5 citations · 9 across the 12 of their papers we have counts for
5 papers · 1 filter
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…
Flood and Echo Net: Algorithmically Aligned GNNs that Generalize
Joël Mathys, Florian Grötschla, Kalyan Varma Nadimpalli +1
Most Graph Neural Networks follow the standard message-passing framework where, in each step, all nodes simultaneously communicate with each other. We want to challenge this paradi…
SALSA-CLRS: A Sparse and Scalable Benchmark for Algorithmic Reasoning
Julian Minder, Florian Grötschla, Joël Mathys +1
We introduce an extension to the CLRS algorithmic learning benchmark, prioritizing scalability and the utilization of sparse representations. Many algorithms in CLRS require global…
Traffic4cast at NeurIPS 2022 -- Predict Dynamics along Graph Edges from Sparse Node Data: Whole City Traffic and ETA from Stationary Vehicle Detectors
Moritz Neun, Christian Eichenberger, Henry Martin +27
The global trends of urbanization and increased personal mobility force us to rethink the way we live and use urban space. The Traffic4cast competition series tackles this problem…
Abstract Visual Reasoning Enabled by Language
Giacomo Camposampiero, Loic Houmard, Benjamin Estermann +2
While artificial intelligence (AI) models have achieved human or even superhuman performance in many well-defined applications, they still struggle to show signs of broad and flexi…