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