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MIAI Grenoble Alpes (Multidisciplinary Institute in Artificial Intelligence) aims to conduct research in artificial intelligence at the highest level, to offer attractive courses for students and professionals of all levels, to support innovation in large companies, SMEs and startups and to inform and interact with citizens on all aspects of AI.
The activities of MIAI Grenoble Alpes are structured around two main themes : future AI systems and AI for human beings the environment.
Instructions to authors: If your research work has been supported by the MIAI Grenoble Alpes, please mention in your article: "This work has been partially supported by MIAI@Grenoble Alpes, (ANR-19-P3IA-0003)."
Derniers dépôts
Lei Zan, Charles K Assaad, Emilie Devijver, Eric Gaussier, Ali Aït-Bachir. On the Fly Detection of Root Causes from Observed Data with Application to IT Systems. CIKM '24: The 33rd ACM International Conference on Information and Knowledge Management, Oct 2024, Boise, ID, United States. pp.5062-5069, ⟨10.1145/3627673.3680010⟩. ⟨hal-04785797⟩
Collaborations
Number of fulltexts
997
Number of references
622
Keywords
Training
Computer vision
Image reconstruction
Adherence
Continuous positive airway pressure
Speech production
Remote sensing
Prediction
Image processing
Privacy
Spatial frequencies
Éthique
Convex optimization
Intelligence artificielle
Pansharpening
Speech synthesis
Backstepping
Deep learning
EHealth
Task analysis
Chronic obstructive pulmonary disease
Dictionaries
Fairness
Self-supervised learning
Hyperspectral imaging
Data models
Algorithmes
Autism
Super-resolution
Hyperspectral
Speech motor control
Automatic speech recognition
Ethique de l'intelligence artificielle
Digital health e-health
Manifold learning
Neural networks
Machine Learning
Co-design
Deep Learning
Dimensionality reduction
Brain
Nash equilibrium
Hyperspectral image
Variational inference
Optimal control
Stabilization
Audio-visual speech enhancement
Image fusion
Ethics
Optimal controller
Optimization
Alternating direction method of multipliers ADMM
Digital public health
Machine learning
Big data
Evaluation
Correlation
Stochastic approximation
Random matrix theory
Endmember variability
OSA
Unsupervised learning
Telemonitoring
Convolutional neural networks
Object detection
Infinite-dimensional systems
Data fusion
Spectral clustering
Sex differences
Image segmentation
Multispectral
Innovation
Artificial Intelligence
Classification
Generative models
Sleep apnea
Semantic segmentation
Kernel methods
Scheduling
Stochastic differential equations
AI
Detectors
Deep learning DL
Asymptotic stability
COVID-19
Spatial resolution
Tensors
Santé numérique
Feature extraction
Artificial intelligence
Predictive coding
Simulation
Output feedback
Remote sensing RS
Convolution
Online learning
Representation learning
Data analysis
Data mining
Philosophie des techniques