English(EN) Cone Extended Rayleigh Quotients for Directed Graph Learning: Minimax Spectral Certificates, Sensitivity, and Adaptive Control

新定向图学习方法揭示用于谱控制

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-28 04:00

研究人员开发了一种新的定向图学习方法，该方法不需要对称性或锥体保持。该方法利用锥体边界来包围区分的锥体级别，并采用光滑的替代函数来处理可微分的单边边界。该方法能够识别最优的图支持干预和自适应谱控制，并通过在定向学习设置和Cora引用网络上的数值实验进行了演示。 AI

影响引入了定向图学习中谱控制的新技术，有可能提高模型的可解释性和干预策略。

排序理由该集群包含一篇详细介绍定向图学习新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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) · Yavdat Sh. Il'yasov, Nur F. Valeev · 2026-08-28 04:00

用于有向图学习的锥形扩展瑞利商：最小-最大谱证书、敏感性和自适应控制

arXiv:2608.27122v1 Announce Type: new Abstract: Directed graph learning naturally leads to trainable nonsymmetric propagation operators with distinct right and left spectral structures. Building on the two-sided cone Rayleigh framework for generalized pencils \[ B_\theta-\lambda …