English(EN) Intrinsic Information-Theoretic Analysis of ReLU Nets

ReLU网络新分析框架规避超参数偏差

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-28 04:00

研究人员开发了一种新颖的ReLU网络信息论分析方法，超越了传统受超参数偏差影响的互信息估计器。该新框架通过将输入空间划分为离散区域和连续表示，内在分析网络。该分析将互信息分解为可解释的几何项，重点关注可以使用即插即用方法估计的“路由信息”。这种方法为理解分段线性网络的内部信息几何提供了更稳健的方式。 AI

影响为分析深度学习模型的内部信息几何引入了一种更稳健的方法。

排序理由学术论文发布在arXiv上，详细介绍了神经网络的新分析框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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arXiv stat.ML TIER_1 English(EN) · Johan Mylius-Kroken, Elisabeth Wetzer, Ali Ramezani-Kebrya, Robert Jenssen, Kristoffer Knutsen Wickstr{\o}m · 2026-10-28 04:00

ReLU网络内在信息论分析

arXiv:2610.36864v1 Announce Type: new Abstract: Mutual information (MI) is fundamental to representation-learning analysis in deep networks, but in high dimensions MI estimators depend on hyperparameters whose bias often dominates the signal. We show that continuous piecewise-lin…

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ReLU网络内在信息论分析

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