38 citations · 77 across the 6 of their papers we have counts for
5 papers · 1 filter
Maximal Independent Sets for Pooling in Graph Neural Networks
Stevan Stanovic, Benoit Gaüzère, Luc Brun
Convolutional Neural Networks (CNNs) have enabled major advances in image classification through convolution and pooling. In particular, image pooling transforms a connected discre…
Maximal Independent Vertex Set applied to Graph Pooling
Stevan Stanovic, Benoit Gaüzère, Luc Brun
Convolutional neural networks (CNN) have enabled major advances in image classification through convolution and pooling. In particular, image pooling transforms a connected discret…
A new Sinkhorn algorithm with Deletion and Insertion operations
Luc Brun, Benoit Gaüzère, Sébastien Bougleux +1
This technical report is devoted to the continuous estimation of an epsilon-assignment. Roughly speaking, an epsilon assignment between two sets V1 and V2 may be understood as a bi…
Breaking the Limits of Message Passing Graph Neural Networks
Muhammet Balcilar, Pierre Héroux, Benoit Gaüzère +3
Since the Message Passing (Graph) Neural Networks (MPNNs) have a linear complexity with respect to the number of nodes when applied to sparse graphs, they have been widely implemen…
Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks
Muhammet Balcilar, Guillaume Renton, Pierre Heroux +3
This paper aims at revisiting Graph Convolutional Neural Networks by bridging the gap between spectral and spatial design of graph convolutions. We theoretically demonstrate some e…