265 citations · 361 across the 14 of their papers we have counts for
3 papers · 1 filter
Optimization and Interpretability of Graph Attention Networks for Small Sparse Graph Structures in Automotive Applications
Marion Neumeier, Andreas Tollkühn, Sebastian Dorn +2
For automotive applications, the Graph Attention Network (GAT) is a prominently used architecture to include relational information of a traffic scenario during feature embedding.…
Gradient Derivation for Learnable Parameters in Graph Attention Networks
Marion Neumeier, Andreas Tollkühn, Sebastian Dorn +2
This work provides a comprehensive derivation of the parameter gradients for GATv2 [4], a widely used implementation of Graph Attention Networks (GATs). GATs have proven to be powe…
Classification and Uncertainty Quantification of Corrupted Data using Semi-Supervised Autoencoders
Philipp Joppich, Sebastian Dorn, Oliver De Candido +2
Parametric and non-parametric classifiers often have to deal with real-world data, where corruptions like noise, occlusions, and blur are unavoidable - posing significant challenge…