5 citations · 10 across the 4 of their papers we have counts for
Showing cs.LGShow all
3 papers · 1 filter
cs.LG2021★ 5 cited
Re-parameterizing VAEs for stability
David Dehaene, Rémy Brossard
We propose a theoretical approach towards the training numerical stability of Variational AutoEncoders (VAE). Our work is motivated by recent studies empowering VAEs to reach state…
cs.LG2021
Graph Context Encoder: Graph Feature Inpainting for Graph Generation and Self-supervised Pretraining
Oriel Frigo, Rémy Brossard, David Dehaene
We propose the Graph Context Encoder (GCE), a simple but efficient approach for graph representation learning based on graph feature masking and reconstruction. GCE models are trai…
cs.LG2020★ 5 cited
Graph convolutions that can finally model local structure
Rémy Brossard, Oriel Frigo, David Dehaene
Despite quick progress in the last few years, recent studies have shown that modern graph neural networks can still fail at very simple tasks, like detecting small cycles. This hin…