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神经网络的高效设计用于声谱的分类
Vlad S Paul1, Philip A Nelson1
1Institute of Sound and Vibration Research, University of Southampton, Southampton, SO17 1BJ, United Kingdomvsp1g18@soton.ac.uk, p.a.nelson@soton.ac.uk.
JASA express letters
|September 15, 2023
概括
这项研究通过将单数值分解 (SVD) 应用于隐藏层来减少多层感知子 (MLP) 网络的大小. 这种方法大大节省了训练时间,与之前的修剪技术相比,精度损失最小.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 神经网络的神经网络的神经网络
背景情况:
- 之前的工作是将单值分解 (SVD) 应用于多层感知子 (MLP) 重量矩阵进行参数修剪.
- 现有的SVD修剪方法专注于在MLP网络中删除单个重量参数.
研究的目的:
- 提出一种新的方法来减少使用SVD的MLP隐藏层的大小.
- 为了评估这种隐藏层减少技术的效率和准确性.
主要方法:
- 这项研究适应了以前应用于MLP重量矩阵的SVD算法.
- 应用了适应的SVD方法来减少MLP网络隐藏层中神经元的数量.
主要成果:
- 通过SVD减少隐藏层神经元显著减少了模型训练时间.
- 拟议的方法实现了与原来的MLP模型可比的准确性,损失最小或没有损失.
- 隐藏层减少方法比以前基于SVD的重量修剪方法更节省时间.
结论:
- 应用SVD来减少MLP隐藏层的大小是一种有效的模型压缩策略.
- 这种技术在训练效率上比以前的SVD修剪方法提供了实质性的改进.
- 这种方法保持了高模型准确性,同时减少了计算资源.
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