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eess.SP2025
Analyse comparative d'algorithmes de restauration en architecture dépliée pour des signaux chromatographiques parcimonieux
Mouna Gharbi, Silvia Villa, Emilie Chouzenoux +2
Data restoration from degraded observations, of sparsity hypotheses, is an active field of study. Traditional iterative optimization methods are now complemented by deep learning t…
eess.SP2019★ 67 cited
Learning to run a power network challenge for training topology controllers
Antoine Marot, Benjamin Donnot, Camilo Romero +4
For power grid operations, a large body of research focuses on using generation redispatching, load shedding or demand side management flexibilities. However, a less costly and pot…