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ORCIDStephan Günnemann
Person information
- affiliation: Technical University of Munich, Germany
- affiliation (former): Carnegie Mellon University, Pittsburgh, USA
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2020 – today
- 2023
[j25]Richard Leibrandt
, Stephan Günnemann:
Generalized density attractor clustering for incomplete data. Data Min. Knowl. Discov. 37(2): 970-1009 (2023)- [c157]Tom Haider, Karsten Roscher, Felippe Schmoeller da Roza, Stephan Günnemann:
Out-of-Distribution Detection for Reinforcement Learning Agents with Probabilistic Dynamics Models. AAMAS 2023: 851-859 - [c156]Armin Moin, Atta Badii, Stephan Günnemann, Moharram Challenger:
Enabling Machine Learning in Software Architecture Frameworks. CAIN 2023: 92-93 - [c155]Nicholas Gao, Stephan Günnemann:
Sampling-free Inference for Ab-Initio Potential Energy Surface Networks. ICLR 2023 - [c154]Lukas Gosch, Daniel Sturm, Simon Geisler, Stephan Günnemann:
Revisiting Robustness in Graph Machine Learning. ICLR 2023 - [c153]Raffaele Paolino, Aleksandar Bojchevski, Stephan Günnemann, Gitta Kutyniok, Ron Levie:
Unveiling the sampling density in non-uniform geometric graphs. ICLR 2023 - [c152]Jan Schuchardt, Tom Wollschläger, Aleksandar Bojchevski, Stephan Günnemann:
Localized Randomized Smoothing for Collective Robustness Certification. ICLR 2023 - [c151]Marin Bilos, Kashif Rasul, Anderson Schneider, Yuriy Nevmyvaka, Stephan Günnemann:
Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion. ICML 2023: 2452-2470 - [c150]Nicholas Gao, Stephan Günnemann:
Generalizing Neural Wave Functions. ICML 2023: 10708-10726 - [c149]Simon Geisler, Yujia Li, Daniel J. Mankowitz, Ali Taylan Cemgil, Stephan Günnemann, Cosmin Paduraru:
Transformers Meet Directed Graphs. ICML 2023: 11144-11172 - [c148]Arthur Kosmala, Johannes Gasteiger, Nicholas Gao, Stephan Günnemann:
Ewald-based Long-Range Message Passing for Molecular Graphs. ICML 2023: 17544-17563 - [c147]Tom Wollschläger, Nicholas Gao, Bertrand Charpentier, Mohamed Amine Ketata, Stephan Günnemann:
Uncertainty Estimation for Molecules: Desiderata and Methods. ICML 2023: 37133-37156 - [c146]Franziska Schwaiger, Andrea Matic, Karsten Roscher, Stephan Günnemann:
Preventing Errors in Person Detection: A Part-Based Self-Monitoring Framework. IV 2023: 1-8 - [c145]Jonas Gregor Wiese, Lisa Wimmer, Theodore Papamarkou, Bernd Bischl, Stephan Günnemann, David Rügamer:
Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry. ECML/PKDD (1) 2023: 459-474 - [i114]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) - [i113]Yan Scholten, Jan Schuchardt, Simon Geisler, Aleksandar Bojchevski, Stephan Günnemann:
Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks. CoRR abs/2301.02039 (2023) - [i112]Felix Mujkanovic, Simon Geisler, Stephan Günnemann, Aleksandar Bojchevski:
Are Defenses for Graph Neural Networks Robust? CoRR abs/2301.13694 (2023) - [i111]Simon Geisler, Yujia Li, Daniel J. Mankowitz, Ali Taylan Cemgil, Stephan Günnemann, Cosmin Paduraru:
Transformers Meet Directed Graphs. CoRR abs/2302.00049 (2023) - [i110]Jan Schuchardt, Aleksandar Bojchevski, Johannes Gasteiger, Stephan Günnemann:
Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks. CoRR abs/2302.02829 (2023) - [i109]Nicholas Gao, Stephan Günnemann:
Generalizing Neural Wave Functions. CoRR abs/2302.04168 (2023) - [i108]Arthur Kosmala, Johannes Gasteiger, Nicholas Gao, Stephan Günnemann:
Ewald-based Long-Range Message Passing for Molecular Graphs. CoRR abs/2303.04791 (2023) - [i107]Bertrand Charpentier, Chenxiang Zhang, Stephan Günnemann:
Training, Architecture, and Prior for Deterministic Uncertainty Methods. CoRR abs/2303.05796 (2023) - [i106]Nicola Franco, Daniel Korth, Jeanette Miriam Lorenz, Karsten Roscher, Stephan Günnemann:
Diffusion Denoised Smoothing for Certified and Adversarial Robust Out-Of-Distribution Detection. CoRR abs/2303.14961 (2023) - [i105]Johannes Getzner, Bertrand Charpentier, Stephan Günnemann:
Accuracy is not the only Metric that matters: Estimating the Energy Consumption of Deep Learning Models. CoRR abs/2304.00897 (2023) - [i104]Jonas Gregor Wiese, Lisa Wimmer, Theodore Papamarkou, Bernd Bischl, Stephan Günnemann, David Rügamer:
Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry. CoRR abs/2304.02902 (2023) - [i103]Nicola Franco, Tom Wollschläger, Benedikt Poggel, Stephan Günnemann, Jeanette Miriam Lorenz:
Efficient MILP Decomposition in Quantum Computing for ReLU Network Robustness. CoRR abs/2305.00472 (2023) - [i102]Lukas Gosch, Daniel Sturm, Simon Geisler, Stephan Günnemann:
Revisiting Robustness in Graph Machine Learning. CoRR abs/2305.00851 (2023) - [i101]Emanuele Rossi, Bertrand Charpentier, Francesco Di Giovanni, Fabrizio Frasca, Stephan Günnemann, Michael M. Bronstein:
Edge Directionality Improves Learning on Heterophilic Graphs. CoRR abs/2305.10498 (2023) - [i100]Leon Hetzel, Johanna Sommer, Bastian Rieck, Fabian J. Theis, Stephan Günnemann:
MAGNet: Motif-Agnostic Generation of Molecules from Shapes. CoRR abs/2305.19303 (2023) - [i99]Marten Lienen, Jan Hansen-Palmus, David Lüdke, Stephan Günnemann:
Generative Diffusion for 3D Turbulent Flows. CoRR abs/2306.01776 (2023) - [i98]Tom Wollschläger, Nicholas Gao, Bertrand Charpentier, Mohamed Amine Ketata, Stephan Günnemann:
Uncertainty Estimation for Molecules: Desiderata and Methods. CoRR abs/2306.14916 (2023) - [i97]Lukas Gosch, Simon Geisler, Daniel Sturm, Bertrand Charpentier, Daniel Zügner, Stephan Günnemann:
Adversarial Training for Graph Neural Networks. CoRR abs/2306.15427 (2023) - [i96]Johanna Sommer, Leon Hetzel, David Lüdke, Fabian J. Theis, Stephan Günnemann:
The power of motifs as inductive bias for learning molecular distributions. CoRR abs/2306.17246 (2023) - [i95]Jianxiang Feng, Matan Atad, Ismael Rodríguez, Maximilian Durner, Stephan Günnemann, Rudolph Triebel:
Density-based Feasibility Learning with Normalizing Flows for Introspective Robotic Assembly. CoRR abs/2307.01317 (2023) - [i94]Franziska Schwaiger, Andrea Matic, Karsten Roscher, Stephan Günnemann:
Preventing Errors in Person Detection: A Part-Based Self-Monitoring Framework. CoRR abs/2307.04533 (2023) - [i93]Xuan Zhang, Limei Wang, Jacob Helwig, Youzhi Luo, Cong Fu, Yaochen Xie, Meng Liu, Yuchao Lin, Zhao Xu, Keqiang Yan, Keir Adams, Maurice Weiler, Xiner Li, Tianfan Fu, Yucheng Wang, Haiyang Yu, Yuqing Xie, Xiang Fu, Alex Strasser, Shenglong Xu, Yi Liu, Yuanqi Du, Alexandra Saxton, Hongyi Ling, Hannah Lawrence, Hannes Stärk, Shurui Gui, Carl Edwards, Nicholas Gao, Adriana Ladera, Tailin Wu, Elyssa F. Hofgard, Aria Mansouri Tehrani, Rui Wang, Ameya Daigavane, Montgomery Bohde, Jerry Kurtin, Qian Huang, Tuong Phung, Minkai Xu, Chaitanya K. Joshi, Simon V. Mathis, Kamyar Azizzadenesheli, Ada Fang, Alán Aspuru-Guzik, Erik Bekkers, Michael M. Bronstein, Marinka Zitnik, Anima Anandkumar, Stefano Ermon, Pietro Liò, Rose Yu, Stephan Günnemann, Jure Leskovec, Heng Ji, Jimeng Sun, Regina Barzilay, Tommi S. Jaakkola, Connor W. Coley, Xiaoning Qian, Xiaofeng Qian, Tess E. Smidt, Shuiwang Ji:
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems. CoRR abs/2307.08423 (2023) - [i92]Armin Moin, Atta Badii, Stephan Günnemann, Moharram Challenger:
AI-Enabled Software and System Architecture Frameworks: Focusing on smart Cyber-Physical Systems (CPS). CoRR abs/2308.05239 (2023) - [i91]Francesco Campi, Lukas Gosch, Tom Wollschläger, Yan Scholten, Stephan Günnemann:
Expressivity of Graph Neural Networks Through the Lens of Adversarial Robustness. CoRR abs/2308.08173 (2023) - [i90]Sebastian Schmidt, Stephan Günnemann:
Stream-based Active Learning by Exploiting Temporal Properties in Perception with Temporal Predicted Loss. CoRR abs/2309.05517 (2023) - 2022
- [j24]Artur Mrowca, Florian Gyrock, Stephan Günnemann:
Temporal state change Bayesian networks for modeling of evolving multivariate state sequences: model, structure discovery and parameter estimation. Data Min. Knowl. Discov. 36(1): 240-294 (2022) - [j23]Maximilian E. Schüle, Harald Lang, Maximilian Springer, Alfons Kemper, Thomas Neumann, Stephan Günnemann:
Recursive SQL and GPU-support for in-database machine learning. Distributed Parallel Databases 40(2-3): 205-259 (2022) - [j22]Aleksei Kuvshinov, Stephan Günnemann:
Robustness verification of ReLU networks via quadratic programming. Mach. Learn. 111(7): 2407-2433 (2022) - [j21]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) - [j20]Armin Moin, Moharram Challenger, Atta Badii, Stephan Günnemann:
A model-driven approach to machine learning and software modeling for the IoT. Softw. Syst. Model. 21(3): 987-1014 (2022) - [j19]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) - [j18]Kevin Kennard Thiel, Florian Naumann, Eduard Jundt, Stephan Günnemann, Gudrun Klinker:
C.DOT - Convolutional Deep Object Tracker for Augmented Reality Based Purely on Synthetic Data. IEEE Trans. Vis. Comput. Graph. 28(12): 4434-4451 (2022) - [c144]Aleksei Kuvshinov, Daniel Knobloch, Daniel Külzer, Elen Vardanyan, Stephan Günnemann:
Domain Reconstruction for UWB Car Key Localization Using Generative Adversarial Networks. AAAI 2022: 12552-12558 - [c143]Poulami Sinhamahapatra, Rajat Koner, Karsten Roscher, Stephan Günnemann:
Is it all a cluster game? - Exploring Out-of-Distribution Detection based on Clustering in the Embedding Space. SafeAI@AAAI 2022 - [c142]Armin Moin, Moharram Challenger, Atta Badii, Stephan Günnemann:
Supporting AI Engineering on the IoT Edge through Model-Driven TinyML. COMPSAC 2022: 884-893 - [c141]Codrut-Andrei Diaconu, Sudipan Saha, Stephan Günnemann, Xiao Xiang Zhu:
Understanding the Role of Weather Data for Earth Surface Forecasting using a ConvLSTM-based Model. CVPR Workshops 2022: 1361-1370 - [c140]Armin Moin, Moharram Challenger, Atta Badii, Stephan Günnemann:
Towards Model-Driven Engineering for Quantum AI. GI-Jahrestagung 2022: 1121-1131 - [c139]Bertrand Charpentier, Oliver Borchert, Daniel Zügner, Simon Geisler, Stephan Günnemann:
Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions. ICLR 2022 - [c138]Bertrand Charpentier, Simon Kibler, Stephan Günnemann:
Differentiable DAG Sampling. ICLR 2022 - [c137]Nicholas Gao, Stephan Günnemann:
Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions. ICLR 2022 - [c136]Simon Geisler, Johanna Sommer, Jan Schuchardt, Aleksandar Bojchevski, Stephan Günnemann:
Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness. ICLR 2022 - [c135]Marten Lienen, Stephan Günnemann:
Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks. ICLR 2022 - [c134]Daniel Zügner, Bertrand Charpentier, Morgane Ayle, Sascha Geringer, Stephan Günnemann:
End-to-End Learning of Probabilistic Hierarchies on Graphs. ICLR 2022 - [c133]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 - [c132]Hannes Stärk, Dominique Beaini, Gabriele Corso, Prudencio Tossou, Christian Dallago, Stephan Günnemann, Pietro Lió:
3D Infomax improves GNNs for Molecular Property Prediction. ICML 2022: 20479-20502 - [c131]Peter Súkeník, Aleksei Kuvshinov, Stephan Günnemann:
Intriguing Properties of Input-Dependent Randomized Smoothing. ICML 2022: 20697-20743 - [c130]Felippe Schmoeller Roza, Hassan Rasheed, Karsten Roscher, Xiangyu Ning, Stephan Günnemann:
Safe Robot Navigation Using Constrained Hierarchical Reinforcement Learning. ICMLA 2022: 737-742 - [c129]Armin Moin, Andrei Mituca, Moharram Challenger, Atta Badii, Stephan Günnemann:
ML-Quadrat & DriotData: A Model-Driven Engineering Tool and a Low-Code Platform for Smart IoT Services. ICSE-Companion 2022: 144-148 - [c128]Johannes Gasteiger, Chendi Qian, Stephan Günnemann:
Influence-Based Mini-Batching for Graph Neural Networks. LoG 2022: 9 - [c127]Alexandru Cristian Mara, Jefrey Lijffijt, Stephan Günnemann, Tijl De Bie:
A Systematic Evaluation of Node Embedding Robustness. LoG 2022: 42 - [c126]Jörg Christian Kirchhof, Evgeny Kusmenko, Jonas Ritz, Bernhard Rumpe, Armin Moin, Atta Badii, Stephan Günnemann, Moharram Challenger:
MDE for machine learning-enabled software systems: a case study and comparison of MontiAnna & ML-Quadrat. MoDELS (Companion) 2022: 380-387 - [c125]Leon Hetzel, Simon Böhm, Niki Kilbertus, Stephan Günnemann, Mohammad Lotfollahi, Fabian J. Theis:
Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution. NeurIPS 2022 - [c124]Felix Mujkanovic, Simon Geisler, Stephan Günnemann, Aleksandar Bojchevski:
Are Defenses for Graph Neural Networks Robust? NeurIPS 2022 - [c123]Yan Scholten, Jan Schuchardt, Simon Geisler, Aleksandar Bojchevski, Stephan Günnemann:
Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks. NeurIPS 2022 - [c122]Jan Schuchardt, Stephan Günnemann:
Invariance-Aware Randomized Smoothing Certificates. NeurIPS 2022 - [c121]Nicola Franco, Tom Wollschläger, Nicholas Gao, Jeanette Miriam Lorenz, Stephan Günnemann:
Quantum Robustness Verification: A Hybrid Quantum-Classical Neural Network Certification Algorithm. QCE 2022: 142-153 - [i89]Oliver Borchert, David Salinas, Valentin Flunkert, Tim Januschowski, Stephan Günnemann:
Multi-Objective Model Selection for Time Series Forecasting. CoRR abs/2202.08485 (2022) - [i88]Tong Zhao, Gang Liu, Stephan Günnemann, Meng Jiang:
Graph Data Augmentation for Graph Machine Learning: A Survey. CoRR abs/2202.08871 (2022) - [i87]Armin Moin, Ukrit Wattanavaekin, Alexandra Lungu, Moharram Challenger, Atta Badii, Stephan Günnemann:
Enabling Automated Machine Learning for Model-Driven AI Engineering. CoRR abs/2203.02927 (2022) - [i86]Bertrand Charpentier, Simon Kibler, Stephan Günnemann:
Differentiable DAG Sampling. CoRR abs/2203.08509 (2022) - [i85]Poulami Sinhamahapatra, Rajat Koner, Karsten Roscher, Stephan Günnemann:
Is it all a cluster game? - Exploring Out-of-Distribution Detection based on Clustering in the Embedding Space. CoRR abs/2203.08549 (2022) - [i84]Marten Lienen, Stephan Günnemann:
Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks. CoRR abs/2203.08852 (2022) - [i83]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) - [i82]Leon Hetzel, Simon Böhm, Niki Kilbertus, Stephan Günnemann, Mohammad Lotfollahi, Fabian J. Theis:
Predicting single-cell perturbation responses for unseen drugs. CoRR abs/2204.13545 (2022) - [i81]Nicholas Gao, Stephan Günnemann:
Sampling-free Inference for Ab-Initio Potential Energy Surface Networks. CoRR abs/2205.14962 (2022) - [i80]Bertrand Charpentier, Ransalu Senanayake, Mykel J. Kochenderfer, Stephan Günnemann:
Disentangling Epistemic and Aleatoric Uncertainty in Reinforcement Learning. CoRR abs/2206.01558 (2022) - [i79]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) - [i78]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) - [i77]Jonathan Külz, Andreas Spitz, Ahmad Abu-Akel, Stephan Günnemann, Robert West:
United States Politicians' Tone Became More Negative with 2016 Primary Campaigns. CoRR abs/2207.08112 (2022) - [i76]Jörg Christian Kirchhof, Evgeny Kusmenko, Jonas Ritz, Bernhard Rumpe, Armin Moin, Atta Badii, Stephan Günnemann, Moharram Challenger:
MDE for Machine Learning-Enabled Software Systems: A Case Study and Comparison of MontiAnna & ML-Quadrat. CoRR abs/2209.07282 (2022) - [i75]Alexandru Mara, Jefrey Lijffijt, Stephan Günnemann, Tijl De Bie:
A Systematic Evaluation of Node Embedding Robustness. CoRR abs/2209.08064 (2022) - [i74]Raffaele Paolino, Aleksandar Bojchevski, Stephan Günnemann, Gitta Kutyniok, Ron Levie:
Unveiling the Sampling Density in Non-Uniform Geometric Graphs. CoRR abs/2210.08219 (2022) - [i73]Marin Bilos, Emanuel Ramneantu, Stephan Günnemann:
Irregularly-Sampled Time Series Modeling with Spline Networks. CoRR abs/2210.10630 (2022) - [i72]Marten Lienen, Stephan Günnemann:
torchode: A Parallel ODE Solver for PyTorch. CoRR abs/2210.12375 (2022) - [i71]Jan Schuchardt, Tom Wollschläger, Aleksandar Bojchevski, Stephan Günnemann:
Localized Randomized Smoothing for Collective Robustness Certification. CoRR abs/2210.16140 (2022) - [i70]Marin Bilos, Kashif Rasul, Anderson Schneider, Yuriy Nevmyvaka, Stephan Günnemann:
Modeling Temporal Data as Continuous Functions with Process Diffusion. CoRR abs/2211.02590 (2022) - [i69]Jan Schuchardt, Stephan Günnemann:
Invariance-Aware Randomized Smoothing Certificates. CoRR abs/2211.14207 (2022) - [i68]Johannes Gasteiger, Chendi Qian, Stephan Günnemann:
Influence-Based Mini-Batching for Graph Neural Networks. CoRR abs/2212.09083 (2022) - [i67]Martin Grohe, Stephan Günnemann, Stefanie Jegelka, Christopher Morris:
Graph Embeddings: Theory meets Practice (Dagstuhl Seminar 22132). Dagstuhl Reports 12(3): 141-155 (2022) - 2021
- [j17]Martin Atzmueller, Stephan Günnemann, Albrecht Zimmermann:
Mining communities and their descriptions on attributed graphs: a survey. Data Min. Knowl. Discov. 35(3): 661-687 (2021) - [j16]Anna-Kathrin Kopetzki, Stephan Günnemann:
Reachable sets of classifiers and regression models: (non-)robustness analysis and robust training. Mach. Learn. 110(6): 1175-1197 (2021) - [c120]Yihan Wu, Aleksandar Bojchevski, Aleksei Kuvshinov, Stephan Günnemann:
Completing the Picture: Randomized Smoothing Suffers from the Curse of Dimensionality for a Large Family of Distributions. AISTATS 2021: 3763-3771 - [c119]Rajat Koner, Poulami Sinhamahapatra, Karsten Roscher, Stephan Günnemann, Volker Tresp:
OODformer: Out-Of-Distribution Detection Transformer. BMVC 2021: 209 - [c118]Jan Schuchardt, Aleksandar Bojchevski, Johannes Klicpera, Stephan Günnemann:
Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks. ICLR 2021 - [c117]Daniel Zügner, Tobias Kirschstein, Michele Catasta, Jure Leskovec, Stephan Günnemann:
Language-Agnostic Representation Learning of Source Code from Structure and Context. ICLR 2021 - [c116]Marin Bilos, Stephan Günnemann:
Scalable Normalizing Flows for Permutation Invariant Densities. ICML 2021: 957-967 - [c115]Johannes Klicpera, Marten Lienen, Stephan Günnemann:
Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, and More. ICML 2021: 5616-5627 - [c114]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 - [c113]Tom Haider, Felippe Schmoeller Roza, Dirk Eilers, Karsten Roscher, Stephan Günnemann:
Domain Shifts in Reinforcement Learning: Identifying Disturbances in Environments. AISafety@IJCAI 2021 - [c112]Oleksandr Shchur, Ali Caner Türkmen, Tim Januschowski, Stephan Günnemann:
Neural Temporal Point Processes: A Review. IJCAI 2021: 4585-4593 - [c111]Johannes Gasteiger, Florian Becker, Stephan Günnemann:
GemNet: Universal Directional Graph Neural Networks for Molecules. NeurIPS 2021: 6790-6802 - [c110]Simon Geisler, Tobias Schmidt, Hakan Sirin, Daniel Zügner, Aleksandar Bojchevski, Stephan Günnemann:
Robustness of Graph Neural Networks at Scale. NeurIPS 2021: 7637-7649 - [c109]Oleksandr Shchur, Ali Caner Türkmen, Tim Januschowski, Jan Gasthaus, Stephan Günnemann:
Detecting Anomalous Event Sequences with Temporal Point Processes. NeurIPS 2021: 13419-13431 - [c108]Johannes Gasteiger, Chandan Yeshwanth, Stephan Günnemann:
Directional Message Passing on Molecular Graphs via Synthetic Coordinates. NeurIPS 2021: 15421-15433 - [c107]Marin Bilos, Johanna Sommer, Syama Sundar Rangapuram, Tim Januschowski, Stephan Günnemann:
Neural Flows: Efficient Alternative to Neural ODEs. NeurIPS 2021: 21325-21337 - [c106]Johannes C. Paetzold, Julian McGinnis, Suprosanna Shit, Ivan Ezhov, Paul Büschl, Chinmay Prabhakar, Anjany Sekuboyina, Mihail I. Todorov, Georgios Kaissis, Ali Ertürk, Stephan Günnemann, Bjoern H. Menze:
Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience. NeurIPS Datasets and Benchmarks 2021 - [c105]Maximilian Stadler, Bertrand Charpentier, Simon Geisler, Daniel Zügner, Stephan Günnemann:
Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification. NeurIPS 2021: 18033-18048 - [c104]Rajat Koner, Hang Li, Marcel Hildebrandt, Deepan Das, Volker Tresp, Stephan Günnemann:
Graphhopper: Multi-hop Scene Graph Reasoning for Visual Question Answering. ISWC 2021: 111-127 - [c103]Maximilian E. Schüle, Harald Lang, Maximilian Springer, Alfons Kemper, Thomas Neumann, Stephan Günnemann:
In-Database Machine Learning with SQL on GPUs. SSDBM 2021: 25-36 - [i66]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) - [i65]Oleksandr Shchur, Ali Caner Türkmen, Tim Januschowski, Stephan Günnemann:
Neural Temporal Point Processes: A Review. CoRR abs/2104.03528 (2021) - [i64]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) - [i63]