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Multiplex visibility graphs to investigate recurrent neural networks dynamics
Filippo Maria Bianchi, Lorenzo Livi, Cesare Alippi +1
A recurrent neural network (RNN) is a universal approximator of dynamical systems, whose performance often depends on sensitive hyperparameters. Tuning of such hyperparameters may…
Training Echo State Networks with Regularization through Dimensionality Reduction
Sigurd Løkse, Filippo Maria Bianchi, Robert Jenssen
In this paper we introduce a new framework to train an Echo State Network to predict real valued time-series. The method consists in projecting the output of the internal layer of…
Determination of the edge of criticality in echo state networks through Fisher information maximization
Lorenzo Livi, Filippo Maria Bianchi, Cesare Alippi
It is a widely accepted fact that the computational capability of recurrent neural networks is maximized on the so-called "edge of criticality". Once the network operates in this c…
Investigating echo state networks dynamics by means of recurrence analysis
Filippo Maria Bianchi, Lorenzo Livi, Cesare Alippi
In this paper, we elaborate over the well-known interpretability issue in echo state networks. The idea is to investigate the dynamics of reservoir neurons with time-series analysi…