Emergence of a stochastic resonance in machine learning
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 9 of its 86 references that are in the map. First posted on arXiv 15 Nov 2022.
Builds on
9 of 86 references.
References · 9
- 2017SmoothGrad: removing noise by adding noisecited by 1,109
- 2020On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practicecited by 182
- 2021Next Generation Reservoir Computingcited by 160
- 2019Backpropagation Algorithms and Reservoir Computing in Recurrent Neural Networks for the Forecasting of Complex Spatiotemporal Dynamicscited by 134
- 2020Machine learning prediction of critical transition and system collapsecited by 54
- 2019Forecasting Chaotic Systems with Very Low Connectivity Reservoir Computerscited by 43
- 2019Model-free prediction of spatiotemporal dynamical systems with recurrent neural networks: Role of network spectral radiuscited by 34
- 2021Anticipating synchronization with machine learningcited by 22
- 2019Dynamical learning of dynamicscited by 16
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Citing papers per year: 2023: 1; 2024: 0; 2025: 0; 2026: 1.
2 papers.