112 citations · 244 across the 7 of their papers we have counts for
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cs.LG2019★ 97 cited
Edge Contraction Pooling for Graph Neural Networks
Frederik Diehl
Graph Neural Network (GNN) research has concentrated on improving convolutional layers, with little attention paid to developing graph pooling layers. Yet pooling layers can enable…
cs.LG2019
Graph Neural Networks for Modelling Traffic Participant Interaction
Frederik Diehl, Thomas Brunner, Michael Truong Le +1
By interpreting a traffic scene as a graph of interacting vehicles, we gain a flexible abstract representation which allows us to apply Graph Neural Network (GNN) models for traffi…
cs.LG2015★ 1 cited
apsis - Framework for Automated Optimization of Machine Learning Hyper Parameters
Frederik Diehl, Andreas Jauch
The apsis toolkit presented in this paper provides a flexible framework for hyperparameter optimization and includes both random search and a bayesian optimizer. It is implemented…