Stephan Günnemann

Citado por

	Total	Desde 2019
Citas	20685	18772
Índice h	59	50
Índice i10	166	145

6000

3000

1500

4500

20102011201220132014201520162017201820192020202120222023202456 74 142 178 179 234 268 292 394 642 1265 2196 3272 5394 5940

Acceso público

Ver todo

69 artículos

10 artículos

disponibles

no disponibles

Basado en requisitos de financiación

Coautores

Aleksandar BojchevskiUniversity of CologneDirección de correo verificada de uni-koeln.de
Thomas SeidlProfessor of Computer Science, Ludwig-Maximilians-Universität München (LMU Munich), MCML, GermanyDirección de correo verificada de dbs.ifi.lmu.de
Johannes Gasteiger, né KlicperaGoogle ResearchDirección de correo verificada de google.com
Daniel ZügnerMicrosoft ResearchDirección de correo verificada de microsoft.com
Oleksandr ShchurApplied Scientist, AWS AIDirección de correo verificada de amazon.com
Emmanuel MüllerProfessor of Computer Science, Technical University of DortmundDirección de correo verificada de cs.tu-dortmund.de
Bertrand CharpentierTechnical University of MunichDirección de correo verificada de in.tum.de
Simon GeislerTechnical University of MunichDirección de correo verificada de in.tum.de
Christos FaloutsosCMUDirección de correo verificada de cs.cmu.edu
Ines FärberData Management and Data ExplorationDirección de correo verificada de cs.rwth-aachen.de
Ira AssentProfessor of Computer Science, Aarhus University, Denmark; IAS-8, Forschungszentrum JülichDirección de correo verificada de cs.au.dk
Hardy KremerDeloitte Analytics Institute, GermanyDirección de correo verificada de deloitte.de
Marin BilošPhD student, Technical University of MunichDirección de correo verificada de in.tum.de
Karsten RoscherResearcher, Fraunhofer IKSDirección de correo verificada de iks.fraunhofer.de
Thomas NeumannProfessor, Technische Universität MünchenDirección de correo verificada de in.tum.de
Nicholas GaoTechnical University of MunichDirección de correo verificada de tum.de
Gitta KutyniokBavarian AI Chair for Mathematical Foundations of Artificial Intelligence, LMU MunichDirección de correo verificada de math.lmu.de
Armin MoinAssistant Professor of Computer Science at the University of Colorado Colorado Springs (UCCS)Dirección de correo verificada de uccs.edu
Jan SchuchardtTechnical University of MunichDirección de correo verificada de in.tum.de
Volker TrespLudwig-Maximilians-Universität München (LMU Munich)Dirección de correo verificada de dbs.ifi.lmu.de

Seguir

Stephan Günnemann

Professor of Computer Science, Technical University of Munich

Dirección de correo verificada de in.tum.de - Página principal

Machine Learning Graphs Graph Neural Networks Robustness


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
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	2857	2023
Predict then propagate: Graph neural networks meet personalized pagerank J Gasteiger, A Bojchevski, S Günnemann International Conference on Learning Representations (ICLR), 2019	2034	2019
Pitfalls of graph neural network evaluation O Shchur, M Mumme, A Bojchevski, S Günnemann Relational Representation Learning Workshop, NeurIPS, 2018	1364	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	1108	2018
Directional message passing for molecular graphs J Gasteiger, J Groß, S Günnemann International Conference on Learning Representations (ICLR), 2020	902	2020
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking A Bojchevski, S Günnemann International Conference on Learning Representations (ICLR), 2018	758	2018
Diffusion improves graph learning J Gasteiger, S Weißenberger, S Günnemann Neural Information Processing Systems (NeurIPS), 2019	740	2019
Adversarial Attacks on Graph Neural Networks via Meta Learning D Zügner, S Günnemann International Conference on Learning Representations (ICLR), 2019	672*	2019
Netgan: Generating graphs via random walks A Bojchevski, O Shchur, D Zügner, S Günnemann International Conference on Machine Learning (ICML), 2018	460	2018
Gemnet: Universal directional graph neural networks for molecules J Gasteiger, F Becker, S Günnemann Advances in Neural Information Processing Systems 34, 6790-6802, 2021	443*	2021
Failing loudly: An empirical study of methods for detecting dataset shift S Rabanser, S Günnemann, ZC Lipton Neural Information Processing Systems (NeurIPS), 2018	386	2018
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	364	2009
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	355	2020
Adversarial attacks on node embeddings via graph poisoning A Bojchevski, S Günnemann International Conference on Machine Learning (ICML), 695-704, 2019	353	2019
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	295	2020
Introduction to tensor decompositions and their applications in machine learning S Rabanser, O Shchur, S Günnemann arXiv preprint arXiv:1711.10781, 2017	281	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	206	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	185	2020
Certifiable robustness and robust training for graph convolutional networks D Zügner, S Günnemann Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019	176	2019
On using class-labels in evaluation of clusterings I Färber, S Günnemann, HP Kriegel, P Kröger, E Müller, E Schubert, ... MultiClust: 1st international workshop on discovering, summarizing and using …, 2010	171	2010

El sistema no puede realizar la operación en estos momentos. Inténtalo de nuevo más tarde.

Artículos 1–20

Citas por año

Citas duplicadas

Citas combinadas

Añadir coautoresCoautores

Seguir

Citado por

Coautores