| Predict then Propagate: Graph Neural Networks meet Personalized PageRank J Gasteiger, A Bojchevski, S Günnemann International Conference on Learning Representations (ICLR), 2019 | 2274* | 2019 |
| Directional Message Passing for Molecular Graphs J Gasteiger, J Groß, S Günnemann International Conference on Learning Representations (ICLR), 2020 | 1036 | 2020 |
| Diffusion Improves Graph Learning J Gasteiger, S Weißenberger, S Günnemann Advances in Neural Information Processing Systems (NeurIPS), 13354-13366, 2019 | 834 | 2019 |
| GemNet: Universal Directional Graph Neural Networks for Molecules J Gasteiger, F Becker, S Günnemann Advances in Neural Information Processing Systems (NeurIPS), 2021 | 513* | 2021 |
| Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules J Gasteiger, S Giri, JT Margraf, S Günnemann Machine Learning for Molecules Workshop at NeurIPS, 2020 | 405 | 2020 |
| Scaling Graph Neural Networks with Approximate PageRank A Bojchevski, J Gasteiger, B Perozzi, A Kapoor, M Blais, B Rózemberczki, ... 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020 | 333 | 2020 |
| Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks J Schuchardt, A Bojchevski, J Gasteiger, S Günnemann International Conference on Learning Representations (ICLR), 2021 | 206* | 2021 |
| Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More A Bojchevski, J Gasteiger, S Günnemann Thirty-seventh International Conference on Machine Learning (ICML), 2020 | 97 | 2020 |
| How robust are modern graph neural network potentials in long and hot molecular dynamics simulations? S Stocker, J Gasteiger, F Becker, S Günnemann, JT Margraf Machine Learning: Science and Technology 3 (4), 045010, 2022 | 89 | 2022 |
| GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasets J Gasteiger, M Shuaibi, A Sriram, S Günnemann, ZW Ulissi, CL Zitnick, ... Transactions on Machine Learning Research, 2022 | 85* | 2022 |
| Directional Message Passing on Molecular Graphs via Synthetic Coordinates J Gasteiger, C Yeshwanth, S Günnemann Advances in Neural Information Processing Systems (NeurIPS), 2021 | 46 | 2021 |
| Attacking Large Language Models with Projected Gradient Descent S Geisler, T Wollschläger, MHI Abdalla, J Gasteiger, S Günnemann arXiv preprint arXiv:2402.09154, 2024 | 44 | 2024 |
| Ewald-based Long-Range Message Passing for Molecular Graphs A Kosmala, J Gasteiger, N Gao, S Günnemann International Conference on Machine Learning (ICML), 2023 | 31 | 2023 |
| Is PageRank All You Need for Scalable Graph Neural Networks? A Bojchevski, J Klicpera, B Perozzi, M Blais, A Kapoor, M Lukasik, ... ACM SIGKDD, MLG Workshop, 2019 | 31 | 2019 |
| Influence-Based Mini-Batching for Graph Neural Networks J Gasteiger, C Qian, S Günnemann Learning on Graphs Conference, 2022 | 17 | 2022 |
| Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, and More J Gasteiger, M Lienen, S Günnemann International Conference on Machine Learning, 5616-5627, 2021 | 14 | 2021 |
| Challenges with unsupervised LLM knowledge discovery S Farquhar, V Varma, Z Kenton, J Gasteiger, V Mikulik, R Shah arXiv preprint arXiv:2312.10029, 2023 | 13 | 2023 |
| Accelerating Molecular Graph Neural Networks via Knowledge Distillation FE Kelvinius, D Georgiev, AP Toshev, J Gasteiger Advances in Neural Information Processing Systems (NeurIPS), 2023 | 6* | 2023 |
| Nanowire Laser Structure and Fabrication Method B Mayer, G Koblmueller, J Finley, J Klicpera, G Abstreiter US Patent App. 15/759,977, 2018 | 6 | 2018 |
| SubMix: Learning to Mix Graph Sampling Heuristics S Abu-El-Haija, JV Dillon, B Fatemi, K Axiotis, N Bulut, J Gasteiger, ... Uncertainty in Artificial Intelligence, 1-10, 2023 | 5 | 2023 |
Stephan GünnemannProfessor of Computer Science, Technical University of Munichایمیل تأیید شده در in.tum.de
