28 citations · 40 across the 3 of their papers we have counts for
3 papers · 1 filter
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…
Neural Networks Regularization Through Class-wise Invariant Representation Learning
Soufiane Belharbi, Clément Chatelain, Romain Hérault +1
Training deep neural networks is known to require a large number of training samples. However, in many applications only few training samples are available. In this work, we tackle…