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(MERCE). The main objective is to develop safe planning and reinforcement learning algorithms with various degrees of confidence for variants of Markov decision processes. More precisely, we will develop
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Inria, the French national research institute for the digital sciences | Saint Martin, Midi Pyrenees | France | 1 day ago
the training. This approach has multiple benefits. First, there is no need to store and move a huge pre-created data set: float matrices of data can take terrabytes of memory, and reading them from the disk
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Laboratoire National de Métrologie et d'Essais - LNE | Paris 15, le de France | France | 23 days ago
on the primary data. They are often captured from multiple scans made from different locations to unspecified surface locations. This introduces novel sources of uncertainty while potentially reducing the impact
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catheters – i.e., equipped with multiple electrodes – are increasingly used as they facilitate the electroanatomic mapping of the atria before the ablation phase. However, most ablation strategies neglect
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well as in the design of machine learning algorithms (ANN, SVM, Decision Tree, and Random Forest) applied to healthcare, will be particularly valued. Proficiency in programming tools (Matlab) and statistical
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minimizing error and maximizing efficiency, is computationally challenging—no known polynomial-time algorithm exists to solve it optimally in all cases. Because of this complexity, researchers typically rely
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Inria, the French national research institute for the digital sciences | Villeurbanne, Rhone Alpes | France | about 2 months ago
arithmetic cores for FPGAs). The team hosts 6 faculty, 6 PhD students, 3 postdocs, 2 engineer, and multiple research interns. Additional information can be found on team website: https://team.inria.fr/emeraude
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" setting [4], where the benchmark is the optimal online algorithm rather than the expected maximum, making the competition more dynamic. - Study settings where multiple items are allocated to buyers, such as
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aims to provide an integrated assessment of urban vegetation from multiple perspectives, including its social functions and usage requirements, the ecosystem health of green spaces linked to plant
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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