Jan Tönshoff
Jan Tönshoff
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Graph neural networks for maximum constraint satisfaction
J Toenshoff, M Ritzert, H Wolf, M Grohe
Frontiers in artificial intelligence 3, 580607, 2021
Walking out of the weisfeiler leman hierarchy: Graph learning beyond message passing
J Tönshoff, M Ritzert, H Wolf, M Grohe
arXiv preprint arXiv:2102.08786, 2021
Wl meet vc
C Morris, F Geerts, J Tönshoff, M Grohe
International Conference on Machine Learning, 25275-25302, 2023
Where did the gap go? reassessing the long-range graph benchmark
J Tönshoff, M Ritzert, E Rosenbluth, M Grohe
arXiv preprint arXiv:2309.00367, 2023
Learning the language of QCD jets with transformers
T Finke, M Krämer, A Mück, J Tönshoff
Journal of High Energy Physics 2023 (6), 1-18, 2023
Some might say all you need is sum
E Rosenbluth, J Toenshoff, M Grohe
arXiv preprint arXiv:2302.11603, 2023
One model, any csp: Graph neural networks as fast global search heuristics for constraint satisfaction
J Tönshoff, B Kisin, J Lindner, M Grohe
arXiv preprint arXiv:2208.10227, 2022
Distinguished In Uniform: Self Attention Vs. Virtual Nodes
E Rosenbluth, J Tönshoff, M Ritzert, B Kisin, M Grohe
arXiv preprint arXiv:2405.11951, 2024
Stable Tuple Embeddings for Dynamic Databases
J Toenshoff, N Friedman, M Grohe, B Kimelfeld
2023 IEEE 39th International Conference on Data Engineering (ICDE), 1286-1299, 2023
Selecting Walk Schemes for Database Embedding
Y Lev Lubarsky, J Tönshoff, M Grohe, B Kimelfeld
arXiv e-prints, arXiv: 2401.11215, 2024
Transformers vs. Message Passing GNNs: Distinguished in Uniform
J Tönshoff, E Rosenbluth, M Ritzert, B Kisin, M Grohe
The Twelfth International Conference on Learning Representations, 2023
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