The current global financial system forms a highly interconnected networ...
This paper proposes a novel dynamical model for determining clearing pay...
Self-supervised learning techniques are gaining popularity due to their
...
The dataset described in this paper contains daily data about COVID-19 c...
In this paper, we present a deep learning model that exploits the power ...
Graph neural networks have become a staple in problems addressing learni...
Modern financial networks are characterized by complex structures of mut...
Synthetic aperture radar (SAR) images are affected by a spatially-correl...
Point clouds are an increasingly relevant data type but they are often
c...
Information extraction from synthetic aperture radar (SAR) images is hea...
SAR despeckling is a problem of paramount importance in remote sensing, ...
Deep learning methods for super-resolution of a remote sensing scene fro...
Within the Private Equity (PE) market, the event of a private company
un...
The nearest-centroid classifier is a simple linear-time classifier based...
Non-local self-similarity is well-known to be an effective prior for the...
Recently, convolutional neural networks (CNN) have been successfully app...
Recovering an image from a noisy observation is a key problem in signal
...
Sampling of signals defined over the nodes of a graph is one of the cruc...
In this paper, we propose a new graph-based coding framework and illustr...