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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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Riccardo Taiello, Melek Önen, Clémentine Gritti, Marco Lorenzi. Let Them Drop: Scalable and Efficient Federated Learning Solutions Agnostic to Stragglers. ARES 2024 - 19th International Conference on Availability, Reliability and Security, Jul 2024, Vienna, Austria. pp.1-12, ⟨10.1145/3664476.3664488⟩. ⟨hal-04742784⟩
Documents en texte intégral
698
Notices
308
Statistiques par discipline
Mots clés
NLP Natural Language Processing
Fluorescence microscopy
SPARQL
CNN
Correlation matrices
53B20
Convolutional Neural Networks
Atrial Fibrillation
MRI
Clustering
Graph neural networks
Computational Topology
Optimization
Argument mining
Neural networks
Hyperbolic systems of conservation laws
Predictive model
Semantic web
Arguments
Dense labeling
Information Extraction
Co-clustering
Multi-Agent Systems
Latent block model
Consensus
Sparsity
Uncertainty
Linked data
Autonomous vehicles
Semantic Web
Computer vision
Apprentissage profond
Graph signal processing
Diffusion strategy
OPAL-Meso
Image fusion
Spiking neural networks
Argument Mining
Super-resolution
Healthcare
Knowledge graph
Diffusion MRI
Cable-driven parallel robot
Macroscopic traffic flow models
Knowledge graphs
Extracellular matrix
Alzheimer's disease
Unsupervised learning
Change point detection
Topological Data Analysis
Coxeter triangulation
Image segmentation
Contrastive learning
Hyperspectral data
Privacy
Linked Data
Machine learning
Visualization
Convolutional neural networks
Spiking Neural Networks
Anomaly detection
Deep Learning
Segmentation
Electronic medical record
Geometric graphs
Electrocardiogram
Echocardiography
Distributed optimization
Dimensionality reduction
Convolutional neural network
COVID-19
Grammatical Evolution
Excursion sets
Embedded Systems
Federated Learning
Atrial fibrillation
Differential privacy
Federated learning
Persistent homology
Explainable AI
Artificial intelligence
Brain-inspired computing
RDF
Data augmentation
Isomanifolds
Artificial Intelligence
Ontology Learning
Domain adaptation
Autoencoder
FPGA
ECG
Extreme value theory
Semantic segmentation
Electrophysiology
Web of Things
Deep learning
Convergence analysis
Computing methodologies
Clinical trials
Biomarkers