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cs.LG2025
Learn to Jump: Adaptive Random Walks for Long-Range Propagation through Graph Hierarchies
Joël Mathys, Federico Errica
Message-passing architectures struggle to sufficiently model long-range dependencies in node and graph prediction tasks. We propose a novel approach exploiting hierarchical graph s…
cs.CV2025
Synthetic Data for Blood Vessel Network Extraction
Joël Mathys, Andreas Plesner, Jorel Elmiger +1
Blood vessel networks in the brain play a crucial role in stroke research, where understanding their topology is essential for analyzing blood flow dynamics. However, extracting de…
cs.LG2025
Beyond Interpolation: Extrapolative Reasoning with Reinforcement Learning and Graph Neural Networks
Niccolò Grillo, Andrea Toccaceli, Joël Mathys +3
Despite incredible progress, many neural architectures fail to properly generalize beyond their training distribution. As such, learning to reason in a correct and generalizable wa…