Gradient based hyperparameter optimization in Echo State Networks

Thiede, L. A.; Parlitz, Ulrich

doi:10.1016/j.neunet.2019.02.001

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Gradient based hyperparameter optimization in Echo State Networks

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Parlitz, Ulrich
Research Group Biomedical Physics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

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Citation

Thiede, L. A., & Parlitz, U. (2019). Gradient based hyperparameter optimization in Echo State Networks. Neural Networks, 115, 23-29. doi:10.1016/j.neunet.2019.02.001.

Cite as: https://hdl.handle.net/21.11116/0000-0003-99C3-4

Abstract

Like most machine learning algorithms, Echo State Networks possess several hyperparameters that have to be carefully tuned for achieving best performance. For minimizing the error on a specific task, we present a gradient based optimization algorithm, for the input scaling, the spectral radius, the leaking rate, and the regularization parameter.