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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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David Chardin, Marie Paquet, Renaud Schiappa, Jacques Darcourt, Caroline Bailleux, et al.. Baseline metabolic tumor volume as a strong predictive and prognostic biomarker in patients with non-small cell lung cancer treated with PD1 inhibitors: a prospective study. Journal for Immunotherapy of Cancer, 2020, 8 (2), pp.e000645. ⟨10.1136/jitc-2020-000645⟩. ⟨hal-02973050⟩
Documents en texte intégral
746
Notices
331
Statistiques par discipline
Mots clés
Distributed optimization
Visualization
Federated learning
Computing methodologies
Semantic segmentation
Linked Data
Explainable AI
Diffusion strategy
Unsupervised learning
Persistent homology
Argument mining
Fluorescence microscopy
Medical imaging
53B20
Hyperspectral data
Artificial Intelligence
Deep Learning
Echocardiography
Brain-inspired computing
Consensus
Neural networks
Autonomous vehicles
Latent block model
Convolutional Neural Networks
Artificial intelligence
Privacy
Graph signal processing
Information Extraction
Semantic Web
Argument Mining
SPARQL
Deep learning
Atrial fibrillation
Super-resolution
Topological Data Analysis
Linked data
Uncertainty
Image fusion
Chernoff information
Predictive model
Adversarial classification
Isomanifolds
Coxeter triangulation
Co-clustering
NLP
Computational Topology
Ontology Learning
Grammatical Evolution
Geometric graphs
Segmentation
FDG PET
MRI
Spiking Neural Networks
Macroscopic traffic flow models
Computer vision
COVID-19
Convergence analysis
Knowledge graph
Healthcare
Contrastive learning
Apprentissage profond
Convolutional neural network
Extreme value theory
Excursion sets
OPAL-Meso
Clustering
Machine learning
Domain adaptation
FPGA
Cable-driven parallel robot
Knowledge graphs
Electrophysiology
Optimization
Arguments
Change point detection
NLP Natural Language Processing
Anomaly detection
Biomarkers
Sparsity
Federated Learning
Autoencoder
Atrial Fibrillation
Image segmentation
Semantic web
Clinical trials
Spiking neural networks
Web of Things
Graph neural networks
Electrocardiogram
Diffusion MRI
Alzheimer's disease
Dense labeling
Hyperbolic systems of conservation laws
Multi-Agent Systems
Convolutional neural networks
SHACL
CNN
RDF
Extracellular matrix
Caching