English(EN) SCORE: Spectral Correlation Estimation for Multivariate Gaussians

新的SCORE框架可有效近似高斯目标的协方差矩阵

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-09 04:00

研究人员开发了SCORE，一个用于高维高斯目标中协方差矩阵近似的新型框架，该框架基于神经网络的预测建模。该方法将学习任务分解为边缘分布和结构化相关矩阵，从而能够以线性存储和O(d log d)的成本进行高效计算。SCORE利用了用于训练的闭式高斯核分数，该分数对退化协方差具有鲁棒性并提供有界梯度。该框架在包括时间序列预测、单目深度估计和空间天气预测在内的各种任务中，均已证明了性能的提高和计算成本的降低。 AI

影响该框架有望提高各种AI应用中预测模型的效率和准确性。

排序理由该集群包含一篇详细介绍近似协方差矩阵新方法的论文。[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) · Christopher B\"ulte, Emil Partow, Astha Gupta, Pascal Esser, Gitta Kutyniok · 2026-10-09 04:00

SCORE：多元高斯分布的谱相关估计

arXiv:2610.12096v1 Announce Type: new Abstract: Neural network-based predictive modeling with high-dimensional structured Gaussian targets requires an efficient and numerically stable, yet expressive approximation of the covariance matrix. We propose SCORE: a scalable framework, …

本文如何被排名

报道来源 [1]

SCORE：多元高斯分布的谱相关估计

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