2 citations · 2 across the 2 of their papers we have counts for
4 papers
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…
Matching Convolutional Neural Networks without Priors about Data
Carlos Eduardo Rosar Kos Lassance, Jean-Charles Vialatte, Vincent Gripon
We propose an extension of Convolutional Neural Networks (CNNs) to graph-structured data, including strided convolutions and data augmentation on graphs. Our method matches the acc…
Learning Local Receptive Fields and their Weight Sharing Scheme on Graphs
Jean-Charles Vialatte, Vincent Gripon, Gilles Coppin
We propose a simple and generic layer formulation that extends the properties of convolutional layers to any domain that can be described by a graph. Namely, we use the support of…
Neighborhood-Preserving Translations on Graphs
Nicolas Grelier, Bastien Pasdeloup, Jean-Charles Vialatte +1
In many domains (e.g. Internet of Things, neuroimaging) signals are naturally supported on graphs. These graphs usually convey information on similarity between the values taken by…