Filippo Maria Bianchi

research

∙ 03/30/2022

Recognition of polar lows in Sentinel-1 SAR images with deep learning

In this paper, we explore the possibility of detecting polar lows in C-b...

Jakob Grahn, et al. ∙

research

∙ 03/10/2022

Probabilistic forecasts of wind power generation in regions with complex topography using deep learning methods: An Arctic case

The energy market relies on forecasting capabilities of both demand and ...

Odin Foldvik Eikeland, et al. ∙

research

∙ 02/17/2022

Ensemble Conformalized Quantile Regression for Probabilistic Time Series Forecasting

This paper presents a novel probabilistic forecasting method called ense...

Vilde Jensen, et al. ∙

research

∙ 11/03/2021

Power Flow Balancing with Decentralized Graph Neural Networks

We propose an end-to-end framework based on a Graph Neural Network (GNN)...

Jonas Berg Hansen, et al. ∙

research

∙ 10/11/2021

Understanding Pooling in Graph Neural Networks

Inspired by the conventional pooling layers in convolutional neural netw...

Daniele Grattarola, et al. ∙

research

∙ 08/16/2021

Detecting and interpreting faults in vulnerable power grids with machine learning

Unscheduled power disturbances cause severe consequences both for custom...

Odin Foldvik Eikeland, et al. ∙

research

∙ 04/10/2021

Pyramidal Reservoir Graph Neural Network

We propose a deep Graph Neural Network (GNN) model that alternates two t...

Filippo Maria Bianchi, et al. ∙

research

∙ 06/24/2020

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...

Filippo Maria Bianchi, et al. ∙

research

∙ 01/13/2020

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

Image translation with convolutional neural networks has recently been u...

Luigi Tommaso Luppino, et al. ∙

research

∙ 10/24/2019

Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling

In graph neural networks (GNNs), pooling operators compute local summari...

Filippo Maria Bianchi, et al. ∙

research

∙ 10/11/2019

Snow avalanche segmentation in SAR images with Fully Convolutional Neural Networks

Knowledge about frequency and location of snow avalanche activity is ess...

Filippo Maria Bianchi, et al. ∙

research

∙ 07/10/2019

Time series cluster kernels to exploit informative missingness and incomplete label information

The time series cluster kernel (TCK) provides a powerful tool for analys...

Karl Øyvind Mikalsen, et al. ∙

research

∙ 06/30/2019

Mincut pooling in Graph Neural Networks

The advance of node pooling operations in a Graph Neural Network (GNN) h...

Filippo Maria Bianchi, et al. ∙

research

∙ 02/20/2019

Noisy multi-label semi-supervised dimensionality reduction

Noisy labeled data represent a rich source of information that often are...

Karl Øyvind Mikalsen, et al. ∙

research

∙ 01/05/2019

Graph Neural Networks with convolutional ARMA filters

Recent graph neural networks implement convolutional layers based on pol...

Filippo Maria Bianchi, et al. ∙

research

∙ 01/05/2019

Graph Neural Networks with distributed ARMA filters

Recent graph neural networks implement convolutional layers based on pol...

Filippo Maria Bianchi, et al. ∙

research

∙ 05/09/2018

Learning representations for multivariate time series with missing data using Temporal Kernelized Autoencoders

Learning compressed representations of multivariate time series (MTS) fa...

Filippo Maria Bianchi, et al. ∙

research

∙ 03/21/2018

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...

Karl Øyvind Mikalsen, et al. ∙

research

∙ 03/21/2018

Reservoir computing approaches for representation and classification of multivariate time series

Classification of multivariate time series (MTS) has been tackled with a...

Filippo Maria Bianchi, et al. ∙

research

∙ 01/21/2018

Time series kernel similarities for predicting Paroxysmal Atrial Fibrillation from ECGs

We tackle the problem of classifying Electrocardiography (ECG) signals w...

Filippo Maria Bianchi, et al. ∙

research

∙ 12/10/2017

A novel algorithm for online inexact string matching and its FPGA implementation

Accelerating inexact string matching procedures is of utmost importance ...

Alessandro Cinti, et al. ∙

research

∙ 11/22/2017

Identifying user habits through data mining on call data records

In this paper we propose a framework for identifying patterns and regula...

Filippo Maria Bianchi, et al. ∙

research

∙ 11/17/2017

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 ...

Andreas Storvik Strauman, et al. ∙

research

∙ 11/17/2017

Bidirectional deep-readout echo state networks

We propose a deep architecture for the classification of multivariate ti...

Filippo Maria Bianchi, et al. ∙

research

∙ 11/17/2017

Bidirectional deep-reservoir echo state networks

We propose a deep architecture for the classification of multivariate ti...

Filippo Maria Bianchi, et al. ∙

research

∙ 11/17/2017

Bidirectional deep echo state networks

In this work we propose a deep architecture for the classification of mu...

Filippo Maria Bianchi, et al. ∙

research

∙ 10/20/2017

Learning compressed representations of blood samples time series with missing data

Clinical measurements collected over time are naturally represented as m...

Filippo Maria Bianchi, et al. ∙

research

∙ 05/11/2017

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 ...

Filippo Maria Bianchi, et al. ∙

research

∙ 04/03/2017

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

Similarity-based approaches represent a promising direction for time ser...

Karl Øyvind Mikalsen, et al. ∙

research

∙ 02/23/2017

Spectral Clustering using PCKID - A Probabilistic Cluster Kernel for Incomplete Data

In this paper, we propose PCKID, a novel, robust, kernel function for sp...

Sigurd Løkse, et al. ∙

research

∙ 02/08/2017

Deep Kernelized Autoencoders

In this paper we introduce the deep kernelized autoencoder, a neural net...

Michael Kampffmeyer, et al. ∙

research

∙ 01/18/2017

Temporal Overdrive Recurrent Neural Network

In this work we present a novel recurrent neural network architecture de...

Filippo Maria Bianchi, et al. ∙

research

∙ 09/10/2016

Multiplex visibility graphs to investigate recurrent neural networks dynamics

A recurrent neural network (RNN) is a universal approximator of dynamica...

Filippo Maria Bianchi, et al. ∙

research

∙ 08/16/2016

Training Echo State Networks with Regularization through Dimensionality Reduction

In this paper we introduce a new framework to train an Echo State Networ...

Sigurd Løkse, et al. ∙

research

∙ 03/11/2016

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...

Lorenzo Livi, et al. ∙

research

∙ 10/24/2015

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...

Enrico Maiorino, et al. ∙

Filippo Maria Bianchi

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