| End-to-end deep representation learning for time series clustering: a comparative study B Lafabregue, J Weber, P Gançarski, G Forestier Data Mining and Knowledge Discovery 36 (1), 29-81, 2022 | 64 | 2022 |
| Constrained distance based clustering for time-series: a comparative and experimental study T Lampert, TBH Dao, B Lafabregue, N Serrette, G Forestier, B Crémilleux, ... Data Mining and Knowledge Discovery 32, 1663-1707, 2018 | 44 | 2018 |
| Constrained distance-based clustering for satellite image time-series T Lampert, B Lafabregue, N Serrette, C Vrain, P Gançarski IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2019 | 15 | 2019 |
| Constrained distance based k-means clustering for satellite image time-series T Lampert, B Lafabregue, P Gançarski IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium …, 2019 | 13 | 2019 |
| Deep constrained clustering applied to satellite image time series B Lafabregue, J Weber, P Gançarski, G Forestier ECML/PKDD Workshop on Machine Learning for Earth Observation Data (MACLEAN), 2019 | 12 | 2019 |
| Incremental constrained clustering with application to remote sensing images time series B Lafabregue, P Gançarski, J Weber, G Forestier 2022 IEEE International Conference on Data Mining Workshops (ICDMW), 814-823, 2022 | 3 | 2022 |
| FODOMUST-une plateforme de clustering collaboratif sous contraintes incrémental de séries temporelles P Gançarski, B Lafabregue, AD Salaou, V Harrison EGC, 507-514, 2020 | 3 | 2020 |
| Grad Centroid Activation Mapping for Convolutional Neural Networks B Lafabregue, J Weber, P Gançarski, G Forestier 2021 IEEE 33rd International Conference on Tools with Artificial …, 2021 | 1 | 2021 |
| Clustering et apprentissage profond sous contraintes pour l'analyse de séries temporelles: application à l'analyse temporelle incrémentale en télédétection B Lafabregue Université de Haute Alsace-Mulhouse, 2021 | 1 | 2021 |
| Learning from few labeled time series with segment-based self-supervised learning: application to remote-sensing A Saget, B Lafabregue, A Cornuéjols, P Gançarski Proceedings of SPAICE2024: The First Joint European Space Agency/IAA …, 2024 | | 2024 |
| Écrêter la valeur cible ou filtrer les données en maintenance prévisionnelle: exemple de C-MAPSS N Mountasir, B Lafabregue, B Albert, N Lachiche Extraction et Gestion des Connaissances (EGC'24), Dijon, France, 347-348, 2024 | | 2024 |
| Contrôle visuel de la qualité perçue par apprentissage automatique N Mountasir, B Albert, B Lafabregue, N Lachiche ORASIS 2023, 2023 | | 2023 |
| Deep Clustering Methods Study Applied to Satellite Images Time Series B Lafabregue, A Puissant, J Weber, G Forestier IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium …, 2022 | | 2022 |
| Constrained clustering and deep learning for time series analysis: with application to incremental temporal analysis for remote sensing B Lafabregue < bound method Organization. get_name_with_acronym of< Organization: TEL …, 2021 | | 2021 |
| Cybersecurity with Machine Learning for industrial networks SDF Théoleyre, B Lafabregue, T Gaberan | | |
| Clustering contraint par apprentissage profond appliqué aux séries temporelles d’images satellites B Lafabregue, J Weber, P Gançarski, G Forestier | | |
Pierre GancarskiUniversité de Strasbourg - ICubeVerified email at unistra.fr
