Training Performance of Echo State Neural Networks

Romain Couillet (1) , Gilles Wainrib (2) , Harry Sevi (3) , Hafiz Tiomoko Ali (4)

1 NXP Semiconductors
2 DI-ENS - Département d'informatique - ENS Paris
3 ENS de Lyon - École normale supérieure de Lyon
4 L2S - Laboratoire des signaux et systèmes

This article proposes a first theoretical performance analysis of the training phase of large dimensional linear echo-state networks. This analysis is based on advanced methods of random matrix theory. The results provide some new insights on the core features of such networks, thereby helping the practitioner when using them.

Mots clés: Index Terms— Neural networks; ESN; random matrix theory

Domaines: Intelligence artificielle [cs.AI]; Réseau de neurones [cs.NE]; Apprentissage [cs.LG]; Statistiques [math.ST]

Romain Couillet, Gilles Wainrib, Harry Sevi, Hafiz Tiomoko Ali. Training Performance of Echo State Neural Networks. IEEE Statistical Signal Processing Workshop 2016 (SSP'16), Palma de Majorca, Spain, Jun 2016, Palma de Majorca, Spain. ⟨hal-01633450v2⟩