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Johannes Gasteiger, né Klicpera
Johannes Gasteiger, né Klicpera
Other namesJohannes Klicpera, Johannes Gasteiger
Verified email at google.com
Title
Cited by
Cited by
Year
Predict then Propagate: Graph Neural Networks meet Personalized PageRank
J Gasteiger, A Bojchevski, S Günnemann
International Conference on Learning Representations (ICLR), 2019
1656*2019
Directional Message Passing for Molecular Graphs
J Gasteiger, J Groß, S Günnemann
International Conference on Learning Representations (ICLR), 2020
7272020
Diffusion Improves Graph Learning
J Gasteiger, S Weißenberger, S Günnemann
Advances in Neural Information Processing Systems (NeurIPS), 13354-13366, 2019
5862019
GemNet: Universal Directional Graph Neural Networks for Molecules
J Gasteiger, F Becker, S Günnemann
Advances in Neural Information Processing Systems (NeurIPS), 2021
332*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
2692020
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
2462020
Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks
J Schuchardt, A Bojchevski, J Gasteiger, S Günnemann
International Conference on Learning Representations (ICLR), 2021
113*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
662020
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
542022
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
52*2022
Directional Message Passing on Molecular Graphs via Synthetic Coordinates
J Gasteiger, C Yeshwanth, S Günnemann
Advances in Neural Information Processing Systems (NeurIPS), 2021
292021
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
242019
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
132021
Influence-Based Mini-Batching for Graph Neural Networks
J Gasteiger, C Qian, S Günnemann
Learning on Graphs Conference, 2022
92022
Ewald-based Long-Range Message Passing for Molecular Graphs
A Kosmala, J Gasteiger, N Gao, S Günnemann
International Conference on Machine Learning (ICML), 2023
72023
Nanowire Laser Structure and Fabrication Method
B Mayer, G Koblmueller, J Finley, J Klicpera, G Abstreiter
US Patent App. 15/759,977, 2018
52018
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
32023
Challenges with unsupervised LLM knowledge discovery
S Farquhar, V Varma, Z Kenton, J Gasteiger, V Mikulik, R Shah
arXiv preprint arXiv:2312.10029, 2023
12023
Attacking Large Language Models with Projected Gradient Descent
S Geisler, T Wollschläger, MHI Abdalla, J Gasteiger, S Günnemann
arXiv preprint arXiv:2402.09154, 2024
2024
Accelerating Molecular Graph Neural Networks via Knowledge Distillation
FE Kelvinius, D Georgiev, AP Toshev, J Gasteiger
Advances in Neural Information Processing Systems (NeurIPS), 2023
2023
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Articles 1–20