Search Results for author: Nicolas Farrugia

Found 20 papers, 8 papers with code

Self-Supervised Learning for Few-Shot Bird Sound Classification

1 code implementation25 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.

Classification Few-Shot Learning +2

Multi-Modal Learning-based Reconstruction of High-Resolution Spatial Wind Speed Fields

no code implementations14 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.

Regularized Contrastive Pre-training for Few-shot Bioacoustic Sound Detection

1 code implementation16 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.

Event Detection Few-Shot Learning +1

A Strong and Simple Deep Learning Baseline for BCI MI Decoding

1 code implementation11 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.

EEG Transfer Learning

Pretraining Representations for Bioacoustic Few-shot Detection using Supervised Contrastive Learning

1 code implementation2 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.

Contrastive Learning Data Augmentation +3

Pretraining Respiratory Sound Representations using Metadata and Contrastive Learning

1 code implementation27 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.

Audio Classification Contrastive Learning +2

Learning-based estimation of in-situ wind speed from underwater acoustics

no code implementations18 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.

Computational Efficiency Retrieval +1

Pruning Graph Convolutional Networks to select meaningful graph frequencies for fMRI decoding

no code implementations9 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.

A roadmap to reverse engineering real-world generalization by combining naturalistic paradigms, deep sampling, and predictive computational models

no code implementations23 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.

Graph-LDA: Graph Structure Priors to Improve the Accuracy in Few-Shot Classification

1 code implementation23 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.

Few-shot Decoding of Brain Activation Maps

1 code implementation23 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.

Few-Shot Learning

Gradients of Connectivity as Graph Fourier Bases of Brain Activity

no code implementations26 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.

Spectral Graph Wavelet Transform as Feature Extractor for Machine Learning in Neuroimaging

no code implementations11 Oct 2019 Yusuf Pilavci, Nicolas Farrugia

Graph Signal Processing has become a very useful framework for signal operations and representations defined on irregular domains.

BIG-bench Machine Learning

Estimating encoding models of cortical auditory processing using naturalistic stimuli and transfer learning

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.

Transfer Learning

Comparing linear structure-based and data-driven latent spatial representations for sequence prediction

no code implementations19 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.

Time Series Time Series Analysis

Quantized Guided Pruning for Efficient Hardware Implementations of Convolutional Neural Networks

no code implementations29 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.


Transfer Incremental Learning using Data Augmentation

no code implementations4 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.

Data Augmentation Incremental Learning

Evaluating Graph Signal Processing for Neuroimaging Through Classification and Dimensionality Reduction

no code implementations6 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.

Dimensionality Reduction General Classification +1

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