177 citations · 551 across the 17 of their papers we have counts for
23 papers
Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks
Giulia Fracastoro, Sophie M. Fosson, Andrea Migliorati +1
The design of sparse neural networks, i.e., of networks with a reduced number of parameters, has been attracting increasing research attention in the last few years. The use of spa…
Deep 3D World Models for Multi-Image Super-Resolution Beyond Optical Flow
Luca Savant Aira, Diego Valsesia, Andrea Bordone Molini +3
Multi-image super-resolution (MISR) allows to increase the spatial resolution of a low-resolution (LR) acquisition by combining multiple images carrying complementary information i…
Control of Dynamic Financial Networks (The Extended Version)
Giuseppe Calafiore, Giulia Fracastoro, Anton V. Proskurnikov
The current global financial system forms a highly interconnected network where a default in one of its nodes can propagate to many other nodes, causing a catastrophic avalanche ef…
Clearing Payments in Dynamic Financial Networks
Giuseppe C. Calafiore, Giulia Fracastoro, Anton V. Proskurnikov
This paper proposes a novel dynamical model for determining clearing payments in financial networks. We extend the classical Eisenberg-Noe model of financial contagion to multiple…
Semi-supervised learning for joint SAR and multispectral land cover classification
Antonio Montanaro, Diego Valsesia, Giulia Fracastoro +1
Semi-supervised learning techniques are gaining popularity due to their capability of building models that are effective, even when scarce amounts of labeled data are available. In…
COVID-19 case data for Italy stratified by age class
Giuseppe Calafiore, Giulia Fracastoro
The dataset described in this paper contains daily data about COVID-19 cases that occurred in Italy over the period from Jan. 28, 2020 to March 20, 2021, divided into ten age class…
Denoise and Contrast for Category Agnostic Shape Completion
Antonio Alliegro, Diego Valsesia, Giulia Fracastoro +2
In this paper, we present a deep learning model that exploits the power of self-supervision to perform 3D point cloud completion, estimating the missing part and a context region a…
RAN-GNNs: breaking the capacity limits of graph neural networks
Diego Valsesia, Giulia Fracastoro, Enrico Magli
Graph neural networks have become a staple in problems addressing learning and analysis of data defined over graphs. However, several results suggest an inherent difficulty in extr…
Optimal Clearing Payments in a Financial Contagion Model
Giuseppe Calafiore, Giulia Fracastoro, Anton V. Proskurnikov
Financial networks are characterized by complex structures of mutual obligations. These obligations are fulfilled entirely or in part (when defaults occur) via a mechanism called c…
Deep Learning Methods For Synthetic Aperture Radar Image Despeckling: An Overview Of Trends And Perspectives
Giulia Fracastoro, Enrico Magli, Giovanni Poggi +3
Synthetic aperture radar (SAR) images are affected by a spatially-correlated and signal-dependent noise called speckle, which is very severe and may hinder image exploitation. Desp…
Learning Graph-Convolutional Representations for Point Cloud Denoising
Francesca Pistilli, Giulia Fracastoro, Diego Valsesia +1
Point clouds are an increasingly relevant data type but they are often corrupted by noise. We propose a deep neural network based on graph-convolutional layers that can elegantly d…
Speckle2Void: Deep Self-Supervised SAR Despeckling with Blind-Spot Convolutional Neural Networks
Andrea Bordone Molini, Diego Valsesia, Giulia Fracastoro +1
Information extraction from synthetic aperture radar (SAR) images is heavily impaired by speckle noise, hence despeckling is a crucial preliminary step in scene analysis algorithms…
DeepSUM++: Non-local Deep Neural Network for Super-Resolution of Unregistered Multitemporal Images
Andrea Bordone Molini, Diego Valsesia, Giulia Fracastoro +1
Deep learning methods for super-resolution of a remote sensing scene from multiple unregistered low-resolution images have recently gained attention thanks to a challenge proposed…
Towards Deep Unsupervised SAR Despeckling with Blind-Spot Convolutional Neural Networks
Andrea Bordone Molini, Diego Valsesia, Giulia Fracastoro +1
SAR despeckling is a problem of paramount importance in remote sensing, since it represents the first step of many scene analysis algorithms. Recently, deep learning techniques hav…
Survival and Neural Models for Private Equity Exit Prediction
Giuseppe C. Calafiore, Marisa H. Morales, Vittorio Tiozzo +2
Within the Private Equity (PE) market, the event of a private company undertaking an Initial Public Offering (IPO) is usually a very high-return one for the investors in the compan…
Sparse and Center Classifiers
Giuseppe C. Calafiore, Giulia Fracastoro
The nearest-centroid classifier is a simple linear-time classifier based on computing the centroids of the data classes in the training phase, and then assigning a new datum to the…
Deep Graph-Convolutional Image Denoising
Diego Valsesia, Giulia Fracastoro, Enrico Magli
Non-local self-similarity is well-known to be an effective prior for the image denoising problem. However, little work has been done to incorporate it in convolutional neural netwo…
DeepSUM: Deep neural network for Super-resolution of Unregistered Multitemporal images
Andrea Bordone Molini, Diego Valsesia, Giulia Fracastoro +1
Recently, convolutional neural networks (CNN) have been successfully applied to many remote sensing problems. However, deep learning techniques for multi-image super-resolution fro…
Image Denoising with Graph-Convolutional Neural Networks
Diego Valsesia, Giulia Fracastoro, Enrico Magli
Recovering an image from a noisy observation is a key problem in signal processing. Recently, it has been shown that data-driven approaches employing convolutional neural networks…
Sampling of graph signals via randomized local aggregations
Diego Valsesia, Giulia Fracastoro, Enrico Magli
Sampling of signals defined over the nodes of a graph is one of the crucial problems in graph signal processing. While in classical signal processing sampling is a well defined ope…
Graph Transform Optimization with Application to Image Compression
Giulia Fracastoro, Dorina Thanou, Pascal Frossard
In this paper, we propose a new graph-based transform and illustrate its potential application to signal compression. Our approach relies on the careful design of a graph that opti…
Steerable Discrete Fourier Transform
Giulia Fracastoro, Enrico Magli
Directional transforms have recently raised a lot of interest thanks to their numerous applications in signal compression and analysis. In this letter, we introduce a generalizatio…
Steerable Discrete Cosine Transform
Giulia Fracastoro, Sophie Marie Fosson, Enrico Magli
In image compression, classical block-based separable transforms tend to be inefficient when image blocks contain arbitrarily shaped discontinuities. For this reason, transforms in…