15 algorithm-development-"Multiple"-"University-of-California,-Santa-Cruz" positions in France
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, sensor failures, or the aggregation of datasets from multiple sources. There is a rich literature on how to impute missing values, for example, considering the EM algorithm [Dempster et al., 1977], low
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train robust machine learning (ML) algorithms without exchanging the actual data. The benefits of such a decentralized technology over personal and confidential data are multiple and already include some
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an internationally recognized research team at LAAS-CNRS in Toulouse, focused on developing autonomous mobile machines that integrate perception, reasoning, learning, action, and reaction capabilities
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École nationale des ponts et chaussées | Champs sur Marne, le de France | France | about 2 months ago
combining multiple ML models have been explored to optimise predictions, enabling algorithms to collaborate and achieve better results. Ensemble methods, in particular, have demonstrated superior performance
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analyzed. The tensor model structure estimated by suitable optimization algorithms, such as that recently developed in [GOU20], will be considered as a starting point. • Exploiting data multimodality and