2018 DesignofDeepEchoStateNetworks

• (Gallicchio et al., 2018) ⇒ Claudio Gallicchio, Alessio Micheli, and Luca Pedrelli. (2018). “Design of Deep Echo State Networks.” In: Neural Networks - Elsevier Journal, 108. doi:10.1016/j.neunet.2018.08.002

Subject Headings: Deep Echo State Network; Echo State Network

Cited By

• http://scholar.google.com/scholar?q=%222018%22+Design+of+Deep+Echo+State+Networks

Quotes

Author Keywords

• Reservoir computing; Echo state networks; Deep echo state networks; Deep recurrent neural networks; Architectural design of recurrent neural networks

Abstract

In this paper, we provide a novel approach to the architectural design of deep Recurrent Neural Networks using signal frequency analysis. In particular, focusing on the Reservoir Computing framework and inspired by the principles related to the inherent effect of layering, we address a fundamental open issue in deep learning, namely the question of how to establish the number of layers in recurrent architectures in the form of deep echo state networks (DeepESNs). The proposed method is first analyzed and refined on a controlled scenario and then it is experimentally assessed on challenging real-world tasks. The achieved results also show the ability of properly designed DeepESNs to outperform RC approaches on a speech recognition task, and to compete with the state-of-the-art in time-series prediction on polyphonic music tasks.

References

;

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2018 DesignofDeepEchoStateNetworks	Claudio Gallicchio Alessio Micheli Luca Pedrelli			Design of Deep Echo State Networks				10.1016/j.neunet.2018.08.002		2018