English(EN) Localizing predictive concepts encoded in neural representations via hierarchical testing

新方法CUTTIE可定位神经网络中的预测概念

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-02 05:00

研究人员开发了一种名为CUTTIE（Cluster-tree Unit Top-down Testing of Independence with Error control）的新方法，用于识别神经网络中编码的特定概念。这种分层测试方法可以控制统计错误，并将关于预测目标的信息定位在模型层内。该方法已应用于卷积模型，用于预测DNA序列可及性，并成功将编码的概念映射到已知的调控基序。 AI

影响增强了深度学习模型在生物序列分析中的可解释性和可信度。

排序理由该集群包含一篇详细介绍分析神经网络新方法的论文。[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 →

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arXiv cs.LG TIER_1 English(EN) · Viktoriia Huryn, Marco Simnacher, Jim Berend, Wojciech Samek, Christoph Lippert, Sonja Greven, Uwe Ohler · 2026-11-02 05:00

Localizing predictive concepts encoded in neural representations via hierarchical testing

arXiv:2610.41046v1 Announce Type: new Abstract: Understanding which regulatory sequence motifs a genomic deep learning model has learned, with statistical error guarantees, is essential for trusting its use in downstream tasks such as variant effect prediction. Existing approache…

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Localizing predictive concepts encoded in neural representations via hierarchical testing

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