新框架将相关性传播统一为熵最优传输

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-05 05:00

研究人员开发了一个新的框架来理解神经网络中的相关性传播，将其视为一种熵最优传输。该方法将各种现有的LRP规则统一在一个单一的原则下，并引入了一种称为Sinkhorn相关性传播的新方法。新方法已在VGG-16和ConvNeXt-Tiny等图像分类器上进行了测试，证明了其准确追踪证据流和识别有影响力的隐藏单元的能力。 AI

影响引入了一个新的理论框架，用于理解和潜在地提高神经网络的可解释性。

排序理由该条目是一篇学术论文，详细介绍了一种用于分析神经网络的新理论框架和方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Olivier Gibaru, Eric Nyiri · 2026-11-05 05:00

Relevance Propagation as Entropic Optimal Transport on Filippov Neural Networks: Sinkhorn Propagation and Region Relevance in Image Classifiers

arXiv:2611.05594v1 Announce Type: new Abstract: Relevance propagation, which redistributes a network's prediction backward through the layers, is usually treated as a heuristic. We show that it is the solution of a single variational problem: Layer-wise Relevance Propagation (LRP…

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报道来源 [1]

Relevance Propagation as Entropic Optimal Transport on Filippov Neural Networks: Sinkhorn Propagation and Region Relevance in Image Classifiers

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