Johannes Gasteiger

Proceedings of the Ausgezeichnete Informatikdissertationen 2023., 2023

On the Convergence of Structure and Geometry in Graph Neural Networks.

[BibT_eX]

[DOI]

PhD thesis, 2023

Challenges with unsupervised LLM knowledge discovery.

[BibT_eX]

[DOI]

CoRR, 2023

SubMix: Learning to Mix Graph Sampling Heuristics.

[BibT_eX]

[DOI]

MohammadHossein Bateni

Proceedings of the Uncertainty in Artificial Intelligence, 2023

Accelerating Molecular Graph Neural Networks via Knowledge Distillation.

[BibT_eX]

[DOI]

Filip Ekström Kelvinius

Dimitar Georgiev

Artur P. Toshev

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Ewald-based Long-Range Message Passing for Molecular Graphs.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

2022

How robust are modern graph neural network potentials in long and hot molecular dynamics simulations?

[BibT_eX]

[DOI]

Mach. Learn. Sci. Technol., December, 2022

GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasets.

[BibT_eX]

[DOI]

Trans. Mach. Learn. Res., 2022

How Do Graph Networks Generalize to Large and Diverse Molecular Systems?

[BibT_eX]

[DOI]

CoRR, 2022

Influence-Based Mini-Batching for Graph Neural Networks.

[BibT_eX]

[DOI]

Chendi Qian

Proceedings of the Learning on Graphs Conference, 2022

2021

Directional Message Passing on Molecular Graphs via Synthetic Coordinates.

[BibT_eX]

[DOI]

Chandan Yeshwanth

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

GemNet: Universal Directional Graph Neural Networks for Molecules.

[BibT_eX]

[DOI]

Florian Becker

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, and More.

[BibT_eX]

[DOI]

Marten Lienen

Proceedings of the 38th International Conference on Machine Learning, 2021

Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks.

[BibT_eX]

[DOI]

Jan Schuchardt

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules.

[BibT_eX]

[DOI]

CoRR, 2020

Scaling Graph Neural Networks with Approximate PageRank.

[BibT_eX]

[DOI]

Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More.

[BibT_eX]

[DOI]

Proceedings of the 37th International Conference on Machine Learning, 2020

Directional Message Passing for Molecular Graphs.

[BibT_eX]

[DOI]

Janek Groß

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Diffusion Improves Graph Learning.

[BibT_eX]

[DOI]

Stefan Weißenberger

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Predict then Propagate: Graph Neural Networks meet Personalized PageRank.

[BibT_eX]

[DOI]

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Personalized Embedding Propagation: Combining Neural Networks on Graphs with Personalized PageRank.

[BibT_eX]

[DOI]