Παρακολούθηση
Martin Ritzert
Martin Ritzert
Georg-August Universität Göttingen
Η διεύθυνση ηλεκτρονικού ταχυδρομείου έχει επαληθευτεί στον τομέα informatik.uni-goettingen.de
Τίτλος
Παρατίθεται από
Παρατίθεται από
Έτος
Weisfeiler and leman go neural: Higher-order graph neural networks
C Morris, M Ritzert, M Fey, WL Hamilton, JE Lenssen, G Rattan, M Grohe
Proceedings of the AAAI Conference on Artificial Intelligence 33, 4602-4609, 2019
18712019
Graph neural networks for maximum constraint satisfaction
J Toenshoff, M Ritzert, H Wolf, M Grohe
Frontiers in artificial intelligence 3, 98, 2021
582021
Graph Learning with 1D Convolutions on Random Walks
J Toenshoff, M Ritzert, H Wolf, M Grohe
arXiv preprint arXiv:2102.08786, 2021
462021
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
362023
Learning first-order definable concepts over structures of small degree
M Grohe, M Ritzert
2017 32nd Annual ACM/IEEE Symposium on Logic in Computer Science (LICS), 1-12, 2017
352017
The effects of randomness on the stability of node embeddings
T Schumacher, H Wolf, M Ritzert, F Lemmerich, M Grohe, M Strohmaier
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2021
262021
Graph machine learning for design of high‐octane fuels
JG Rittig, M Ritzert, AM Schweidtmann, S Winkler, JM Weber, P Morsch, ...
AIChE Journal 69 (4), e17971, 2023
212023
RUN-CSP: Unsupervised Learning of Message Passing Networks for Binary Constraint Satisfaction Problems
J Toenshoff, M Ritzert, H Wolf, M Grohe
arXiv preprint arXiv:1909.08387, 2019
192019
Learning MSO-definable hypotheses on strings
M Grohe, C Löding, M Ritzert
International Conference on Algorithmic Learning Theory, 434-451, 2017
192017
Walking out of the weisfeiler leman hierarchy: Graph learning beyond message passing
J Tönshoff, M Ritzert, H Wolf, M Grohe
Transactions on Machine Learning Research, 2023
102023
Distinguished In Uniform: Self Attention Vs. Virtual Nodes
E Rosenbluth, J Tönshoff, M Ritzert, B Kisin, M Grohe
arXiv preprint arXiv:2405.11951, 2024
92024
Optimal weak to strong learning
K Green Larsen, M Ritzert
Advances in Neural Information Processing Systems 35, 32830-32841, 2022
92022
On the Parameterized Complexity of Learning First-Order Logic
S van Bergerem, M Grohe, M Ritzert
Proceedings of the 41st ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of …, 2022
72022
Learning Definable Hypotheses on Trees
E Grienenberger, M Ritzert
22nd International Conference on Database Theory (ICDT 2019), 2019
62019
Adaboost is not an optimal weak to strong learner
MM Høgsgaard, KG Larsen, M Ritzert
International Conference on Machine Learning, 13118-13140, 2023
42023
On the Parameterized Complexity of Learning Logic
S van Bergerem, M Grohe, M Ritzert
CoRR, 2021
22021
Boosting, Voting Classifiers and Randomized Sample Compression Schemes
A da Cunha, KG Larsen, M Ritzert
arXiv preprint arXiv:2402.02976, 2024
12024
MNIST-Nd: a set of naturalistic datasets to benchmark clustering across dimensions
P Turishcheva, L Hansel, M Ritzert, MA Weis, AS Ecker
arXiv preprint arXiv:2410.16124, 2024
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
2023
AdaBoost is not an Optimal Weak to Strong Learner
M Møller Høgsgaard, K Green Larsen, M Ritzert
arXiv e-prints, arXiv: 2301.11571, 2023
2023
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