Publications of PN 6

  1. 2024

    1. B. Xiong, M. Nayyeri, L. Luo, Z. Wang, S. Pan, and S. Staab, “NestE: Modeling Nested Relational Structures for Knowledge Graph Reasoning,” in The 38th Annual AAAI Conference on Artificial Intelligence, in The 38th Annual AAAI Conference on Artificial Intelligence. 2024. [Online]. Available: https://arxiv.org/abs/2312.09219
    2. K. Peng et al., “Navigating Open Set Scenarios for Skeleton-based Action Recognition,” The 38th Annual AAAI Conference on Artificial Intelligence, 2024, [Online]. Available: https://arxiv.org/abs/2312.06330
  2. 2023

    1. D. Holzmüller and F. Bach, “Convergence rates for non-log-concave sampling and log-partition estimation,” arXiv:2303.03237, 2023.
    2. V. Zaverkin, D. Holzmüller, L. Bonfirraro, and J. Kästner, “Transfer learning for chemically accurate interatomic neural network potentials,” Phys. Chem. Chem. Phys., vol. 25, no. 7, Art. no. 7, 2023, doi: 10.1039/D2CP05793J.
    3. J. Rettberg et al., “Replication Data for: Port-Hamiltonian Fluid-Structure Interaction Modeling and Structure-Preserving Model Order Reduction of a Classical Guitar.” 2023. doi: 10.18419/darus-3248.
    4. T. Munz-Körner, S. Künzel, and D. Weiskopf, “Supplemental Material for ‘Visual-Explainable AI: The Use Case of Language Models.’” 2023. doi: 10.18419/darus-3456.
    5. M. Haas, D. Holzmüller, U. von Luxburg, and I. Steinwart, “Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension.” 2023.
    6. R. Bauer et al., “Visual Ensemble Analysis of Fluid Flow in Porous Media across Simulation Codes and Experiment,” Transport in Porous Media, 2023.
    7. C. Tanama, K. Peng, Z. Marinov, R. Stiefelhagen, and A. Roitberg, “Quantized Distillation: Optimizing Driver Activity Recognition Models for Resource-Constrained Environments,” 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023.
    8. J. Cervino, L. F. O. Chamon, B. D. Haeffele, R. Vidal, and A. Ribeiro, “Learning Globally Smooth Functions on Manifolds,” in International Conference on Machine Learning~(ICML), in International Conference on Machine Learning~(ICML). 2023.
    9. D. Holzmüller, V. Zaverkin, J. Kästner, and I. Steinwart, “A Framework and Benchmark for Deep Batch Active Learning for Regression,” Journal of Machine Learning Research, vol. 24, no. 164, Art. no. 164, 2023, [Online]. Available: http://jmlr.org/papers/v24/22-0937.html
    10. P.-C. Bürkner, M. Scholz, and S. T. Radev, “Some models are useful, but how do we know which ones? Towards a unified Bayesian model taxonomy,” Statistics Surveys, vol. 17, no. none, Art. no. none, 2023, doi: 10.1214/23-SS145.
    11. J. Rettberg et al., “Port-Hamiltonian fluid–structure interaction modelling and structure-preserving model order reduction of a classical guitar,” Mathematical and Computer Modelling of Dynamical Systems, vol. 29, no. 1, Art. no. 1, 2023, doi: 10.1080/13873954.2023.2173238.
    12. B. N. Hahn, G. Rigaud, and R. Schmähl, “A class of regularizations based on nonlinear isotropic diffusion for inverse problems,” IMA Journal of Numerical Analysis, Feb. 2023, doi: 10.1093/imanum/drad002.
    13. A. Straub, G. K. Karch, J. Steigerwald, F. Sadlo, B. Weigand, and T. Ertl, “Visual Analysis of Interface Deformation in Multiphase Flow,” Journal of Visualization, 2023, doi: https://doi.org/10.1007/s12650-023-00939-x.
    14. A. Straub, G. K. Karch, J. Steigerwald, F. Sadlo, B. Weigand, and T. Ertl, “Visual Analysis of Interface Deformation in Multiphase Flow,” Journal of Visualization, vol. 26, no. 6, Art. no. 6, 2023, doi: https://doi.org/10.1007/s12650-023-00939-x.
    15. B. Xiong, M. Nayyeri, S. Pan, and S. Staab, “Shrinking Embeddings for Hyper-Relational Knowledge Graphs,” The 61st Annual Meeting of the Association for Computational Linguistics, 2023, [Online]. Available: https://arxiv.org/abs/2306.02199
    16. J. Gödeke and G. Rigaud, “Imaging based on Compton scattering: model uncertainty and data-driven reconstruction methods,” Inverse Problems, vol. 39, no. 3, Art. no. 3, Feb. 2023, doi: 10.1088/1361-6420/acb2ed.
    17. A. Straub, N. Karadimitriou, G. Reina, S. Frey, H. Steeb, and T. Ertl, “Visual Analysis of Displacement Processes in Porous Media using Spatio-Temporal Flow Graphs,” IEEE Transactions on Visualization and Computer Graphics, vol. 30, no. 1, Art. no. 1, 2023, doi: 10.1109/TVCG.2023.3326931.
    18. J. Rettberg, D. Wittwar, P. Buchfink, R. Herkert, J. Fehr, and B. Haasdonk, “Improved a posteriori Error Bounds for Reduced port-Hamiltonian Systems.” 2023. doi: https://doi.org/10.48550/arXiv.2303.17329.
    19. N. Schäfer et al., “Model Parameters and Evaluation Data for our Visual Analysis System for Scene-Graph-Based Visual Question Answering.” 2023. doi: 10.18419/darus-3597.
    20. N. Schäfer et al., “Visual Analysis System for Scene-Graph-Based Visual Question Answering.” 2023. doi: 10.18419/darus-3589.
    21. D. Holzmüller, “Regression from linear models to neural networks: double descent, active learning, and sampling,” University of Stuttgart, 2023.
    22. N. Schäfer et al., “Visual Analysis of Scene-Graph-Based Visual Question Answering,” in Proceedings of the 16th International Symposium on Visual Information Communication and Interaction, in Proceedings of the 16th International Symposium on Visual Information Communication and Interaction. Guangzhou, China: Association for Computing Machinery, Oct. 2023, pp. 1–8. doi: 10.1145/3615522.3615547.
    23. W. Morales-Alvarez, N. Certad, A. Roitberg, R. Stiefelhagen, and C. Olaverri-Monreal, “On Transferability of Driver Observation Models from Simulated to Real Environments in Autonomous Cars,” 2023 IEEE International Conference on Intelligent Transportation Systems (ITSC), 2023.
    24. J. Lu, J. Shen, B. Xiong, W. Ma, S. Staab, and C. Yang, “HiPrompt: Few-Shot Biomedical Knowledge Fusion via Hierarchy-Oriented Prompting,” in The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval 2023, in The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval 2023. 2023.
    25. L. Pfitzer, J. Heitkämper, J. Kästner, and R. Peters, “Use of the N–O Bonds in N-Mesyloxyamides and N-Mesyloxyimides To Gain Access to 5-Alkoxy-3,4-dialkyloxazol-2-ones and 3-Hetero-Substituted Succinimides: A Combined Experimental and Theoretical Study,” Synthesis, vol. 55, no. 26, Art. no. 26, 2023, doi: 10.1055/s-0042-1751447.
    26. M. Nayyeri et al., “Knowledge Graph Embeddings using Neural Itô Process: From Multiple Walks to Stochastic Trajectories,” in Findings of the Association for Computational Linguistics: ACL 2023, in Findings of the Association for Computational Linguistics: ACL 2023. 2023, pp. 7165--7179.
    27. R. Bauer et al., “Visual Ensemble Analysis of Fluid Flow in Porous Media Across Simulation Codes and Experiment,” Transport in Porous Media, 2023, doi: 10.1007/s11242-023-02019-y.
    28. R. R. Herkert, P. Buchfink, B. Haasdonk, J. Rettberg, and J. C. Fehr, “Randomized Symplectic Model Order Reduction for Hamiltonian Systems,” pp. 1–8, 2023, doi: 10.48550/arXiv.2303.04036.
    29. K. Gubaev, V. Zaverkin, P. Srinivasan, A. I. Duff, J. Kästner, and B. Grabowski, “Performance of two complementary machine-learned potentials in modelling chemically complex systems,” Npj Comput. Mater., vol. 9, p. 129, 2023, doi: 10.1038/s41524-023-01073-w.
  3. 2022

    1. R. Leiteritz, P. Buchfink, B. Haasdonk, and D. Pflüger, “Surrogate-data-enriched Physics-Aware Neural Networks,” in Proceedings of the Northern Lights Deep Learning Workshop 2022, in Proceedings of the Northern Lights Deep Learning Workshop 2022, vol. 3. Mar. 2022. doi: 10.7557/18.6268.
    2. J. Potyka et al., “Towards DNS of Droplet-Jet Collisions of Immiscible Liquids with FS3D,” High Performance Computing in Science and Engineering ’22, Springer International Publishing, 2022. [Online]. Available: https://arxiv.org/abs/2212.09727
    3. T. Wenzel, M. Kurz, A. Beck, G. Santin, and B. Haasdonk, “Structured Deep Kernel Networks for Data-Driven Closure Terms of Turbulent Flows,” in Large-Scale Scientific Computing, I. Lirkov and S. Margenov, Eds., in Large-Scale Scientific Computing. Cham: Springer International Publishing, 2022, pp. 410--418.
    4. P. Gavrilenko et al., “A Full Order, Reduced Order and Machine Learning Model Pipeline for Efficient Prediction of Reactive Flows,” in Large-Scale Scientific Computing, I. Lirkov and S. Margenov, Eds., in Large-Scale Scientific Computing. Cham: Springer International Publishing, 2022, pp. 378--386.
    5. L. Eirich, M. Münch, D. Jäckle, M. Sedlmair, J. Bonart, and T. Schreck, “RfX: A Design Study for the Interactive Exploration of a Random Forest to Enhance Testing Procedures for Electrical Engines,” Computer Graphics Forum (CGF), p. 14, 2022, doi: https://doi.org/10.1111/cgf.14452.
    6. S. Frey et al., “Visual Analysis of Two-Phase Flow Displacement Processes in Porous Media,” Computer graphics forum, vol. 41, no. 1, Art. no. 1, 2022, doi: 10.1111/cgf.14432.
    7. T. Munz, D. Väth, P. Kuznecov, N. T. Vu, and D. Weiskopf, “NMTVis - Extended Neural Machine Translation Visualization System.” 2022. doi: 10.18419/darus-2124.
    8. A. Straub, S. Boblest, G. K. Karch, F. Sadlo, and T. Ertl, “Droplet-Local Line Integration for Multiphase Flow,” in 2022 IEEE Visualization and Visual Analytics (VIS), in 2022 IEEE Visualization and Visual Analytics (VIS). 2022, pp. 135–139. doi: 10.1109/VIS54862.2022.00036.
    9. G. Tkachev, R. Cutura, M. Sedlmair, S. Frey, and T. Ertl, “Metaphorical Visualization: Mapping Data to Familiar Concepts,” in CHI Conference on Human Factors in Computing Systems Extended Abstracts, in CHI Conference on Human Factors in Computing Systems Extended Abstracts. ACM, Apr. 2022. doi: 10.1145/3491101.3516393.
    10. B. Xiong, S. Zhu, N. Potyka, S. Pan, C. Zhou, and S. Staab, “Pseudo-Riemannian Graph Convolutional Networks,” in Advances in Neural Information Processing Systems, in Advances in Neural Information Processing Systems. 2022. [Online]. Available: https://arxiv.org/abs/2106.03134
    11. V. Zaverkin, D. Holzmüller, I. Steinwart, and J. Kästner, “Exploring chemical and conformational spaces by batch mode deep active learning,” Digital Discovery, vol. 1, pp. 605–620, 2022, doi: 10.1039/D₂DD00034B.
    12. V. Zaverkin, D. Holzmüller, R. Schuldt, and J. Kästner, “Predicting properties of periodic systems from cluster data: A case study of liquid water,” The Journal of Chemical Physics, vol. 156, no. 11, Art. no. 11, 2022, doi: 10.1063/5.0078983.
    13. V. Zaverkin, J. Netz, F. Zills, A. Köhn, and J. Kästner, “Thermally Averaged Magnetic Anisotropy Tensors via Machine Learning Based on Gaussian Moments,” J. Chem. Theory Comput., vol. 18, pp. 1–12, 2022, doi: 10.1021/acs.jctc.1c00853.
    14. J. Rettberg et al., “Port-Hamiltonian Fluid-Structure Interaction Modeling and Structure-Preserving Model Order Reduction of a Classical Guitar,” pp. 1–27, 2022, doi: 10.48550/arXiv.2203.10061.
    15. V. Zaverkin, G. Molpeceres, and J. Kästner, “Neural-network assisted study of nitrogen atom dynamics on amorphous solid water – II. Diffusion,” Mon. Not. R. Astron. Soc., vol. 510, no. 2, Art. no. 2, 2022, doi: 10.1093/mnras/stab3631.
    16. T. Wenzel, G. Santin, and B. Haasdonk, “Stability of convergence rates: Kernel interpolation on non-Lipschitz domains.” arXiv, 2022. doi: 10.48550/ARXIV.2203.12532.
    17. K. Pluhackova, V. Schittny, P.-C. Bürkner, C. Siligan, and A. Horner, “Multiple pore lining residues modulate water permeability of GlpF,” Protein Science, vol. 31, no. 10, Art. no. 10, 2022, doi: https://doi.org/10.1002/pro.4431.
    18. D. Holzmüller and I. Steinwart, “Training two-layer ReLU networks with gradient descent is inconsistent,” Journal of Machine Learning Research, vol. 23, no. 181, Art. no. 181, 2022, [Online]. Available: http://jmlr.org/papers/v23/20-830.html
    19. C. Arndt, A. Denker, J. Nickel, J. Leuschner, M. Schmidt, and G. Rigaud, “In Focus - hybrid deep learning approaches to the HDC2021 challenge,” Inverse Problems and Imaging, vol. 0, no. 0, Art. no. 0, 2022, doi: 10.3934/ipi.2022061.
  4. 2021

    1. T. Wenzel, G. Santin, and B. Haasdonk, “Analysis of target data-dependent greedy kernel algorithms: Convergence rates for $f$-, $f P$- and $f/P$-greedy.” arXiv, 2021. doi: 10.48550/ARXIV.2105.07411.
    2. V. Zaverkin, D. Holzmüller, I. Steinwart, and J. Kästner, “Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments,” Journal of Chemical Theory and Computation, vol. 17, no. 10, Art. no. 10, 2021, doi: 10.1021/acs.jctc.1c00527.
    3. A. M. Miksch, T. Morawietz, J. Kästner, A. Urban, and N. Artrith, “Strategies for the construction of machine-learning potentials for accurate and efficient atomic-scale simulations,” Mach. Learn.: Sci. Technol., vol. 2, p. 031001, 2021, doi: 10.1088/2632-2153/abfd96.
    4. D. Holzmüller and D. Pflüger, “Fast Sparse Grid Operations Using the Unidirectional Principle: A Generalized and Unified Framework,” in Sparse Grids and Applications - Munich 2018, H.-J. Bungartz, J. Garcke, and D. Pflüger, Eds., in Sparse Grids and Applications - Munich 2018. Cham: Springer International Publishing, 2021, pp. 69--100.
    5. A. Straub, G. K. Karch, F. Sadlo, and T. Ertl, “Implicit Visualization of 2D Vector Field Topology for Periodic Orbit Detection,” in Topological Methods in Data Analysis and Visualization VI, I. Hotz, T. Bin Masood, F. Sadlo, and J. Tierny, Eds., in Topological Methods in Data Analysis and Visualization VI. , Springer International Publishing, 2021, pp. 159–180. doi: 10.1007/978-3-030-83500-2_9.
    6. T. Munz, D. Väth, P. Kuznecov, T. Vu, and D. Weiskopf, “Visual-Interactive Neural Machine Translation,” in Graphics Interface 2021, in Graphics Interface 2021. 2021. [Online]. Available: https://openreview.net/forum?id=DQHaCvN9xd
    7. T. Munz, R. Garcia, and D. Weiskopf, “Visual Analytics System for Hidden States in Recurrent Neural Networks.” DaRUS, 2021. doi: 10.18419/DARUS-2052.
    8. R. Garcia, T. Munz, and D. Weiskopf, “Visual analytics tool for the interpretation of hidden states in recurrent neural networks,” Visual Computing for Industry, Biomedicine, and Art, vol. 4, no. 24, Art. no. 24, Sep. 2021, doi: 10.1186/s42492-021-00090-0.
    9. B. Haasdonk, M. Ohlberger, and F. Schindler, “An adaptive model hierarchy for data-augmented training of kernel models for reactive flow.” 2021.
    10. R. Leiteritz, M. Hurler, and D. Pflüger, “Learning Free-Surface Flow with Physics-Informed Neural Networks,” in 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA), in 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA). 2021, pp. 1664–1669. doi: 10.1109/ICMLA52953.2021.00266.
    11. G. Tkachev, S. Frey, and T. Ertl, “S4: Self-Supervised learning of Spatiotemporal Similarity,” IEEE Transactions on Visualization and Computer Graphics, 2021.
    12. D. Holzmüller, “On the Universality of the Double Descent Peak in Ridgeless Regression,” in International Conference on Learning Representations, in International Conference on Learning Representations. 2021. [Online]. Available: https://openreview.net/forum?id=0IO5VdnSAaH
    13. T. Wenzel, G. Santin, and B. Haasdonk, “A novel class of stabilized greedy kernel approximation algorithms: Convergence, stability and uniform point distribution,” ELSEVIER, vol. 262, p. 105508, Feb. 2021, doi: 10.1016/j.jat.2020.105508.
    14. M. Heinemann, S. Frey, G. Tkachev, A. Straub, F. Sadlo, and T. Ertl, “Visual analysis of droplet dynamics in large-scale multiphase spray simulations,” Journal of Visualization, vol. 24, no. 5, Art. no. 5, May 2021, doi: 10.1007/s12650-021-00750-6.
    15. B. Haasdonk, T. Wenzel, G. Santin, and S. Schmitt, “Biomechanical surrogate modelling using stabilized vectorial greedy kernel methods,” in Numerical Mathematics and Advanced Applications ENUMATH 2019, in Numerical Mathematics and Advanced Applications ENUMATH 2019. , Springer, 2021, pp. 499--508. doi: https://doi.org/10.1007/978-3-030-55874-1_49.
    16. V. Zaverkin and J. Kästner, “Exploration of transferable and uniformly accurate neural network interatomic potentials using optimal experimental design,” Machine Learning: Science and Technology, vol. 2, no. 3, Art. no. 3, 2021.
    17. T. Munz, D. Väth, P. Kuznecov, N. T. Vu, and D. Weiskopf, “NMTVis - Trained Models for our Visual Analytics System.” DaRUS, 2021. doi: 10.18419/DARUS-1850.
    18. M. Scholz and R. Torkar, “An empirical study of Linespots: A novel past-fault algorithm,” Software Testing, Verification and Reliability, vol. n/a, no. n/a, Art. no. n/a, 2021, doi: https://doi.org/10.1002/stvr.1787.
  5. 2020

    1. T. Munz, N. Schäfer, T. Blascheck, K. Kurzhals, E. Zhang, and D. Weiskopf, “Comparative Visual Gaze Analysis for Virtual Board Games,” in Proceedings of the 13th International Symposium on Visual Information Communication and Interaction, in Proceedings of the 13th International Symposium on Visual Information Communication and Interaction. Eindhoven, Netherlands: Association for Computing Machinery, Dec. 2020, pp. 1–8. doi: 10.1145/3430036.3430038.
    2. D. F. B. Häufle, I. Wochner, D. Holzmüller, D. Drieß, M. Günther, and S. Schmitt, “Muscles reduce neuronal information load : quantification of control effort in biological vs. robotic pointing and walking,” Frontiers in Robotics and AI, vol. 7, p. 77, 2020, doi: 10.3389/frobt.2020.00077.
    3. F. Heyen et al., “ClaVis: An Interactive Visual Comparison System for Classifiers,” in Proceedings of the International Conference on Advanced Visual Interfaces, in Proceedings of the International Conference on Advanced Visual Interfaces. 2020, pp. 1--9. doi: 10.1145/3399715.3399814.
    4. T. Munz, N. Schäfer, T. Blascheck, K. Kurzhals, E. Zhang, and D. Weiskopf, “Supplemental Material for Comparative Visual Gaze Analysis for Virtual Board Games.” DaRUS, 2020. doi: 10.18419/DARUS-1130.
    5. T. Munz, N. Schäfer, T. Blascheck, K. Kurzhals, E. Zhang, and D. Weiskopf, “Comparative Visual Gaze Analysis for Virtual Board Games,” The 13th International Symposium on Visual Information Communication and Interaction (VINCI 2020), 2020, doi: 10.1145/3430036.3430038.
    6. E. Sood, S. Tannert, D. Frassinelli, A. Bulling, and N. T. Vu, “Interpreting attention models with human visual attention in machine reading comprehension,” arXiv preprint arXiv:2010.06396, 2020, [Online]. Available: https://arxiv.org/abs/2010.06396
    7. T. Munz, N. Schaefer, T. Blascheck, K. Kurzhals, E. Zhang, and D. Weiskopf, “Demo of a Visual Gaze Analysis System for Virtual Board Games,” in ACM Symposium on Eye Tracking Research and Applications, in ACM Symposium on Eye Tracking Research and Applications. Stuttgart, Germany: Association for Computing Machinery, 2020. doi: 10.1145/3379157.3391985.
    8. G. Molpeceres, V. Zaverkin, and J. Kästner, “Neural-network assisted study of nitrogen atom dynamics on amorphous solid water – I. adsorption and desorption,” Mon. Not. R. Astron. Soc., vol. 499, pp. 1373–1384, 2020, doi: 10.1093/mnras/staa2891.
    9. D. F. B. Häufle, I. Wochner, D. Holzmüller, D. Driess, M. Günther, and S. Schmitt, “Muscles Reduce Neuronal Information Load : Quantification of Control Effort in Biological vs. Robotic Pointing and Walking,” Frontiers In Robotics and AI, vol. 7, p. 77, 2020, doi: 10.3389/frobt.2020.00077.
    10. F. Heyen et al., “ClaVis: An Interactive Visual Comparison System for Classifiers,” in Proceedings of the International Conference on Advanced Visual Interfaces, in Proceedings of the International Conference on Advanced Visual Interfaces. Salerno, Italy: Association for Computing Machinery, 2020. doi: 10.1145/3399715.3399814.
    11. V. Zaverkin and J. Kästner, “Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials,” J. Chem. Theory Comput., vol. 16, pp. 5410–5421, 2020, doi: 10.1021/acs.jctc.0c00347.
  6. 2019

    1. G. Tkachev, S. Frey, and T. Ertl, “Local Prediction Models for Spatiotemporal Volume Visualization,” IEEE Transactions on Visualization and Computer Graphics, 2019, doi: 10.1109/TVCG.2019.2961893.
    2. T. Munz, M. Burch, T. van Benthem, Y. Poels, F. Beck, and D. Weiskopf, “Overlap-Free Drawing of Generalized Pythagoras Trees for Hierarchy Visualization,” in 2019 IEEE Visualization Conference (VIS), in 2019 IEEE Visualization Conference (VIS). Oct. 2019, pp. 251–255. doi: 10.1109/VISUAL.2019.8933606.
    3. T. Munz, L. L. Chuang, S. Pannasch, and D. Weiskopf, “VisME: Visual microsaccades explorer,” Journal of Eye Movement Research, vol. 12, no. 6, Art. no. 6, Dec. 2019, doi: 10.16910/jemr.12.6.5.

Project Network Coordinators

This image shows Dirk Pflüger

Dirk Pflüger

Prof. Dr. rer. nat.

Scientific Computing | Vice-Head of GS SimTech

[Photo: SimTech/Max Kovalenko]

This image shows Ingo Steinwart

Ingo Steinwart

Univ.-Prof. Dr. rer. nat.

Stochastics

[Photo: SimTech/Max Kovalenko]

Mathias Niepert

Prof. Dr.

Machine Learning in Simulation Science

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