Ronan Fablet
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GeoTrackNet-A Maritime Anomaly Detector using Probabilistic Neural Network Representation of AIS Tracks and A Contrario DetectionIEEE Transactions on Intelligent Transportation Systems, 2021, ⟨10.1109/TITS.2021.3055614⟩
Article dans une revue
hal-02388260v4
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Learning Latent Dynamics for Partially-Observed Chaotic SystemsChaos: An Interdisciplinary Journal of Nonlinear Science, 2020, 30 (10), ⟨10.1063/5.0019309⟩
Article dans une revue
hal-02274705v1
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Detection of Abnormal Vessel Behaviors from AIS data using GeoTrackNet: from the Laboratory to the OceanMBDW 2020 : 2nd Maritime Big Data Workshop part of MDM 2020 : 21st IEEE International Conference on Mobile Data Management, Jun 2020, Versailles, France. pp.264-268, ⟨10.1109/MDM48529.2020.00061⟩
Communication dans un congrès
hal-02523279v2
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Assimilation-based Learning of Chaotic Dynamical Systems from Noisy and Partial DataICASSP 2020 : International Conference on Acoustics, Speech, and Signal Processing, May 2020, Barcelona, Spain. ⟨10.1109/ICASSP40776.2020.9054718⟩
Communication dans un congrès
hal-02436060v2
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Learning Chaotic and Stochastic Dynamics from Noisy and Partial Observation using Variational Deep LearningCI'2020 : 10th International Conference on Climate Informatics, Sep 2020, Oxford, United Kingdom
Communication dans un congrès
hal-02941313v1
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Learning Constrained Dynamical Embeddings for Geophysical DynamicsCI 2019 : 9th International Workshop on Climate Informatics, 2019, Paris, France
Communication dans un congrès
hal-02285700v1
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Data assimilation schemes as a framework for learning dynamical model from partial and noisy observationsEGU 2019 : General Assembly 2019 of the European Geosciences Union, Apr 2019, Vienna, Austria
Communication dans un congrès
hal-02110359v1
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Learning ocean dynamical priors from noisy data using assimilation-derived neural netsIGARSS 2019 - International Geoscience and remote Sensing Symposium, Jul 2019, Yokohama, Japan. pp.1-3, ⟨10.1109/IGARSS.2019.8900345⟩
Communication dans un congrès
hal-02285693v1
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Variational Deep Learning for the Identification and Reconstruction of Chaotic and Stochastic Dynamical Systems from Noisy and Partial Observations2021
Pré-publication, Document de travail
hal-02931101v7
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