Zobrazeno 1 - 10
of 30
pro vyhledávání: '"KHOUADJIA, Mostepha"'
The last decades have seen a drastic improvement of Machine Learning (ML), mainly driven by Deep Learning (DL). However, despite the resounding successes of ML in many domains, the impossibility to provide guarantees of conformity and the fragility o
Externí odkaz:
http://arxiv.org/abs/2409.13585
Neurosymbolic artificial intelligence is a growing field of research aiming to combine neural network learning capabilities with the reasoning abilities of symbolic systems. Informed multi-label classification is a sub-field of neurosymbolic AI which
Externí odkaz:
http://arxiv.org/abs/2404.08404
Neurosymbolic AI is a growing field of research aiming to combine neural networks learning capabilities with the reasoning abilities of symbolic systems. This hybridization can take many shapes. In this paper, we propose a new formalism for supervise
Externí odkaz:
http://arxiv.org/abs/2402.13019
Autor:
Khouadjia, Mostepha Redouane
Beaucoup de problèmes dans le monde réel ont une nature dynamique et peuvent être modélisés comme des problèmes dynamiques d'optimisation combinatoire. Cependant, les travaux de recherches sur l'optimisation dynamique se concentrent essentielle
Externí odkaz:
http://www.theses.fr/2011LIL10140/document
Autor:
Khouadjia, Mostepha Redouane, Schoenauer, Marc, Vidal, Vincent, Dréo, Johann, Savéant, Pierre
Publikováno v:
LION7 - Learning and Intelligent OptimizatioN Conference (2013)
Parameter tuning is recognized today as a crucial ingredient when tackling an optimization problem. Several meta-optimization methods have been proposed to find the best parameter set for a given optimization algorithm and (set of) problem instances.
Externí odkaz:
http://arxiv.org/abs/1305.2265
Autor:
Khouadjia, Mostepha Redouane, Schoenauer, Marc, Vidal, Vincent, Dréo, Johann, Savéant, Pierre
Publikováno v:
EvoCOP -- 13th European Conference on Evolutionary Computation in Combinatorial Optimisation 7832 (2013) 202-213
Most real-world Planning problems are multi-objective, trying to minimize both the makespan of the solution plan, and some cost of the actions involved in the plan. But most, if not all existing approaches are based on single-objective planners, and
Externí odkaz:
http://arxiv.org/abs/1305.1169
Autor:
Khouadjia, Mostepha Redouane, Schoenauer, Marc, Vidal, Vincent, Dréo, Johann, Savéant, Pierre
All standard AI planners to-date can only handle a single objective, and the only way for them to take into account multiple objectives is by aggregation of the objectives. Furthermore, and in deep contrast with the single objective case, there exist
Externí odkaz:
http://arxiv.org/abs/1212.5276
Autor:
Pasini, Kevin1,2 (AUTHOR), Khouadjia, Mostepha1 (AUTHOR), Samé, Allou2 (AUTHOR), Trépanier, Martin3 (AUTHOR), Oukhellou, Latifa2 (AUTHOR) latifa.oukhellou@univ-eiffel.fr
Publikováno v:
Neural Computing & Applications. Jan2022, Vol. 34 Issue 2, p1483-1507. 25p.
Autor:
Khouadjia, Mostepha R., Sarasola, Briseida, Alba, Enrique, Jourdan, Laetitia, Talbi, El-Ghazali
Publikováno v:
In Applied Soft Computing Journal April 2012 12(4):1426-1439
Publikováno v:
International Conference on Transport and Smart Cities
International Conference on Transport and Smart Cities, Sep 2021, Frankfort, Germany
The IEEE 2021 International Symposium on Computer Science and Intelligent Controls (ISCSIC)
The IEEE 2021 International Symposium on Computer Science and Intelligent Controls (ISCSIC), Nov 2021, Rome, Italy. pp.51-57, ⟨10.1109/ISCSIC54682.2021.00021⟩
International Conference on Transport and Smart Cities, Sep 2021, Frankfort, Germany
The IEEE 2021 International Symposium on Computer Science and Intelligent Controls (ISCSIC)
The IEEE 2021 International Symposium on Computer Science and Intelligent Controls (ISCSIC), Nov 2021, Rome, Italy. pp.51-57, ⟨10.1109/ISCSIC54682.2021.00021⟩
International audience; Demand estimation in public transport is critical for transport stakeholders. Thanks to the emerging technologies in recent years, many sources of mobility data are available to model passengers flow in public transport networ
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6caa7a921daac83a83f937b3339332b1
https://hal.archives-ouvertes.fr/hal-03358508/document
https://hal.archives-ouvertes.fr/hal-03358508/document