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English(EN) Difference-of-Convex Regularization for Graph Learning by Differentiable Programming

新的DCR框架通过近似拉普拉斯伪逆来增强图学习

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一个名为差分凸正则化器（DCR）的新框架用于图学习。该方法通过近似拉普拉斯图的谱作用而不直接求逆，解决了计算其伪逆的挑战，因为拉普拉斯图可能密集且病态。DCR利用正则化最大似然估计和可微分的对偶引导学习方案来有效地重建原始解。已建立了稳定性和唯一不动点的理论保证，数值实验表明在各种图拓扑上，其性能优于现有的凸求解器和图过滤基线。 AI

影响 这个新框架可以提高基于图的机器学习任务的效率和性能。

排序理由 该集群包含一篇详细介绍图学习新方法的学术论文。[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 →

新的DCR框架通过近似拉普拉斯伪逆来增强图学习

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该集群包含一篇详细介绍图学习新方法的学术论文。[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) · Liping Tao, Chee Wei Tan ·

    基于可微编程的图学习的凸差分正则化

    arXiv:2608.12757v1 Announce Type: cross Abstract: Laplacian-regularized minimization is fundamental in signal processing and machine learning, but is limited by the dense and ill-conditioned nature of the graph Laplacian pseudoinverse. While the Laplacian itself is sparse, its ps…

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