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Patrik O. Hoyer: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Aapo Hyvärinen, Patrik O. Hoyer, Mika Inki
    Topographic ICA as a Model of Natural Image Statistics. [Citation Graph (0, 0)][DBLP]
    Biologically Motivated Computer Vision, 2000, pp:535-544 [Conf]
  2. Patrik O. Hoyer, Shohei Shimizu, Aapo Hyvärinen, Yutaka Kano, Antti J. Kerminen
    New Permutation Algorithms for Causal Discovery Using ICA. [Citation Graph (0, 0)][DBLP]
    ICA, 2006, pp:115-122 [Conf]
  3. Shohei Shimizu, Aapo Hyvärinen, Yutaka Kano, Patrik O. Hoyer, Antti J. Kerminen
    Testing Significance of Mixing and Demixing Coefficients in ICA. [Citation Graph (0, 0)][DBLP]
    ICA, 2006, pp:901-908 [Conf]
  4. Patrik O. Hoyer, Aapo Hyvärinen
    Feature Extraction from Color and Stereo Images Using ICA. [Citation Graph (0, 0)][DBLP]
    IJCNN (3), 2000, pp:369-376 [Conf]
  5. Aapo Hyvärinen, Patrik O. Hoyer, Mika Inki
    Topographic ICA as a Model of V1 Receptive Fields. [Citation Graph (0, 0)][DBLP]
    IJCNN (4), 2000, pp:83-88 [Conf]
  6. Patrik O. Hoyer, Aapo Hyvärinen
    Interpreting Neural Response Variability as Monte Carlo Sampling of the Posterior. [Citation Graph (0, 0)][DBLP]
    NIPS, 2002, pp:277-284 [Conf]
  7. Aapo Hyvärinen, Patrik O. Hoyer
    Emergence of Topography and Complex Cell Properties from Natural Images using Extensions of ICA. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:827-833 [Conf]
  8. Aapo Hyvärinen, Patrik O. Hoyer, Erkki Oja
    Sparse Code Shrinkage: Denoising by Nonlinear Maximum Likelihood Estimation. [Citation Graph (0, 0)][DBLP]
    NIPS, 1998, pp:473-479 [Conf]
  9. Patrik O. Hoyer
    Non-negative matrix factorization with sparseness constraints [Citation Graph (0, 0)][DBLP]
    CoRR, 2004, v:0, n:, pp:- [Journal]
  10. Patrik O. Hoyer
    Non-negative sparse coding [Citation Graph (0, 0)][DBLP]
    CoRR, 2002, v:0, n:, pp:- [Journal]
  11. Patrik O. Hoyer
    Modeling receptive fields with non-negative sparse coding. [Citation Graph (0, 0)][DBLP]
    Neurocomputing, 2003, v:52, n:, pp:547-552 [Journal]
  12. Patrik O. Hoyer, Aapo Hyvärinen
    Sparse coding of natural contours. [Citation Graph (0, 0)][DBLP]
    Neurocomputing, 2002, v:44, n:, pp:459-466 [Journal]
  13. Aapo Hyvärinen, Patrik O. Hoyer
    Topographic independent component analysis as a model of V1 organization and receptive fields. [Citation Graph (0, 0)][DBLP]
    Neurocomputing, 2001, v:38, n:, pp:1307-1315 [Journal]
  14. Patrik O. Hoyer
    Non-negative Matrix Factorization with Sparseness Constraints. [Citation Graph (0, 0)][DBLP]
    Journal of Machine Learning Research, 2004, v:5, n:, pp:1457-1469 [Journal]
  15. Shohei Shimizu, Patrik O. Hoyer, Aapo Hyvärinen, Antti Kerminen
    A Linear Non-Gaussian Acyclic Model for Causal Discovery. [Citation Graph (0, 0)][DBLP]
    Journal of Machine Learning Research, 2006, v:7, n:, pp:2003-2030 [Journal]
  16. Aapo Hyvärinen, Patrik O. Hoyer
    Emergence of Phase- and Shift-Invariant Features by Decomposition of Natural Images into Independent Feature Subspaces. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 2000, v:12, n:7, pp:1705-1720 [Journal]
  17. Aapo Hyvärinen, Patrik O. Hoyer, Mika Inki
    Topographic Independent Component Analysis. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 2001, v:13, n:7, pp:1527-1558 [Journal]
  18. Erkki Oja, Aapo Hyvärinen, Patrik O. Hoyer
    Image Feature Extraction and Denoising by Sparse Coding. [Citation Graph (0, 0)][DBLP]
    Pattern Anal. Appl., 1999, v:2, n:2, pp:104-110 [Journal]
  19. M. Asuncion Vicente, Patrik O. Hoyer, Aapo Hyvärinen
    Equivalence of Some Common Linear Feature Extraction Techniques for Appearance-Based Object Recognition Tasks. [Citation Graph (0, 0)][DBLP]
    IEEE Trans. Pattern Anal. Mach. Intell., 2007, v:29, n:5, pp:896-900 [Journal]
  20. Shohei Shimizu, Aapo Hyvärinen, Patrik O. Hoyer, Yutaka Kano
    Finding a causal ordering via independent component analysis. [Citation Graph (0, 0)][DBLP]
    Computational Statistics & Data Analysis, 2006, v:50, n:11, pp:3278-3293 [Journal]
  21. Shohei Shimizu, Aapo Hyvärinen, Yutaka Kano, Patrik O. Hoyer
    Discovery of Non-gaussian Linear Causal Models using ICA. [Citation Graph (0, 0)][DBLP]
    UAI, 2005, pp:525-533 [Conf]
  22. Patrik O. Hoyer, Shohei Shimizu, Antti J. Kerminen
    Estimation of linear, non-gaussian causal models in the presence of confounding latent variables [Citation Graph (0, 0)][DBLP]
    CoRR, 2006, v:0, n:, pp:- [Journal]

  23. Causal modelling combining instantaneous and lagged effects: an identifiable model based on non-Gaussianity. [Citation Graph (, )][DBLP]


  24. Telling cause from effect based on high-dimensional observations. [Citation Graph (, )][DBLP]


  25. Nonlinear causal discovery with additive noise models. [Citation Graph (, )][DBLP]


  26. Estimation of linear, non-gaussian causal models in the presence of confounding latent variables. [Citation Graph (, )][DBLP]


  27. Discovering Cyclic Causal Models by Independent Components Analysis. [Citation Graph (, )][DBLP]


  28. Causal discovery of linear acyclic models with arbitrary distributions. [Citation Graph (, )][DBLP]


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