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The study of deep recurrent neural networks (RNNs) and, in particular, of deep Reservoir Computing (RC) is gaining an increasing research attention in the neural networks community.
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C. Gallicchio, A. Micheli, Deep Tree Echo State Networks, in: Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN), IEEE, 2018, pp. 499–506
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doi:10.1007/978-3-319-95098-3\_11
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C. Gallicchio, A. Micheli, Deep Reservoir Neural Networks for Trees, Information Sciences 480 (2019) 174–193
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C. Gallicchio, S. Scardapane, Deep randomized neural networks, in: Recent Trends in Learning From Data, Springer, 2020, pp. 43–68
2020
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C. Gallicchio, A. Micheli, Fast and deep graph neural networks., in: Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20), 2020, pp. 3898–3905
2020
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M. Alizamir, S. Kim, O. Kisi, M. Zounemat-Kermani, Deep echo state network: a novel machine learning approach to model dew point temperature using meteorological variables, Hydrological Sciences Journal 65 (7) (2020) 1173–1190
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T. Kim, B. R. King, Time series prediction using deep echo state networks, Neural Computing and Applications (2020) 1–19
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Q. Li, Z. Wu, R. Ling, L. Feng, K. Liu, Multi-reservoir echo state computing for solar irradiance prediction: A fast yet efficient deep learning approach, Applied Soft Computing 95 (2020) 106481
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H. Hu, L. Wang, S.-X. Lv, Forecasting energy consumption and wind power generation using deep echo state network, Renewable Energy 154 (2020) 598–613
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Z. Song, K. Wu, J. Shao, Destination prediction using deep echo state network, Neurocomputing 406 (2020) 343–353
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