Search Results for author: Myriam Bontonou

Found 9 papers, 5 papers with code

Graphs as Tools to Improve Deep Learning Methods

no code implementations8 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.

Denoising

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.

Ranking Deep Learning Generalization using Label Variation in Latent Geometry Graphs

1 code implementation25 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.

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

Predicting the Accuracy of a Few-Shot Classifier

1 code implementation8 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.

Few-Shot Learning

Deep geometric knowledge distillation with graphs

1 code implementation8 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.

Knowledge Distillation

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

A Unified Deep Learning Formalism For Processing Graph Signals

no code implementations1 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).

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