24 citations · 91 across the 28 of their papers we have counts for
4 papers · 1 filter
Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification
Maximilian Stadler, Bertrand Charpentier, Simon Geisler +2
The interdependence between nodes in graphs is key to improve class predictions on nodes and utilized in approaches like Label Propagation (LP) or in Graph Neural Networks (GNN). N…
Robustness of Graph Neural Networks at Scale
Simon Geisler, Tobias Schmidt, Hakan Şirin +3
Graph Neural Networks (GNNs) are increasingly important given their popularity and the diversity of applications. Yet, existing studies of their vulnerability to adversarial attack…
Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness
Simon Geisler, Johanna Sommer, Jan Schuchardt +2
End-to-end (geometric) deep learning has seen first successes in approximating the solution of combinatorial optimization problems. However, generating data in the realm of NP-hard…
Natural Posterior Network: Deep Bayesian Uncertainty for Exponential Family Distributions
Bertrand Charpentier, Oliver Borchert, Daniel Zügner +2
Uncertainty awareness is crucial to develop reliable machine learning models. In this work, we propose the Natural Posterior Network (NatPN) for fast and high-quality uncertainty e…