280 citations · 1.7k across the 60 of their papers we have counts for
10 papers · 1 filter
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…
Urban Land Cover Classification with Missing Data Modalities Using Deep Convolutional Neural Networks
Michael Kampffmeyer, Arnt-Børre Salberg, Robert Jenssen
Automatic urban land cover classification is a fundamental problem in remote sensing, e.g. for environmental monitoring. The problem is highly challenging, as classes generally hav…
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…
Time Series Cluster Kernel for Learning Similarities between Multivariate Time Series with Missing Data
Karl Øyvind Mikalsen, Filippo Maria Bianchi, Cristina Soguero-Ruiz +1
Similarity-based approaches represent a promising direction for time series analysis. However, many such methods rely on parameter tuning, and some have shortcomings if the time se…
Spectral Clustering using PCKID - A Probabilistic Cluster Kernel for Incomplete Data
Sigurd Løkse, Filippo Maria Bianchi, Arnt-Børre Salberg +1
In this paper, we propose PCKID, a novel, robust, kernel function for spectral clustering, specifically designed to handle incomplete data. By combining posterior distributions of…
A clustering approach to heterogeneous change detection
Luigi Tommaso Luppino, Stian Normann Anfinsen, Gabriele Moser +4
Change detection in heterogeneous multitemporal satellite images is a challenging and still not much studied topic in remote sensing and earth observation. This paper focuses on co…
Deep Kernelized Autoencoders
Michael Kampffmeyer, Sigurd Løkse, Filippo Maria Bianchi +2
In this paper we introduce the deep kernelized autoencoder, a neural network model that allows an explicit approximation of (i) the mapping from an input space to an arbitrary, use…
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…