7 citations · 21 across the 12 of their papers we have counts for
14 papers
Spatial Graph Signal Interpolation with an Application for Merging BCI Datasets with Various Dimensionalities
Yassine El Ouahidi, Lucas Drumetz, Giulia Lioi +3
BCI Motor Imagery datasets usually are small and have different electrodes setups. When training a Deep Neural Network, one may want to capitalize on all these datasets to increase…
Pretraining Respiratory Sound Representations using Metadata and Contrastive Learning
Ilyass Moummad, Nicolas Farrugia
Methods based on supervised learning using annotations in an end-to-end fashion have been the state-of-the-art for classification problems. However, they may be limited in their ge…
Learning-based estimation of in-situ wind speed from underwater acoustics
Matteo Zambra, Dorian Cazau, Nicolas Farrugia +4
Wind speed retrieval at sea surface is of primary importance for scientific and operational applications. Besides weather models, in-situ measurements and remote sensing technologi…
Pruning Graph Convolutional Networks to select meaningful graph frequencies for fMRI decoding
Yassine El Ouahidi, Hugo Tessier, Giulia Lioi +3
Graph Signal Processing is a promising framework to manipulate brain signals as it allows to encompass the spatial dependencies between the activity in regions of interest in the b…
Graph-LDA: Graph Structure Priors to Improve the Accuracy in Few-Shot Classification
Myriam Bontonou, Nicolas Farrugia, Vincent Gripon
It is very common to face classification problems where the number of available labeled samples is small compared to their dimension. These conditions are likely to cause underdete…
A roadmap to reverse engineering real-world generalization by combining naturalistic paradigms, deep sampling, and predictive computational models
Peer Herholz, Eddy Fortier, Mariya Toneva +3
Real-world generalization, e.g., deciding to approach a never-seen-before animal, relies on contextual information as well as previous experiences. Such a seemingly easy behavioral…
Few-shot Decoding of Brain Activation Maps
Myriam Bontonou, Giulia Lioi, Nicolas Farrugia +1
Few-shot learning addresses problems for which a limited number of training examples are available. So far, the field has been mostly driven by applications in computer vision. Her…
Gradients of Connectivity as Graph Fourier Bases of Brain Activity
Giulia Lioi, Vincent Gripon, Abdelbasset Brahim +2
The application of graph theory to model the complex structure and function of the brain has shed new light on its organization and function, prompting the emergence of network neu…
Efficient Hardware Implementation of Incremental Learning and Inference on Chip
Ghouthi Boukli Hacene, Vincent Gripon, Nicolas Farrugia +2
In this paper, we tackle the problem of incrementally learning a classifier, one example at a time, directly on chip. To this end, we propose an efficient hardware implementation o…
Spectral Graph Wavelet Transform as Feature Extractor for Machine Learning in Neuroimaging
Yusuf Pilavci, Nicolas Farrugia
Graph Signal Processing has become a very useful framework for signal operations and representations defined on irregular domains. Exploiting transformations that are defined on gr…
Comparing linear structure-based and data-driven latent spatial representations for sequence prediction
Myriam Bontonou, Carlos Lassance, Vincent Gripon +1
Predicting the future of Graph-supported Time Series (GTS) is a key challenge in many domains, such as climate monitoring, finance or neuroimaging. Yet it is a highly difficult pro…
Quantized Guided Pruning for Efficient Hardware Implementations of Convolutional Neural Networks
Ghouthi Boukli Hacene, Vincent Gripon, Matthieu Arzel +2
Convolutional Neural Networks (CNNs) are state-of-the-art in numerous computer vision tasks such as object classification and detection. However, the large amount of parameters the…
Transfer Incremental Learning using Data Augmentation
Ghouthi Boukli Hacene, Vincent Gripon, Nicolas Farrugia +2
Deep learning-based methods have reached state of the art performances, relying on large quantity of available data and computational power. Such methods still remain highly inappr…
Evaluating Graph Signal Processing for Neuroimaging Through Classification and Dimensionality Reduction
Mathilde Ménoret, Nicolas Farrugia, Bastien Pasdeloup +1
Graph Signal Processing (GSP) is a promising framework to analyze multi-dimensional neuroimaging datasets, while taking into account both the spatial and functional dependencies be…