论文标题
机器学习中随机共振的出现
Emergence of a stochastic resonance in machine learning
论文作者
论文摘要
噪声可以有益于混乱系统的机器学习预测吗？利用储层计算机作为范式，我们发现向训练数据注射噪声可以引起随机共鸣，并具有对状态变量的短期预测以及对系统吸引子的长期预测的重大益处。诱导随机共振的关键是将噪声的振幅包括在超参数集中以进行优化。通过这样做，可以大大提高预测准确性，稳定性和地平线。使用两个原型高维混沌系统证明了随机共振现象。
Can noise be beneficial to machine-learning prediction of chaotic systems? Utilizing reservoir computers as a paradigm, we find that injecting noise to the training data can induce a stochastic resonance with significant benefits to both short-term prediction of the state variables and long-term prediction of the attractor of the system. A key to inducing the stochastic resonance is to include the amplitude of the noise in the set of hyperparameters for optimization. By so doing, the prediction accuracy, stability and horizon can be dramatically improved. The stochastic resonance phenomenon is demonstrated using two prototypical high-dimensional chaotic systems.
