439 citations · 2k across the 50 of their papers we have counts for
55 papers
When Missingness Follows the Graph: Beyond Feature Propagation for Missing Features
Enrico Chialà, Francesco Ferrini, Antonio Longa +4
Graph Neural Networks (GNNs) commonly assume that node features are fully observed, despite missing features being pervasive in real-world applications. By leveraging the dependenc…
Interpreting Temporal Graph Neural Networks with Koopman Theory
Michele Guerra, Simone Scardapane, Filippo Maria Bianchi
Spatiotemporal graph neural networks (STGNNs) have shown promising results in many domains, from forecasting to epidemiology. However, understanding the dynamics learned by these m…
Graph-based Forecasting with Missing Data through Spatiotemporal Downsampling
Ivan Marisca, Cesare Alippi, Filippo Maria Bianchi
Given a set of synchronous time series, each associated with a sensor-point in space and characterized by inter-series relationships, the problem of spatiotemporal forecasting cons…
Probabilistic load forecasting with Reservoir Computing
Michele Guerra, Simone Scardapane, Filippo Maria Bianchi
Some applications of deep learning require not only to provide accurate results but also to quantify the amount of confidence in their prediction. The management of an electric pow…
Combining Stochastic Explainers and Subgraph Neural Networks can Increase Expressivity and Interpretability
Indro Spinelli, Michele Guerra, Filippo Maria Bianchi +1
Subgraph-enhanced graph neural networks (SGNN) can increase the expressive power of the standard message-passing framework. This model family represents each graph as a collection…
The expressive power of pooling in Graph Neural Networks
Filippo Maria Bianchi, Veronica Lachi
In Graph Neural Networks (GNNs), hierarchical pooling operators generate local summaries of the data by coarsening the graph structure and the vertex features. While considerable a…
Power Availability of PV plus Thermal Batteries in real-world electric power grids
Odin Foldvik Eikeland, Colin C. Kelsall, Kyle Buznitsky +4
As variable renewable energy sources comprise a growing share of total electricity generation, energy storage technologies are becoming increasingly critical for balancing energy g…
Total Variation Graph Neural Networks
Jonas Berg Hansen, Filippo Maria Bianchi
Recently proposed Graph Neural Networks (GNNs) for vertex clustering are trained with an unsupervised minimum cut objective, approximated by a Spectral Clustering (SC) relaxation.…
Explainability in subgraphs-enhanced Graph Neural Networks
Michele Guerra, Indro Spinelli, Simone Scardapane +1
Recently, subgraphs-enhanced Graph Neural Networks (SGNNs) have been introduced to enhance the expressive power of Graph Neural Networks (GNNs), which was proved to be not higher t…
Scalable Spatiotemporal Graph Neural Networks
Andrea Cini, Ivan Marisca, Filippo Maria Bianchi +1
Neural forecasting of spatiotemporal time series drives both research and industrial innovation in several relevant application domains. Graph neural networks (GNNs) are often the…
Simplifying Clustering with Graph Neural Networks
Filippo Maria Bianchi
The objective functions used in spectral clustering are usually composed of two terms: i) a term that minimizes the local quadratic variation of the cluster assignments on the grap…
Probabilistic forecasts of wind power generation in regions with complex topography using deep learning methods: An Arctic case
Odin Foldvik Eikeland, Finn Dag Hovem, Tom Eirik Olsen +2
The energy market relies on forecasting capabilities of both demand and power generation that need to be kept in dynamic balance. Today, when it comes to renewable energy generatio…
Recognition of polar lows in Sentinel-1 SAR images with deep learning
Jakob Grahn, Filippo Maria Bianchi
In this paper, we explore the possibility of detecting polar lows in C-band SAR images by means of deep learning. Specifically, we introduce a novel dataset consisting of Sentinel-…
Ensemble Conformalized Quantile Regression for Probabilistic Time Series Forecasting
Vilde Jensen, Filippo Maria Bianchi, Stian Norman Anfinsen
This paper presents a novel probabilistic forecasting method called ensemble conformalized quantile regression (EnCQR). EnCQR constructs distribution-free and approximately margina…
Power Flow Balancing with Decentralized Graph Neural Networks
Jonas Berg Hansen, Stian Normann Anfinsen, Filippo Maria Bianchi
We propose an end-to-end framework based on a Graph Neural Network (GNN) to balance the power flows in energy grids. The balancing is framed as a supervised vertex regression task,…
Understanding Pooling in Graph Neural Networks
Daniele Grattarola, Daniele Zambon, Filippo Maria Bianchi +1
Inspired by the conventional pooling layers in convolutional neural networks, many recent works in the field of graph machine learning have introduced pooling operators to reduce t…
Detecting and interpreting faults in vulnerable power grids with machine learning
Odin Foldvik Eikeland, Inga Setså Holmstrand, Sigurd Bakkejord +2
Unscheduled power disturbances cause severe consequences both for customers and grid operators. To defend against such events, it is necessary to identify the causes of interruptio…
Pyramidal Reservoir Graph Neural Network
Filippo Maria Bianchi, Claudio Gallicchio, Alessio Micheli
We propose a deep Graph Neural Network (GNN) model that alternates two types of layers. The first type is inspired by Reservoir Computing (RC) and generates new vertex features by…
Intervention fatigue is the primary cause of strong secondary waves in the COVID-19 pandemic
Kristoffer Rypdal, Filippo Maria Bianchi, Martin Rypdal
As of November 2020, the number of COVID-19 cases is increasing rapidly in many countries. In Europe, the virus spread slowed considerably in the late spring due to strict lockdown…
Large-scale detection and categorization of oil spills from SAR images with deep learning
Filippo Maria Bianchi, Martine M. Espeseth, Njål Borch
We propose a deep learning framework to detect and categorize oil spills in synthetic aperture radar (SAR) images at a large scale. By means of a carefully designed neural network…
Code-Aligned Autoencoders for Unsupervised Change Detection in Multimodal Remote Sensing Images
Luigi T. Luppino, Mads A. Hansen, Michael Kampffmeyer +4
Image translation with convolutional autoencoders has recently been used as an approach to multimodal change detection in bitemporal satellite images. A main challenge is the align…
Deep Image Translation with an Affinity-Based Change Prior for Unsupervised Multimodal Change Detection
Luigi Tommaso Luppino, Michael Kampffmeyer, Filippo Maria Bianchi +4
Image translation with convolutional neural networks has recently been used as an approach to multimodal change detection. Existing approaches train the networks by exploiting supe…
Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling
Filippo Maria Bianchi, Daniele Grattarola, Lorenzo Livi +1
In graph neural networks (GNNs), pooling operators compute local summaries of input graphs to capture their global properties, and they are fundamental for building deep GNNs that…
Snow avalanche segmentation in SAR images with Fully Convolutional Neural Networks
Filippo Maria Bianchi, Jakob Grahn, Markus Eckerstorfer +2
Knowledge about frequency and location of snow avalanche activity is essential for forecasting and mapping of snow avalanche hazard. Traditional field monitoring of avalanche activ…