| Ilastik: interactive machine learning for (bio) image analysis S Berg, D Kutra, T Kroeger, CN Straehle, BX Kausler, C Haubold, ... Nature methods 16 (12), 1226-1232, 2019 | 1917 | 2019 |
| Probabilistic recurrent state-space models A Doerr, C Daniel, M Schiegg, NT Duy, S Schaal, M Toussaint, ... International conference on machine learning, 1280-1289, 2018 | 144 | 2018 |
| Graphical model for joint segmentation and tracking of multiple dividing cells M Schiegg, P Hanslovsky, C Haubold, U Koethe, L Hufnagel, ... Bioinformatics 31 (6), 948-956, 2015 | 114 | 2015 |
| Conservation tracking M Schiegg, P Hanslovsky, BX Kausler, L Hufnagel, FA Hamprecht Proceedings of the IEEE international conference on computer vision, 2928-2935, 2013 | 102 | 2013 |
| A discrete chain graph model for 3d+ t cell tracking with high misdetection robustness BX Kausler, M Schiegg, B Andres, M Lindner, U Koethe, H Leitte, ... Computer Vision–ECCV 2012: 12th European Conference on Computer Vision …, 2012 | 59 | 2012 |
| Segmenting and Tracking Multiple Dividing Targets Using ilastik C Haubold, M Schiegg, A Kreshuk, S Berg, U Koethe, FA Hamprecht Focus on bio-image informatics, 199-229, 2016 | 58 | 2016 |
| Time series anomaly detection based on shapelet learning L Beggel, BX Kausler, M Schiegg, M Pfeiffer, B Bischl Computational Statistics 34, 945-976, 2019 | 56 | 2019 |
| Active structured learning for cell tracking: algorithm, framework, and usability X Lou, M Schiegg, FA Hamprecht IEEE transactions on medical imaging 33 (4), 849-860, 2014 | 35 | 2014 |
| Tracking indistinguishable translucent objects over time using weakly supervised structured learning L Fiaschi, F Diego, K Gregor, M Schiegg, U Koethe, M Zlatic, ... Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2014 | 31 | 2014 |
| Relational generalized few-shot learning X Shi, L Salewski, M Schiegg, Z Akata, M Welling arXiv preprint arXiv:1907.09557, 2019 | 27 | 2019 |
| Differentiable likelihoods for fast inversion of’likelihood-free’dynamical systems H Kersting, N Krämer, M Schiegg, C Daniel, M Tiemann, P Hennig International Conference on Machine Learning, 5198-5208, 2020 | 25 | 2020 |
| Markov logic mixtures of Gaussian processes: Towards machines reading regression data M Schiegg, M Neumann, K Kersting Artificial Intelligence and Statistics, 1002-1011, 2012 | 9 | 2012 |
| Proof-reading guidance in cell tracking by sampling from tracking-by-assignment models M Schiegg, B Heuer, C Haubold, S Wolf, U Koethe, FA Hamprecht 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), 394-398, 2015 | 5 | 2015 |
| Modelling operation profiles of a vehicle M Schiegg, MB Zafar US Patent App. 17/457,089, 2022 | 4 | 2022 |
| Calculation of exhaust emissions of a motor vehicle M Schiegg, H Markert, S Angermaier US Patent 11,078,857, 2021 | 4 | 2021 |
| Processing a model trained based on a loss function MB Zafar, C Zimmer, MR Rudolph, M Schiegg, S Gerwinn US Patent App. 17/141,959, 2021 | 4 | 2021 |
| Validation of composite systems by discrepancy propagation D Reeb, K Patel, KS Barsim, M Schiegg, S Gerwinn Uncertainty in Artificial Intelligence, 1730-1740, 2023 | 3 | 2023 |
| Learning diverse models: The coulomb structured support vector machine M Schiegg, F Diego, FA Hamprecht Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The …, 2016 | 3 | 2016 |
| Device, method and machine learning system for determining a state of a transmission for a vehicle M Schiegg, MB Zafar, RD Kilgus, S Gerwinn US Patent 12,104,691, 2024 | 2 | 2024 |
| Method, device and computer program for ascertaining an anomaly B Kausler, L Beggel, M Schiegg, M Pfeiffer US Patent 11,215,485, 2022 | 2 | 2022 |
Fred A. HamprechtProfessor, Heidelberg UniversityBestätigte E-Mail-Adresse bei iwr.uni-heidelberg.de
