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Journal Articles

  1. M. Kuusela, E. Malmi, R. Orava, T. Raiko, T. Vatanen.
    Semi-Supervised Anomaly Detection - Towards Model-Independent Searches of New Physics.
    To appear in the Journal of Physics: Conference Series (JPCS).

  2. U. Remes, K. Palomäki, T. Raiko, A. Honkela, and M. Kurimo
    Missing-Feature Reconstruction with a Bounded Nonlinear State-Space Model
    In IEEE Signal Processing Letters, vol 18, issue 10, pp. 563-566, October 2011.

  3. A. Honkela*, T. Raiko*, M. Kuusela, M. Tornio, and J. Karhunen.
    Approximate Riemannian Conjugate Gradient Learning for Fixed-Form Variational Bayes.
    In the Journal of Machine Learning Research (JMLR), 11(Nov):3235-3268, 2010.
    * These authors contributed equally.

  4. A. Ilin and T. Raiko.
    Practical Approaches to Principal Component Analysis in the Presence of Missing Values.
    In the Journal of Machine Learning Research (JMLR), volume 11, pages 1957-2000, July 2010.
    Related: Matlab package for PCA with missing values.

  5. T. Raiko and M. Tornio.
    Variational Bayesian learning of nonlinear hidden state-space models for model predictive control.
    In Neurocomputing, volume 72, issues 16-18, pages 3704-3712, October 2009.

  6. T. Raiko, H. Valpola, M. Harva, and J. Karhunen.
    Building Blocks for Variational Bayesian Learning of Latent Variable Models.
    In the Journal of Machine Learning Research (JMLR), volume 8, pages 155-201, January 2007.
    Related: Bayes Blocks software library

  7. K. Kersting, L. De Raedt, and T. Raiko.
    Logical Hidden Markov Models.
    In the Journal of Artificial Intelligence Research (JAIR), volume 25, pages 425-456, April 2006.

Book Chapters

  1. T. Raiko and H. Valpola.
    Chapter 7: Oscillatory Neural Network for Image Segmentation with Biased Competition for Attention.
    In From Brains to Systems: Brain-Inspired Cognitive Systems 2010 (ISBN 978-1-4614-0163-6), Advances in Experimental Medicine and Biology, Volume 718, pages 75-86, Springer New York, 2011.
    PDF
    The original publication available at springerlink.com

  2. K. Cho, A. Ilin, and T. Raiko.
    Improved Learning of Gaussian-Bernoulli Restricted Boltzmann Machines.
    In Lecture Notes in Computer Science, Volume 6791, Artificial Neural Networks and Machine Learning - ICANN 2011, pages 10-17, Espoo, Finland, June 14-17, 2011.
    The original publication available at springerlink.com

  3. T. Raiko, A. Ilin, and J. Karhunen.
    Principal Component Analysis for Sparse High-Dimensional Data.
    In Lecture Notes in Computer Science, Volume 4984, Neural Information Processing (ICONIP 2007), pages 566-575, 2008.
    The original publication available at springerlink.com
    Related: Software package

  4. A. Honkela, M. Tornio, T. Raiko, and J. Karhunen.
    Natural Conjugate Gradient in Variational Inference.
    In Lecture Notes in Computer Science, Volume 4985, Neural Information Processing (ICONIP 2007), pages 305-314, 2008.
    The original publication available at springerlink.com
    Errata: The denominator of Equation (14) should have k-1 twice instead of k-1 and k.

  5. T. Raiko, A. Ilin, and J. Karhunen.
    Principal Component Analysis for Large Scale Problems with Lots of Missing Values.
    In J. Kok et al. (Eds.), Lecture Notes in Artificial Intelligence, vol. 4701, Springer-Verlag, proceedings of the 18th European Conference on Machine Learning (ECML 2007), pages 691-698, 2007.
    The original publication is available at springerlink.com
    Related: Software package

  6. T. Raiko, M. Tornio, A. Honkela, and J. Karhunen.
    State Inference in Variational Bayesian Nonlinear State-Space Models
    Tapani Raiko, Matti Tornio, Antti Honkela and Juha Karhunen
    In Lecture Notes in Computer Science, Volume 3889, Independent Component Analysis and Blind Signal Separation (ICA 2006), pages 222-229, 2006.
    The original publication is available at springerlink.com

  7. T. Raiko.
    Nonlinear Relational Markov Networks with an Application to the Game of Go.
    In Lecture Notes in Computer Science, 2005, Volume 3697, Artificial Neural Networks: Formal Models and Their Applications (ICANN 2005), pages 989-996, 2005.
    The original publication is available at springerlink.com

Theses

  1. T. Raiko.
     Bayesian Inference in Nonlinear and Relational Latent Variable Models.
    Doctoral thesis, Helsinki University of Technology, Espoo, 1st December, 2006.
    Electronic version

  2. T. Raiko.
    Hierarchical Nonlinear Factor Analysis.
    Master's thesis, Helsinki University of Technology, Espoo, 2001.
    PS, PDF

Edited Books

  1. T. Raiko, P. Haikonen, and J. Väyrynen editors.
    AI and Machine Consciousness.
    Proceedings of the 13th Finnish Artificial Intelligence Conference (STeP 2008), Helsinki University of Technology, Espoo, Finland, August 20-22, 2008.

  2. T. Honkela, T. Raiko, J. Kortela, and H. Valpola editors.
    Proceedings of the Ninth Scandinavian Conference on Artificial Intelligence (SCAI 2006), Espoo, Finland, October 25-27, 2006.

  3. E. Hyvönen, T. Kauppinen, J. Kortela, M. Laukkanen, T. Raiko, and K. Viljanen editors.
    New Developments in Artificial Intelligence and the Semantic Web.
    Proceedings of the 12th Finnish Artificial Intelligence Conference (STeP 2006), Helsinki University of Technology, Espoo, Finland, October 26-27, 2006.

Peer-Reviewed International Conference Papers

  1. T. Raiko, H. Valpola, and Y. LeCun.
    Deep Learning Made Easier by Linear Transformations in Perceptrons.
    To appear in the Fifteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2012), La Palma, Canary Islands, April 21-23, 2012.

  2. K. Cho, T. Raiko, and A. Ilin
    Gaussian-Bernoulli Deep Boltzmann Machine.
    In the NIPS 2011 Workshop on Deep Learning and Unsupervised Feature Learning, Granada, Spain, December 16, 2011.

  3. T. Raiko, H. Valpola, and Y. LeCun.
    Deep Learning Made Easier by Linear Transformations in Perceptrons.
    In the NIPS 2011 Workshop on Deep Learning and Unsupervised Feature Learning, Granada, Spain, December 16, 2011.

  4. K. Cho, T. Raiko, and A. Ilin.
    Enhanced Gradient and Adaptive Learning Rate for Training Restricted Boltzmann Machines.
    In the proceedings of the International Conference on Machine Learning (ICML 2011), Bellevue, Washington, USA, June 28-July 2, 2011.
    See also a technical report for derivations and proofs.

  5. V. Lämsä and T. Raiko.
    Novelty Detection by Nonlinear Factor Analysis for Structural Health Monitoring.
    In the IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2010), Kittilä, Finland, Aug 29-Sep 1, 2010.

  6. K. Nyberg, T. Raiko, T. Tiinanen, and E. Hyvönen.
    Document Classification Utilising Ontologies and Relations between Documents.
    In the Eighth Workshop on Mining and Learning with Graphs (MLG 2010), Washington, USA, July 24-25, 2010.

  7. K. Cho, T. Raiko, and A. Ilin.
    Parallel Tempering is Efficient for Learning Restricted Boltzmann Machines.
    In the proceedings of the International Joint Conference on Neural Networks (IJCNN 2010), Barcelona, Spain, 18-23 July, 2010.

  8. D. Sovilj, T. Raiko, and E. Oja.
    Extending Self-Organizing Maps with Uncertainty Information of Probabilistic PCA.
    In the proceedings of the International Joint Conference on Neural Networks (IJCNN 2010), Barcelona, Spain, 18-23 July, 2010.

  9. L. Kozma, A. Ilin, and T. Raiko.
    Binary Principal Component Analysis in the Netflix Collaborative Filtering Task.
    In the proceedings of the IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2009), Grenoble, France, 2-4 September, 2009.

  10. M. Kuusela, T. Raiko, A. Honkela, and J. Karhunen.
    A Gradient-Based Algorithm Competitive with Variational Bayesian EM for Mixture of Gaussians.
    In the proceedings of the International Joint Conference on Neural Networks (IJCNN 2009), Atlanta, Georgia, June 15-19, 2009.

  11. J. Luttinen, A. Ilin, and T. Raiko.
    Linear Transformations in the Variational Factor Analysis Subspace for Speeding up Learning.
    In the proceedings of the European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning (ESANN 2009), Bruges, Belgium, 22-24 April, 2009.

  12. A. Honkela, M. Harva, T. Raiko, H. Valpola, J. Karhunen.
    Bayes Blocks: A Python Toolbox for Variational Bayesian Learning.
    In NIPS*2006 Workshop on Machine Learning Open Source Software, Whistler, B.C., Canada, 2006.
    PDF

  13. A. Honkela, M. Tornio, T. Raiko.
    Variational Bayes for Continuous-Time Nonlinear State-Space Models.
    In NIPS*2006 Workshop on Dynamical Systems, Stochastic Processes and Bayesian Inference, Whistler, B.C., Canada, 2006.
    PDF

  14. T. Raiko.
    Higher Order Statistics in Play-out Analysis.
    In the proceedings of the Scandinavian Conference on Artificial intelligence (SCAI 2006), pages 189-195, Espoo, Finland, October 25-27, 2006.
    PS, PDF

  15. M. Tornio and T. Raiko.
    Variational Bayesian Approach for Nonlinear Identification and Control.
    In the proceedings of the IFAC Workshop on Nonlinear Model Predictive Control for Fast Systems (NMPC FS06), pages 41-46, Grenoble, France, October 9-11, 2006.
    PS, PDF

  16. T. Raiko and M. Tornio.
    Learning Nonlinear State-Space Models for Control.
    In the proceedings of the International Joint Conference on Neural Networks, IJCNN 2005, pages 815-820, Montreal, Canada, July 31-August 4, 2005.
    Paper: PS, PDF, Poster: PS, PDF

  17. K. Kersting and T. Raiko.
    'Say EM' for Selecting Probabilistic Models for Logical Sequences.
    In the proceedings of the 21st Conference on Uncertainty in Artificial Intelligence, UAI 2005, pages 300-307, Edinburgh, Scotland, July 26-29, 2005.
    PS, PDF

  18. M. Harva, T. Raiko, A. Honkela, H. Valpola, and J. Karhunen.
    Bayes Blocks: An Implementation of the Variational Bayesian Building Blocks Framework.
    In the proceedings of the 21st Conference on Uncertainty in Artificial Intelligence, UAI 2005, pages 259-266, Edinburgh, Scotland, July 26-29, 2005.
    PS, PDF

  19. T. Raiko.
    Partially Observed Values.
    In the proceedings of the International Joint Conference on Neural Networks, IJCNN 2004, pages 2825-2830, Budapest, Hungary, July 25-29, 2004.
    Paper: PS, PDF, poster: PS, PDF

  20. T. Raiko, H. Valpola, T. Östman, and J. Karhunen.
    Missing Values in Hierarchical Nonlinear Factor Analysis.
    In the proceedings of the International Conference on Artificial Neural Networks and Neural Information Processing, ICANN/ICONIP 2003, pages 185-189, Istanbul, Turkey, June 26-29, 2003.
    Paper: PS, PDF, poster: PS, PDF

  21. K. Kersting, T. Raiko, S. Kramer, and L. De Raedt.
    Towards Discovering Structural Signatures of Protein Folds based on Logical Hidden Markov Models.
    In the proceedings of the Pacific Symposium on Biocomputing, PSB-2003, pages 192-203, Kauai, Hawaii, January 3-7, 2003.
    PS, PDF

  22. H. Valpola, T. Raiko, and J. Karhunen.
    Building blocks for hierarchical latent variable models.
    In the proceedings of the 3rd International Conference on Independent Component Analysis and Blind Signal Separation, ICA 2001, pages 716-721, San Diego, California, USA, December 9-12, 2001.
    PS, PDF

  23. T. Raiko and H. Valpola.
    Missing values in nonlinear factor analysis.
    In the proceedings of the 8th International Conference on Neural Information Processing, ICONIP 2001, pages 822-827, Shanghai, China, November 14-18, 2001.
    PS, PDF

Other Publications

  1. T. Raiko.
    Jonglöörauksen matematiikka (in Finnish).
    To appear in Arpakannus 1/2012, magazine of the Finnish Artificial Intelligence Society, 2012.

  2. T. Vatanen, M. Kuusela, E. Malmi, T. Raiko, T. Aaltonen, and Y. Nagai.
    Fixed-Background EM Algorithm for Semi-Supervised Anomaly Detection.
    Technical report, Aalto University, 2011.

  3. T. Raiko, K. Cho, and A. Ilin.
    Derivations of the Enhanced Gradient for the Boltzmann Machine.
    Technical report, Aalto University, 2011.

  4. T. Raiko, K. Cho, and A. Ilin.
    Enhanced Gradient for Learning Boltzmann Machines (Abstract).
    In the Learning Workshop, Fort Lauderdale, Florida, April, 2011.
    See also the poster, the ICML 2011 paper, and the technical report on the topic.

  5. L. De Alba, A. Ilin, and T. Raiko.
    Comparison of Variational Bayes and Gibbs Sampling in Reconstruction of Missing Values with Probabilistic Principal Component Analysis.
    In the proceedings of the 14th Finnish Artificial Intelligence Conference (STeP 2010), Helsinki, Finland, August 2010.
    Slides (PDF)
    Received the best paper award.

  6. T. Raiko and H. Valpola.
    Oscillatory Neural Network for Image Segmentation with Biased Competition for Attention.
    In the Brain Inspired Cognitive Systems (BICS 2010) symposium, Madrid, Spain, 14-16 July, 2010.
    See also the book chapter and slides

  7. T. Raiko, A. Ilin, N. Korsakova, E. Oja, and H. Valpola.
    Drifting Linear Dynamics (Abstract).
    Presented at the 13th International Conference on Artificial Intelligence and Statistics (AISTATS 2010), Chia Laguna Resort, Sardinia, Italy, May 13-15, 2010.

  8. T. Raiko.
    Sudoku ihmisen ja koneen ratkaisemana (in Finnish).
    Arpakannus 1/2009, magazine of the Finnish Artificial Intelligence Society, February, 2009.

  9. T. Raiko and J. Peltonen.
    Application of UCT Search to the Connection Games of Hex, Y, *Star, and Renkula!
    Proc. of the Finnish Artificial Intelligence Conference (STeP 2008), Espoo, Finland, August 2008.
    Related: Connection games

  10. A. Ilin and T. Raiko.
    Practical Approaches to Principal Component Analysis in the Presence of Missing Values.
    Technical report TKK-ICS-R6, Helsinki University of Technology, June 2008.
    Related: Software package, JMLR paper

  11. A. Honkela, M. Harva, T. Raiko, J. Karhunen
    Variational Inference and Learning for Continuous-Time Nonlinear State-Space Models.
    Proc. of PASCAL 2008 Workshop on Approximate Inference in Stochastic Processes and Dynamical Systems, Cumberland Lodge, UK, May 27-29, 2008.

  12. T. Raiko, K. Puolamäki, J. Karhunen, J. Hollmén, A. Honkela, S. Kaski, H. Mannila, E. Oja, and O. Simula
    Macadamia: Master's Programme in Machine Learning and Data Mining
    Proc. of Teaching Machine Learning: Workshop on open problems and new directions, Saint-Etienne, France, May 6-8, 2008
    PDF

  13. J. Hollmén and T. Raiko
    Learning mixture models - courseware for finite mixtures of Bernoulli distributions
    Proc. of Teaching Machine Learning: Workshop on open problems and new directions, Saint-Etienne, France, May 6-8, 2008
    PDF

  14. T. Raiko.
    Higher Order Statistics in Play-out Analysis (Extended Abstract).
    In the proceedings of the 5th International Workshop on Mining and Learning with Graphs, MLG'07, Firenze, Italy, August 1-3, 2007.
    PDF

  15. A. Honkela, M. Tornio, T. Raiko, and J. Karhunen.
    Natural Conjugate Gradient in Variational Inference.
    Publications in Computer and Information Science E10, Helsinki University of Technology, Espoo, Finland, March 2007.
    PDF

  16. A. Honkela, M. Tornio, T. Raiko, and J. Karhunen.
    Natural Conjugate Gradient in Variational Inference (Abstract).
    The Learning Workshop, San Juan, Puerto Rico, March 19-22 2007.
    PDF, PS

  17. T. Raiko, H. Valpola, M. Harva, and J. Karhunen.
    Building Blocks for Variational Bayesian Learning of Latent Variable Models.
    Publications in Computer and Information Science E4, Helsinki University of Technology, Espoo, Finland, April 2006.
    PDF

  18. T. Raiko.
    The Go-Playing Program Called Go81.
    In the proceedings of the Finnish Artificial Intelligence Conference, STeP 2004, pages 197-206, Helsinki, Finland, September 1-3, 2004.
    PS, PDF

  19. K. Kersting, T. Raiko, and L. De Raedt.
    A Structural GEM for Learning Logical Hidden Markov Models.
    In S. Dzeroski, L. De Raedt, and S. Wrobel, editors, Working notes of the Second KDD-Workshop on Multi-Relational Data Mining, MRDM-03, Washington DC, USA, August 2003.
    PS, PDF

  20. T. Raiko, K. Kersting, J. Karhunen, and L. De Raedt.
    Bayesian Learning of Logical Hidden Markov Models.
    In the proceedings of the Finnish Artificial Intelligence Conference, STeP 2002, pages 64-71, Oulu, Finland, December 2002.
    PS, PDF

  21. K. Kersting, T. Raiko, and L. De Raedt.
    Logical Hidden Markov Models (Extended Abstract).
    In the proceedings of the First European Workshop on Graphical Models, PGM-02, pages 99-107, Cuenca, Spain, November 6-8, 2002.
    PS, PDF

  22. K. Kersting, T. Raiko, S. Kramer, and L. De Raedt.
    Towards Discovering Structural Signatures of Protein Folds based on Logical Hidden Markov Models
    Technical Report No. 175, Institute for Computer Science, University of Freiburg, Germany, June 2002.
    PS

  23. K. Kersting, T. Raiko, S. Kramer, and L. De Raedt.
    Towards Discovering Structural Signatures of Protein Folds based on Logical Hidden Markov Models (Extended Abstract)
    In Stan Matwin and Claude Sammut, editors, Work-in-Progress Reports of the Twelfth International Conference on Inductive Logic Programming (ILP -2002), Sydney, Australia, July 9-11, 2002.
    PS

  24. H. Valpola, T. Raiko, and J. Karhunen.
    Constructing Graphical Models for Bayesian Ensemble Learning from Simple Building Blocks (Abstract).
    The Learning Workshop, Snowbird, Utah, April 2-5, 2002.
    PS