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English(EN) Adaptive multi-resolution Gaussian processes: Scalable exact inference with naturally data-sparse covariance matrices

新的高斯过程框架提供可扩展的精确推理

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一个自适应多分辨率高斯过程框架，旨在提高概率机器学习模型的可扩展性和保真度。这种新方法使用直接锚定在样本上的自适应多分辨率基函数来构建自然数据稀疏的协方差矩阵。该框架能够以 O(n log^2 n) 的训练成本和 O(log^d n) 的预测成本进行精确推理，为高保真高斯过程回归提供了一种原则性的方法。 AI

影响 这项研究可以为机器学习应用中的大型数据集实现更有效、更准确的建模。

排序理由 该集群包含一篇详细介绍概率机器学习新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的高斯过程框架提供可扩展的精确推理

本文如何被排名

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该集群包含一篇详细介绍概率机器学习新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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完整方法见我们的编辑标准。

报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Yanchuang Cao, Jun Liu, Tengchao Yu, Heng Yong ·

    自适应多分辨率高斯过程：具有天然数据稀疏协方差矩阵的可扩展精确推断

    arXiv:2609.30348v1 Announce Type: new Abstract: Gaussian processes constitute a cornerstone of probabilistic machine learning, yet scaling them to large datasets typically forces a trade-off between computational efficiency and model fidelity. This work bridges this gap by presen…

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