
Pyramidal Reservoir Graph Neural Network
We propose a deep Graph Neural Network (GNN) model that alternates two t...
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Largescale detection and categorization of oil spills from SAR images with deep learning
We propose a deep learning framework to detect and categorize oil spills...
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Deep Image Translation with an AffinityBased Change Prior for Unsupervised Multimodal Change Detection
Image translation with convolutional neural networks has recently been u...
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Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling
In graph neural networks (GNNs), pooling operators compute local summari...
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Snow avalanche segmentation in SAR images with Fully Convolutional Neural Networks
Knowledge about frequency and location of snow avalanche activity is ess...
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Time series cluster kernels to exploit informative missingness and incomplete label information
The time series cluster kernel (TCK) provides a powerful tool for analys...
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Mincut pooling in Graph Neural Networks
The advance of node pooling operations in a Graph Neural Network (GNN) h...
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Noisy multilabel semisupervised dimensionality reduction
Noisy labeled data represent a rich source of information that often are...
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Graph Neural Networks with convolutional ARMA filters
Recent graph neural networks implement convolutional layers based on pol...
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Graph Neural Networks with distributed ARMA filters
Recent graph neural networks implement convolutional layers based on pol...
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Learning representations for multivariate time series with missing data using Temporal Kernelized Autoencoders
Learning compressed representations of multivariate time series (MTS) fa...
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An Unsupervised Multivariate Time Series Kernel Approach for Identifying Patients with Surgical Site Infection from Blood Samples
A large fraction of the electronic health records consists of clinical m...
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Reservoir computing approaches for representation and classification of multivariate time series
Classification of multivariate time series (MTS) has been tackled with a...
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Time series kernel similarities for predicting Paroxysmal Atrial Fibrillation from ECGs
We tackle the problem of classifying Electrocardiography (ECG) signals w...
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A novel algorithm for online inexact string matching and its FPGA implementation
Accelerating inexact string matching procedures is of utmost importance ...
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Identifying user habits through data mining on call data records
In this paper we propose a framework for identifying patterns and regula...
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Classification of postoperative surgical site infections from blood measurements with missing data using recurrent neural networks
Clinical measurements that can be represented as time series constitute ...
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Bidirectional deepreadout echo state networks
We propose a deep architecture for the classification of multivariate ti...
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Bidirectional deepreservoir echo state networks
We propose a deep architecture for the classification of multivariate ti...
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Bidirectional deep echo state networks
In this work we propose a deep architecture for the classification of mu...
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Learning compressed representations of blood samples time series with missing data
Clinical measurements collected over time are naturally represented as m...
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An overview and comparative analysis of Recurrent Neural Networks for Short Term Load Forecasting
The key component in forecasting demand and consumption of resources in ...
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Time Series Cluster Kernel for Learning Similarities between Multivariate Time Series with Missing Data
Similaritybased approaches represent a promising direction for time ser...
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Spectral Clustering using PCKID  A Probabilistic Cluster Kernel for Incomplete Data
In this paper, we propose PCKID, a novel, robust, kernel function for sp...
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Deep Kernelized Autoencoders
In this paper we introduce the deep kernelized autoencoder, a neural net...
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Temporal Overdrive Recurrent Neural Network
In this work we present a novel recurrent neural network architecture de...
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Multiplex visibility graphs to investigate recurrent neural networks dynamics
A recurrent neural network (RNN) is a universal approximator of dynamica...
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Training Echo State Networks with Regularization through Dimensionality Reduction
In this paper we introduce a new framework to train an Echo State Networ...
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Determination of the edge of criticality in echo state networks through Fisher information maximization
It is a widely accepted fact that the computational capability of recurr...
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Datadriven detrending of nonstationary fractal time series with echo state networks
In this paper, we propose a novel datadriven approach for removing tren...
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Filippo Maria Bianchi
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Postdoctoral Fellow at the Machine Learning Group at UiT the Arctic University of Norway, Tromsø.