3 papers
stat.ML2026
Position: Don't be Afraid of Over-Smoothing And Over-Squashing
Niklas Kormann, Benjamin Doerr, Johannes F. Lutzeyer
Over-smoothing and over-squashing have been extensively studied in the literature on Graph Neural Networks (GNNs) over the past years. We challenge this prevailing focus in GNN res…
cs.LG2025
HIEGNet: A Heterogenous Graph Neural Network Including the Immune Environment in Glomeruli Classification
Niklas Kormann, Masoud Ramuz, Zeeshan Nisar +6
Graph Neural Networks (GNNs) have recently been found to excel in histopathology. However, an important histopathological task, where GNNs have not been extensively explored, is th…
cs.NE2025
Speeding Up Hyper-Heuristics With Markov-Chain Operator Selection and the Only-Worsening Acceptance Operator
Abderrahim Bendahi, Benjamin Doerr, Adrien Fradin +1
The move-acceptance hyper-heuristic was recently shown to be able to leave local optima with astonishing efficiency (Lissovoi et al., Artificial Intelligence (2023)). In this work,…