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Welcome on HAL open archive of PaRis AI Research InstitutE
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 web site.
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 web site.
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References
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Francesco Galati, Daniele Falcetta, Rosa Cortese, Barbara Casolla, Ferran Prados, et al.. A2V: A Semi-Supervised Domain Adaptation Framework for Brain Vessel Segmentation via Two-Phase Training Angiography-to-Venography Translation. BMVC 2023, 34th British Machine Vision Conference, Nov 2023, Aberdeen, United Kingdom. ⟨hal-04195756v2⟩
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Ravi Hassanaly, Camille Brianceau, Olivier Colliot, Ninon Burgos. Unsupervised anomaly detection in 3D brain FDG PET: A benchmark of 17 VAE-based approaches. Deep Generative Models workshop at the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023), Oct 2023, Vancouver, Canada. ⟨hal-04185304⟩
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Keywords
Prediction
Bias
Reinforcement learning
French
Huntington's disease
Microscopy
Ensemble learning
Electronic health records
Zero-Shot Learning
Deep Learning
MRI
Bayesian logistic regression
Dimensionality reduction
Clinical data warehouse
Hippocampus
Confidence interval
Simulation
Adaptation
Kalman filter
Segmentation
Alzheimer’s disease
Curvature penalization
Clinical trial
Stochastic optimization
Classification
Apprentissage par renforcement
Alzheimer's disease
Deep learning
Longitudinal data
Variational autoencoder
Machine Learning
Active learning
Breast cancer
BERT
Dementia
Image synthesis
Brain
Data imputation
Apprentissage faiblement supervisé
Clustering
Language Model
RNA localization
Action recognition
PET
HIV
Human-in-the-loop
Artificial intelligence
Complex systems
Magnetic resonance imaging
Semantics
Medical imaging
Functional connectivity
Optimization
Representation learning
ADNI
ASPM
Alzheimer
Literature
Longitudinal study
Neural networks
Wavelets
Interpretability
Computer vision
Software
Mixture models
Transcriptomics
Genomics
Computer Vision
Riemannian geometry
Convex optimization
SmFISH
Cancer
Computational Pathology
Self-supervised learning
Attention Mechanism
Sparsity
CamemBERT
Object discovery
Multiple Sclerosis
Graph alignment
Anatomical MRI
Association
Object detection
Alzheimer's Disease
Machine learning
Choroid plexus
Weakly-supervised learning
Poetry generation
Data visualization
Neuroimaging
Image processing
Whole slide images
Computational modeling
Clinical Data Warehouse
BCI
Brain MRI
Reproducibility
Kernel methods
Contrastive predictive coding
Multiple sclerosis
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