Random Matrix Theory for Deep Learning: Beyond Eigenvalues of Linear Models
深度学习中的随机矩阵理论:超越线性模型的特征值
机构 * School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China(电子信息与通信学院,华中科技大学,武汉,中国) ; ICSI, LBNL, and Department of Statistics University of California, Berkeley, USA(ICSI、LBNL 和加州大学伯克利分校统计系,美国)
AI总结 本文扩展了传统随机矩阵理论,以应对非线性深度学习模型在高维比例 regime 中的挑战,提出高维等价概念,统一并泛化确定性等价和线性等价,系统解决高维性、非线性和分析通用特征谱函数的难题。
Comments 30 pages, 6 figures; extended technical report version of the IEEE SPM article, with appendices, minor corrections, and clarified derivations
Journal ref IEEE Signal Processing Magazine (Volume: 43, Issue: 2, March 2026) 93 - 106