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3IA Côte d'Azur - Interdisciplinary Institute for Artificial Intelligence
3IA Côte d'Azur est l'un des quatre "Instituts interdisciplinaires d'intelligence artificielle" créés en France en 2019. Son ambition est de créer un écosystème innovant et influent au niveau local, national et international. L'institut 3IA Côte d'Azur est piloté par Université Côte d'Azur en partenariat avec les grands partenaires de l'enseignement supérieur et de la recherche de la région niçoise et de Sophia Antipolis : CNRS, Inria, INSERM, EURECOM, SKEMA Business School. L'institut 3IA Côte d'Azur est également soutenu par l'ECA, le CHU de Nice, le CSTB, le CNES, l'Institut Data ScienceTech et l'INRAE. Le projet a également obtenu le soutien de plus de 62 entreprises et start-ups.
Derniers dépôts
Documents en texte intégral
646
Notices
301
Statistiques par discipline
Mots clés
Coxeter triangulation
Event cameras
Autoencoder
Graph neural networks
Multiple Sclerosis
Image segmentation
Electrophysiology
Electronic medical record
Machine learning
Semantic segmentation
Privacy
CNN
Dimensionality reduction
Predictive model
Electrocardiogram
Segmentation
53B20
Echocardiography
Fibronectin
COVID-19
Semantic web
Image fusion
Alzheimer's disease
Topological Data Analysis
Atrial Fibrillation
Federated Learning
Autonomous vehicles
Explainable AI
Neural networks
Computing methodologies
Web of Things
Medical imaging
Embedded Systems
Hyperbolic systems of conservation laws
Extreme value theory
Super-resolution
Atrial fibrillation
Biomarkers
Brain-inspired computing
Knowledge graphs
Multi-Agent Systems
Information Extraction
Cable-driven parallel robot
Co-clustering
Convolutional neural network
Fluorescence microscopy
Excursion sets
OPAL-Meso
Semantic Web
Federated learning
Distributed optimization
Grammatical Evolution
Optimization
NLP Natural Language Processing
Physics-based learning
Clustering
Simulations
Clinical trials
Healthcare
Latent block model
Consensus
Diffusion MRI
Isomanifolds
Knowledge graph
Deep learning
Sparsity
Argument Mining
Domain adaptation
Differential privacy
Unsupervised learning
Extracellular matrix
Spiking Neural Networks
Uncertainty
Apprentissage profond
Deep Learning
Hyperspectral data
Computational Topology
Data augmentation
Artificial intelligence
Contrastive learning
Artificial Intelligence
FPGA
Convergence analysis
Spiking neural networks
Diffusion strategy
Macroscopic traffic flow models
Linked data
Anomaly detection
Dense labeling
Visualization
Computer vision
Persistent homology
SPARQL
Convolutional Neural Networks
RDF
Ontology Learning
Convolutional neural networks
MRI
Arguments
Linked Data