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用反向传播训练的深层物理神经网络
Logan G Wright1,2, Tatsuhiro Onodera3,4, Martin M Stein5
1School of Applied and Engineering Physics, Cornell University, Ithaca, NY, USA. lgw32@cornell.edu.
Nature
|January 27, 2022
概括
我们开发了物理意识培训, 一种新的算法, 使物理神经网络能够反向传播. 这种方法训练各种物理系统进行机器学习任务,提供比传统电子更快,更节能的计算.
科学领域:
- 人工智能
- 材料科学
- 物理
背景情况:
- 深度学习模型在科学中是必不可少的,但由于高能耗,它们面临着可扩展性的限制.
- 目前的深度学习加速器主要专注于节能推断,而不是训练非传统硬件.
- 一个主要的障碍是无法在现场应用反向传播来训练新的物理硬件.
研究的目的:
- 引入一个混合算法,物理意识的训练,以实现训练物理系统的反向传播.
- 使用可控制的物理基质演示深层物理神经网络的训练.
- 在光学,机械和电子方面展示该方法的普遍性.
主要方法:
- 开发了一种名为"物理意识训练"的混合现场算法.
- 应用反向传播来训练由可控制的物理系统组成的深层物理神经网络.
- 使用多种物理基质,包括光学,机械和电子.
主要成果:
- 通过物理意识训练成功训练了多种物理神经网络.
- 使用这些物理神经网络进行实验性音频和图像分类任务.
- 展示了反向传播的可扩展性与现场算法的降噪的结合.
结论:
- 物理意识的训练可以使用反向传播来训练物理神经网络.
- 物理神经网络为更快,更节能的机器学习提供了潜力.
- 这种方法可以为各种应用程序提供自动设计的功能.
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