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10 résultats
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triés par
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Abstractions refinement for hybrid systems diagnosability analysisDX'17 28th International Workshop on Principles of Diagnosis, Sep 2017, Brescia, Italy. ⟨10.1007/978-3-319-74962-4_11⟩
Communication dans un congrès
hal-01676931v1
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A hyperbolic approach for learning communities on graphsData Mining and Knowledge Discovery, 2023, 37, pp.1090-1124. ⟨10.1007/s10618-022-00902-8⟩
Article dans une revue
hal-04022426v1
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A three level trajectory planning for autonomous transportation systemsCONGRÈS : SIA Simulation Numérique digital, Apr 2021, En ligne, France
Communication dans un congrès
hal-03202335v1
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A Practical Hands-on for Learning Graph Data Communities on ManifoldsGeometric Structures of Statistical Physics, Information Geometry, and Learning, pp.428-459, 2021, ⟨10.1007/978-3-030-77957-3_21⟩
Chapitre d'ouvrage
hal-03295547v1
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Counterexample-Guided Abstraction-Refinement for Hybrid Systems Diagnosability Analysis28th International Workshop on Principles of Diagnosis DX’17, Sep 2017, Brescia, Italy. pp.124-143
Communication dans un congrès
hal-01676889v1
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Apprentissage automatique sur des données de type graphe utilisant le plongement de Poincaré et les algorithmes stochastiques riemanniensConférence Nationale d'Intelligence Artificielle Année 2019, Jul 2019, Toulouse, France
Communication dans un congrès
hal-02473573v1
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Learning graph-structured data using Poincar\'e embeddings and Riemannian K-means algorithms2019
Pré-publication, Document de travail
hal-02339208v1
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Automating Abstraction Computations of Hybrid SystemsCICM 2018 - 11th Conference on Intelligent Computer Mathematics ; Workshop FVPS 2018 - Formal Verification of Physical Systems, Aug 2018, Hagenberg, Austria
Communication dans un congrès
hal-01839897v1
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Introduction to Geometric Learning in Python with GeomstatsSciPy 2020 - 19th Python in Science Conference, Jul 2020, Austin, Texas, United States. pp.48-57, ⟨10.25080/Majora-342d178e-007⟩
Communication dans un congrès
hal-02908006v1
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Geomstats: A Python Package for Riemannian Geometry in Machine LearningJournal of Machine Learning Research, 2020, 21 (223), pp.1-9
Article dans une revue
hal-02536154v2
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