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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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Rémi Felin, Pierre Monnin, Catherine Faron, Andrea G. B. Tettamanzi. RDFminer: an Interactive Tool for the Evolutionary Discovery of SHACL Shapes. The Semantic Web - 21st International Conference, ESWC 2024, May 2024, Hersonissos (Crete), Greece. The Semantic Web: ESWC 2024 Satellite Events - Hersonissos, Crete, Greece, May 26 - May 30, 2024, Proceedings. ⟨hal-04566981⟩
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Célian Ringwald. Learning Pattern-Based Extractors from Natural Language and Knowledge Graphs: Applying Large Language Models to Wikipedia and Linked Open Data. Proceedings of the 38th AAAI Conference on Artificial Intelligence, Feb 2024, Vancouver, France. pp.23411-23412, ⟨10.1609/aaai.v38i21.30406⟩. ⟨hal-04526050⟩
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Celian Ringwald, Fabien Gandon, Catherine Faron, Franck Michel, Hanna Abi Akl. Learning Pattern-Based Extractors from Natural Language and Knowledge Graphs Applying Large Language Models to Wikipedia & the Linked Open Data (POSTER). AAAI 2024 - 38th Annual AAAI Conference on Artificial Intelligence, Feb 2024, Vancouver, France. . ⟨hal-04526139⟩
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Celian Ringwald, Fabien Gandon, Catherine Faron, Franck Michel, Hanna Abi Akl. Well-written Knowledge Graphs Most Effective RDF Syntaxes for Triple Linearization in End-to-End Extraction of Relations from Text (Student Abstract). AAAI 24 - 38th Annual AAAI Conference on Artificial Intelligence, Feb 2024, Vancouver, Canada. . ⟨hal-04526132⟩
Documents en texte intégral
642
Notices
299
Statistiques par discipline
Mots clés
Spiking neural networks
Extreme value theory
Convolutional Neural Networks
Segmentation
Explainable AI
Atrial fibrillation
Neural networks
Differential privacy
Information Extraction
Domain adaptation
Coxeter triangulation
Multi-Agent Systems
Medical imaging
Super-resolution
Crossings
Federated learning
Visualization
Embedded Systems
Ontology Learning
Autonomous vehicles
Excursion sets
Electrophysiology
Knowledge graphs
Electrocardiogram
Convergence analysis
Privacy
Machine learning
Diffusion MRI
Biomarkers
Artificial Intelligence
Federated Learning
Hyperbolic systems of conservation laws
Autoencoder
Apprentissage profond
Deep learning
Hyperspectral data
Unsupervised learning
FPGA
Co-clustering
CNN
Distributed optimization
Uncertainty
Persistent homology
Arguments
COVID-19
Clinical trials
Isomanifolds
Image segmentation
Semantic web
NLP Natural Language Processing
Knowledge graph
Computational Topology
Linked Data
Computer vision
Contrastive learning
Deep Learning
Computing methodologies
Linked data
Diffusion strategy
Anomaly detection
Atrial Fibrillation
MRI
Echocardiography
Argument Mining
Event cameras
Artificial intelligence
Healthcare
Electronic medical record
Fluorescence microscopy
Physics-based learning
Simulations
Multiple Sclerosis
Image fusion
Semantic segmentation
Topological Data Analysis
Sparsity
Dimensionality reduction
Binary image
Data augmentation
Convolutional neural network
Macroscopic traffic flow models
Grammatical Evolution
53B20
Alzheimer's disease
RDF
Cable-driven parallel robot
Brain-inspired computing
Predictive model
Web of Things
Extracellular matrix
Clustering
OPAL-Meso
Semantic Web
Graph neural networks
Convolutional neural networks
Dense labeling
Optimization
Spiking Neural Networks
Consensus
Latent block model