利用波散射的解锁反向传播
Unlocked Backpropagation using Wave Scattering
AI总结：
本文提出了一种基于波散射的解锁反向传播方法，通过引入优化时间维度和反向传播波变量，推导出适用于神经网络训练的全新算法。
摘要：
在机器学习中的反向传播算法和最优控制理论中的最大原理都被表述为一个两点边界问题，导致出现一种
英文摘要：
Both the backpropagation algorithm in machine learning and the maximum principle in optimal control theory are posed as a two-point boundary problem, resulting in a "forward-backward" lock. We derive a reformulation of the maximum principle in optimal control theory as a hyperbolic initial value problem by introducing an additional "optimization time" dimension. We introduce counter-propagating wave variables with finite propagation speed and recast the optimization problem in terms of scattering relationships between them. This relaxation of the original problem can be interpreted as a physical system that equilibrates and changes its physical properties in order to minimize reflections. We discretize this continuum theory to derive a family of fully unlocked algorithms suitable for training neural networks. Different parameter dynamics, including gradient descent, can be derived by demanding dissipation and minimization of reflections at parameter ports. These results also imply that any physical substrate that supports the scattering and dissipation of waves can be interpreted as solving an optimization problem.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
