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cs.LG2026
Towards an Optimal Control Foundation of Early Exits - Turnpikes and ResNets
Jens Püttschneider, Simon Heilig, Asja Fischer +1
The feed-forward propagation through neural networks is crucial for training and inference. To trade off efficiency and accuracy, it has been proposed to consider intermediate outp…
cs.LG2026
Get a GRIP, this will be a long TRIP: A Quantifiable Long-Range Framework for Verifying Over-squashing
Ferran Hernandez Caralt, Simon Heilig, Adrián Bazaga +3
Empirical claims about the connection between over-squashing and long-range interactions in GNNs, can only be trusted if the benchmarks used to validate them genuinely require long…
cs.LG2026
A Critical Audit of Spatiotemporal Forecasting Benchmark Datasets and Models
Kenneth Martin, Simon Heilig, Asja Fischer +3
Graph neural networks (GNNs) are routinely employed for spatiotemporal forecasting, yet their performance across widely used benchmark datasets is inconsistent. Here, we perform an…