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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
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Alessandro Viani, Boris A Gutman, Emile d'Angremont, Marco Lorenzi. Disease Progression Modelling and Stratification for detecting sub-trajectories in the natural history of pathologies: application to Parkinson's Disease trajectory modelling. Longitudinal Disease Tracking and Modelling with Medical Images and Data, Oct 2024, Marrachech, Morocco. ⟨hal-04833565⟩
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Javier Villar-Valero, Jesus Jairo Rodríguez Padilla, Buntheng Ly, Juan F Gomez, Mihaela Pop, et al.. Exploring Chemotherapy-Induced Cardiotoxicity Combining A 3D Computational Model and Preclinical Cardiac Imaging Data. MICCAI 2024, STACOM Workshop, Oct 2024, Marrakesh, Maroc, Morocco. ⟨hal-04840698⟩
Documents en texte intégral
735
Notices
332
Statistiques par discipline
Mots clés
Artificial intelligence
Argument Mining
Healthcare
Spiking Neural Networks
Diffusion MRI
Deep learning
Extracellular matrix
Computing methodologies
Information Extraction
Machine learning
Neural networks
Adversarial classification
Artificial Intelligence
Distributed optimization
FPGA
Fluorescence microscopy
Diffusion strategy
Brain-inspired computing
COVID-19
FDG PET
SHACL
Visualization
Linked data
Alzheimer's disease
Extreme value theory
Federated learning
Hyperbolic systems of conservation laws
Privacy
Knowledge graph
Clustering
Spiking neural networks
Geometric graphs
Electrocardiogram
Predictive model
Isomanifolds
Grammatical Evolution
Autonomous vehicles
Convolutional neural networks
Autoencoder
Semantic web
Segmentation
OPAL-Meso
Super-resolution
Atrial fibrillation
MRI
Deep Learning
Semantic Web
Convolutional Neural Networks
Contrastive learning
RDF
Dense labeling
CNN
Unsupervised learning
Coxeter triangulation
Knowledge graphs
NLP
Computer vision
Change point detection
Latent block model
Arguments
Sparsity
Web of Things
Biomarkers
Explainable AI
Medical imaging
Macroscopic traffic flow models
53B20
Anomaly detection
Caching
Hyperspectral data
Echocardiography
Cable-driven parallel robot
SPARQL
Ontology Learning
Excursion sets
Image fusion
Graph neural networks
Persistent homology
Uncertainty
Chernoff information
Co-clustering
Apprentissage profond
Atrial Fibrillation
NLP Natural Language Processing
Electrophysiology
Optimization
Argument mining
Linked Data
Semantic segmentation
Topological Data Analysis
Clinical trials
Graph signal processing
Convolutional neural network
Consensus
Domain adaptation
Image segmentation
Multi-Agent Systems
Convergence analysis
Computational Topology
Federated Learning