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对无限宽度双层ReLU神经网络的同位体放松训练算法

Yahong Yang¹, Qipin Chen², Wenrui Hao¹

¹Department of Mathematics, The Pennsylvania State University, University Park, State College, PA 16802, USA.

Journal of scientific computing

|May 14, 2025

概括

我们介绍了同位素放松训练算法 (HRTA),以加速深度学习. 这种新的方法提高了培训的融合率,特别是在更广泛的神经网络中.

科学领域:

机器学习机器学习
深度神经网络深度神经网络
计算数学计算数学计算数学

背景情况:

传统的深度学习培训方法可能是缓慢和计算密集的.
激活功能在神经网络的性能中起着至关重要的作用.
通过像神经触角内核 (NTK) 这样的工具了解训练动态对于优化至关重要.

研究的目的:

介绍一种新的训练算法,即同位素放松训练算法 (HRTA),用于加速深度神经网络训练.
引入同位素激活功能和同位素参数放松技术,以提高训练效率.
在神经触角内核 (NTK) 框架内分析HRTA的有效性.

主要方法:

开发了同位素放松训练算法 (HRTA).
构建了一个连接线性和激活函数的同类型激活函数.
实施了同位素参数放松技术,用于精细训练.
使用神经触角内核 (NTK) 分析了HRTA的收特性.

主要成果:

与传统方法相比,HRTA显著加快了培训过程.
该算法显示了更好的融合率,特别是在NTK环境中.
实验结果验证了理论发现,特别是对于更广泛的神经网络.
拟议的方法对各种激活功能和深度网络架构有希望.

结论:

同位素放松训练算法 (HRTA) 在加速深度学习训练方面取得了重大进展.
HRTA提供了增强的融合率,并证明了广泛的适用性.
这种新的方法有可能扩展到其他激活功能和深度神经网络架构.

关键词:

65K9999 这是一个很好的选择.68T07 这是一个很好的例子.68W1010 的使用情况.同类型的同类型 (homotopy) 是同类型的.神经网络的神经网络的神经网络优化优化优化优化放松放松放松

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