Technische Universität Berlin, Institut für Theoretische Physik, Hardenbergstraße 36, 10623, Berlin, Germany.
Technische Universität Ilmenau, Institut für Physik, Weimarer Straße 25, 98693, Ilmenau, Germany.
Nat Commun. 2022 Jan 11;13(1):227. doi: 10.1038/s41467-021-27715-5.
Among the existing machine learning frameworks, reservoir computing demonstrates fast and low-cost training, and its suitability for implementation in various physical systems. This Comment reports on how aspects of reservoir computing can be applied to classical forecasting methods to accelerate the learning process, and highlights a new approach that makes the hardware implementation of traditional machine learning algorithms practicable in electronic and photonic systems.
在现有的机器学习框架中，储层计算具有快速、低成本的训练特点，并且适用于各种物理系统的实现。本评论报告了如何将储层计算的某些方面应用于经典预测方法，以加速学习过程，并强调了一种新方法，使得传统机器学习算法的硬件实现可以在电子和光子系统中实际应用。
