Martin Schiegg
Martin Schiegg
Research Scientist, Bosch Center for AI
Zweryfikowany adres z bosch.com - Strona główna
Cytowane przez
Cytowane przez
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
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
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
Conservation tracking
M Schiegg, P Hanslovsky, BX Kausler, L Hufnagel, FA Hamprecht
Proceedings of the IEEE international conference on computer vision, 2928-2935, 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
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
Time series anomaly detection based on shapelet learning
L Beggel, BX Kausler, M Schiegg, M Pfeiffer, B Bischl
Computational Statistics 34, 945-976, 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
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
Relational generalized few-shot learning
X Shi, L Salewski, M Schiegg, Z Akata, M Welling
arXiv preprint arXiv:1907.09557, 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
Markov logic mixtures of Gaussian processes: Towards machines reading regression data
M Schiegg, M Neumann, K Kersting
Artificial Intelligence and Statistics, 1002-1011, 2012
Calculation of exhaust emissions of a motor vehicle
M Schiegg, H Markert, S Angermaier
US Patent 11,078,857, 2021
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
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
Method for ascertaining driving profiles
M Schiegg, MB Zafar, S Angermaier
US Patent 11,661,067, 2023
Model calculating unit and control unit for selectively calculating an RBF model, a gaussian process model and an MLP model
A Guntoro, E Kloppenburg, H Markert, M Schiegg
US Patent 11,537,090, 2022
Model calculation unit and control unit for calculating a multilayer perceptron model with feedforward and feedback
A Guntoro, H Markert, M Schiegg
US Patent 11,449,737, 2022
Modelling operation profiles of a vehicle
M Schiegg, MB Zafar
US Patent App. 17/457,089, 2022
Method for ascertaining a NOx concentration and a NH3 slip downstream from an SCR catalytic converter
C Daniel, E Klenske, H Markert, M Schiegg, S Angermaier, V Imhof
US Patent 11,261,774, 2022
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