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The 3AI plan  

The Prairie Institute (PaRis AI Research InstitutE) is one of the four French Institutes of Artificial Intelligence, which were created as part of the national French initiative on AI announced by President Emmanuel Macron on May 29, 2018.
A major part of this ambitious plan, which has a total budget of one billion euros, was the creation of a small number of interdisciplinary AI research institutes (or “3IAs” for “Instituts Interdisciplinaires d’Intelligence Artificielle”). After an open call for participation in July 2018 and two rounds of review by an international scientific committee, the Grenoble, Nice, Paris and Toulouse projects have officially received the 3IA label on April 24, 2019, with a total budget of 75 million Euros.

For more information about PaRis AI Research InstitutE, see our website.

 

 

 

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Keywords

MRI Genomics Curvature penalization Mixed-effects models Convexity shape prior HIV Representation learning Deep learning Prediction ADNI Speech perception Dimensionality reduction Longitudinal analysis Association Neighbourhood consensus Cancer Action recognition Brain MRI Poetry generation Classification Cross-cohort replication Interpretability Data Augmentation Bayesian logistic regression Medical imaging French Independent Component Analysis Disease progression model Evaluation metrics Data leakage EM algorithm Data imputation Language Modeling Digital Humanities Anatomical MRI High-dimensional data Contrastive predictive coding Graph alignment Clinical data warehouse Manifold learning Speech recognition Stochastic optimization BCI Longitudinal data Intrinsic dimension Eikonal equation Alzheimer’s disease Online learning Magnetic resonance imaging Emergence Convex optimization First-order methods Performance estimation Alzheimer's disease Neuroimaging Adaptation Computer vision Graphical models Inverse problems Multimodal Functional connectivity Artificial intelligence Brain Data visualization Human-in-the-loop Kernel methods Microscopy Open-source Mixture models BERT Deep Learning Image processing Principal trees Impulse control disorders Local translation Imitation learning Object detection Machine learning Bias Riemannian geometry Optimization Dementia Object discovery Diabetes Molecular networks Computer Vision Kalman filter MCMC-SAEM Optimal control Ensemble learning Reproducibility ASPM Neural networks Computational modeling Data treatment Clustering Erdős-Rényi random graphs Longitudinal study High Content Screening Machine Learning

 

 

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