1 citations · 1 across the 1 of their papers we have counts for
2 papers
cs.LG2020
Natural Graph Networks
Pim de Haan, Taco Cohen, Max Welling
A key requirement for graph neural networks is that they must process a graph in a way that does not depend on how the graph is described. Traditionally this has been taken to mean…
stat.ML2019★ 1 cited
Reparameterizing Distributions on Lie Groups
Luca Falorsi, Pim de Haan, Tim R. Davidson +1
Reparameterizable densities are an important way to learn probability distributions in a deep learning setting. For many distributions it is possible to create low-variance gradien…