Deep Reservoir Computing

Author:

Gallicchio Claudio,Micheli Alessio

Publisher

Springer Singapore

Reference65 articles.

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3. F.M. Bianchi, S. Scardapane, S. Lokse, R. Jenssen, Bidirectional deep-readout echo state networks, in Proceedings of the 26th European Symposium on Artificial Neural Networks (ESANN) (2018), pp. 425–430

4. P.S. Churchland, T.J. Sejnowski, The Computational Brain (The MIT Press, 1992)

5. V. Colla, I. Matino, S. Dettori, S. Cateni, R. Matino, Reservoir computing approaches applied to energy management in industry, in International Conference on Engineering Applications of Neural Networks (Springer, 2019), pp. 66–79

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1. Multi-scale dynamics by adjusting the leaking rate to enhance the performance of deep echo state networks;Frontiers in Artificial Intelligence;2024-07-16

2. Physical reservoir computing with emerging electronics;Nature Electronics;2024-03-12

3. Material and Physical Reservoir Computing for Beyond CMOS Electronics: Quo Vadis?;Proceedings of the 18th ACM International Symposium on Nanoscale Architectures;2023-12-18

4. Mechanism for Enhancement of Functionality in Deep Echo State Network by Optimizing Leaking Rate;2023 International Conference on Emerging Techniques in Computational Intelligence (ICETCI);2023-09-21

5. Echo State Network Optimization: A Systematic Literature Review;Neural Processing Letters;2023-06-29

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