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Daniel Zügner
Daniel Zügner
Microsoft Research
Bestätigte E-Mail-Adresse bei microsoft.com
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Jahr
Adversarial attacks on neural networks for graph data
D Zügner, A Akbarnejad, S Günnemann
Proceedings of the 24th ACM SIGKDD international conference on knowledge …, 2018
10572018
Adversarial attacks on graph neural networks: Perturbations and their patterns
D Zügner, O Borchert, A Akbarnejad, S Günnemann
ACM Transactions on Knowledge Discovery from Data (TKDD) 14 (5), 1-31, 2020
6292020
Netgan: Generating graphs via random walks
A Bojchevski, O Shchur, D Zügner, S Günnemann
International conference on machine learning, 610-619, 2018
4372018
Posterior network: Uncertainty estimation without ood samples via density-based pseudo-counts
B Charpentier, D Zügner, S Günnemann
Neural Information Processing Systems, 2020
1652020
Certifiable robustness and robust training for graph convolutional networks
D Zügner, S Günnemann
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019
1652019
Language-Agnostic Representation Learning of Source Code from Structure and Context
D Zügner, T Kirschstein, M Catasta, J Leskovec, S Günnemann
International Conference on Learning Representations, 2021
1342021
Robustness of Graph Neural Networks at Scale
S Geisler, T Schmidt, H Şirin, D Zügner, A Bojchevski, S Günnemann
Neural Information Processing Systems, 2021
1052021
Reliable graph neural networks via robust aggregation
S Geisler, D Zügner, S Günnemann
Neural Information Processing Systems, 2020
782020
Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification
M Stadler, B Charpentier, S Geisler, D Zügner, S Günnemann
Neural Information Processing Systems, 2021
732021
Certifiable robustness of graph convolutional networks under structure perturbations
D Zügner, S Günnemann
Proceedings of the 26th ACM SIGKDD international conference on knowledge …, 2020
622020
Natural posterior network: Deep bayesian uncertainty for exponential family distributions
B Charpentier, O Borchert, D Zügner, S Geisler, S Günnemann
arXiv preprint arXiv:2105.04471, 2021
592021
Two for one: Diffusion models and force fields for coarse-grained molecular dynamics
M Arts, V Garcia Satorras, CW Huang, D Zugner, M Federici, C Clementi, ...
Journal of Chemical Theory and Computation 19 (18), 6151-6159, 2023
532023
Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable?
AK Kopetzki, B Charpentier, D Zügner, S Giri, S Günnemann
International Conference on Machine Learning 2021, 2020
462020
Mattergen: a generative model for inorganic materials design
C Zeni, R Pinsler, D Zügner, A Fowler, M Horton, X Fu, S Shysheya, ...
arXiv preprint arXiv:2312.03687, 2023
392023
Winning the lottery ahead of time: Efficient early network pruning
J Rachwan, D Zügner, B Charpentier, S Geisler, M Ayle, S Günnemann
International Conference on Machine Learning, 18293-18309, 2022
242022
Group centrality maximization for large-scale graphs
E Angriman, A van der Grinten, A Bojchevski, D Zügner, S Günnemann, ...
2020 Proceedings of the twenty-second workshop on Algorithm Engineering and …, 2020
222020
Pushing the limits of RFID: Empowering RFID-based electronic article surveillance with data analytics techniques
M Hauser, D Zügner, C Flath, F Thiesse
202015
A study of joint graph inference and forecasting
D Zügner, FX Aubet, VG Satorras, T Januschowski, S Günnemann, ...
arXiv preprint arXiv:2109.04979, 2021
142021
On out-of-distribution detection with energy-based models
S Elflein, B Charpentier, D Zügner, S Günnemann
arXiv preprint arXiv:2107.08785, 2021
132021
Adversarial training for graph neural networks: Pitfalls, solutions, and new directions
L Gosch, S Geisler, D Sturm, B Charpentier, D Zügner, S Günnemann
Advances in Neural Information Processing Systems 36, 2024
112024
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