1 code implementation • 8 Nov 2019 • Carlos Lassance, Myriam Bontonou, Ghouthi Boukli Hacene, Vincent Gripon, Jian Tang, Antonio Ortega
Specifically we introduce a graph-based RKD method, in which graphs are used to capture the geometry of latent spaces.
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.
1 code implementation • 25 Nov 2020 • Carlos Lassance, Louis Béthune, Myriam Bontonou, Mounia Hamidouche, Vincent Gripon
Measuring the generalization performance of a Deep Neural Network (DNN) without relying on a validation set is a difficult task.
1 code implementation • 8 Jul 2020 • 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.
no code implementations • 1 May 2019 • Myriam Bontonou, Carlos Lassance, Ghouthi Boukli Hacene, Vincent Gripon, Jian Tang, Antonio Ortega
We introduce a novel loss function for training deep learning architectures to perform classification.
no code implementations • 1 May 2019 • Myriam Bontonou, Carlos Lassance, Jean-Charles Vialatte, Vincent Gripon
Convolutional Neural Networks are very efficient at processing signals defined on a discrete Euclidean space (such as images).
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.
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.
no code implementations • 8 Oct 2021 • Carlos Lassance, Myriam Bontonou, Mounia Hamidouche, Bastien Pasdeloup, Lucas Drumetz, Vincent Gripon
This chapter is composed of four main parts: tools for visualizing intermediate layers in a DNN, denoising data representations, optimizing graph objective functions and regularizing the learning process.
1 code implementation • 19 Mar 2023 • Myriam Bontonou, Anaïs Haget, Maria Boulougouri, Jean-Michel Arbona, Benjamin Audit, Pierre Borgnat
The scientific questions are formulated as classical learning problems on tabular data or on graphs, e. g. phenotype prediction from gene expression data.
1 code implementation • 1 Feb 2024 • Myriam Bontonou, Anaïs Haget, Maria Boulougouri, Benjamin Audit, Pierre Borgnat, Jean-Michel Arbona
A collection of machine learning models including logistic regression, multilayer perceptron, and graph neural network are trained to classify samples according to their cancer type.