753 citations · 3k across the 55 of their papers we have counts for
5 papers · 1 filter
Input-to-State Representation in linear reservoirs dynamics
Pietro Verzelli, Cesare Alippi, Lorenzo Livi +1
Reservoir computing is a popular approach to design recurrent neural networks, due to its training simplicity and approximation performance. The recurrent part of these networks is…
Echo State Networks with Self-Normalizing Activations on the Hyper-Sphere
Pietro Verzelli, Cesare Alippi, Lorenzo Livi
Among the various architectures of Recurrent Neural Networks, Echo State Networks (ESNs) emerged due to their simplified and inexpensive training procedure. These networks are know…
A characterization of the Edge of Criticality in Binary Echo State Networks
Pietro Verzelli, Lorenzo Livi, Cesare Alippi
Echo State Networks (ESNs) are simplified recurrent neural network models composed of a reservoir and a linear, trainable readout layer. The reservoir is tunable by some hyper-para…
Learning representations for multivariate time series with missing data using Temporal Kernelized Autoencoders
Filippo Maria Bianchi, Lorenzo Livi, Karl Øyvind Mikalsen +2
Learning compressed representations of multivariate time series (MTS) facilitates data analysis in the presence of noise and redundant information, and for a large number of variat…
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…