A Random Matrix Approach to Echo-State Neural Networks
Résumé
Recurrent neural networks, especially in their linear version, have provided many qualitative insights on their performance under different configurations. This article provides, through a novel random matrix framework, the quantitative counterpart of these performance results, particularly in the case of echo-state networks. Beyond mere insights, our approach conveys a deeper understanding on the core mechanism under play for both training and testing.
Origine | Fichiers produits par l'(les) auteur(s) |
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