English(EN) Large-Scale Bayesian Tensor Reconstruction via Approximate Message Passing

新的CP-GAMP算法加速了贝叶斯张量重建

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-01 04:00

研究人员开发了一种名为CP广义近似消息传递（CP-GAMP）的新算法，用于贝叶斯CANDECOMP/PARAFAC（CP）分解。该方法通过采用高斯消息近似来绕过高维矩阵求逆，与传统的变分贝叶斯方法相比，显著减少了计算时间。该算法还结合了具有期望最大化更新的伯努利-高斯先验，用于估计CP秩和噪声方差，并通过合成和图像修复实验验证了其性能。 AI

影响这项新算法可以提高机器学习中张量重建任务的效率。

排序理由该集群包含一篇详细介绍张量重建新算法的研究论文。[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) · Bingyang Cheng, Zhongtao Chen, Yichen Jin, Hao Zhang, Chen Zhang, Edmund Y. Lam, Yik-Chung Wu · 2026-09-01 04:00

大规模贝叶斯张量重构与近似消息传递

arXiv:2505.16305v3 Announce Type: replace Abstract: While CANDECOMP/PARAFAC (CP) decomposition (CPD) is fundamental for tensor reconstruction, Bayesian CPD often scales poorly because variational updates require repeated matrix inversions. We develop CP generalized approximate me…

本文如何被排名

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大规模贝叶斯张量重构与近似消息传递

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