| Entropy maximization and meta classification for out-of-distribution detection in semantic segmentation R Chan, M Rottmann, H Gottschalk Proceedings of the ieee/cvf international conference on computer vision …, 2021 | 119 | 2021 |
| Convoluted generalized white noise, Schwinger functions and their analytic continuation to Wightman functions S Albeverio, H Gottschalk, JL Wu Reviews in Mathematical Physics 8 (06), 763-817, 1996 | 98 | 1996 |
| Prediction error meta classification in semantic segmentation: Detection via aggregated dispersion measures of softmax probabilities M Rottmann, P Colling, TP Hack, R Chan, F Hüger, P Schlicht, ... 2020 International Joint Conference on Neural Networks (IJCNN), 1-9, 2020 | 88 | 2020 |
| Combined notch and size effect modeling in a local probabilistic approach for LCF L Mäde, S Schmitz, H Gottschalk, T Beck Computational Materials Science 142, 377-388, 2018 | 81 | 2018 |
| A probabilistic model for LCF S Schmitz, T Seibel, T Beck, G Rollmann, R Krause, H Gottschalk Computational Materials Science 79, 584-590, 2013 | 78 | 2013 |
| Classification uncertainty of deep neural networks based on gradient information P Oberdiek, M Rottmann, H Gottschalk Artificial Neural Networks in Pattern Recognition: 8th IAPR TC3 Workshop …, 2018 | 67 | 2018 |
| Models of local relativistic quantum fields with indefinite metric (in all dimensions) S Albeverio, H Gottschalk, JL Wu Communications in mathematical physics 184, 509-531, 1997 | 64 | 1997 |
| Yodar: Uncertainty-based sensor fusion for vehicle detection with camera and radar sensors K Kowol, M Rottmann, S Bracke, H Gottschalk arXiv preprint arXiv:2010.03320, 2020 | 43 | 2020 |
| Application of decision rules for handling class imbalance in semantic segmentation R Chan, M Rottmann, F Hüger, P Schlicht, H Gottschalk arXiv preprint arXiv:1901.08394, 2019 | 41 | 2019 |
| Time-dynamic estimates of the reliability of deep semantic segmentation networks K Maag, M Rottmann, H Gottschalk 2020 IEEE 32nd International Conference on Tools with Artificial …, 2020 | 38 | 2020 |
| Generative modeling of turbulence C Drygala, B Winhart, F di Mare, H Gottschalk Physics of Fluids 34 (3), 2022 | 33 | 2022 |
| Risk estimation for LCF crack initiation S Schmitz, H Gottschalk, G Rollmann, R Krause Turbo Expo: Power for Land, Sea, and Air 55263, V07AT27A007, 2013 | 33 | 2013 |
| Dynamical backreaction in Robertson–Walker spacetime B Eltzner, H Gottschalk Reviews in Mathematical Physics 23 (05), 531-551, 2011 | 32 | 2011 |
| Systems of classical particles in the grand canonical ensemble, scaling limits and quantum field theory S Albeverio, H Gottschalk, MW Yoshida Reviews in Mathematical Physics 17 (02), 175-226, 2005 | 32 | 2005 |
| Probabilistic lcf risk evaluation of a turbine vane by combined size effect and notch support modeling L Mäde, H Gottschalk, S Schmitz, T Beck, G Rollmann Turbo Expo: Power for Land, Sea, and Air 50923, V07AT32A004, 2017 | 31 | 2017 |
| Scattering Theory for Quantum Fields¶ with Indefinite Metric S Albeverio, H Gottschalk Communications in Mathematical Physics 216, 491-513, 2001 | 31 | 2001 |
| Metabox+: A new region based active learning method for semantic segmentation using priority maps P Colling, L Roese-Koerner, H Gottschalk, M Rottmann arXiv preprint arXiv:2010.01884, 2020 | 29 | 2020 |
| Deep bayesian active semi-supervised learning M Rottmann, K Kahl, H Gottschalk 2018 17th IEEE International Conference on Machine Learning and Applications …, 2018 | 29 | 2018 |
| Minimal failure probability for ceramic design via shape control M Bolten, H Gottschalk, S Schmitz Journal of Optimization Theory and Applications 166, 983-1001, 2015 | 26 | 2015 |
| Deep neural networks and data for automated driving: Robustness, uncertainty quantification, and insights towards safety T Fingscheidt, H Gottschalk, S Houben Springer Nature, 2022 | 25 | 2022 |
