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A new explanation of geometric nature of the reservoir computing phenomenon is presented.
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2008
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J. Pathak, B. Hunt, M. Girvan, Z. Lu, and E. Ott, “Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach,” Physical Review Letters , vol. 120, no. 2, p. 24102, 2018. [Online]. Available: https://doi.org/10.1103/PhysRevLett.120.024102
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2018
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2018
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L. Grigoryeva and J.-P. Ortega, “Echo state networks are universal,” Neural Networks , vol. 108, pp. 495–508, 2018
2018
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C. Cuchiero, L. Gonon, L. Grigoryeva, J.-P. Ortega, and J. Teichmann, “Approximation of dynamics by randomized signature,” 2019
2019
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——, “Differentiable reservoir computing,” Journal of Machine Learning Research , vol. 20, no. 179, pp. 1–62, 2019
2019
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L. Gonon, L. Grigoryeva, and J.-P. Ortega, “Risk bounds for reservoir computing,” Preprint , 2019
2019
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L. Gonon, L. Grigoryeva, and J.-P. Ortega, “Approximation error estimates for random neural networks and reservoir systems,” arXiv preprint 2002.05933 , 2020
2020
Closest in time.
L. Gonon and J.-P. Ortega, “Reservoir computing universality with stochastic inputs,” IEEE Transactions on Neural Networks and Learning Systems , vol. 31, no. 1, pp. 100–112, 2020
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——, “Memory and forecasting capacities of nonlinear recurrent networks,” To appear in Physica D , 2020
2020
Closest in time.