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Found 10 papers, 2 papers with code
Greedy feature selection: Classifier-dependent feature selection via greedy methods
The purpose of this study is to introduce a new approach to feature ranking for classification tasks, called in what follows greedy feature selection.
AI-FLARES: Artificial Intelligence for the Analysis of Solar Flares Data
AI-FLARES (Artificial Intelligence for the Analysis of Solar Flares Data) is a research project funded by the Agenzia Spaziale Italiana and by the Istituto Nazionale di Astrofisica within the framework of the ``Attivit\`a di Studio per la Comunit\`a Scientifica Nazionale Sole, Sistema Solare ed Esopianeti'' program.
3D Pose Nowcasting: Forecast the Future to Improve the Present
Technologies to enable safe and effective collaboration and coexistence between humans and robots have gained significant importance in the last few years.
A comprehensive theoretical framework for the optimization of neural networks classification performance with respect to weighted metrics
In many contexts, customized and weighted classification scores are designed in order to evaluate the goodness of the predictions carried out by neural networks.
Physics-driven machine learning for the prediction of coronal mass ejections' travel times
Coronal Mass Ejections (CMEs) correspond to dramatic expulsions of plasma and magnetic field from the solar corona into the heliosphere.
Interpolation with the polynomial kernels
We will then study the Reproducing Kernel Hilbert Spaces (or native spaces) of these kernels and their norms, and provide inclusion relations between spaces corresponding to different kernel parameters.
Operational solar flare forecasting via video-based deep learning
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.
SMEMO: Social Memory for Trajectory Forecasting
Effective modeling of human interactions is of utmost importance when forecasting behaviors such as future trajectories.
Ranked #5 on
Trajectory Prediction
on Stanford Drone
Score-oriented loss (SOL) functions
Loss functions engineering and the assessment of forecasting performances are two crucial and intertwined aspects of supervised machine learning.
MANTRA: Memory Augmented Networks for Multiple Trajectory Prediction
Autonomous vehicles are expected to drive in complex scenarios with several independent non cooperating agents.
