Filippo Maria Bianchi

research

∙ 08/24/2023

Probabilistic load forecasting with Reservoir Computing

Some applications of deep learning require not only to provide accurate ...

0 Michele Guerra, et al. ∙

research

∙ 04/14/2023

Combining Stochastic Explainers and Subgraph Neural Networks can Increase Expressivity and Interpretability

Subgraph-enhanced graph neural networks (SGNN) can increase the expressi...

0 Indro Spinelli, et al. ∙

research

∙ 04/04/2023

The expressive power of pooling in Graph Neural Networks

In Graph Neural Networks (GNNs), hierarchical pooling operators generate...

0 Filippo Maria Bianchi, et al. ∙

research

∙ 11/11/2022

Clustering with Total Variation Graph Neural Networks

Graph Neural Networks (GNNs) are deep learning models designed to proces...

0 Jonas Berg Hansen, et al. ∙

research

∙ 09/16/2022

Explainability in subgraphs-enhanced Graph Neural Networks

Recently, subgraphs-enhanced Graph Neural Networks (SGNNs) have been int...

0 Michele Guerra, et al. ∙

research

∙ 09/14/2022

Scalable Spatiotemporal Graph Neural Networks

Neural forecasting of spatiotemporal time series drives both research an...

2 Andrea Cini, et al. ∙

research

∙ 07/18/2022

Simplifying Clustering with Graph Neural Networks

The objective functions used in spectral clustering are usually composed...

0 Filippo Maria Bianchi, et al. ∙

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...

0 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 ...

0 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...

1 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)...

1 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...

15 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...

0 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...

14 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...

0 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...

25 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...

0 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...

15 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...

0 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...

0 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...

0 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...

0 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...

0 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...

0 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...

0 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...

0 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...

0 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 ...

0 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...

0 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 ...

0 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...

0 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...

0 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...

0 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...

0 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 ...

0 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...

0 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...

0 Sigurd Løkse, et al. ∙

research

∙ 02/08/2017

Deep Kernelized Autoencoders

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

0 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...

0 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...

0 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...

0 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...

0 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...

1 Enrico Maiorino, et al. ∙

Filippo Maria Bianchi

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