4 citations · 10 across the 9 of their papers we have counts for
4 papers · 1 filter
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…
Comparing linear structure-based and data-driven latent spatial representations for sequence prediction
Myriam Bontonou, Carlos Lassance, Vincent Gripon +1
Predicting the future of Graph-supported Time Series (GTS) is a key challenge in many domains, such as climate monitoring, finance or neuroimaging. Yet it is a highly difficult pro…
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…