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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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Celian Ringwald. Learning Pattern-Based Extractors from Natural Language and Knowledge Graphs: Applying Large Language Models to Wikipedia and Linked Open Data. AAAI-24 - 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
643
Notices
300
Statistiques par discipline
Mots clés
Explainable AI
Binary image
Distributed optimization
Persistent homology
Data augmentation
Event cameras
Clustering
Knowledge graphs
Computer vision
Contrastive learning
Convolutional neural network
Extracellular matrix
MRI
Autonomous vehicles
Simulations
Optimization
Anomaly detection
Uncertainty
Hyperspectral data
Atrial fibrillation
Consensus
Unsupervised learning
Linked Data
Segmentation
Physics-based learning
Spiking neural networks
Differential privacy
Semantic web
Convolutional Neural Networks
Federated Learning
Graph neural networks
Convolutional neural networks
Dense labeling
COVID-19
Privacy
Image segmentation
Deep learning
Macroscopic traffic flow models
Neural networks
Isomanifolds
Biomarkers
Deep Learning
Topological Data Analysis
Clinical trials
Computing methodologies
Linked data
Excursion sets
Grammatical Evolution
Convergence analysis
Argument Mining
Machine learning
RDF
Knowledge graph
Multi-Agent Systems
Arguments
FPGA
Atrial Fibrillation
Ontology Learning
Diffusion strategy
Visualization
Healthcare
Autoencoder
Sparsity
Extreme value theory
Apprentissage profond
NLP Natural Language Processing
Super-resolution
Embedded Systems
Co-clustering
Electronic medical record
Diffusion MRI
Artificial Intelligence
Coxeter triangulation
Fluorescence microscopy
Electrocardiogram
CNN
Echocardiography
Crossings
Cable-driven parallel robot
Semantic segmentation
Predictive model
Spiking Neural Networks
Alzheimer's disease
Electrophysiology
Latent block model
OPAL-Meso
Semantic Web
Multiple Sclerosis
Artificial intelligence
Medical imaging
Information Extraction
Computational Topology
Federated learning
53B20
Hyperbolic systems of conservation laws
Domain adaptation
Web of Things
Dimensionality reduction
Image fusion
Brain-inspired computing