Martin Schiegg
Martin Schiegg
Research Scientist, Bosch Center for AI
Verified email at bosch.com - Homepage
Title
Cited by
Cited by
Year
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
2522019
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
842015
Conservation tracking
M Schiegg, P Hanslovsky, BX Kausler, L Hufnagel, FA Hamprecht
Proceedings of the IEEE International Conference on Computer Vision, 2928-2935, 2013
702013
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
622018
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, ...
European Conference on Computer Vision, 144-157, 2012
512012
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
372016
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
272014
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
222014
Time series anomaly detection based on shapelet learning
L Beggel, BX Kausler, M Schiegg, M Pfeiffer, B Bischl
Computational Statistics 34 (3), 945-976, 2019
142019
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
102020
Markov logic mixtures of Gaussian processes: Towards machines reading regression data
M Schiegg, M Neumann, K Kersting
Artificial Intelligence and Statistics, 1002-1011, 2012
92012
Relational generalized few-shot learning
X Shi, L Salewski, M Schiegg, Z Akata, M Welling
arXiv preprint arXiv:1907.09557, 2019
82019
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
32015
Learning diverse models: The coulomb structured support vector machine
M Schiegg, F Diego, FA Hamprecht
European Conference on Computer Vision, 585-599, 2016
22016
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 App. 16/651,104, 2020
12020
Model calculation unit and control unit for calculating a multilayer perceptron model with feedforward and feedback
A Guntoro, H Markert, M Schiegg
US Patent App. 16/330,625, 2020
12020
Multi-target tracking with probabilistic graphical models
MJ Schiegg
12015
Processing a classifier
MB Zafar, C Zimmer, MR Rudolph, M Schiegg, S Gerwinn
US Patent App. 17/141,991, 2021
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
2021
Determining an output signal by aggregating parent instances
M Schiegg, X Shi
US Patent App. 17/080,365, 2021
2021
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