Loading...
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
![Chargement de la page](/img/loading.gif)
Documents en texte intégral
667
Notices
304
Statistiques par discipline
Mots clés
Persistent homology
Convolutional Neural Networks
Diffusion MRI
Brain-inspired computing
Convergence analysis
CNN
Uncertainty
Convolutional neural network
Macroscopic traffic flow models
MRI
Semantic web
Event cameras
Fluorescence microscopy
Fibronectin
Extracellular matrix
Coxeter triangulation
Grammatical Evolution
Multi-Agent Systems
Artificial intelligence
Computing methodologies
Change point detection
OPAL-Meso
Domain adaptation
Super-resolution
Autonomous vehicles
Autoencoder
Image segmentation
Latent block model
Medical imaging
Knowledge graph
Differential privacy
Diffusion strategy
Computer vision
Optimization
Image fusion
NLP Natural Language Processing
FPGA
Cable-driven parallel robot
Neural networks
Predictive model
Unsupervised learning
Biomarkers
Web of Things
Federated Learning
Data augmentation
Atrial fibrillation
Topological Data Analysis
Excursion sets
Consensus
Information Extraction
Semantic segmentation
Privacy
Sparsity
Distributed optimization
Artificial Intelligence
Linked Data
Deep learning
Computational Topology
Electrocardiogram
Federated learning
Knowledge graphs
Argument Mining
Machine learning
Arguments
Contrastive learning
Healthcare
Graph neural networks
SPARQL
Clustering
Isomanifolds
Hyperspectral data
Semantic Web
Alzheimer's disease
Dense labeling
Ontology Learning
Echocardiography
Visualization
Deep Learning
Multiple Sclerosis
Linked data
Electronic medical record
Convolutional neural networks
Spiking Neural Networks
Extreme value theory
Hyperbolic systems of conservation laws
Anomaly detection
53B20
COVID-19
Co-clustering
Graph signal processing
Segmentation
Atrial Fibrillation
Dimensionality reduction
Electrophysiology
Clinical trials
RDF
Spiking neural networks
Embedded Systems
Apprentissage profond
Explainable AI