学习的物理效应
Menachem Stern1, Andrea J Liu1,2, Vijay Balasubramanian1,3,4
1Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA.
Physical review. E
|March 16, 2024
概括
在物理系统中学习,就像神经网络一样,在它们的结构上留下了印记. 这些印记在系统中可以观察到.
科学领域:
- 复杂的系统复杂的系统.
- 物理网络是指物理网络.
- 机器学习 机器学习
背景情况:
- 相互作用的多体物理系统表现出学习能力.
- 学习是通过进化选择或从经验中积极学习而发生的.
研究的目的:
- 研究在线性物理网络中学习的结构性印记.
- 分析弱输入信号对系统架构的影响.
主要方法:
- 在物理系统中分析赫森矩阵.
- 对弱输入信号的系统响应的描述.
主要成果:
- 学习减少了输入响应的有效物理维度.
- 学习增加了对物理自由度的敏感性.
- 赫西安的低自值自向量与已学习的任务保持一致.
结论:
- 观察到的效应是物理系统在弱输入条件下的学习的特征.
- 这些发现表明了识别训练有素物理网络的方法.
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