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Daniel Zügner
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2020 – today
- 2024
- [c20]Marcel Kollovieh, Bertrand Charpentier, Daniel Zügner, Stephan Günnemann:
Expected Probabilistic Hierarchies. NeurIPS 2024 - 2023
- [c19]Lukas Gosch, Simon Geisler, Daniel Sturm, Bertrand Charpentier, Daniel Zügner, Stephan Günnemann:
Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions. NeurIPS 2023 - [i22]Morgane Ayle, Jan Schuchardt, Lukas Gosch, Daniel Zügner, Stephan Günnemann:
Training Differentially Private Graph Neural Networks with Random Walk Sampling. CoRR abs/2301.00738 (2023) - [i21]Marloes Arts, Victor Garcia Satorras, Chin-Wei Huang, Daniel Zügner, Marco Federici, Cecilia Clementi, Frank Noé, Robert Pinsler, Rianne van den Berg:
Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics. CoRR abs/2302.00600 (2023) - [i20]Lukas Gosch, Simon Geisler, Daniel Sturm, Bertrand Charpentier, Daniel Zügner, Stephan Günnemann:
Adversarial Training for Graph Neural Networks. CoRR abs/2306.15427 (2023) - [i19]Claudio Zeni, Robert Pinsler, Daniel Zügner, Andrew Fowler, Matthew Horton, Xiang Fu, Sasha Shysheya, Jonathan Crabbé
, Lixin Sun, Jake Smith, Ryota Tomioka, Tian Xie:
MatterGen: a generative model for inorganic materials design. CoRR abs/2312.03687 (2023) - 2022
- [b1]Daniel Zügner:
Adversarial Robustness of Graph Neural Networks. Technical University of Munich, Germany, 2022 - [c18]Bertrand Charpentier, Oliver Borchert, Daniel Zügner, Simon Geisler, Stephan Günnemann:
Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions. ICLR 2022 - [c17]Daniel Zügner, Bertrand Charpentier, Morgane Ayle, Sascha Geringer, Stephan Günnemann:
End-to-End Learning of Probabilistic Hierarchies on Graphs. ICLR 2022 - [c16]John Rachwan, Daniel Zügner, Bertrand Charpentier, Simon Geisler, Morgane Ayle, Stephan Günnemann:
Winning the Lottery Ahead of Time: Efficient Early Network Pruning. ICML 2022: 18293-18309 - [i18]John Rachwan, Daniel Zügner, Bertrand Charpentier, Simon Geisler, Morgane Ayle, Stephan Günnemann:
Winning the Lottery Ahead of Time: Efficient Early Network Pruning. CoRR abs/2206.10451 (2022) - [i17]Morgane Ayle, Bertrand Charpentier, John Rachwan, Daniel Zügner, Simon Geisler, Stephan Günnemann:
On the Robustness and Anomaly Detection of Sparse Neural Networks. CoRR abs/2207.04227 (2022) - 2021
- [c15]Daniel Zügner, Tobias Kirschstein, Michele Catasta, Jure Leskovec, Stephan Günnemann:
Language-Agnostic Representation Learning of Source Code from Structure and Context. ICLR 2021 - [c14]Anna-Kathrin Kopetzki, Bertrand Charpentier, Daniel Zügner, Sandhya Giri, Stephan Günnemann:
Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable? ICML 2021: 5707-5718 - [c13]Simon Geisler, Tobias Schmidt, Hakan Sirin, Daniel Zügner, Aleksandar Bojchevski, Stephan Günnemann:
Robustness of Graph Neural Networks at Scale. NeurIPS 2021: 7637-7649 - [c12]Maximilian Stadler, Bertrand Charpentier, Simon Geisler, Daniel Zügner, Stephan Günnemann:
Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification. NeurIPS 2021: 18033-18048 - [i16]Daniel Zügner, Tobias Kirschstein, Michele Catasta, Jure Leskovec, Stephan Günnemann:
Language-Agnostic Representation Learning of Source Code from Structure and Context. CoRR abs/2103.11318 (2021) - [i15]Bertrand Charpentier, Oliver Borchert, Daniel Zügner, Simon Geisler, Stephan Günnemann:
Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions. CoRR abs/2105.04471 (2021) - [i14]Sven Elflein, Bertrand Charpentier, Daniel Zügner, Stephan Günnemann:
On Out-of-distribution Detection with Energy-based Models. CoRR abs/2107.08785 (2021) - [i13]Daniel Zügner, François-Xavier Aubet, Victor Garcia Satorras, Tim Januschowski, Stephan Günnemann, Jan Gasthaus:
A Study of Joint Graph Inference and Forecasting. CoRR abs/2109.04979 (2021) - [i12]Maximilian Stadler, Bertrand Charpentier, Simon Geisler, Daniel Zügner, Stephan Günnemann:
Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification. CoRR abs/2110.14012 (2021) - [i11]Simon Geisler, Tobias Schmidt, Hakan Sirin, Daniel Zügner, Aleksandar Bojchevski, Stephan Günnemann:
Robustness of Graph Neural Networks at Scale. CoRR abs/2110.14038 (2021) - [i10]François-Xavier Aubet, Daniel Zügner, Jan Gasthaus:
Monte Carlo EM for Deep Time Series Anomaly Detection. CoRR abs/2112.14436 (2021) - 2020
- [j1]Daniel Zügner, Oliver Borchert, Amir Akbarnejad, Stephan Günnemann:
Adversarial Attacks on Graph Neural Networks: Perturbations and their Patterns. ACM Trans. Knowl. Discov. Data 14(5): 57:1-57:31 (2020) - [c11]Eugenio Angriman, Alexander van der Grinten, Aleksandar Bojchevski, Daniel Zügner, Stephan Günnemann, Henning Meyerhenke:
Group Centrality Maximization for Large-scale Graphs. ALENEX 2020: 56-69 - [c10]Daniel Zügner, Stephan Günnemann:
Certifiable Robustness of Graph Convolutional Networks under Structure Perturbations. KDD 2020: 1656-1665 - [c9]Bertrand Charpentier, Daniel Zügner, Stephan Günnemann:
Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts. NeurIPS 2020 - [c8]Simon Geisler, Daniel Zügner, Stephan Günnemann:
Reliable Graph Neural Networks via Robust Aggregation. NeurIPS 2020 - [i9]Bertrand Charpentier, Daniel Zügner, Stephan Günnemann:
Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts. CoRR abs/2006.09239 (2020) - [i8]Anna-Kathrin Kopetzki, Bertrand Charpentier, Daniel Zügner, Sandhya Giri, Stephan Günnemann:
Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable? CoRR abs/2010.14986 (2020) - [i7]Simon Geisler, Daniel Zügner, Stephan Günnemann:
Reliable Graph Neural Networks via Robust Aggregation. CoRR abs/2010.15651 (2020)
2010 – 2019
- 2019
- [c7]Daniel Zügner, Amir Akbarnejad, Stephan Günnemann:
Adversarial Attacks on Graph Neural Networks. GI-Jahrestagung 2019: 251-252 - [c6]Daniel Zügner, Stephan Günnemann:
Adversarial Attacks on Graph Neural Networks via Meta Learning. ICLR (Poster) 2019 - [c5]Daniel Zügner, Amir Akbarnejad, Stephan Günnemann:
Adversarial Attacks on Neural Networks for Graph Data. IJCAI 2019: 6246-6250 - [c4]Daniel Zügner, Stephan Günnemann:
Certifiable Robustness and Robust Training for Graph Convolutional Networks. KDD 2019: 246-256 - [i6]Daniel Zügner, Stephan Günnemann:
Adversarial Attacks on Graph Neural Networks via Meta Learning. CoRR abs/1902.08412 (2019) - [i5]Daniel Zügner, Stephan Günnemann:
Certifiable Robustness and Robust Training for Graph Convolutional Networks. CoRR abs/1906.12269 (2019) - [i4]Eugenio Angriman, Alexander van der Grinten, Aleksandar Bojchevski, Daniel Zügner, Stephan Günnemann, Henning Meyerhenke
Group Centrality Maximization for Large-scale Graphs. CoRR abs/1910.13874 (2019) - [i3]Alexander Ziller, Julius Hansjakob, Vitalii Rusinov, Daniel Zügner, Peter Vogel, Stephan Günnemann:
Oktoberfest Food Dataset. CoRR abs/1912.05007 (2019) - 2018
- [c3]Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, Stephan Günnemann:
NetGAN: Generating Graphs via Random Walks. ICML 2018: 609-618 - [c2]Daniel Zügner, Amir Akbarnejad, Stephan Günnemann:
Adversarial Attacks on Neural Networks for Graph Data. KDD 2018: 2847-2856 - [i2]Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, Stephan Günnemann:
NetGAN: Generating Graphs via Random Walks. CoRR abs/1803.00816 (2018) - [i1]Daniel Zügner, Amir Akbarnejad, Stephan Günnemann:
Adversarial Attacks on Neural Networks for Graph Data. CoRR abs/1805.07984 (2018) - 2015
- [c1]Matthias Hauser, Daniel Zügner, Christoph Flath, Frédéric Thiesse:
Pushing the limits of RFID: Empowering RFID-based Electronic Article Surveillance with Data Analytics Techniques. ICIS 2015
Coauthor Index
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