207 citations · 516 across the 63 of their papers we have counts for
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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…
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…
Toward An Uncertainty Principle For Weighted Graphs
Bastien Pasdeloup, Réda Alami, Vincent Gripon +1
The uncertainty principle states that a signal cannot be localized both in time and frequency. With the aim of extending this result to signals on graphs, Agaskar&Lu introduce noti…