Zobrazeno 1 - 10
of 1 053
pro vyhledávání: '"Séjourné, A."'
Publikováno v:
Proceedings of the National Academy of Sciences, 2022, 119, e2112930118
What was the nature of the Late Hesperian climate? Warm and wet or cold and dry? Formulated this way the question leads to an apparent paradox since both options seem implausible. A warm and wet climate would have produced extensive fluvial erosion b
Externí odkaz:
http://arxiv.org/abs/2310.00461
Optimal transport (OT) has emerged as a powerful framework to compare probability measures, a fundamental task in many statistical and machine learning problems. Substantial advances have been made over the last decade in designing OT variants which
Externí odkaz:
http://arxiv.org/abs/2306.07176
Optimal Transport (OT) has recently emerged as a central tool in data sciences to compare in a geometrically faithful way point clouds and more generally probability distributions. The wide adoption of OT into existing data analysis and machine learn
Externí odkaz:
http://arxiv.org/abs/2211.08775
Autor:
Salgado, Federico E., Iannelli, Sofía B., Pallares, Carlos, Ramseyer, Victor, Rabatel, Antoine, Séjourné, Antoine, Folguera, Andrés
Publikováno v:
In Journal of South American Earth Sciences 15 October 2024 146
Autor:
Sarmast, Sepideh, Séjourné, Stephan, Wigston, Andrew, Fraser, Roydon A., Dusseault, Maurice B.
Publikováno v:
In Energy Conversion and Management 1 September 2024 315
Unbalanced optimal transport (UOT) extends optimal transport (OT) to take into account mass variations to compare distributions. This is crucial to make OT successful in ML applications, making it robust to data normalization and outliers. The baseli
Externí odkaz:
http://arxiv.org/abs/2201.00730
Autor:
Rivard, C., Lavoie, D., Bordeleau, G., Huchet, F., Lefebvre, R., Duchesne, M.J., Pinet, N., Séjourné, S., Crow, H., Bellefleur, G., Brake, V., Hinds, S.
Publikováno v:
In Groundwater for Sustainable Development May 2024 25
The Rosetta mission provided us with detailed data of the surface of the nucleus of comet 67P/Churyumov-Gerasimenko.In order to better understand the physical processes associated with the comet activity and the surface evolution of its nucleus, we p
Externí odkaz:
http://arxiv.org/abs/2104.13741
Optimal transport distances have found many applications in machine learning for their capacity to compare non-parametric probability distributions. Yet their algorithmic complexity generally prevents their direct use on large scale datasets. Among t
Externí odkaz:
http://arxiv.org/abs/2103.03606
Comparing metric measure spaces (i.e. a metric space endowed with aprobability distribution) is at the heart of many machine learning problems. The most popular distance between such metric measure spaces is theGromov-Wasserstein (GW) distance, which
Externí odkaz:
http://arxiv.org/abs/2009.04266