Dr. Werner Zellinger

Researcher Knowledge-Based Vision Systems
Phone: +43 50 343 867


Data Science - KVS

H. Eghbal-zadeh, K. Koutini, V. Haunschmid, P. Primus, M. Lewandowski, W. Zellinger, G. Widmer. Adversarial robustness in data augmentation. Published as a workshop presenation at ICLR 2020 Workshop Towards Trustworthy ML: Rethinking Security and Privacy for ML, Addis Ababa, Ethiopia, April, 2020.

L. Fischer, L. Ehrlinger, V. Geist, R. Ramler, F. Sobiezky, W. Zellinger, D. Brunner, M. Kumar, B. Moser. AI System Engineering - Key Challenges and Lessons Learned. Machine Learning & Knowledge Extraction, volume 3, number 1, pages 56-83, DOI: 10.3390/make3010004, December, 2020.

L. Fischer, L. Ehrlinger, V. Geist, R. Ramler, F. Sobieczky, W. Zellinger, B. Moser. Applying AI in practice: Key challenges and lessons learned. Machine Learning and Knowledge Extraction. CD-MAKE 2020, volume 12279, pages 451-471, DOI 10.1007/978-3-030-57321-8_25, Springer, August, 2020.

R. Nikzad-Langerodi, W. Zellinger, S. Saminger-Platz, B. Moser. Domain adaptation for regression under Beer–Lambert’s law. Knowledge-Based Systems, volume 210, DOI: 10.1016/j.knosys.2020.106447, December, 2020.

R. Nikzad-Langerodi, B. Moser, W. Zellinger, S. Saminger-Platz. Domain-invariant regression under Beer-Lambert’s Law. In M. Wani, T. Khoshgoftaar, D. Wang, H. Wang, N. Seliya (editors), Proceedings of the 18th IEEE International Conference of Machine Learning and Applications (ICMLA 2019), pages 581-856, DOI 10.1109/ICMLA.2019.00108,, IEEE, February, 2020.

W. Zellinger. Moment-based domain adaption: Learning bounds and algorithms. PhD thesis, Joahnnes Kepler University Linz, February, 2020.

W. Zellinger, T. Grubinger, M. Zwick, E. Lughofer, H. Schöner, T. Natschläger, S. Saminger-Platz. Multi-source transfer learning of time series in cyclical manufacturing. Journal of Intelligent Manufacturing, volume 31, pages 777-787, DOI: 10.1007/s10845-019-01499-4, March, 2020.

W. Zellinger, B. Moser, S. Saminger-Platz. On generalization in moment-based domain adaptation. Annals of Mathematics and Artificial Intelligence, volume online first, DOI: 10.1007/s10472-020-09719-x, November, 2020.

N. Shepeleva, W. Zellinger, M. Lewandowski, B. Moser. ReLU code space: A basis for rating network quality besides accuracy. Machine Learning (strat.ML), https://arxiv.org/abs/2005.09903, April, 2020.

H. Eghbal-zadeh, W. Zellinger, G. Widmer. Mixture density generative adversarial networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 5820-5829, June, 2019.

W. Zellinger, B. Moser, T. Grubinger, E. Lughofer, T. Natschläger, S. Saminger-Platz. Robust unsupervised domain adaptation for neural networks via moment alignment. Information Sciences, volume 483, number 5, pages 174-191, DOI: 10.1016/j.ins.2019.01.025, May, 2019.

R. Nikzad-Langerodi, W. Zellinger, E. Lughofer, S. Saminger-Platz. Domain-invariant partial-least-squares regression. Analytical Chemistry, volume 90, number 11, pages 6693-6701, DOI: doi:10.1021/acs.analchem.8b00498, May, 2018.

H. Eghbal-zadeh, W. Zellinger, G. Widmer. Mixture density generative adversarial networks. Proceedings of NIPS 2018 Workshop Bayesian Deep Learning, pages http://bayesiandeeplearning.org/, September, 2018.

W. Zellinger, H. Eghbal-zadeh, B. Moser, M. Zwick, E. Lughofer, T. Natschläger, S. Saminger-Platz. Moment distances for comparing high-entropy. BoA of the International Conference on Computational Statistics (COMPSTAT 2018), August, 2018.

W. Zellinger, T. Grubinger, E. Lughofer, T. Natschläger, S. Saminger-Platz. Central Moment Discrepancy (CMD) for domain-invariant representation learning. Learning (cs.LG), Published as a conference paper at ICLR 2017 - International Conference on Learning Representations, pages https://arxiv.org/abs/1702.08811, February, 2017.

W. Zellinger, M. Zwick, T. Grubinger. Domain-invariant feature learning with recurrent autoencoders for time series prediction. Technical Report, number SCCH-TR-17050, August, 2017.

W. Zellinger, M. Zwick, T. Grubinger. Domain-invariant representation learning for metal sheet bending. Technical Report, number SCCH-TR-17051, August, 2017.

W. Zellinger, B. Moser. Improving visual discomfort prediction for stereoscopic images via disparity-based contrast. Proceedings of the IS&T International Symposium on Electronic Imaging 2016 (EI 2016), volume Image Quality and System Performance XIII, pages 1-9(9), February, 2016.

W. Reisner, B. Moser, A. Chouikhi, M. Gelautz, F. Seitner, M. Nezveda. Linear optimization approach for depth range adaption of stereoscopic videos. Proceedings of the IS&T International Symposium on Electronic Imaging 2016 (EI 2016), volume Stereoscopic Displays and Applications XXVII, pages 1-6(6), February, 2016.

G. Chasparis, W. Zellinger, V. Haunschmid, M. Riedenbauer, R. Stumptner. On the optimization of material usage in power transformer manufacturing. Proceedings of the 8th IEEE International Conference on Intelligent Systems (IS'16), pages 680-685, IEEE, September, 2016.

W. Zellinger. Visual discomfort prevention: A new motion-based comfort zone. Technical Report, number SCCH-TR-1626, May, 2016.

W. Zellinger, B. Moser. Improving visual discomfort prediction for stereoscopic images via disparity-based contrast. Journal of Imaging Science and Technology, volume 59, number 6, pages 60401-1-60401-8(8), DOI: http://dx.doi.org/10.2352/J.ImagingSci.Technol.2015.59.6.060401, November, 2015.

W. Reisner. Models and analysis of visual discomfort measures for stereoscopic images. Master thesis, Johannes Kepler Universität Linz/Department for Knowledge-Based Mathematical Systems, March, 2015.

F. Seitner, M. Nezveda, M. Gelautz, G. Braun, C. Kapeller, W. Zellinger, B. Moser. Trifocal system for high-quality inter-camera mapping and virtual view synthesis. Proceedings of the 2015 International Conference on 3D imaging (IC3D 2015), December, 2015.

M. Roßbory, W. Reisner. Parallelization of algorithms for linear discrete optimization using ParaPhrase. In F. Morvan, A. M. Toja, R. R. Wagner (editors), 24th International Workshop on Database and Expert Systems Applications, pages 241-245, CPS, August, 2013.