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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
[i91]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)- [i90]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) - 2022
- [j22]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) - [j21]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) - [j20]Aleksei Kuvshinov, Stephan Günnemann:
Robustness verification of ReLU networks via quadratic programming. Mach. Learn. 111(7): 2407-2433 (2022) - [j19]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) - [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) - [c137]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 - [c136]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 - [c135]Armin Moin, Moharram Challenger, Atta Badii, Stephan Günnemann:
Supporting AI Engineering on the IoT Edge through Model-Driven TinyML. COMPSAC 2022: 884-893 - [c134]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 - [c133]Armin Moin, Moharram Challenger, Atta Badii, Stephan Günnemann:
Towards Model-Driven Engineering for Quantum AI. GI-Jahrestagung 2022: 1121-1131 - [c132]Bertrand Charpentier, Oliver Borchert, Daniel Zügner, Simon Geisler, Stephan Günnemann:
Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions. ICLR 2022 - [c131]Bertrand Charpentier, Simon Kibler, Stephan Günnemann:
Differentiable DAG Sampling. ICLR 2022 - [c130]Nicholas Gao, Stephan Günnemann:
Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions. ICLR 2022 - [c129]Simon Geisler, Johanna Sommer, Jan Schuchardt, Aleksandar Bojchevski, Stephan Günnemann:
Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness. ICLR 2022 - [c128]Marten Lienen, Stephan Günnemann:
Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks. ICLR 2022 - [c127]Daniel Zügner, Bertrand Charpentier, Morgane Ayle, Sascha Geringer, Stephan Günnemann:
End-to-End Learning of Probabilistic Hierarchies on Graphs. ICLR 2022 - [c126]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 - [c125]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 - [c124]Peter Súkeník, Aleksei Kuvshinov, Stephan Günnemann:
Intriguing Properties of Input-Dependent Randomized Smoothing. ICML 2022: 20697-20743 - [c123]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 - [c122]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 - [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]Oleksandr Shchur, Ali Caner Türkmen, Tim Januschowski, Jan Gasthaus, Stephan Günnemann:
Detecting Anomalous Event Sequences with Temporal Point Processes. CoRR abs/2106.04465 (2021) - [i62]Johannes Gasteiger, Florian Becker, Stephan Günnemann:
GemNet: Universal Directional Graph Neural Networks for Molecules. CoRR abs/2106.08903 (2021) - [i61]Armin Moin, Atta Badii, Stephan Günnemann:
A Model-Driven Engineering Approach to Machine Learning and Software Modeling. CoRR abs/2107.02689 (2021) - [i60]Armin Moin, Atta Badii, Stephan Günnemann:
Enabling Un-/Semi-Supervised Machine Learning for MDSE of the Real-World CPS/IoT Applications. CoRR abs/2107.02690 (2021) - [i59]Armin Moin, Andrei Mituca, Atta Badii, Stephan Günnemann:
ML-Quadrat & DriotData: A Model-Driven Engineering Tool and a Low-Code Platform for Smart IoT Services. CoRR abs/2107.02692 (2021) - [i58]Rajat Koner, Hang Li, Marcel Hildebrandt, Deepan Das, Volker Tresp, Stephan Günnemann:
Graphhopper: Multi-Hop Scene Graph Reasoning for Visual Question Answering. CoRR abs/2107.06325 (2021) - [i57]Armin Moin, Moharram Challenger, Atta Badii, Stephan Günnemann:
MDE4QAI: Towards Model-Driven Engineering for Quantum Artificial Intelligence. CoRR abs/2107.06708 (2021) - [i56]Johannes Klicpera, Marten Lienen, Stephan Günnemann:
Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, and More. CoRR abs/2107.06876 (2021) - [i55]Sven Elflein, Bertrand Charpentier, Daniel Zügner, Stephan Günnemann:
On Out-of-distribution Detection with Energy-based Models. CoRR abs/2107.08785 (2021) - [i54]Rajat Koner, Poulami Sinhamahapatra, Karsten Roscher, Stephan Günnemann, Volker Tresp:
OODformer: Out-Of-Distribution Detection Transformer. CoRR abs/2107.08976 (2021) - [i53]Johannes C. Paetzold, Julian McGinnis, Suprosanna Shit, Ivan Ezhov, Paul Büschl, Chinmay Prabhakar, Mihail I. Todorov, Anjany Sekuboyina, Georgios Kaissis, Ali Ertürk, Stephan Günnemann, Bjoern H. Menze:
Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience (VesselGraph). CoRR abs/2108.13233 (2021) - [i52]Sebastian Bischoff, Stephan Günnemann, Martin Jaggi, Sebastian U. Stich:
On Second-order Optimization Methods for Federated Learning. CoRR abs/2109.02388 (2021) - [i51]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) - [i50]Hannes Stärk, Dominique Beaini, Gabriele Corso, Prudencio Tossou, Christian Dallago, Stephan Günnemann, Pietro Liò:
3D Infomax improves GNNs for Molecular Property Prediction. CoRR abs/2110.04126 (2021) - [i49]Nicholas Gao, Stephan Günnemann:
Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions. CoRR abs/2110.05064 (2021) - [i48]Peter Súkeník, Aleksei Kuvshinov, Stephan Günnemann:
Intriguing Properties of Input-dependent Randomized Smoothing. CoRR abs/2110.05365 (2021) - [i47]Simon Geisler, Johanna Sommer, Jan Schuchardt, Aleksandar Bojchevski, Stephan Günnemann:
Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness. CoRR abs/2110.10942 (2021) - [i46]Marin Bilos, Johanna Sommer, Syama Sundar Rangapuram, Tim Januschowski, Stephan Günnemann:
Neural Flows: Efficient Alternative to Neural ODEs. CoRR abs/2110.13040 (2021) - [i45]Maximilian Stadler, Bertrand Charpentier, Simon Geisler, Daniel Zügner, Stephan Günnemann:
Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification. CoRR abs/2110.14012 (2021) - [i44]Simon Geisler, Tobias Schmidt, Hakan Sirin, Daniel Zügner, Aleksandar Bojchevski, Stephan Günnemann:
Robustness of Graph Neural Networks at Scale. CoRR abs/2110.14038 (2021) - [i43]Johannes Klicpera, Chandan Yeshwanth, Stephan Günnemann:
Directional Message Passing on Molecular Graphs via Synthetic Coordinates. CoRR abs/2111.04718 (2021) - 2020
- [j15]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) - [c102]Zhen Han, Yunpu Ma, Yuyi Wang, Stephan Günnemann, Volker Tresp:
Graph Hawkes Neural Network for Forecasting on Temporal Knowledge Graphs. AKBC 2020 - [c101]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 - [c100]Felippe Schmoeller Roza, Maximilian Henne, Karsten Roscher, Stephan Günnemann:
Assessing Box Merging Strategies and Uncertainty Estimation Methods in Multimodel Object Detection. ECCV Workshops (6) 2020: 3-10 - [c99]Johannes Klicpera, Janek Groß, Stephan Günnemann:
Directional Message Passing for Molecular Graphs. ICLR 2020 - [c98]Richard Kurle, Botond Cseke, Alexej Klushyn, Patrick van der Smagt, Stephan Günnemann:
Continual Learning with Bayesian Neural Networks for Non-Stationary Data. ICLR 2020 - [c97]Oleksandr Shchur, Marin Bilos, Stephan Günnemann:
Intensity-Free Learning of Temporal Point Processes. ICLR 2020 - [c96]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 - [c95]Daniel Zügner, Stephan Günnemann:
Certifiable Robustness of Graph Convolutional Networks under Structure Perturbations. KDD 2020: 1656-1665 - [c94]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 - [c93]Armin Moin, Stephan Rössler, Marouane Sayih, Stephan Günnemann:
From things' modeling language (ThingML) to things' machine learning (ThingML2). MoDELS (Companion) 2020: 19:1-19:2 - [c92]Bertrand Charpentier, Daniel Zügner, Stephan Günnemann:
Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts. NeurIPS 2020 - [c91]Simon Geisler, Daniel Zügner, Stephan Günnemann:
Reliable Graph Neural Networks via Robust Aggregation. NeurIPS 2020 - [c90]Richard Kurle, Syama Sundar Rangapuram, Emmanuel de Bézenac, Stephan Günnemann, Jan Gasthaus:
Deep Rao-Blackwellised Particle Filters for Time Series Forecasting. NeurIPS 2020 - [c89]Oleksandr Shchur, Nicholas Gao, Marin Bilos, Stephan Günnemann:
Fast and Flexible Temporal Point Processes with Triangular Maps. NeurIPS 2020 - [c88]Richard Leibrandt, Stephan Günnemann:
Gauss Shift: Density Attractor Clustering Faster Than Mean Shift. ECML/PKDD (1) 2020: 125-142 - [i42]Johannes Klicpera, Janek Groß, Stephan Günnemann:
Directional Message Passing for Molecular Graphs. CoRR abs/2003.03123 (2020) - [i41]Zhen Han, Yuyi Wang, Yunpu Ma, Stephan Günnemann, Volker Tresp:
Graph Hawkes Network for Reasoning on Temporal Knowledge Graphs. CoRR abs/2003.13432 (2020) - [i40]Bertrand Charpentier, Daniel Zügner, Stephan Günnemann:
Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts. CoRR abs/2006.09239 (2020) - [i39]Oleksandr Shchur, Nicholas Gao, Marin Bilos, Stephan Günnemann:
Fast and Flexible Temporal Point Processes with Triangular Maps. CoRR abs/2006.12631 (2020) - [i38]Marcel Hildebrandt, Hang Li, Rajat Koner, Volker Tresp, Stephan Günnemann:
Scene Graph Reasoning for Visual Question Answering. CoRR abs/2007.01072 (2020) - [i37]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) - [i36]Nick Harmening, Marin Bilos, Stephan Günnemann:
Deep Representation Learning and Clustering of Traffic Scenarios. CoRR abs/2007.07740 (2020) - [i35]Anna-Kathrin Kopetzki, Stephan Günnemann:
Reachable Sets of Classifiers & Regression Models: (Non-)Robustness Analysis and Robust Training. CoRR abs/2007.14120 (2020) - [i34]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) - [i33]Armin Moin, Stephan Rössler, Marouane Sayih, Stephan Günnemann:
From Things' Modeling Language (ThingML) to Things' Machine Learning (ThingML2). CoRR abs/2009.10632 (2020) - [i32]Armin Moin, Stephan Rössler, Stephan Günnemann:
ThingML+ Augmenting Model-Driven Software Engineering for the Internet of Things with Machine Learning. CoRR abs/2009.10633 (2020) - [i31]Marin Bilos, Stephan Günnemann:
Equivariant Normalizing Flows for Point Processes and Sets. CoRR abs/2010.03242 (2020) - [i30]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) - [i29]Simon Geisler, Daniel Zügner, Stephan Günnemann:
Reliable Graph Neural Networks via Robust Aggregation. CoRR abs/2010.15651 (2020) - [i28]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
- [j14]Saskia Metzler, Stephan Günnemann, Pauli Miettinen:
Stability and dynamics of communities on online question-answer sites. Soc. Networks 58: 50-58 (2019) - [c87]Richard Kurle