1 citations · 1 across the 3 of their papers we have counts for
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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…
SURF: A Generalization Benchmark for GNNs Predicting Fluid Dynamics
Stefan Künzli, Florian Grötschla, Joël Mathys +1
Simulating fluid dynamics is crucial for the design and development process, ranging from simple valves to complex turbomachinery. Accurately solving the underlying physical equati…
Learning Graph Algorithms With Recurrent Graph Neural Networks
Florian Grötschla, Joël Mathys, Roger Wattenhofer
Classical graph algorithms work well for combinatorial problems that can be thoroughly formalized and abstracted. Once the algorithm is derived, it generalizes to instances of any…
Hierarchical Graph Structures for Congestion and ETA Prediction
Florian Grötschla, Joël Mathys
Traffic4cast is an annual competition to predict spatio temporal traffic based on real world data. We propose an approach using Graph Neural Networks that directly works on the roa…