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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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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
Multiple sclerosis
French
Hippocampus
Attention Mechanism
Interpretability
Image processing
Alzheimer’s disease
Reproducibility
Stochastic optimization
Cancer
Action recognition
Reinforcement learning
Alzheimer's disease
Kalman filter
Dementia
Longitudinal data
Magnetic resonance imaging
Multiple Sclerosis
Genomics
Functional connectivity
Curvature penalization
Variational autoencoder
Alzheimer's Disease
Choroid plexus
SmFISH
Confidence interval
Active learning
Contrastive predictive coding
Anatomical MRI
Bias
Convexity shape prior
Longitudinal study
Alzheimer
Computer vision
HIV
Dimensionality reduction
Validation
Brain MRI
Clinical trial
Deep learning
Apprentissage par renforcement
Neuroimaging
Computer Vision
Poetry generation
Brain
Mixture models
Prediction
Microscopy
Computational modeling
ASPM
RNA localization
Clinical Data Warehouse
Simulation
Clustering
Classification
Computational Pathology
Semantics
Association
Segmentation
Bayesian logistic regression
Machine Learning
Graph alignment
Data visualization
Artificial intelligence
Transcriptomics
Weakly-supervised learning
Self-supervised learning
Breast cancer
Clinical data warehouse
Convex optimization
BCI
Representation learning
ADNI
Huntington's disease
BERT
Riemannian geometry
Literature
Optimization
Object discovery
Apprentissage faiblement supervisé
Whole slide images
Electronic health records
Deep Learning
Kernel methods
Wavelets
Medical imaging
Adaptation
Software
Object detection
Human-in-the-loop
PET
Neural networks
Complex systems
Machine learning
CamemBERT
MRI
Ensemble learning
Image synthesis
Sparsity
Data imputation
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