Filippo Maria Bianchi

Pyramidal Reservoir Graph Neural Network

We propose a deep Graph Neural Network (GNN) model that alternates two t...
04/10/2021 ∙ by Filippo Maria Bianchi, et al. ∙ 13 ∙ share

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Large-scale detection and categorization of oil spills from SAR images with deep learning

We propose a deep learning framework to detect and categorize oil spills...
06/24/2020 ∙ by Filippo Maria Bianchi, et al. ∙ 0 ∙ share

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Deep Image Translation with an Affinity-Based Change Prior for Unsupervised Multimodal Change Detection

Image translation with convolutional neural networks has recently been u...
01/13/2020 ∙ by Luigi Tommaso Luppino, et al. ∙ 24 ∙ share

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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...
10/24/2019 ∙ by Filippo Maria Bianchi, et al. ∙ 0 ∙ share

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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...
10/11/2019 ∙ by Filippo Maria Bianchi, et al. ∙ 15 ∙ share

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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...
07/10/2019 ∙ by Karl Øyvind Mikalsen, et al. ∙ 0 ∙ share

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Mincut pooling in Graph Neural Networks

The advance of node pooling operations in a Graph Neural Network (GNN) h...
06/30/2019 ∙ by Filippo Maria Bianchi, et al. ∙ 0 ∙ share

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Noisy multi-label semi-supervised dimensionality reduction

Noisy labeled data represent a rich source of information that often are...
02/20/2019 ∙ by Karl Øyvind Mikalsen, et al. ∙ 0 ∙ share

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Graph Neural Networks with convolutional ARMA filters

Recent graph neural networks implement convolutional layers based on pol...
01/05/2019 ∙ by Filippo Maria Bianchi, et al. ∙ 0 ∙ share

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Graph Neural Networks with distributed ARMA filters

Recent graph neural networks implement convolutional layers based on pol...
01/05/2019 ∙ by Filippo Maria Bianchi, et al. ∙ 0 ∙ share

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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...
05/09/2018 ∙ by Filippo Maria Bianchi, et al. ∙ 0 ∙ share

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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...
03/21/2018 ∙ by Karl Øyvind Mikalsen, et al. ∙ 0 ∙ share

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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...
03/21/2018 ∙ by Filippo Maria Bianchi, et al. ∙ 0 ∙ share

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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...
01/21/2018 ∙ by Filippo Maria Bianchi, et al. ∙ 0 ∙ share

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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 ...
12/10/2017 ∙ by Alessandro Cinti, et al. ∙ 0 ∙ share

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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...
11/22/2017 ∙ by Filippo Maria Bianchi, et al. ∙ 0 ∙ share

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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 ...
11/17/2017 ∙ by Andreas Storvik Strauman, et al. ∙ 0 ∙ share

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Bidirectional deep-readout echo state networks

We propose a deep architecture for the classification of multivariate ti...
11/17/2017 ∙ by Filippo Maria Bianchi, et al. ∙ 0 ∙ share

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Bidirectional deep-reservoir echo state networks

We propose a deep architecture for the classification of multivariate ti...
11/17/2017 ∙ by Filippo Maria Bianchi, et al. ∙ 0 ∙ share

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Bidirectional deep echo state networks

In this work we propose a deep architecture for the classification of mu...
11/17/2017 ∙ by Filippo Maria Bianchi, et al. ∙ 0 ∙ share

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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...
10/20/2017 ∙ by Filippo Maria Bianchi, et al. ∙ 0 ∙ share

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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 ...
05/11/2017 ∙ by Filippo Maria Bianchi, et al. ∙ 0 ∙ share

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Time Series Cluster Kernel for Learning Similarities between Multivariate Time Series with Missing Data

Similarity-based approaches represent a promising direction for time ser...
04/03/2017 ∙ by Karl Øyvind Mikalsen, et al. ∙ 0 ∙ share

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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...
02/23/2017 ∙ by Sigurd Løkse, et al. ∙ 0 ∙ share

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Deep Kernelized Autoencoders

In this paper we introduce the deep kernelized autoencoder, a neural net...
02/08/2017 ∙ by Michael Kampffmeyer, et al. ∙ 0 ∙ share

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Temporal Overdrive Recurrent Neural Network

In this work we present a novel recurrent neural network architecture de...
01/18/2017 ∙ by Filippo Maria Bianchi, et al. ∙ 0 ∙ share

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Multiplex visibility graphs to investigate recurrent neural networks dynamics

A recurrent neural network (RNN) is a universal approximator of dynamica...
09/10/2016 ∙ by Filippo Maria Bianchi, et al. ∙ 0 ∙ share

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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...
08/16/2016 ∙ by Sigurd Løkse, et al. ∙ 0 ∙ share

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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...
03/11/2016 ∙ by Lorenzo Livi, et al. ∙ 0 ∙ share

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Data-driven detrending of nonstationary fractal time series with echo state networks

In this paper, we propose a novel data-driven approach for removing tren...
10/24/2015 ∙ by Enrico Maiorino, et al. ∙ 0 ∙ share

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Filippo Maria Bianchi

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