no code implementations • 15 Mar 2024 • Giuseppe Calafiore, Giulia Fracastoro, Anton Proskurnikov
In this paper we analyze the resilience of a network of banks to joint price fluctuations of the external assets in which they have shared exposures, and evaluate the worst-case effects of the possible default contagion.
no code implementations • 30 Jan 2024 • Luca Savant Aira, Diego Valsesia, Andrea Bordone Molini, Giulia Fracastoro, Enrico Magli, Andrea Mirabile
Multi-image super-resolution (MISR) allows to increase the spatial resolution of a low-resolution (LR) acquisition by combining multiple images carrying complementary information in the form of sub-pixel offsets in the scene sampling, and can be significantly more effective than its single-image counterpart.
no code implementations • 20 Aug 2021 • Antonio Montanaro, Diego Valsesia, Giulia Fracastoro, Enrico Magli
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.
no code implementations • 13 Apr 2021 • 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 classes of the population, the first class being 0-9 years, the tenth class being 90 years and over.
1 code implementation • CVPR 2021 • Antonio Alliegro, Diego Valsesia, Giulia Fracastoro, Enrico Magli, Tatiana Tommasi
The combined embedding inherits category-agnostic properties from the chosen pretext tasks.
no code implementations • 29 Mar 2021 • Diego Valsesia, Giulia Fracastoro, Enrico Magli
In this paper, we investigate the recently proposed randomly wired architectures in the context of graph neural networks.
Ranked #20 on Graph Property Prediction on ogbg-molpcba
no code implementations • ICLR Workshop GTRL 2021 • Diego Valsesia, Giulia Fracastoro, Enrico Magli
In this paper, we investigate the recently proposed randomly wired architectures in the context of graph neural networks.
no code implementations • 10 Dec 2020 • Giulia Fracastoro, Enrico Magli, Giovanni Poggi, Giuseppe Scarpa, Diego Valsesia, Luisa Verdoliva
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.
1 code implementation • ECCV 2020 • Francesca Pistilli, Giulia Fracastoro, Diego Valsesia, Enrico Magli
Point clouds are an increasingly relevant data type but they are often corrupted by noise.
1 code implementation • 4 Jul 2020 • Andrea Bordone Molini, Diego Valsesia, Giulia Fracastoro, Enrico Magli
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.
no code implementations • 15 Jan 2020 • Andrea Bordone Molini, Diego Valsesia, Giulia Fracastoro, Enrico Magli
The proposed method is trained employing only noisy images and can therefore learn features of real SAR images rather than synthetic data.
no code implementations • 15 Jan 2020 • Andrea Bordone Molini, Diego Valsesia, Giulia Fracastoro, Enrico Magli
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 by the European Space Agency.
no code implementations • 19 Nov 2019 • Giuseppe C. Calafiore, Marisa H. Morales, Vittorio Tiozzo, Giulia Fracastoro, Serge Marquie
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 company.
no code implementations • 17 Nov 2019 • Giuseppe C. Calafiore, Giulia Fracastoro
We show that training of the proposed sparse models, with both distance criteria, can be performed exactly (i. e., the globally optimal set of features is selected) and at a quasi-linear computational cost.
1 code implementation • 19 Jul 2019 • Diego Valsesia, Giulia Fracastoro, Enrico Magli
The graph convolution operation generalizes the classic convolution to arbitrary graphs.
Ranked #3 on Grayscale Image Denoising on Set12 sigma25
1 code implementation • 15 Jul 2019 • Andrea Bordone Molini, Diego Valsesia, Giulia Fracastoro, Enrico Magli
This novel framework integrates the spatial registration task directly inside the CNN, and allows to exploit the representation learning capabilities of the network to enhance registration accuracy.
Ranked #8 on Multi-Frame Super-Resolution on PROBA-V
1 code implementation • 29 May 2019 • Diego Valsesia, Giulia Fracastoro, Enrico Magli
The graph-convolutional layers dynamically construct neighborhoods in the feature space to detect latent correlations in the feature maps produced by the hidden layers.
1 code implementation • ICLR 2019 • Diego Valsesia, Giulia Fracastoro, Enrico Magli
We also study the problem of defining an upsampling layer in the graph-convolutional generator, such that it learns to exploit a self-similarity prior on the data distribution to sample more effectively.