠的模型可解释性。

排序理由 该集群包含一篇详细介绍神经网络分析新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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新审计方法阐明神经网络交互分析

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该集群包含一篇详细介绍神经网络分析新方法的学术论文。[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) · Anqi Peter Li ·

    激活空间顺序交换几何:一个位点不对称审计

    arXiv:2608.25315v1 Announce Type: new Abstract: Order-dependent activation statistics are often interpreted as evidence of interaction, but that interpretation can be confounded by where interventions enter the network. We introduce a no-fit site-asymmetry audit. For a twice-diff…