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ORCIDJohannes Gasteiger
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- affiliation: Google Research, Zürich, Switzerland
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2020 – today
- 2024
[i16]Simon Geisler, Tom Wollschläger, M. H. I. Abdalla, Johannes Gasteiger, Stephan Günnemann:
Attacking Large Language Models with Projected Gradient Descent. CoRR abs/2402.09154 (2024)- 2023
- [b1]Johannes Gasteiger:
On the Convergence of Structure and Geometry in Graph Neural Networks. Technical University of Munich, Germany, 2023 - [c13]Arthur Kosmala, Johannes Gasteiger, Nicholas Gao, Stephan Günnemann:
Ewald-based Long-Range Message Passing for Molecular Graphs. ICML 2023: 17544-17563 - [c12]Filip Ekström Kelvinius, Dimitar Georgiev, Artur P. Toshev, Johannes Gasteiger:
Accelerating Molecular Graph Neural Networks via Knowledge Distillation. NeurIPS 2023 - [c11]Sami Abu-El-Haija, Joshua V. Dillon, Bahare Fatemi, Kyriakos Axiotis, Neslihan Bulut, Johannes Gasteiger, Bryan Perozzi, MohammadHossein Bateni:
SubMix: Learning to Mix Graph Sampling Heuristics. UAI 2023: 1-10 - [i15]Jan Schuchardt, Aleksandar Bojchevski, Johannes Gasteiger, Stephan Günnemann:
Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks. CoRR abs/2302.02829 (2023) - [i14]Arthur Kosmala, Johannes Gasteiger, Nicholas Gao, Stephan Günnemann:
Ewald-based Long-Range Message Passing for Molecular Graphs. CoRR abs/2303.04791 (2023) - [i13]Filip Ekström Kelvinius, Dimitar Georgiev, Artur Petrov Toshev, Johannes Gasteiger:
Accelerating Molecular Graph Neural Networks via Knowledge Distillation. CoRR abs/2306.14818 (2023) - [i12]Sebastian Farquhar, Vikrant Varma, Zachary Kenton, Johannes Gasteiger, Vladimir Mikulik, Rohin Shah:
Challenges with unsupervised LLM knowledge discovery. CoRR abs/2312.10029 (2023) - 2022
- [j2]Sina Stocker, Johannes Gasteiger
, Florian Becker, Stephan Günnemann, Johannes T. Margraf:
How robust are modern graph neural network potentials in long and hot molecular dynamics simulations? Mach. Learn. Sci. Technol. 3(4): 45010 (2022) - [j1]Johannes Gasteiger, Muhammed Shuaibi, Anuroop Sriram, Stephan Günnemann, Zachary W. Ulissi, C. Lawrence Zitnick, Abhishek Das:
GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasets. Trans. Mach. Learn. Res. 2022 (2022) - [c10]Johannes Gasteiger, Chendi Qian, Stephan Günnemann:
Influence-Based Mini-Batching for Graph Neural Networks. LoG 2022: 9 - [i11]Johannes Gasteiger, Muhammed Shuaibi, Anuroop Sriram, Stephan Günnemann, Zachary W. Ulissi, C. Lawrence Zitnick, Abhishek Das:
How Do Graph Networks Generalize to Large and Diverse Molecular Systems? CoRR abs/2204.02782 (2022) - [i10]Johannes Gasteiger, Chendi Qian, Stephan Günnemann:
Influence-Based Mini-Batching for Graph Neural Networks. CoRR abs/2212.09083 (2022) - 2021
- [c9]Jan Schuchardt, Aleksandar Bojchevski, Johannes Klicpera, Stephan Günnemann:
Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks. ICLR 2021 - [c8]Johannes Klicpera, Marten Lienen, Stephan Günnemann:
Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, and More. ICML 2021: 5616-5627 - [c7]Johannes Gasteiger, Florian Becker, Stephan Günnemann:
GemNet: Universal Directional Graph Neural Networks for Molecules. NeurIPS 2021: 6790-6802 - [c6]Johannes Gasteiger, Chandan Yeshwanth, Stephan Günnemann:
Directional Message Passing on Molecular Graphs via Synthetic Coordinates. NeurIPS 2021: 15421-15433 - [i9]Johannes Gasteiger, Florian Becker, Stephan Günnemann:
GemNet: Universal Directional Graph Neural Networks for Molecules. CoRR abs/2106.08903 (2021) - [i8]Johannes Klicpera, Marten Lienen, Stephan Günnemann:
Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, and More. CoRR abs/2107.06876 (2021) - [i7]Johannes Klicpera, Chandan Yeshwanth, Stephan Günnemann:
Directional Message Passing on Molecular Graphs via Synthetic Coordinates. CoRR abs/2111.04718 (2021) - 2020
- [c5]Johannes Klicpera, Janek Groß, Stephan Günnemann:
Directional Message Passing for Molecular Graphs. ICLR 2020 - [c4]Aleksandar Bojchevski, Johannes Klicpera, Stephan Günnemann:
Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More. ICML 2020: 1003-1013 - [c3]Aleksandar Bojchevski, Johannes Klicpera, Bryan Perozzi, Amol Kapoor, Martin Blais, Benedek Rózemberczki, Michal Lukasik, Stephan Günnemann:
Scaling Graph Neural Networks with Approximate PageRank. KDD 2020: 2464-2473 - [i6]Johannes Klicpera, Janek Groß, Stephan Günnemann:
Directional Message Passing for Molecular Graphs. CoRR abs/2003.03123 (2020) - [i5]Aleksandar Bojchevski, Johannes Klicpera, Bryan Perozzi, Amol Kapoor, Martin Blais, Benedek Rózemberczki, Michal Lukasik, Stephan Günnemann:
Scaling Graph Neural Networks with Approximate PageRank. CoRR abs/2007.01570 (2020) - [i4]Aleksandar Bojchevski, Johannes Klicpera, Stephan Günnemann:
Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More. CoRR abs/2008.12952 (2020) - [i3]Johannes Klicpera, Shankari Giri, Johannes T. Margraf, Stephan Günnemann:
Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules. CoRR abs/2011.14115 (2020)
2010 – 2019
- 2019
- [c2]Johannes Klicpera, Aleksandar Bojchevski, Stephan Günnemann:
Predict then Propagate: Graph Neural Networks meet Personalized PageRank. ICLR (Poster) 2019 - [c1]Johannes Klicpera, Stefan Weißenberger, Stephan Günnemann:
Diffusion Improves Graph Learning. NeurIPS 2019: 13333-13345 - [i2]Johannes Klicpera, Stefan Weißenberger, Stephan Günnemann:
Diffusion Improves Graph Learning. CoRR abs/1911.05485 (2019) - 2018
- [i1]Johannes Klicpera, Aleksandar Bojchevski, Stephan Günnemann:
Personalized Embedding Propagation: Combining Neural Networks on Graphs with Personalized PageRank. CoRR abs/1810.05997 (2018)
Coauthor Index
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last updated on 2024-03-03 00:10 CET by the dblp team
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