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Deep Echo State Network (DeepESN): A Brief Survey
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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. The recently introduced Deep Echo State Network (DeepESN) model opened the way to an extremely efficient approach for designing deep neural networks for temporal data. At the same time, the study of DeepESNs allowed to shed light on the intrinsic properties of state dynamics developed by hierarchical compositions of recurrent layers, i.e. on the bias of depth in RNNs architectural design. In this paper, we summarize the advancements in the development, analysis and applications of DeepESNs.
Forward citations
Cited by 3 Pith papers
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A first-order autoregressive echo state network matches ARIMA/TBATS on monthly M4 series and achieves the lowest mean MASE on quarterly series among nine methods.
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Echo State Networks for Time Series Forecasting: Hyperparameter Sweep and Benchmarking
Echo State Networks match or beat ARIMA and TBATS on quarterly M4 series and match them on monthly series while using less compute after an extensive hyperparameter sweep.
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