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pro vyhledávání: '"Terrail, Jean Ogier du"'
Autor:
Terrail, Jean Ogier du, Klopfenstein, Quentin, Li, Honghao, Mayer, Imke, Loiseau, Nicolas, Hallal, Mohammad, Debouver, Michael, Camalon, Thibault, Fouqueray, Thibault, Castro, Jorge Arellano, Yanes, Zahia, Dahan, Laetitia, Taïeb, Julien, Laurent-Puig, Pierre, Bachet, Jean-Baptiste, Zhao, Shulin, Nicolle, Remy, Cros, Jérome, Gonzalez, Daniel, Carreras-Torres, Robert, Velasco, Adelaida Garcia, Abdilleh, Kawther, Doss, Sudheer, Balazard, Félix, Andreux, Mathieu
External control arms (ECA) can inform the early clinical development of experimental drugs and provide efficacy evidence for regulatory approval. However, the main challenge in implementing ECA lies in accessing real-world or historical clinical tri
Externí odkaz:
http://arxiv.org/abs/2311.16984
We consider a cross-silo federated learning (FL) setting where a machine learning model with a fully connected first layer is trained between different clients and a central server using FedAvg, and where the aggregation step can be performed with se
Externí odkaz:
http://arxiv.org/abs/2306.07644
Autor:
Terrail, Jean Ogier du, Ayed, Samy-Safwan, Cyffers, Edwige, Grimberg, Felix, He, Chaoyang, Loeb, Regis, Mangold, Paul, Marchand, Tanguy, Marfoq, Othmane, Mushtaq, Erum, Muzellec, Boris, Philippenko, Constantin, Silva, Santiago, Teleńczuk, Maria, Albarqouni, Shadi, Avestimehr, Salman, Bellet, Aurélien, Dieuleveut, Aymeric, Jaggi, Martin, Karimireddy, Sai Praneeth, Lorenzi, Marco, Neglia, Giovanni, Tommasi, Marc, Andreux, Mathieu
Federated Learning (FL) is a novel approach enabling several clients holding sensitive data to collaboratively train machine learning models, without centralizing data. The cross-silo FL setting corresponds to the case of few ($2$--$50$) reliable cli
Externí odkaz:
http://arxiv.org/abs/2210.04620
Autor:
Marchand, Tanguy, Muzellec, Boris, Beguier, Constance, Terrail, Jean Ogier du, Andreux, Mathieu
The Yeo-Johnson (YJ) transformation is a standard parametrized per-feature unidimensional transformation often used to Gaussianize features in machine learning. In this paper, we investigate the problem of applying the YJ transformation in a cross-si
Externí odkaz:
http://arxiv.org/abs/2210.01639
Since 2014, the NIH funded iDASH (integrating Data for Analysis, Anonymization, SHaring) National Center for Biomedical Computing has hosted yearly competitions on the topic of private computing for genomic data. For one track of the 2020 iteration o
Externí odkaz:
http://arxiv.org/abs/2101.02997
While federated learning is a promising approach for training deep learning models over distributed sensitive datasets, it presents new challenges for machine learning, especially when applied in the medical domain where multi-centric data heterogene
Externí odkaz:
http://arxiv.org/abs/2008.07424
Detecting small vehicles in aerial images is a difficult job that can be challenging even for humans. Rotating objects, low resolution, small inter-class variability and very large images comprising complicated backgrounds render the work of photo-in
Externí odkaz:
http://arxiv.org/abs/1809.07628
Object detection-the computer vision task dealing with detecting instances of objects of a certain class (e.g., 'car', 'plane', etc.) in images-attracted a lot of attention from the community during the last 5 years. This strong interest can be expla
Externí odkaz:
http://arxiv.org/abs/1809.03193