Patrik Hoyer

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Aapo HyvärinenUniversity of HelsinkiVerified email at helsinki.fi
SHIMIZU ShoheiThe University of Osaka, Shiga University & RIKENVerified email at ds.sanken.osaka-u.ac.jp
Antti HyttinenSilo AIVerified email at alumni.helsinki.fi
Erkki OjaProfessor of Computer Science and Engineering, Aalto UniversityVerified email at aalto.fi
Dominik JanzingAmazon Development Center, TuebingenVerified email at amazon.com
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHVerified email at tuebingen.mpg.de
Jonas PetersProfessor of Statistics, ETH ZurichVerified email at stat.math.ethz.ch
Joris M. MooijProfessor in Mathematical Statistics, Korteweg-de Vries Institute, University of Amsterdam (NL)Verified email at uva.nl
Alessio MonetaScuola Superiore Sant'Anna, PisaVerified email at sssup.it
Joseph RamseyCarnegie Mellon UniversityVerified email at andrew.cmu.edu
Yutaka KanoDoshisha UniversityVerified email at sigmath.es.osaka-u.ac.jp
Alex CoadWaseda Business School, Waseda University, Tokyo, JapanVerified email at waseda.jp
Kenneth BollenDepartment of Psychology and Neuroscience, Department of Sociology, University of North Carolina atVerified email at unc.edu
Kun ZhangCarnegie Mellon University & Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)Verified email at cmu.edu
Richard ScheinesDean, Dietrich College of Humanities and Social Sciences, Carnegie Mellon UniversityVerified email at andrew.cmu.edu
Michael U. GutmannUniversity of EdinburghVerified email at ed.ac.uk
María Asunción Vicente RipollLecturer and researcher at Miguel Hernández UniversityVerified email at umh.es
Matti JärvisaloProfessor of Computer Science, University of HelsinkiVerified email at helsinki.fi
Gustavo Lacerdaindependent
Ella BinghamHead of Research Services, Aalto UniversityVerified email at aalto.fi

Patrik Hoyer

Alumni, Aalto University and University of Helsinki

Verified email at alumni.aalto.fi

Machine Learning


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Non-negative matrix factorization with sparseness constraints. PO Hoyer Journal of machine learning research 5 (9), 2004	3768	2004
A linear non-Gaussian acyclic model for causal discovery. S Shimizu, PO Hoyer, A Hyvärinen, A Kerminen, M Jordan Journal of Machine Learning Research 7 (10), 2006	2046	2006
Nonlinear causal discovery with additive noise models P Hoyer, D Janzing, JM Mooij, J Peters, B Schölkopf Advances in neural information processing systems 21, 2008	1276	2008
Non-negative sparse coding PO Hoyer Proceedings of the 12th IEEE workshop on neural networks for signal …, 2002	1176	2002
Natural image statistics: A probabilistic approach to early computational vision. A Hyvärinen, J Hurri, PO Hoyer Springer Science & Business Media, 2009	933	2009
Emergence of phase-and shift-invariant features by decomposition of natural images into independent feature subspaces A Hyvärinen, P Hoyer Neural computation 12 (7), 1705-1720, 2000	767	2000
DirectLiNGAM: A direct method for learning a linear non-Gaussian structural equation model S Shimizu, T Inazumi, Y Sogawa, A Hyvarinen, Y Kawahara, T Washio, ... Journal of Machine Learning Research-JMLR 12 (Apr), 1225-1248, 2011	676	2011
Topographic independent component analysis A Hyvärinen Encyclopedia of Computational Neuroscience, 3446-3449, 2022	644	2022
Sparse code shrinkage: Denoising by nonlinear maximum likelihood estimation A Hyvärinen, P Hoyer, E Oja Advances in Neural Information Processing Systems 11, 1998	598	1998
Estimation of a structural vector autoregression model using non-gaussianity. A Hyvärinen, K Zhang, S Shimizu, PO Hoyer Journal of Machine Learning Research 11 (5), 2010	472	2010
Independent component analysis applied to feature extraction from colour and stereo images PO Hoyer, A Hyvärinen Network: computation in neural systems 11 (3), 191-210, 2000	390	2000
A two-layer sparse coding model learns simple and complex cell receptive fields and topography from natural images A Hyvärinen, PO Hoyer Vision research 41 (18), 2413-2423, 2001	357	2001
Estimation of causal effects using linear non-Gaussian causal models with hidden variables PO Hoyer, S Shimizu, AJ Kerminen, M Palviainen International Journal of Approximate Reasoning 49 (2), 362-378, 2008	264	2008
Causal inference by independent component analysis: Theory and applications A Moneta, D Entner, PO Hoyer, A Coad Oxford Bulletin of Economics and Statistics 75 (5), 705-730, 2013	243	2013
Interpreting neural response variability as Monte Carlo sampling of the posterior P Hoyer, A Hyvärinen Advances in neural information processing systems 15, 2002	242	2002
Discovering cyclic causal models by independent components analysis G Lacerda, PL Spirtes, J Ramsey, PO Hoyer arXiv preprint arXiv:1206.3273, 2012	215	2012
A multi-layer sparse coding network learns contour coding from natural images PO Hoyer, A Hyvärinen Vision research 42 (12), 1593-1605, 2002	208	2002
Image denoising by sparse code shrinkage S Haykin, B Kosko Wiley-IEEE Press, 2001	188*	2001
On causal discovery from time series data using FCI D Entner, PO Hoyer Probabilistic graphical models 16, 2010	176	2010
Modeling receptive fields with non-negative sparse coding PO Hoyer Neurocomputing 52, 547-552, 2003	170	2003

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