280 citations · 1.7k across the 60 of their papers we have counts for
9 papers · 1 filter
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…
Reservoir computing approaches for representation and classification of multivariate time series
Filippo Maria Bianchi, Simone Scardapane, Sigurd Løkse +1
Classification of multivariate time series (MTS) has been tackled with a large variety of methodologies and applied to a wide range of scenarios. Reservoir Computing (RC) provides…
Classification of postoperative surgical site infections from blood measurements with missing data using recurrent neural networks
Andreas Storvik Strauman, Filippo Maria Bianchi, Karl Øyvind Mikalsen +3
Clinical measurements that can be represented as time series constitute an important fraction of the electronic health records and are often both uncertain and incomplete. Recurren…
Bidirectional deep-readout echo state networks
Filippo Maria Bianchi, Simone Scardapane, Sigurd Løkse +1
We propose a deep architecture for the classification of multivariate time series. By means of a recurrent and untrained reservoir we generate a vectorial representation that embed…
Learning compressed representations of blood samples time series with missing data
Filippo Maria Bianchi, Karl Øyvind Mikalsen, Robert Jenssen
Clinical measurements collected over time are naturally represented as multivariate time series (MTS), which often contain missing data. An autoencoder can learn low dimensional ve…
An overview and comparative analysis of Recurrent Neural Networks for Short Term Load Forecasting
Filippo Maria Bianchi, Enrico Maiorino, Michael C. Kampffmeyer +2
The key component in forecasting demand and consumption of resources in a supply network is an accurate prediction of real-valued time series. Indeed, both service interruptions an…
Temporal Overdrive Recurrent Neural Network
Filippo Maria Bianchi, Michael Kampffmeyer, Enrico Maiorino +1
In this work we present a novel recurrent neural network architecture designed to model systems characterized by multiple characteristic timescales in their dynamics. The proposed…
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…