Ronan Fablet
24
Documents
Publications
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Learning stochastic dynamical systems with neural networks mimicking the Euler-Maruyama schemeEUSIPCO 2021: 29th European Signal Processing Conference, Aug 2021, Dublin, Ireland. ⟨10.23919/EUSIPCO54536.2021.9616068⟩
Communication dans un congrès
hal-03265004v1
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END-TO-END LEARNING OF VARIATIONAL MODELS AND SOLVERS FOR THE RESOLUTION OF INTERPOLATION PROBLEMSICASSP 2021 : IEEE International Conference on Acoustics, Speech and Signal Processing, Jun 2021, Toronto, United States. ⟨10.1109/ICASSP39728.2021.9414629⟩
Communication dans un congrès
hal-03139133v1
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Joint learning of variational data assimilation models and solversECMWF-ESA 2020 - Workshop on Machine Learning for Earth System Observation and Prediction, Oct 2020, Reading, United Kingdom
Communication dans un congrès
hal-02927356v1
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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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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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Filtering Internal tides from wide-swath altimeter data using Convolutional Neural Networks.IGARSS 2020: IEEE International Geoscience and Remote Sensing Symposium, Sep 2020, Waikoloa, United States. ⟨10.1109/IGARSS39084.2020.9323531⟩
Communication dans un congrès
hal-02941320v1
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Learning Endmember Dynamics in Multitemporal Hyperspectral Data Using A State-Space Model FormulationICASSP 2020 - IEEE International Conference on Acoustics, Speech and Signal Processing, IEEE, May 2020, Barcelone (virtual), Spain. ⟨10.1109/ICASSP40776.2020.9053787⟩
Communication dans un congrès
hal-02490607v1
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Learning differential transport operators for the joint super-resolution of sea surface tracers and prediction of subgrid-scale features.IGARSS 2019 : 2019 IEEE International Geoscience and Remote Sensing Symposium, Jul 2019, Yokohama, Japan. ⟨10.1109/IGARSS.2019.8900571⟩
Communication dans un congrès
hal-02285698v1
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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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Sea surface dynamics reconstruction using neural networks based kalman filterIGARSS 2019 - International Geoscience and remote Sensing Symposium, Jul 2019, Yokohama, Japan. pp.1-5, ⟨10.1109/IGARSS.2019.8898086⟩
Communication dans un congrès
hal-02285697v1
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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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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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End-to-end learning of optimal interpolators for geophysical dynamicsCI 2019 : 9th International Workshop on Climate Informatics, 2019, Paris, France
Communication dans un congrès
hal-02285701v1
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Spatial multi-modality as a way to improve both performance and interpretability of deep learning models to reconstruct phytoplankton time-series in the global oceanEGU General Assembly 2022, May 2022, Vienna, Austria
Poster de conférence
hal-04231217v1
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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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Filtering Internal Tides From Wide-Swath Altimeter Data Using Convolutional Neural Networks2020
Pré-publication, Document de travail
hal-03084229v1
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Joint learning of variational representations and solvers for inverse problems with partially-observed data2020
Pré-publication, Document de travail
hal-02802121v1
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