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12 résultats
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triés par
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Robust Anomaly Detection on Unreliable DataDSN 2019 - 49th IEEE/IFIP International Conference on Dependable Systems and Networks, Jun 2019, Portland, Oregon, United States. ⟨10.1109/DSN.2019.00068⟩
Communication dans un congrès
hal-02056558v1
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An Exploratory Analysis on Users’ Contributions in Federated Learning2020 Second IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA), Oct 2020, Atlanta, United States. pp.20-29, ⟨10.1109/TPS-ISA50397.2020.00014⟩
Communication dans un congrès
hal-03341707v1
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Robust (Deep) Learning Framework Against Dirty Labels and BeyondCogMI 2019 - IEEE International Conference on Cognitive Machine Intelligence, Dec 2019, Los Angeles, California, United States
Communication dans un congrès
hal-02374376v1
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Active Learning from Unreliable DataEuroDW 2019 - 13th EuroSys Doctoral Workshop, Mar 2019, Dresde, Germany
Communication dans un congrès
hal-02045455v1
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Event-Based Control for Online Training of Neural NetworksIEEE Control Systems Letters, 2020, 4 (3), pp.773 - 778. ⟨10.1109/LCSYS.2020.2981984⟩
Article dans une revue
hal-02509604v1
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Extracting knowledge from macroeconomic data, images and unreliable dataAutomatic Control Engineering. Université Grenoble Alpes [2020-..], 2020. English. ⟨NNT : 2020GRALT074⟩
Thèse
tel-03216611v2
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Modelling and Optimal Control of MIMO System - France Macroeconomic Model CaseECC 2023 - 21st European Control Conference, International Federation of Automatic Control; IEEE Control Systems Society (CSS), Jun 2023, Bucarest, Romania. ⟨10.23919/ECC57647.2023.10178184⟩
Communication dans un congrès
hal-04039452v1
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Modelling and Optimal Control of MIMO System -France Macroeconomic Model Case2022
Pré-publication, Document de travail
hal-03632679v1
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Feedback Control for Online Training of Neural NetworksCCTA 2019 - 3rd IEEE Conference on Control Technology and Applications, Aug 2019, Hong Kong, China. ⟨10.1109/CCTA.2019.8920662⟩
Communication dans un congrès
hal-02115916v2
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Enhancing Robustness of On-line Learning Models on Highly Noisy DataIEEE Transactions on Dependable and Secure Computing, 2021, 18 (5), pp.2177-2192. ⟨10.1109/TDSC.2021.3063947⟩
Article dans une revue
hal-03752315v1
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QActor: On-line Active Learning for Noisy Labeled Stream Data2021
Pré-publication, Document de travail
hal-03177177v1
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Modelling and dynamic analysis of the domestic demand influence on the economic growth of ChinaConference on International Development Economics, CERDI, Nov 2018, Clermont-Ferrand, France
Communication dans un congrès
hal-01980620v1
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