no code implementations • 15 Mar 2024 • Yassine El Ouahidi, Giulia Lioi, Nicolas Farrugia, Bastien Pasdeloup, Vincent Gripon
In the context of Brain-Computer Interfaces, we propose an adaptive method that reaches offline performance level while being usable online without requiring supervision.
1 code implementation • 14 Mar 2024 • Ilyass Moummad, Nicolas Farrugia, Romain Serizel, Jeremy Froidevaux, Vincent Lostanlen
Multi-label imbalanced classification poses a significant challenge in machine learning, particularly evident in bioacoustics where animal sounds often co-occur, and certain sounds are much less frequent than others.
1 code implementation • 25 Dec 2023 • Ilyass Moummad, Romain Serizel, Nicolas Farrugia
Self-supervised learning (SSL) in audio holds significant potential across various domains, particularly in situations where abundant, unlabeled data is readily available at no cost.
1 code implementation • 14 Dec 2023 • Matteo Zambra, Nicolas Farrugia, Dorian Cazau, Alexandre Gensse, Ronan Fablet
We show that in-situ observations with richer temporal resolution represent an added value in terms of the model reconstruction performance.
1 code implementation • 16 Sep 2023 • Ilyass Moummad, Romain Serizel, Nicolas Farrugia
Bioacoustic sound event detection allows for better understanding of animal behavior and for better monitoring biodiversity using audio.
1 code implementation • 11 Sep 2023 • Yassine El Ouahidi, Vincent Gripon, Bastien Pasdeloup, Ghaith Bouallegue, Nicolas Farrugia, Giulia Lioi
We propose EEG-SimpleConv, a straightforward 1D convolutional neural network for Motor Imagery decoding in BCI.
1 code implementation • 2 Sep 2023 • Ilyass Moummad, Romain Serizel, Nicolas Farrugia
The bioacoustic community recasted the problem of sound event detection within the framework of few-shot learning, i. e. training a system with only few labeled examples.
no code implementations • 28 Oct 2022 • Yassine El Ouahidi, Lucas Drumetz, Giulia Lioi, Nicolas Farrugia, Bastien Pasdeloup, Vincent Gripon
BCI Motor Imagery datasets usually are small and have different electrodes setups.
1 code implementation • 27 Oct 2022 • Ilyass Moummad, Nicolas Farrugia
In addition, when combining class labels with metadata using multiple supervised contrastive learning, an extension of supervised contrastive learning solving an additional task of grouping patients within the same sex and age group, more informative features are learned.
no code implementations • 18 Aug 2022 • Matteo Zambra, Dorian Cazau, Nicolas Farrugia, Alexandre Gensse, Sara Pensieri, Roberto Bozzano, Ronan Fablet
As sea surface winds produce sounds that propagate underwater, underwater acoustics recordings can also deliver fine-grained wind-related information.
no code implementations • 9 Mar 2022 • Yassine El Ouahidi, Hugo Tessier, Giulia Lioi, Nicolas Farrugia, Bastien Pasdeloup, Vincent Gripon
In this work, we are interested in better understanding what are the graph frequencies that are the most useful to decode fMRI signals.
no code implementations • 23 Aug 2021 • Peer Herholz, Eddy Fortier, Mariya Toneva, Nicolas Farrugia, Leila Wehbe, Valentina Borghesani
Real-world generalization, e. g., deciding to approach a never-seen-before animal, relies on contextual information as well as previous experiences.
1 code implementation • 23 Aug 2021 • 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.
1 code implementation • 23 Oct 2020 • Myriam Bontonou, Giulia Lioi, Nicolas Farrugia, Vincent Gripon
Few-shot learning addresses problems for which a limited number of training examples are available.
no code implementations • 26 Sep 2020 • Giulia Lioi, Vincent Gripon, Abdelbasset Brahim, François Rousseau, Nicolas Farrugia
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 neuroscience.
no code implementations • 18 Nov 2019 • Ghouthi Boukli Hacene, Vincent Gripon, Nicolas Farrugia, Matthieu Arzel, Michel Jezequel
In this paper, we tackle the problem of incrementally learning a classifier, one example at a time, directly on chip.
no code implementations • 11 Oct 2019 • Yusuf Pilavci, Nicolas Farrugia
Graph Signal Processing has become a very useful framework for signal operations and representations defined on irregular domains.
1 code implementation • NeurIPS Workshop Neuro_AI 2019 • Nicolas Farrugia, Victor Nepveu, Deycy Camila Arias Villamil
Taken together, this contribution extends previous attempts on estimating encoding models, by showing the ability to model brain activity using a generic DNN (ie not specifically trained for this purpose) to extract auditory features, suggesting a degree of similarity between internal DNN representations and brain activity in naturalistic settings.
no code implementations • 19 Aug 2019 • Myriam Bontonou, Carlos Lassance, Vincent Gripon, Nicolas Farrugia
Predicting the future of Graph-supported Time Series (GTS) is a key challenge in many domains, such as climate monitoring, finance or neuroimaging.
no code implementations • 29 Dec 2018 • Ghouthi Boukli Hacene, Vincent Gripon, Matthieu Arzel, Nicolas Farrugia, Yoshua Bengio
Convolutional Neural Networks (CNNs) are state-of-the-art in numerous computer vision tasks such as object classification and detection.
no code implementations • 4 Oct 2018 • Ghouthi Boukli Hacene, Vincent Gripon, Nicolas Farrugia, Matthieu Arzel, Michel Jezequel
Deep learning-based methods have reached state of the art performances, relying on large quantity of available data and computational power.
no code implementations • 6 Mar 2017 • Mathilde Ménoret, Nicolas Farrugia, Bastien Pasdeloup, Vincent Gripon
Graph Signal Processing (GSP) is a promising framework to analyze multi-dimensional neuroimaging datasets, while taking into account both the spatial and functional dependencies between brain signals.