频率传播:非线性物理网络中的多机制学习
Vidyesh Rao Anisetti1, Ananth Kandala2, Benjamin Scellier3
1Physics Department, Syracuse University, Syracuse, NY 13244 U.S.A. vvaniset@syr.edu.
Neural computation
|March 8, 2024
概括
我们开发了频率传播,这是物理网络的新学习算法. 这种方法使用不同的频率来激活和错误信号,为可适应的网络参数实现高效的梯度下降.
科学领域:
- 物理 物理学 物理
- 机器学习 机器学习
- 网络科学 网络科学
背景情况:
- 传统的机器学习算法通常需要集中处理和全球信息.
- 物理网络,如电力或流量网络,为分布式计算和学习提供了独特的机会.
- 开发物理系统的高效,本地学习规则对于其实际应用至关重要.
研究的目的:
- 为非线性物理网络引入频率传播,一种新的学习算法.
- 为了证明频率传播能够在损失函数上实现梯度下降.
- 建立频率传播作为一种适用于各种物理网络的多机制学习策略.
主要方法:
- 将一个频率的激活电流和另一个频率的误差电流应用于具有可变电阻的电阻电路.
- 分析了电压响应作为频域中激活和错误信号的叠加.
- 在本地更新电路导电量,与从激活和错误信号中获得的系数的乘积成比例.
主要成果:
- 频率传播成功地使用本地信息更新网络参数.
- 学习规则被证明可以在定义的损失函数上执行梯度下降.
- 证明了该算法的适用于非线性物理网络,包括电阻,弹性和流量网络.
结论:
- 频率传播是物理网络的有效和本地学习算法.
- 该算法是多机制学习策略的典范,利用不同的物理量作为信号.
- 这种方法为理解各种物理网络学习机制提供了一个统一的框架,包括在流网络中进行的先前工作.
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