English(EN) Exact Dynamics and Finite-Sample Trajectory Recovery of Linear Recursive Feature Machines

线性递归特征机动力学分析

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-08 04:00

研究人员分析了线性递归特征机（RFMs）的动力学和有限样本轨迹恢复。在具有各向同性亚高斯输入数据和低秩教师矩阵的噪声多输出回归设置中，他们扩展了线性RFMs与迭代重加权最小二乘法之间的联系。研究表明，学习到的特征矩阵非常接近其理想对应物，在n个样本下，误差以高概率衰减到O(sqrt(d/n))的速率。在真实文本和单细胞基因表达数据上的实验验证了该线性模型所学的特征。 AI

影响为理解和改进神经网络训练提供了与特征学习动力学相关的理论见解。

排序理由学术论文，详细介绍了机器学习模型的理论分析和实验验证。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Andrew Cheng, Bobak T. Kiani, Yue M. Lu, Adityanarayanan Radhakrishnan · 2026-10-08 04:00

线性递归特征机的精确动力学与有限样本轨迹恢复

arXiv:2610.09196v1 Announce Type: new Abstract: Recursive feature machines (RFMs) learn representations of data by alternating between fitting a predictor to a dataset and updating features of that predictor using the average gradient outer product (AGOP). Connections between AGO…

本文如何被排名

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线性递归特征机的精确动力学与有限样本轨迹恢复

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