Mixed Deep Gaussian Mixture Model: a clustering model for mixed datasets R Fuchs, D Pommeret, C Viroli Advances in Data Analysis and Classification 16 (1), 31-53, 2022 | 13 | 2022 |
Automatic recognition of flow cytometric phytoplankton functional groups using convolutional neural networks R Fuchs, M Thyssen, V Creach, M Dugenne, L Izard, M Latimier, ... Limnology and Oceanography: Methods 20 (7), 387-399, 2022 | 10 | 2022 |
Phytoplankton reaction to an intense storm in the north-western Mediterranean Sea S Barrillon, R Fuchs, AA Petrenko, C Comby, A Bosse, C Yohia, JL Fuda, ... Biogeosciences 20 (1), 141-161, 2023 | 7 | 2023 |
A RUpture‐Based detection method for the Active mesopeLagIc Zone (RUBALIZ): A crucial step toward rigorous carbon budget assessments R Fuchs, CMJ Baumas, M Garel, D Nerini, FAC Le Moigne, C Tamburini Limnology and Oceanography: Methods 21 (1), 24-39, 2023 | 6 | 2023 |
Reconstructing the ocean's mesopelagic zone carbon budget: sensitivity and estimation of parameters associated with prokaryotic remineralization C Baumas, R Fuchs, M Garel, JC Poggiale, L Memery, FAC Le Moigne, ... Biogeosciences 20 (19), 4165-4182, 2023 | 5 | 2023 |
Intermittent Upwelling Events Trigger Delayed, Major, and Reproducible Pico‐Nanophytoplankton Responses in Coastal Oligotrophic Waters R Fuchs, V Rossi, C Caille, N Bensoussan, C Pinazo, O Grosso, ... Geophysical Research Letters 50 (5), e2022GL102651, 2023 | 2 | 2023 |
MIAMI: MIxed Data Augmentation MIxture R Fuchs, D Pommeret, S Stocksieker International Conference on Computational Science and Its Applications, 113-129, 2022 | 1 | 2022 |
Méthodes neuronales et données mixtes: vers une meilleure résolution spatio-temporelle des écosystèmes marins et du phytoplancton R Fuchs Aix-Marseille, 2022 | | 2022 |
Standard vocabulary, consensual functional groups and automated classification for phytoplankton high throughput datasets using automated flow cytometry M Thyssen, R Fuchs, V Créach, LF Artigas, G Grégori, P Marrec, ... ASLO 2021, 2021 | | 2021 |