Apr 24, 2018 · In this paper, the noticeable dynamism and the large memory provided by ESN and the strength of Autoencoders in feature extraction are gathered ...
It is an artificial unsupervised feed-forward neural network which provides efficient data coding. It learns certain data representation so as to render a ...
The empirical study reveals the main contribution of recurrent connections in improving the classification performance results within an ESN Recurrent ...
Genesis of basic and multi-layer echo state network recurrent autoencoders for efficient data representations. N Chouikhi, B Ammar, AM Alimi. arXiv preprint ...
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Bibliographic details on Genesis of Basic and Multi-Layer Echo State Network Recurrent Autoencoders for Efficient Data Representations.
Naima Chouikhi , Boudour Ammar , Amir Hussain, Adel M. Alimi: Novel single and multi-layer echo-state recurrent autoencoders for representation learning.
Feature extraction-based methods such as Auto-Encoders (AEs) are used to find new, more accurate data representations from original ones. They perform ...
Genesis of Basic and Multi-Layer Echo State Network Recurrent Autoencoders for Efficient Data Representations.
Echo State Networks (ESN) are a powerful and efficient type of Recurrent Neural Networks (RNN) used for processing time-series data and have gained ...