19 citations · 19 across the 3 of their papers we have counts for
4 papers
RAwR: Role-Aware Rewiring via Approximate Equitable Partition
Riccardo Porcedda, Giuseppe Squillace, Bastian Epping +4
While Graph Neural Networks (GNNs) have demonstrated significant efficacy in node classification tasks, where predictions rely on local neighborhood information, the performance of…
Beyond-mean-field fluctuations for the solution of constraint satisfaction problems
Niklas Foos, Bastian Epping, Jannik Grundler +5
Constraint Satisfaction Problems (CSPs) lie at the heart of complexity theory and find application in a plethora of prominent tasks ranging from cryptography to genetics. Classical…
Graph Neural Networks Do Not Always Oversmooth
Bastian Epping, Alexandre René, Moritz Helias +1
Graph neural networks (GNNs) have emerged as powerful tools for processing relational data in applications. However, GNNs suffer from the problem of oversmoothing, the property tha…
Unified field theoretical approach to deep and recurrent neuronal networks
Kai Segadlo, Bastian Epping, Alexander van Meegen +3
Understanding capabilities and limitations of different network architectures is of fundamental importance to machine learning. Bayesian inference on Gaussian processes has proven…