default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDStephan Günnemann
Person information
- affiliation: Technical University of Munich, Germany
- affiliation (former): Carnegie Mellon University, Pittsburgh, USA
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
[j28]Sebastian Schmidt
, Lukas Stappen, Leo Schwinn, Stephan Günnemann:
Generalized Synchronized Active Learning for Multi-Agent-Based Data Selection on Mobile Robotic Systems. IEEE Robotics Autom. Lett. 9(10): 8659-8666 (2024)- [c173]Nicola Franco, Jeanette Miriam Lorenz, Karsten Roscher, Stephan Günnemann:
Understanding ReLU Network Robustness Through Test Set Certification Performance. CVPR Workshops 2024: 3451-3460 - [c172]Lena Heidemann, Iwo Kurzidem, Maureen Monnet, Karsten Roscher, Stephan Günnemann:
Towards Engineered Safe AI with Modular Concept Models. CVPR Workshops 2024: 3564-3573 - [c171]Marten Lienen, David Lüdke, Jan Hansen-Palmus, Stephan Günnemann:
From Zero to Turbulence: Generative Modeling for 3D Flow Simulation. ICLR 2024 - [c170]Dominik Fuchsgruber, Tom Wollschläger, Bertrand Charpentier, Antonio Oroz, Stephan Günnemann:
Uncertainty for Active Learning on Graphs. ICML 2024 - [c169]Tom Wollschläger, Niklas Kemper, Leon Hetzel, Johanna Sommer, Stephan Günnemann:
Expressivity and Generalization: Fragment-Biases for Molecular GNNs. ICML 2024 - [c168]Jonas Gregor Wiese, Lisa Wimmer, Theodore Papamarkou, Bernd Bischl, Stephan Günnemann, David Rügamer:
Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry (Extended Abstract). IJCAI 2024: 8466-8470 - [i153]Leo Schwinn, David Dobre, Sophie Xhonneux, Gauthier Gidel, Stephan Günnemann:
Soft Prompt Threats: Attacking Safety Alignment and Unlearning in Open-Source LLMs through the Embedding Space. CoRR abs/2402.09063 (2024) - [i152]Simon Geisler, Tom Wollschläger, M. H. I. Abdalla, Johannes Gasteiger, Stephan Günnemann:
Attacking Large Language Models with Projected Gradient Descent. CoRR abs/2402.09154 (2024) - [i151]Rayen Dhahri, Alexander Immer, Bertrand Charpentier, Stephan Günnemann, Vincent Fortuin:
Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks Using the Marginal Likelihood. CoRR abs/2402.15978 (2024) - [i150]Jan Schuchardt, Mihail Stoian, Arthur Kosmala, Stephan Günnemann:
Group Privacy Amplification and Unified Amplification by Subsampling for Rényi Differential Privacy. CoRR abs/2403.04867 (2024) - [i149]Nicholas Gao, Stephan Günnemann:
On Representing Electronic Wave Functions with Sign Equivariant Neural Networks. CoRR abs/2403.05249 (2024) - [i148]Xun Wang, John Rachwan, Stephan Günnemann, Bertrand Charpentier:
Structurally Prune Anything: Any Architecture, Any Framework, Any Time. CoRR abs/2403.18955 (2024) - [i147]Poulami Sinhamahapatra, Suprosanna Shit, Anjany Sekuboyina, Malek El Husseini, David Schinz, Nicolas Lenhart, Bjoern H. Menze, Jan Kirschke, Karsten Roscher, Stephan Günnemann:
Enhancing Interpretability of Vertebrae Fracture Grading using Human-interpretable Prototypes. CoRR abs/2404.02830 (2024) - [i146]Poulami Sinhamahapatra, Franziska Schwaiger, Shirsha Bose, Huiyu Wang, Karsten Roscher, Stephan Günnemann:
Finding Dino: A plug-and-play framework for unsupervised detection of out-of-distribution objects using prototypes. CoRR abs/2404.07664 (2024) - [i145]Dominik Fuchsgruber, Tom Wollschläger, Bertrand Charpentier, Antonio Oroz, Stephan Günnemann:
Uncertainty for Active Learning on Graphs. CoRR abs/2405.01462 (2024) - [i144]Sebastian Schmidt, Leonard Schenk, Leo Schwinn, Stephan Günnemann:
A Unified Approach Towards Active Learning and Out-of-Distribution Detection. CoRR abs/2405.11337 (2024) - [i143]Nicholas Gao, Stephan Günnemann:
Neural Pfaffians: Solving Many Many-Electron Schrödinger Equations. CoRR abs/2405.14762 (2024) - [i142]Sophie Xhonneux, Alessandro Sordoni, Stephan Günnemann, Gauthier Gidel, Leo Schwinn:
Efficient Adversarial Training in LLMs with Continuous Attacks. CoRR abs/2405.15589 (2024) - [i141]Leon Götz, Marcel Kollovieh, Stephan Günnemann, Leo Schwinn:
Efficient Time Series Processing for Transformers and State-Space Models through Token Merging. CoRR abs/2405.17951 (2024) - [i140]Simon Geisler, Arthur Kosmala, Daniel Herbst, Stephan Günnemann:
Spatio-Spectral Graph Neural Networks. CoRR abs/2405.19121 (2024) - [i139]Dominik Fuchsgruber, Tom Wollschläger, Stephan Günnemann:
Energy-based Epistemic Uncertainty for Graph Neural Networks. CoRR abs/2406.04043 (2024) - [i138]Zhong Li, Simon Geisler, Yuhang Wang, Stephan Günnemann, Matthijs van Leeuwen:
Explainable Graph Neural Networks Under Fire. CoRR abs/2406.06417 (2024) - [i137]Tom Wollschläger, Niklas Kemper, Leon Hetzel, Johanna Sommer, Stephan Günnemann:
Expressivity and Generalization: Fragment-Biases for Molecular GNNs. CoRR abs/2406.08210 (2024) - [i136]Mohamed Amine Ketata, Nicholas Gao, Johanna Sommer, Tom Wollschläger, Stephan Günnemann:
Lift Your Molecules: Molecular Graph Generation in Latent Euclidean Space. CoRR abs/2406.10513 (2024) - [i135]Abdullah Saydemir, Marten Lienen, Stephan Günnemann:
Unfolding Time: Generative Modeling for Turbulent Flows in 4D. CoRR abs/2406.11390 (2024) - [i134]Florence Regol, Joud Chataoui, Bertrand Charpentier, Mark Coates, Pablo Piantanida, Stephan Günnemann:
Predicting Probabilities of Error to Combine Quantization and Early Exiting: QuEE. CoRR abs/2406.14404 (2024) - [i133]Lukas Gosch, Mahalakshmi Sabanayagam, Debarghya Ghoshdastidar, Stephan Günnemann:
Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor Attacks. CoRR abs/2407.10867 (2024) - [i132]Philipp Foth, Lukas Gosch, Simon Geisler, Leo Schwinn, Stephan Günnemann:
Relaxing Graph Transformers for Adversarial Attacks. CoRR abs/2407.11764 (2024) - [i131]Tom Wollschläger, Aman Saxena, Nicola Franco, Jeanette Miriam Lorenz, Stephan Günnemann:
Discrete Randomized Smoothing Meets Quantum Computing. CoRR abs/2408.00895 (2024) - [i130]Aman Saxena, Tom Wollschläger, Nicola Franco, Jeanette Miriam Lorenz, Stephan Günnemann:
Certifiably Robust Encoding Schemes. CoRR abs/2408.01200 (2024) - [i129]Marcel Kollovieh, Marten Lienen, David Lüdke, Leo Schwinn, Stephan Günnemann:
Flow Matching with Gaussian Process Priors for Probabilistic Time Series Forecasting. CoRR abs/2410.03024 (2024) - [i128]Yan Scholten, Stephan Günnemann, Leo Schwinn:
A Probabilistic Perspective on Unlearning and Alignment for Large Language Models. CoRR abs/2410.03523 (2024) - [i127]Nicholas Gao, Eike Eberhard, Stephan Günnemann:
Learning Equivariant Non-Local Electron Density Functionals. CoRR abs/2410.07972 (2024) - [i126]Yan Scholten, Stephan Günnemann:
Provably Reliable Conformal Prediction Sets in the Presence of Data Poisoning. CoRR abs/2410.09878 (2024) - [i125]Dominik Fuchsgruber, Tim Postuvan, Stephan Günnemann, Simon Geisler:
Graph Neural Networks for Edge Signals: Orientation Equivariance and Invariance. CoRR abs/2410.16935 (2024) - [i124]David Lüdke, Enric Rabasseda Raventós, Marcel Kollovieh, Stephan Günnemann:
Unlocking Point Processes through Point Set Diffusion. CoRR abs/2410.22493 (2024) - 2023
- [j27]Richard Leibrandt, Stephan Günnemann:
Generalized density attractor clustering for incomplete data. Data Min. Knowl. Discov. 37(2): 970-1009 (2023) - [j26]Tong Zhao, Wei Jin, Yozen Liu, Yingheng Wang, Gang Liu, Stephan Günnemann, Neil Shah, Meng Jiang:
Graph Data Augmentation for Graph Machine Learning: A Survey. IEEE Data Eng. Bull. 46(2): 140-165 (2023) - [j25]Hao Lin, Hongfu Liu, Junjie Wu, Hong Li, Stephan Günnemann:
Algorithm 1038: KCC: A MATLAB Package for k-Means-based Consensus Clustering. ACM Trans. Math. Softw. 49(4): 40:1-40:27 (2023) - [c167]Nicola Franco, Daniel Korth, Jeanette Miriam Lorenz, Karsten Roscher, Stephan Günnemann:
Diffusion Denoised Smoothing for Certified and Adversarial Robust Out Of Distribution. AISafety/SafeRL@IJCAI 2023 - [c166]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 - [c165]Sebastian Schmidt, Stephan Günnemann:
Stream-based Active Learning by Exploiting Temporal Properties in Perception with Temporal Predicted Loss. BMVC 2023: 664 - [c164]Armin Moin, Atta Badii, Stephan Günnemann, Moharram Challenger:
Enabling Machine Learning in Software Architecture Frameworks. CAIN 2023: 92-93 - [c163]Jianxiang Feng, Jongseok Lee, Simon Geisler, Stephan Günnemann, Rudolph Triebel:
Topology-Matching Normalizing Flows for Out-of-Distribution Detection in Robot Learning. CoRL 2023: 3214-3241 - [c162]Leo Schwinn, David Dobre, Stephan Günnemann, Gauthier Gidel:
Adversarial Attacks and Defenses in Large Language Models: Old and New Threats. ICBINB 2023: 103-117 - [c161]Nicholas Gao, Stephan Günnemann:
Sampling-free Inference for Ab-Initio Potential Energy Surface Networks. ICLR 2023 - [c160]Lukas Gosch, Daniel Sturm, Simon Geisler, Stephan Günnemann:
Revisiting Robustness in Graph Machine Learning. ICLR 2023 - [c159]Raffaele Paolino, Aleksandar Bojchevski, Stephan Günnemann, Gitta Kutyniok, Ron Levie:
Unveiling the sampling density in non-uniform geometric graphs. ICLR 2023 - [c158]Jan Schuchardt, Tom Wollschläger, Aleksandar Bojchevski, Stephan Günnemann:
Localized Randomized Smoothing for Collective Robustness Certification. ICLR 2023 - [c157]Marin Bilos, Kashif Rasul, Anderson Schneider, Yuriy Nevmyvaka, Stephan Günnemann:
Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion. ICML 2023: 2452-2470 - [c156]Nicholas Gao, Stephan Günnemann:
Generalizing Neural Wave Functions. ICML 2023: 10708-10726 - [c155]Simon Geisler, Yujia Li, Daniel J. Mankowitz, Ali Taylan Cemgil, Stephan Günnemann, Cosmin Paduraru:
Transformers Meet Directed Graphs. ICML 2023: 11144-11172 - [c154]Arthur Kosmala, Johannes Gasteiger, Nicholas Gao, Stephan Günnemann:
Ewald-based Long-Range Message Passing for Molecular Graphs. ICML 2023: 17544-17563 - [c153]Tom Wollschläger, Nicholas Gao, Bertrand Charpentier, Mohamed Amine Ketata, Stephan Günnemann:
Uncertainty Estimation for Molecules: Desiderata and Methods. ICML 2023: 37133-37156 - [c152]Franziska Schwaiger, Andrea Matic, Karsten Roscher, Stephan Günnemann:
Preventing Errors in Person Detection: A Part-Based Self-Monitoring Framework. IV 2023: 1-8 - [c151]Emanuele Rossi, Bertrand Charpentier, Francesco Di Giovanni, Fabrizio Frasca, Stephan Günnemann, Michael M. Bronstein:
Edge Directionality Improves Learning on Heterophilic Graphs. LoG 2023: 25 - [c150]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 - [c149]David Lüdke, Marin Bilos, Oleksandr Shchur, Marten Lienen, Stephan Günnemann:
Add and Thin: Diffusion for Temporal Point Processes. NeurIPS 2023 - [c148]Yan Scholten, Jan Schuchardt, Aleksandar Bojchevski, Stephan Günnemann:
Hierarchical Randomized Smoothing. NeurIPS 2023 - [c147]Jan Schuchardt, Yan Scholten, Stephan Günnemann:
(Provable) Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More. NeurIPS 2023 - [c146]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 - [c145]Nicola Franco, Tom Wollschläger, Benedikt Poggel, Stephan Günnemann, Jeanette Miriam Lorenz:
Efficient MILP Decomposition in Quantum Computing for ReLU Network Robustness. QCE 2023: 524-534 - [i123]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) - [i122]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) - [i121]Felix Mujkanovic, Simon Geisler, Stephan Günnemann, Aleksandar Bojchevski:
Are Defenses for Graph Neural Networks Robust? CoRR abs/2301.13694 (2023) - [i120]Simon Geisler, Yujia Li, Daniel J. Mankowitz, Ali Taylan Cemgil, Stephan Günnemann, Cosmin Paduraru:
Transformers Meet Directed Graphs. CoRR abs/2302.00049 (2023) - [i119]Jan Schuchardt, Aleksandar Bojchevski, Johannes Gasteiger, Stephan Günnemann:
Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks. CoRR abs/2302.02829 (2023) - [i118]Nicholas Gao, Stephan Günnemann:
Generalizing Neural Wave Functions. CoRR abs/2302.04168 (2023) - [i117]Arthur Kosmala, Johannes Gasteiger, Nicholas Gao, Stephan Günnemann:
Ewald-based Long-Range Message Passing for Molecular Graphs. CoRR abs/2303.04791 (2023) - [i116]Bertrand Charpentier, Chenxiang Zhang, Stephan Günnemann:
Training, Architecture, and Prior for Deterministic Uncertainty Methods. CoRR abs/2303.05796 (2023) - [i115]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) - [i114]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) - [i113]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) - [i112]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) - [i111]Lukas Gosch, Daniel Sturm, Simon Geisler, Stephan Günnemann:
Revisiting Robustness in Graph Machine Learning. CoRR abs/2305.00851 (2023) - [i110]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) - [i109]Leon Hetzel, Johanna Sommer, Bastian Rieck, Fabian J. Theis, Stephan Günnemann:
MAGNet: Motif-Agnostic Generation of Molecules from Shapes. CoRR abs/2305.19303 (2023) - [i108]Marten Lienen, Jan Hansen-Palmus, David Lüdke, Stephan Günnemann:
Generative Diffusion for 3D Turbulent Flows. CoRR abs/2306.01776 (2023) - [i107]Tom Wollschläger, Nicholas Gao, Bertrand Charpentier, Mohamed Amine Ketata, Stephan Günnemann:
Uncertainty Estimation for Molecules: Desiderata and Methods. CoRR abs/2306.14916 (2023) - [i106]Lukas Gosch, Simon Geisler, Daniel Sturm, Bertrand Charpentier, Daniel Zügner, Stephan Günnemann:
Adversarial Training for Graph Neural Networks. CoRR abs/2306.15427 (2023) - [i105]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) - [i104]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) - [i103]Franziska Schwaiger, Andrea Matic, Karsten Roscher, Stephan Günnemann:
Preventing Errors in Person Detection: A Part-Based Self-Monitoring Framework. CoRR abs/2307.04533 (2023) - [i102]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 J. 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) - [i101]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) - [i100]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) - [i99]Sebastian Schmidt, Stephan Günnemann:
Stream-based Active Learning by Exploiting Temporal Properties in Perception with Temporal Predicted Loss. CoRR abs/2309.05517 (2023) - [i98]Marcel Kollovieh, Lukas Gosch, Yan Scholten, Marten Lienen, Stephan Günnemann:
Assessing Robustness via Score-Based Adversarial Image Generation. CoRR abs/2310.04285 (2023) - [i97]Yan Scholten, Jan Schuchardt, Aleksandar Bojchevski, Stephan Günnemann:
Hierarchical Randomized Smoothing. CoRR abs/2310.16221 (2023) - [i96]Leo Schwinn, David Dobre, Stephan Günnemann, Gauthier Gidel:
Adversarial Attacks and Defenses in Large Language Models: Old and New Threats. CoRR abs/2310.19737 (2023) - [i95]David Lüdke, Marin Bilos, Oleksandr Shchur, Marten Lienen, Stephan Günnemann:
Add and Thin: Diffusion for Temporal Point Processes. CoRR abs/2311.01139 (2023) - [i94]Jianxiang Feng, Jongseok Lee, Simon Geisler, Stephan Günnemann, Rudolph Triebel:
Topology-Matching Normalizing Flows for Out-of-Distribution Detection in Robot Learning. CoRR abs/2311.06481 (2023) - [i93]Filippo Guerranti, Zinuo Yi, Anna Starovoit, Rafiq Kamel, Simon Geisler, Stephan Günnemann:
On the Adversarial Robustness of Graph Contrastive Learning Methods. CoRR abs/2311.17853 (2023) - [i92]Jan Schuchardt, Yan Scholten, Stephan Günnemann:
(Provable) Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More. CoRR abs/2312.02708 (2023) - [i91]Michael Plainer, Hannes Stärk, Charlotte Bunne, Stephan Günnemann:
Transition Path Sampling with Boltzmann Generator-based MCMC Moves. CoRR abs/2312.05340 (2023) - [i90]Ege Erdogan, Simon Geisler, Stephan Günnemann:
Poisoning × Evasion: Symbiotic Adversarial Robustness for Graph Neural Networks. CoRR abs/2312.05502 (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]