4 citations · 10 across the 9 of their papers we have counts for
8 papers · 1 filter
Graphs as Tools to Improve Deep Learning Methods
Carlos Lassance, Myriam Bontonou, Mounia Hamidouche +3
In recent years, deep neural networks (DNNs) have known an important rise in popularity. However, although they are state-of-the-art in many machine learning challenges, they still…
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…
Ranking Deep Learning Generalization using Label Variation in Latent Geometry Graphs
Carlos Lassance, Louis Béthune, Myriam Bontonou +2
Measuring the generalization performance of a Deep Neural Network (DNN) without relying on a validation set is a difficult task. In this work, we propose exploiting Latent Geometry…
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…
Predicting the Accuracy of a Few-Shot Classifier
Myriam Bontonou, Louis Béthune, Vincent Gripon
In the context of few-shot learning, one cannot measure the generalization ability of a trained classifier using validation sets, due to the small number of labeled samples. In thi…
Deep geometric knowledge distillation with graphs
Carlos Lassance, Myriam Bontonou, Ghouthi Boukli Hacene +3
In most cases deep learning architectures are trained disregarding the amount of operations and energy consumption. However, some applications, like embedded systems, can be resour…
A Unified Deep Learning Formalism For Processing Graph Signals
Myriam Bontonou, Carlos Lassance, Jean-Charles Vialatte +1
Convolutional Neural Networks are very efficient at processing signals defined on a discrete Euclidean space (such as images). However, as they can not be used on signals defined o…
Introducing Graph Smoothness Loss for Training Deep Learning Architectures
Myriam Bontonou, Carlos Lassance, Ghouthi Boukli Hacene +3
We introduce a novel loss function for training deep learning architectures to perform classification. It consists in minimizing the smoothness of label signals on similarity graph…