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2020 – today
- 2026
[i2]Nassime Mountasir, Baptiste Lafabregue, Bruno Albert, Nicolas Lachiche:
A Visualization for Comparative Analysis of Regression Models. CoRR abs/2603.19291 (2026)- 2025
- [j4]Baptiste Lafabregue
, Pierre Gançarski:
I-SAMARAH, an incremental constrained clustering applied to remote sensing images. Neural Comput. Appl. 37(27): 22941-22965 (2025) - [c9]Antoine Saget, Baptiste Lafabregue, Antoine Cornuéjols, Pierre Gançarski:
Apprendre avec peu d'exemples : Une approche auto-supervisée basée sur les segments avec application à la télédétection. EGC 2025: 493-494 - [c8]Antoine Saget, Baptiste Lafabregue, Antoine Cornuéjols, Pierre Gançarski:
Resampling Augmentation for Time Series Contrastive Learning: Application to Remote Sensing. TerraBytes 2025: 32-45 - [e1]Thomas Guyet, Baptiste Lafabregue, Aurélie Leborgne:
Extraction et Gestion des Connaissances, EGC 2025, Strasbourg, France, January 29-31, 2025. RNTI E-41, Editions RNTI 2025, ISBN 9791096289226 [contents] - [i1]Antoine Saget, Baptiste Lafabregue, Antoine Cornuéjols, Pierre Gançarski:
Resampling Augmentation for Time Series Contrastive Learning: Application to Remote Sensing. CoRR abs/2506.18587 (2025) - 2024
- [c7]Nassime Mountasir, Baptiste Lafabregue, Bruno Albert, Nicolas Lachiche:
Écrêter la valeur cible ou filtrer les données en maintenance prévisionnelle : exemple de C-MAPSS. EGC 2024: 347-348 - 2022
- [j3]Baptiste Lafabregue, Jonathan Weber, Pierre Gançarski, Germain Forestier:
End-to-end deep representation learning for time series clustering: a comparative study. Data Min. Knowl. Discov. 36(1): 29-81 (2022) - [c6]Baptiste Lafabregue, Pierre Gançarski, Jonathan Weber, Germain Forestier:
Incremental constrained clustering with application to remote sensing images time series. ICDM (Workshops) 2022: 814-823 - [c5]Baptiste Lafabregue, Anne Puissant, Jonathan Weber, Germain Forestier:
Deep Clustering Methods Study Applied to Satellite Images Time Series. IGARSS 2022: 195-198 - 2021
- [b1]Baptiste Lafabregue:
Clustering et apprentissage profond sous contraintes pour l'analyse de séries temporelles: application à l'analyse temporelle incrémentale en télédétection. (Constrained clustering and deep learning for time series analysis: with application to incremental temporal analysis for remote sensing). University of Upper Alsace, Mulhouse, France, 2021 - [c4]Baptiste Lafabregue, Jonathan Weber, Pierre Gançarski, Germain Forestier:
Grad Centroid Activation Mapping for Convolutional Neural Networks. ICTAI 2021: 184-191 - 2020
- [c3]Pierre Gançarski, Baptiste Lafabregue, Abdoul-Djawadou Salaou, Harrison Vernier:
FODOMUST - Une plateforme de clustering collaboratif sous contraintes incrémental de séries temporelles. EGC 2020: 507-514
2010 – 2019
- 2019
- [j2]Thomas Andrew Lampert, Baptiste Lafabregue, Thi-Bich-Hanh Dao, Nicolas Serrette, Christel Vrain, Pierre Gançarski:
Constrained Distance-Based Clustering for Satellite Image Time-Series. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 12(11): 4606-4621 (2019) - [c2]Thomas Andrew Lampert, Baptiste Lafabregue, Pierre Gançarski:
Constrained Distance based K-Means Clustering for Satellite Image Time-Series. IGARSS 2019: 2419-2422 - [c1]Baptiste Lafabregue, Jonathan Weber, Pierre Gançarski, Germain Forestier:
Deep Constrained Clustering applied to Satellite Image Time Series. MACLEAN@PKDD/ECML 2019 - 2018
- [j1]Thomas Andrew Lampert, Thi-Bich-Hanh Dao, Baptiste Lafabregue, Nicolas Serrette, Germain Forestier, Bruno Crémilleux, Christel Vrain, Pierre Gançarski:
Constrained distance based clustering for time-series: a comparative and experimental study. Data Min. Knowl. Discov. 32(6): 1663-1707 (2018)
Coauthor Index
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