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Deep Echo State Networks (DeepESNs) recently extended the applicability of Reservoir Computing (RC) methods towards the field of deep learning.
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Triefenbach, F., Jalalvand, A., Schrauwen, B., Martens, J.P.: Phoneme recognition with large hierarchical reservoirs. In: Advances in neural information processing systems. pp. 2307–2315 (2010)
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Bacciu, D., Bongiorno, A.: Concentric esn: Assessing the effect of modularity in cycle reservoirs. In: 2018 International Joint Conference on Neural Networks (IJCNN). pp. 1–8. IEEE (2018)
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Gallicchio, C., Micheli, A.: Why Layering in RNN? A DeepESN Survey. In: Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN). pp. 1–8. IEEE (2018)
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Gallicchio, C., Micheli, A., Pedrelli, L.: Design of deep echo state networks. Neural Networks 108
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Farkaš, I., Bosák, R., Gergel’, P.: Computational analysis of memory capacity in echo state networks. Neural Networks 83
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Gallicchio, C., Micheli, A., Pedrelli, L.: Deep reservoir computing: A critical experimental analysis. Neurocomputing 268
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Gallicchio, C., Micheli, A.: Echo state property of deep reservoir computing networks. Cognitive Computation 9
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Grigoryeva, L., Ortega, J.P.: Echo state networks are universal. Neural Networks 108
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Gallicchio, C., Micheli, A.: Deep reservoir neural networks for trees. Information Sciences 480
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Kawai, Y., Park, J., Asada, M.: A small-world topology enhances the echo state property and signal propagation in reservoir computing. Neural Networks (2019)
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