33 citations · 38 across the 6 of their papers we have counts for
7 papers
3D Pose Nowcasting: Forecast the Future to Improve the Present
Alessandro Simoni, Francesco Marchetti, Guido Borghi +4
Technologies to enable safe and effective collaboration and coexistence between humans and robots have gained significant importance in the last few years. A critical component use…
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…
Operational solar flare forecasting via video-based deep learning
Sabrina Guastavino, Francesco Marchetti, Federico Benvenuto +2
Operational flare forecasting aims at providing predictions that can be used to make decisions, typically at a daily scale, about the space weather impacts of flare occurrence. Thi…
SMEMO: Social Memory for Trajectory Forecasting
Francesco Marchetti, Federico Becattini, Lorenzo Seidenari +1
Effective modeling of human interactions is of utmost importance when forecasting behaviors such as future trajectories. Each individual, with its motion, influences surrounding ag…
Implementation paradigm for supervised flare forecasting studies: a deep learning application with video data
Sabrina Guastavino, Francesco Marchetti, Federico Benvenuto +2
Solar flare forecasting can be realized by means of the analysis of magnetic data through artificial intelligence techniques. The aim is to predict whether a magnetic active region…
MANTRA: Memory Augmented Networks for Multiple Trajectory Prediction
Francesco Marchetti, Federico Becattini, Lorenzo Seidenari +1
Autonomous vehicles are expected to drive in complex scenarios with several independent non cooperating agents. Path planning for safely navigating in such environments can not jus…