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Attention Based Echo State Network
Proceedings of the 2019 11th International Conference on Machine Learning and Computing, 2019Recurrent neural networks (RNNs) are widely studied in recent years, since RNNs are capable of modeling the significant nonlinear dynamical systems. Echo state network (ESN) is a novel type of RNN with an interconnected reservoir to model temporal dynamics of complex sequential information. In this paper, a novel ESN structure is developed and employed
Chongdang Liu +3 more
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2006
We are interested in the optimization of the recurrent connection structure of Echo State Networks (ESNs), because their topology can strongly influence performance. We study ESN predictive capacity by numerical simulations on Mackey-Glass time series, and find that a particular small subset of ESNs is much better than ordinary ESNs provided that the ...
Márton Albert Hajnal, András Lőrincz
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We are interested in the optimization of the recurrent connection structure of Echo State Networks (ESNs), because their topology can strongly influence performance. We study ESN predictive capacity by numerical simulations on Mackey-Glass time series, and find that a particular small subset of ESNs is much better than ordinary ESNs provided that the ...
Márton Albert Hajnal, András Lőrincz
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Hierarchical Dynamics in Deep Echo State Networks
2022Reservoir computing (RC) is a popular approach to the efficient design of recurrent neural networks (RNNs), where the dynamical part of the model is initialized and left untrained. Deep echo state networks (ESNs) combined the deep learning approach with RC, by structuring the reservoir in multiple layers, thus offering the striking advantage of ...
Tortorella D., Gallicchio C., Micheli A.
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2018 International Joint Conference on Neural Networks (IJCNN), 2018
This work proposes a first study, through empirical assessment, of a deep recursive Neural Network (RecNN) architecture for tree structured data exploiting the efficient design of the Echo State Network (ESN) framework. Three benchmark tasks for trees allow us to assess the potentiality of the novel Deep Tree ESN (DeepTESN) model with respect to the ...
Gallicchio, Claudio, Micheli, Alessio
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This work proposes a first study, through empirical assessment, of a deep recursive Neural Network (RecNN) architecture for tree structured data exploiting the efficient design of the Echo State Network (ESN) framework. Three benchmark tasks for trees allow us to assess the potentiality of the novel Deep Tree ESN (DeepTESN) model with respect to the ...
Gallicchio, Claudio, Micheli, Alessio
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Learning grammatical structure with Echo State Networks
Neural Networks, 2007Echo State Networks (ESNs) have been shown to be effective for a number of tasks, including motor control, dynamic time series prediction, and memorizing musical sequences. However, their performance on natural language tasks has been largely unexplored until now.
Tong, Matthew H. +3 more
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FPGA-Based Echo-State Networks
2019The hardware implementation of Echo State Networks (ESN) can be applied to situations where a quick response is needed in relation to how a certain signal will evolve. This is due to the possibility of connecting the ESN’s neurons in parallel, which accelerates the calculation process considerably. In this article, we present a proposal for the compact
Erik S. Skibinsky-Gitlin +8 more
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Decoupled echo state networks with lateral inhibition
Neural Networks, 2007Building on some prior work, in this paper we describe a novel structure termed the decoupled echo state network (DESN) involving the use of lateral inhibition. Two low-complexity implementation schemes, namely, the DESN with reservoir prediction (DESN + RP) and DESN with maximum available information (DESN + MaxInfo), are developed: (1) In the ...
Xue, Yanbo, Yang, Le, Haykin, Simon
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Modular state space of echo state network
Neurocomputing, 2013Echo state network (ESN) mainly consists of a reservoir with a large number of neurons that are randomly connected and a linear readout (output) that is easily adapted. From this point, the reservoir will reconstruct the input signals in the high-dimensional state space.
Qian-Li Ma, Wei-Biao Chen
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Echo state networks (ESNs) are a class of recurrent neural networks (RNNs) designed according to the Reservoir Computing (RC) approach, where the dynamical part of the model is initialized and left untrained. The reservoir’s topology and spectral properties both play an important role in producing an informative encoding of the input sequence.
Domenico Tortorella, Alessio Micheli
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Domenico Tortorella, Alessio Micheli
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