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Stephan Günnemann
Stephan Günnemann
Professor of Computer Science, Technical University of Munich
Bestätigte E-Mail-Adresse bei in.tum.de - Startseite
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Zitiert von
Zitiert von
Jahr
ChatGPT for good? On opportunities and challenges of large language models for education
E Kasneci, K Seßler, S Küchemann, M Bannert, D Dementieva, F Fischer, ...
Learning and individual differences 103, 102274, 2023
33882023
Predict then propagate: Graph neural networks meet personalized pagerank
J Gasteiger, A Bojchevski, S Günnemann
International Conference on Learning Representations (ICLR), 2019
21482019
Pitfalls of graph neural network evaluation
O Shchur, M Mumme, A Bojchevski, S Günnemann
Relational Representation Learning Workshop, NeurIPS, 2018
14572018
Adversarial attacks on neural networks for graph data
D Zügner, A Akbarnejad, S Günnemann
ACM SIGKDD International Conference on Knowledge Discovery & Data Mining …, 2018
11402018
Directional message passing for molecular graphs
J Gasteiger, J Groß, S Günnemann
International Conference on Learning Representations (ICLR), 2020
9622020
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking
A Bojchevski, S Günnemann
International Conference on Learning Representations (ICLR), 2018
7922018
Diffusion improves graph learning
J Gasteiger, S Weißenberger, S Günnemann
Neural Information Processing Systems (NeurIPS), 2019
7912019
Adversarial Attacks on Graph Neural Networks via Meta Learning
D Zügner, S Günnemann
International Conference on Learning Representations (ICLR), 2019
701*2019
Gemnet: Universal directional graph neural networks for molecules
J Gasteiger, F Becker, S Günnemann
Advances in Neural Information Processing Systems 34, 6790-6802, 2021
478*2021
Netgan: Generating graphs via random walks
A Bojchevski, O Shchur, D Zügner, S Günnemann
International Conference on Machine Learning (ICML), 2018
4672018
Failing loudly: An empirical study of methods for detecting dataset shift
S Rabanser, S Günnemann, ZC Lipton
Neural Information Processing Systems (NeurIPS), 2018
4082018
Fast and uncertainty-aware directional message passing for non-equilibrium molecules
J Gasteiger, S Giri, JT Margraf, S Günnemann
Machine Learning for Molecules Workshop, NeurIPS, 2020
3842020
Evaluating clustering in subspace projections of high dimensional data
E Müller, S Günnemann, I Assent, T Seidl
Proceedings of the VLDB Endowment 2 (1), 1270-1281, 2009
3662009
Adversarial attacks on node embeddings via graph poisoning
A Bojchevski, S Günnemann
International Conference on Machine Learning (ICML), 695-704, 2019
3602019
Scaling graph neural networks with approximate pagerank
A Bojchevski, J Gasteiger, B Perozzi, A Kapoor, M Blais, B Rózemberczki, ...
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge …, 2020
3162020
Introduction to tensor decompositions and their applications in machine learning
S Rabanser, O Shchur, S Günnemann
arXiv preprint arXiv:1711.10781, 2017
2892017
3d infomax improves gnns for molecular property prediction
H Stärk, D Beaini, G Corso, P Tossou, C Dallago, S Günnemann, P Liò
International Conference on Machine Learning, 20479-20502, 2022
2312022
Posterior network: Uncertainty estimation without ood samples via density-based pseudo-counts
B Charpentier, D Zügner, S Günnemann
Neural Information Processing Systems (NeurIPS), 2020
2002020
Certifiable robustness and robust training for graph convolutional networks
D Zügner, S Günnemann
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019
1842019
Intensity-free learning of temporal point processes
O Shchur, M Biloš, S Günnemann
International Conference on Learning Representations (ICLR), 2019
1792019
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