Stephan Günnemann

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	Toate	Din 2020
Referințe bibliografice	24270	21702
h-index	61	51
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Aleksandar BojchevskiUniversity of CologneAdresă de e-mail confirmată pe uni-koeln.de
Thomas SeidlProfessor of Computer Science, LMU Munich, Munich Center for Machine Learning (MCML)Adresă de e-mail confirmată pe dbs.ifi.lmu.de
Johannes Gasteiger, né KlicperaAnthropicAdresă de e-mail confirmată pe anthropic.com
Daniel ZügnerMicrosoft ResearchAdresă de e-mail confirmată pe microsoft.com
Oleksandr ShchurApplied Scientist, AWS AIAdresă de e-mail confirmată pe amazon.com
Simon GeislerTechnical University of MunichAdresă de e-mail confirmată pe in.tum.de
Bertrand CharpentierPruna AI, Ex-Technical University of Munich, Ex-TwitterAdresă de e-mail confirmată pe in.tum.de
Emmanuel MüllerProfessor of Computer Science, Technical University of DortmundAdresă de e-mail confirmată pe cs.tu-dortmund.de
Christos FaloutsosCMUAdresă de e-mail confirmată pe cs.cmu.edu
Ines FärberData Management and Data ExplorationAdresă de e-mail confirmată pe cs.rwth-aachen.de
Ira AssentProfessor of Computer Science, Aarhus University, Denmark; IAS-8, Forschungszentrum JülichAdresă de e-mail confirmată pe cs.au.dk
Marin BilošPhD student, Technical University of MunichAdresă de e-mail confirmată pe in.tum.de
Hardy KremerDeloitte Analytics Institute, GermanyAdresă de e-mail confirmată pe deloitte.de
Karsten RoscherResearcher, Fraunhofer IKSAdresă de e-mail confirmată pe iks.fraunhofer.de
Nicholas GaoTechnical University of MunichAdresă de e-mail confirmată pe tum.de
Gitta KutyniokBavarian AI Chair for Mathematical Foundations of Artificial Intelligence, LMU MunichAdresă de e-mail confirmată pe math.lmu.de
Thomas NeumannProfessor, Technische Universität MünchenAdresă de e-mail confirmată pe in.tum.de
Leon HetzelPhD Student @Helmholtz Munich & TUMAdresă de e-mail confirmată pe in.tum.de
Jan SchuchardtTechnical University of MunichAdresă de e-mail confirmată pe in.tum.de
Fabian J TheisHelmholtz Munich, Technical University of MunichAdresă de e-mail confirmată pe helmholtz-muenchen.de

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Stephan Günnemann

Professor of Computer Science, Technical University of Munich

Adresă de e-mail confirmată pe in.tum.de - Pagina de pornire

Machine Learning Graphs Graph Neural Networks Robustness


Titlu Sortați după descrierea bibliografică Sortați după an Sortați după titlu	Citat de Citat de	Anul
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	4026	2023
Predict then propagate: Graph neural networks meet personalized pagerank J Gasteiger, A Bojchevski, S Günnemann International Conference on Learning Representations (ICLR), 2019	2280*	2019
Pitfalls of graph neural network evaluation O Shchur, M Mumme, A Bojchevski, S Günnemann Relational Representation Learning Workshop, NeurIPS, 2018	1572	2018
Adversarial attacks on neural networks for graph data D Zügner, A Akbarnejad, S Günnemann ACM SIGKDD International Conference on Knowledge Discovery & Data Mining …, 2018	1214	2018
Directional message passing for molecular graphs J Gasteiger, J Groß, S Günnemann International Conference on Learning Representations (ICLR), 2020	1033*	2020
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking A Bojchevski, S Günnemann International Conference on Learning Representations (ICLR), 2018	844	2018
Diffusion improves graph learning J Gasteiger, S Weißenberger, S Günnemann Neural Information Processing Systems (NeurIPS), 2019	833	2019
Adversarial Attacks on Graph Neural Networks via Meta Learning D Zügner, S Günnemann International Conference on Learning Representations (ICLR), 2019	761*	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	527*	2021
Netgan: Generating graphs via random walks A Bojchevski, O Shchur, D Zügner, S Günnemann International Conference on Machine Learning (ICML), 2018	488	2018
Failing loudly: An empirical study of methods for detecting dataset shift S Rabanser, S Günnemann, ZC Lipton Neural Information Processing Systems (NeurIPS), 2018	428	2018
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	415	2020
Adversarial attacks on node embeddings via graph poisoning A Bojchevski, S Günnemann International Conference on Machine Learning (ICML), 695-704, 2019	379	2019
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	367	2009
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	333	2020
Introduction to tensor decompositions and their applications in machine learning S Rabanser, O Shchur, S Günnemann arXiv preprint arXiv:1711.10781, 2017	299	2017
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	261	2022
Posterior network: Uncertainty estimation without ood samples via density-based pseudo-counts B Charpentier, D Zügner, S Günnemann Neural Information Processing Systems (NeurIPS), 2020	211	2020
Intensity-free learning of temporal point processes O Shchur, M Biloš, S Günnemann International Conference on Learning Representations (ICLR), 2019	201	2019
Certifiable robustness and robust training for graph convolutional networks D Zügner, S Günnemann Proceedings of the 25th ACM SIGKDD international conference on knowledge …, 2019	189	2019

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