24 citations · 45 across the 7 of their papers we have counts for
8 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…
Convolutional neural networks on irregular domains based on approximate vertex-domain translations
Bastien Pasdeloup, Vincent Gripon, Jean-Charles Vialatte +2
We propose a generalization of convolutional neural networks (CNNs) to irregular domains, through the use of a translation operator on a graph structure. In regular settings such a…
A neighborhood-preserving translation operator on graphs
Bastien Pasdeloup, Vincent Gripon, Jean-Charles Vialatte +2
In this paper, we introduce translation operators on graphs. Contrary to spectrally-defined translations in the framework of graph signal processing, our operators mimic neighborho…
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…
A Study of Deep Learning Robustness Against Computation Failures
Jean-Charles Vialatte, François Leduc-Primeau
For many types of integrated circuits, accepting larger failure rates in computations can be used to improve energy efficiency. We study the performance of faulty implementations o…
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…
Generalizing the Convolution Operator to extend CNNs to Irregular Domains
Jean-Charles Vialatte, Vincent Gripon, Grégoire Mercier
Convolutional Neural Networks (CNNs) have become the state-of-the-art in supervised learning vision tasks. Their convolutional filters are of paramount importance for they allow to…