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Werner Zellinger

Werner Zellinger's picture

PhD student since 2016
Master's Degree (Dipl.-Ing.) in Computer Mathematics at JKU Linz (2016)
Industrial Researcher at Software Competence Center Hagenberg since 2012

werner.zellinger@jku.at FLLL/JKU +43 7236 3343 430

Research interests:
I'm interested in the theoretical analysis, the mathematical description and the design of novel machine learning algorithms. Recently, I was working on new regularization-based algorithms and theoretical performance guarantees in the areas of transfer learning and generative image modeling. Applications can be found in industrial manufacturing, analytical chemistry and stereoscopic video analysis.
 
PhD supervisor:

 
Journals:

  • Multi-Source Transfer Learning of Time Series in Cyclical Manufacturing
    Werner Zellinger, Thomas Grubinger, Michael Zwick, Edwin Lughofer, Holger Schöner, Thomas Natschläger, and Susanne Saminger-Platz.
    Journal of Intelligent Manufacturing, 2019.
     
  • Robust unsupervised domain adaptation for neural networks via moment alignment [link]
    Werner Zellinger, Bernhard Moser, Thomas Grubinger, Edwin Lughofer, Thomas Natschläger, and Susanne Saminger-Platz.
    Information Sciences, 483:174–191, 2019.
     
  • Domain-invariant partial least squares regression [link]
    Ramin Nikzad-Langerodi, Werner Zellinger, Edwin Lughofer, and Susanne Saminger-Platz.
    Analytical Chemistry, 90 (2018), 6693-6701.
     
  • Improving visual discomfort prediction for stereoscopic images via disparity-based contrast [link]
    Werner Zellinger, and Bernhard Moser.
    Journal of Imaging Science and Technology, 59.6 (2015), 60401-1.

 
Conferences:

  • Domain-Invariant Regression under Beer-Lambert’s Law
    Ramin Nikzad-Langerodi, Bernhard A. Moser, Werner Zellinger, and Susanne Saminger-Platz.
    International Conference on Machine Learning and Applications (ICMLA), 2019.
     
  • Mixture Density Generative Adversarial Networks [link]
    Hamid Eghbal-zadeh, Werner Zellinger, Gerhard Widmer.
    The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
     
  • Mixture Density Generative Adversarial Networks [arXiv]
    Hamid Eghbal-zadeh, Werner Zellinger, Gerhard Widmer.
    Neural Information Processing Systems (NeurIPS) workshop on Bayesian Deep Learning, 2018.
     
  • Moment distances for comparing high-entropy distributions with application in domain adaptation
    Werner Zellinger, Hamid Eghbal-zadeh, Bernhard Moser, Michael Zwick, Edwin Lughofer, Thomas Natschläger, and Susanne Saminger-Platz.
    International Conference on Computational Statistics (COMPSTAT), 2018.
     
  • Central moment discrepancy for domain invariant representation learning [arXiv] [poster] [code] [discussion]
    Werner Zellinger, Thomas Grubinger, Edwin Lughofer, Thomas Natschläger, and Susanne Saminger-Platz.
    International Conference on Learning Representations (ICLR), 2017.
     
  • Linear optimization approach for depth range adaption of stereoscopic videos [link] [presentation]
    Werner Zellinger, Bernhard Moser, Ayadi Chouikhi, Florian Seitner, Matej Nezveda, and Margrit Gelautz.
    Stereoscopic Displays and Applications (SD&A), 2016.
     
  • On the optimization of material usage in power transformer manufacturing
    Georgios Chasparis, Werner Zellinger, Verena Haunschmid, Markus Riedenbauer, and Reinhard Stumptner.
    International Conference on Intelligent Systems (IS), 2016.
     
  • Trifocal system for high-quality inter-camera mapping and virtual view synthesis
    Florian Seitner, Matej Nezveda, Margrit Gelautz, Georg Braun, Christian Kapeller, Werner Zellinger, and Bernhard Moser.
    International Conference on 3D Imaging (IC3D), 2015.
     
  • Parallelization of algorithms for linear discrete optimization using paraphrase
    Michael Rossbory, and Werner Zellinger.
    International Workshop on Database and Expert Systems Applications (DEXA), 2013.

 
Presentations:

  • Central moment discrepancy - domain adaptation via moment alignment
    Werner Zellinger, Thomas Grubinger, Edwin Lughofer, Thomas Natschläger, and Susanne Saminger-Platz.
    The Machine Learning Summer School (MLSS), Max Planck Institute, Tübingen, 2017.

 
Thesis:

  • Models and analysis of visual discomfort measures for stereoscopic images [download]
    Werner Zellinger.
    Master's Thesis (Johannes Kepler University Linz), 2015.

 
Teaching:

  • Wovon Maschinen Träumen [link]
    Werner Zellinger, and Robert Pollak.
    Kinderuni (Johannes Kepler University Linz), 2017.