1 code implementation • 2 May 2024 • Alessio Xompero, Myriam Bontonou, Jean-Michel Arbona, Emmanouil Benetos, Andrea Cavallaro
To explain the decision of these models, we use feature-attribution to identify and quantify which objects (and which of their features) are more relevant to privacy classification with respect to a reference input (i. e., no objects localised in an image) predicted as public.
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.
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.
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 • 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 • 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 • 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 • 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.
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.
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 • 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).