1 citations · 1 across the 5 of their papers we have counts for
5 papers
A comprehensive theoretical framework for the optimization of neural networks classification performance with respect to weighted metrics
Francesco Marchetti, Sabrina Guastavino, Cristina Campi +2
In many contexts, customized and weighted classification scores are designed in order to evaluate the goodness of the predictions carried out by neural networks. However, there exi…
Physics-driven machine learning for the prediction of coronal mass ejections' travel times
Sabrina Guastavino, Valentina Candiani, Alessandro Bemporad +7
Coronal Mass Ejections (CMEs) correspond to dramatic expulsions of plasma and magnetic field from the solar corona into the heliosphere. CMEs are scientifically relevant because th…
Mapped Variably Scaled Kernels: Applications to Solar Imaging
Francesco Marchetti, Emma Perracchione, Anna Volpara +3
Variably scaled kernels and mapped bases constructed via the so-called fake nodes approach are two different strategies to provide adaptive bases for function interpolation. In thi…
Moving Least Squares Approximation using Variably Scaled Discontinuous Weight Function
Mohammad Karimnejad Esfahani, Stefano De Marchi, Francesco Marchetti
Functions with discontinuities appear in many applications such as image reconstruction, signal processing, optimal control problems, interface problems, engineering applications a…
Data-driven kernel designs for optimized greedy schemes: A machine learning perspective
Tizian Wenzel, Francesco Marchetti, Emma Perracchione
Thanks to their easy implementation via Radial Basis Functions (RBFs), meshfree kernel methods have been proved to be an effective tool for e.g. scattered data interpolation, PDE c…