Zeitschriftenartikel (13)

  1. 1.
    Schütt, H.; Harmeling, S.; Macke, J.; Wichmann, F.: Painfree and accurate Bayesian estimation of psychometric functions for (potentially) overdispersed data. Vision Research 122, S. 105 - 123 (2016)
  2. 2.
    Kitching, T.; Amara, A.; Gill, M.; Harmeling, S.; Heymans, C.; Massey, R.; Rowe, B.; Schrabback, T.; Voigt, L.; Balan, S. et al.; Bernstein, G.; Bethge, M.; Bridle, S.; Courbin, F.; Gentile, M.; Heavens, A.; Hirsch, M.; Hosseini, R.; Kiessling, A.; Kirk, D.; Kuijken, K.; Mandelbaum, R.; Moghaddam, B.; Nurbaeva, G.; Paulin-Henriksson , S.; Rassat, A.; Rhodes, J.; Schölkopf, B.; Shawe-Taylor, J.; Shmakova , M.; Taylor, A.; Velander, M.; van Waerbeke, L.; Witherick, D.; Wittman, D.: Gravitational Lensing Accuracy Testing 2010 (GREAT10) Challenge Handbook. Annals of Applied Statistics 5 (3), S. 2231 - 2263 (2011)
  3. 3.
    Hirsch, M.; Harmeling, S.; Sra, S.; Schölkopf, B.: Online Multi-frame Blind Deconvolution with Super-resolution and Saturation Correction. Astronomy & Astrophysics 531 (A9), S. 1 - 11 (2011)
  4. 4.
    Harmeling, S.; Williams , C.: Greedy Learning of Binary Latent Trees. IEEE Transactions on Pattern Analysis and Machine Intelligence 33 (6), S. 1087 - 1097 (2011)
  5. 5.
    Bridle, S.; Balan, S.; Bethge, M.; Gentile, M.; Harmeling, S.; Heymans, C.; Hirsch, M.; Hosseini, R.; Jarvis, M.; Kirk, D. et al.; Kitching, T.; Kuijken, K.; Lewis, A.; Paulin-Henriksson, S.; Schölkopf, B.; Velander, M.; Voigt, L.; Witherick, D.; Amara, A.; Bernstein, G.; Courbin, F.; Gill, M.; Heavens, A.; Mandelbaum, R.; Massey, R.; Moghaddam, B.; Rassat, A.; Refregier, A.; Rhodes, J.; Schrabback, T.; Shaw-Taylor, J.; Shmakova, M.; van Waerbeke, L.; Wittman, D.: Results of the GREAT08 Challenge: An image analysis competition for cosmological lensing. Monthly Notices of the Royal Astronomical Society 405 (3), S. 2044 - 2061 (2010)
  6. 6.
    Baehrens, D.; Schroeter, T.; Harmeling, S.; Kawanabe, M.; Hansen, K.; Müller, K.-R.: How to Explain Individual Classification Decisions. Journal of Machine Learning Research 11, S. 1803 - 1831 (2010)
  7. 7.
    Harmeling, S.: Inferring textual entailment with a probabilistically sound calculus. Natural Language Engineering 15 (4), S. 459 - 477 (2009)
  8. 8.
    Harmeling, S.; Dornhege, G.; Tax, D.; Meinecke, F.; Müller, K.-R.: From outliers to prototypes: Ordering data. Neurocomputing 69 (13-15), S. 1608 - 1618 (2006)
  9. 9.
    Meinecke, F.; Harmeling, S.; Müller, K.-R.: Inlier-based ICA with an application to superimposed images. International Journal of Imaging Systems and Technology 15 (1), S. 48 - 55 (2005)
  10. 10.
    Harmeling, S.; Meinecke, F.; Müller, K.-R.: Injecting noise for analysing the stability of ICA components. Signal Processing 84 (2), S. 255 - 266 (2004)
  11. 11.
    Oja, E.; Harmeling, S.; Almeida, L.: Independent component analysis and beyond. Signal Processing 84 (2), S. 215 - 216 (2004)
  12. 12.
    Ziehe, A.; Kawanabe, M.; Harmeling, S.; Müller, K.-R.: Blind separation of post-nonlinear mixtures using linearizing transformations and temporal decorrelation. The Journal of Machine Learning Research 4, S. 1319 - 1338 (2003)
  13. 13.
    Harmeling, S.; Ziehe, A.; Kawanabe, M.; Müller, K.-R.: Kernel-based nonlinear blind source separation. Neural computation 15 (5), S. 1089 - 1124 (2003)

Buchkapitel (1)

  1. 14.
    Harmeling, S.: Solving Satisfiability Problems with Genetic Algorithms. In: Genetic Algorithms and Genetic Programming at Stanford 2000, S. 206 - 213. Stanford Bookstore, Stanford, CA, USA (2000)

Konferenzbeitrag (21)

  1. 15.
    Köhler, R.; Hirsch, M.; Mohler, B.; Schoelkopf, B.; Harmeling, S.: Recording and Playback of Camera Shake: Benchmarking Blind Deconvolution with a Real-World Database. In: Computer Vision - ECCV 2012, S. 27 - 40 (Hg. Fitzgibbon, A.; Lazebnik, S.; Perona, P.; Sato, Y.; Schmid, C.). 12th European Conference on Computer Vision (ECCV 2012), Firenze, Italy, 07. Oktober 2012 - 13. Oktober 2012. Springer, Berlin, Germany (2012)
  2. 16.
    Burger, H.; Schuler, C.; Harmeling, S.: Image denoising: Can plain Neural Networks compete with BM3D? In: 25th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2012), S. 2392 - 2399. 25th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2012), Providence, RI, USA. IEEE, Piscataway, NJ, USA (2012)
  3. 17.
    Zscheischler, J.; Mahecha, M.; Harmeling, S.: Climate classifications: the value of unsupervised clustering. In: Procedia Computer Science, Bd. 9, S. 897 - 906 (Hg. Ali, H.; Shi, Y.; Khazanchi, D.; Lees, M.; van Albada, G. et al.). International Conference on Computational Science (ICCS 2012), Omaha, NE, USA. Elsevier, Amsterdam, Netherlands (2012)
  4. 18.
    Hirsch, M.; Schuler, C.; Harmeling, S.; Schölkopf, B.: Fast removal of non-uniform camera shake. In: 13th IEEE International Conference on Computer Vision (ICCV 2011), S. 463 - 470. 13th IEEE International Conference on Computer Vision (ICCV 2011), Barcelona, Spain. IEEE, Piscataway, NJ, USA (2011)
  5. 19.
    Schuler, C.; Hirsch, M.; Harmeling, S.; Schölkopf, B.: Non-stationary correction of optical aberrations. In: 13th IEEE International Conference on Computer Vision (ICCV 2011), S. 659 - 666. 13th IEEE International Conference on Computer Vision (ICCV 2011), Barcelona, Spain. IEEE, Piscataway, NJ, USA (2011)
  6. 20.
    Burger, H.; Harmeling, S.: Improving Denoising Algorithms via a Multi-scale Meta-procedure. In: Pattern Recognition, S. 206 - 215 (Hg. Mester, M.; Felsberg, R.). 33rd DAGM Symposium, Frankfurt a.M., Germany. Springer, Berlin, Germany (2011)
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