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Journal Articles

  1. H. Schulz, K. Cho, T. Raiko, and S. Behnke.
    Two-Layer Contractive Encodings for Learning Stable Nonlinear Features.
    In Neural Networks journal, volume 64, pages 4-11, special issue on Deep Learning of Representations, Yoshua Bengio and Honglak Lee editors, Elsevier, April 2015.

  2. M. Berglund, T. Raiko, K. Cho.
    Measuring the Usefulness of Hidden Units in Boltzmann Machines with Mutual Information.
    In Neural Networks journal, volume 64, pages 12-18, special issue on Deep Learning of Representations, Yoshua Bengio and Honglak Lee editors, Elsevier, April 2015.

  3. T. Vatanen, M. Osmala, T. Raiko, K. Lagus, M. Sysi-Aho, M. Oresic, T. Honkela, H. Lähdesmäki.
    Self-Organization and Missing Values in SOM and GTM.
    In Neurocomputing journal, volume 147, pages 60-70, January 2015.

  4. K. Cho, T. Raiko, and A. Ilin.
    Enhanced Gradient for Training Restricted Boltzmann Machines.
    In Neural Computation, Vol. 25, No. 3, Pages 805-831, March 2013.

  5. M. Kuusela, E. Malmi, R. Orava, T. Raiko, T. Vatanen.
    Semi-Supervised Anomaly Detection - Towards Model-Independent Searches of New Physics.
    In the Journal of Physics: Conference Series (JPCS), volume 368, pages 1-9, June 2012.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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

  11. 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. J. Karhunen, T. Raiko, and K. Cho.
    Chapter 7: Unsupervised Deep Learning: A Short Survey.
    In Advances in Independent Component Analysis and Learning Machines, Ella Bingham, Samuel Kaski, Jorma Laaksonen, and Jouko Lampinen editors, Academic Press, May 2015.

  2. K. Cho, T. Raiko, A. Ilin, and J. Karhunen.
    Chapter 10: How to Pretrain Deep Boltzmann Machines in Two Stages.
    In Artificial Neural Networks, Methods and Applications in Bio-/Neuroinformatics, Springer Series in Bio-/Neuroinformatics, Volume 4, Petia Koprinkova-Hristova, Valeri Mladenov, Nikola Kasabov editors, isbn:987-3-319-09902-6, pages 201-219, Springer, 2015.
    draft

  3. 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.
    The original publication 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.
    Thesis info

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

Edited Books and Journal Issues

  1. J. Peltonen, T. Raiko, S. Kaski editors.
    Special Issue on Machine Learning for Signal Processing 2010.
    Neurocomputing journal, Volume 80, March, 2012.

  2. 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.

  3. 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.

  4. 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. C. K. Sønderby, T. Raiko, L. Maaløe, S. K. Sønderby, O. Winther.
    Ladder Variational Autoencoders.
    In Conference on Neural Information Processing Systems (NIPS), Barcelona, Spain, December 2016.
    Preprint available as arXiv:1602.02282 [stat.ML], February 2016.

  2. H. Wang, T. Raiko, L. Lensu, T. Wang, and J. Karhunen.
    Semi-Supervised Domain Adaptation for Weakly Labeled Semantic Video Object Segmentation.
    In Asian Conference on Computer Vision (ACCV), Taipei, Taiwan, November 2016.
    Preprint available as arXiv:1606.02280 [cs.CV], June 2016.

  3. E. Malmi, P. Takala, H. Toivonen, T. Raiko, and A. Gionis.
    DopeLearning: A Computational Approach to Rap Lyrics Generation.
    In the ACM KDD Conference on Knowledge Discovery and Data Mining, San Francisco, California, August, 2016.
    Preprint available as arXiv:1505.04771 [cs.LG], May 2015.

  4. J. Luketina, M. Berglund, K. Greff, and T. Raiko.
    Scalable Gradient-Based Tuning of Continuous Regularization Hyperparameters
    In the International Conference on Machine Learning (ICML), New York, June, 2016.
    Also presented in the workshop track of the International Conference on Learning Representations (ICLR), Puerto Rico, May 2016.
    Preprint available as arXiv:1511.06727 [cs.LG], Nov 2015.

  5. A. Rasmus, H. Valpola, M. Honkala, M. Berglund, and T. Raiko.
    Semi-Supervised Learning with Ladder Networks.
    Advances in Neural Information Processing Systems 28 (NIPS 2015), pages 3532-3540, December 2015.
    Extended version available as arXiv:1507.02672 [cs.NE], July 2015.

  6. M. Berglund, T. Raiko, M. Honkala, L. Kärkkäinen, A. Vetek, J. Karhunen.
    Bidirectional Recurrent Neural Networks as Generative Models.
    Advances in Neural Information Processing Systems 28 (NIPS 2015), pages 856-864, December 2015.
    Preprint available as arXiv:1504.01575 [cs.LG], April 2015.

  7. T. Raiko, M. Berglund, G. Alain, and L. Dinh.
    Techniques for Learning Binary Stochastic Feedforward Neural Networks.
    In the International Conference on Learning Representations (ICLR 2015), San Diego, May, 2015.
    ArXiv preprint, arXiv:1406.2989 [stat.ML], June 2014.
    Note: Also presented in NIPS 2014 Workshop on Deep Learning and Representation Learning.

  8. T. Raiko, L. Yao, K. Cho, and Y. Bengio.
    Iterative Neural Autoregressive Distribution Estimator (NADE-k).
    In Advances in Neural Information Processing Systems 27 (NIPS 2014), Montreal, December, 2014.
    Code

  9. J. Luttinen, T. Raiko, and A. Ilin.
    Linear State-Space Model with Time-Varying Dynamics.
    In Machine Learning and Knowledge Discovery in Databases (ECML), Lecture Notes in Computer Science, Volume 8725, pp 338-353, September 2014.

  10. M. Berglund, T. Raiko, and K. Cho.
    Measuring the Usefulness of Hidden Units in Boltzmann Machines with Mutual Information.
    In Lecture Notes in Computer Science, volume 8226, Neural Information Processing (ICONIP 2013), Special Session on Deep Learning and Related Technologies, pages 482-489, Springer, Heidelberg, November 2013.

  11. H. Schulz, K. Cho, T. Raiko, and S. Behnke.
    Two-Layer Contractive Encodings with Linear Transformation of Perceptrons for Semi-Supervised Learning.
    In Lecture Notes in Computer Science, volume 8226, Neural Information Processing (ICONIP 2013), Special Session on Deep Learning and Related Technologies, pages 450-457, Springer, Heidelberg, November 2013.

  12. T. Vatanen, T. Raiko, H. Valpola, and Y. LeCun.
    Pushing Stochastic Gradient towards Second-Order Methods - Backpropagation Learning with Transformations in Nonlinearities.
    In Lecture Notes in Computer Science, volume 8226, Neural Information Processing (ICONIP 2013), Special Session on Deep Learning and Related Technologies, pages 442-449, Springer, Heidelberg, November 2013.
    Note: Also presented in the workshop of the International Conference on Learning Representations (ICLR 2013), Scottsdale, Arizona, May, 2013.

  13. J. Klapuri, I. Nieminen, T. Raiko, and K. Lagus.
    Variational Bayesian PCA versus k-NN on a Very Sparse Reddit Voting Dataset.
    In Lecture Notes in Computer Science, volume 8207, Advances in Intelligent Data Analysis XII, pages 249-260, Springer, October 2013.

  14. K. Cho, T. Raiko, A. Ilin, and J. Karhunen.
    A Two-Stage Pretraining Algorithm for Deep Boltzmann Machines.
    In Artificial Neural Networks and Machine Learning - ICANN 2013, Lecture Notes in Computer Science, September 2013.

  15. K. Cho, T. Raiko, and A. Ilin.
    Gaussian-Bernoulli Deep Boltzmann Machine.
    In the proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN 2013), Dallas, Texax, August, 2013.

  16. S. Keronen, K. Cho, T. Raiko, A. Ilin, and K. Palomäki.
    Gaussian-Bernoulli restricted Boltzmann machines and automatic feature extraction for noise robust missing data mask estimation.
    In the proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013), Vancouver, Canada, 26-31 May, 2013.

  17. T. Vatanen, I.T. Nieminen, T. Honkela, T. Raiko, and K. Lagus.
    Controlling Self-Organization and Handling Missing Values in SOM and GTM.
    In the proceedings of the 9th Workshop on Self-Organizing Maps (WSOM 2012), Santiago, Chile, 12-14 December, 2012.

  18. J. Raitio, T. Raiko, and T. Honkela.
    Hybrid Bilinear and Trilinear Models for Exploratory Analysis of Three-Way Poisson Counts.
    In Artificial Neural Networks and Machine Learning - ICANN 2012, Lecture Notes in Computer Science, volume 7553, pages 475-482, September 2012.

  19. R. Calandra, T. Raiko, F. Montesino Pouzols, and M. P. Deisenroth.
    Learning Deep Belief Networks from Non-Stationary Streams.
    In Artificial Neural Networks and Machine Learning - ICANN 2012, Lecture Notes in Computer Science, volume 7553, pages 379-386, September 2012.

  20. T. Hao, T. Raiko, A. Ilin, and J. Karhunen.
    Gated Boltzmann Machine in Texture Modeling.
    In Artificial Neural Networks and Machine Learning - ICANN 2012, Lecture Notes in Computer Science, volume 7553, pages 124-131, September 2012.

  21. K. Cho, A. Ilin, and T. Raiko.
    Tikhonov-Type Regularization for Restricted Boltzmann Machines.
    In Artificial Neural Networks and Machine Learning - ICANN 2012, Lecture Notes in Computer Science, volume 7552, pages 81-88, September 2012.

  22. A. Gusmão and T. Raiko.
    Towards Generalizing the Success of Monte-Carlo Tree Search beyond the Game of Go.
    In Frontiers in Artificial Intelligence and Applications, vol. 242, Proceedings of the European Conference on Artificial Intelligence (ECAI 2012), pages 384-389, IOS Press, August 2012.

  23. T. Vatanen, M. Kuusela, E. Malmi, T. Raiko, T. Aaltonen, and Y. Nagai.
    Semi-Supervised Detection of Collective Anomalies with an Application in High Energy Particle Physics.
    In the proceedings of the International Joint Conference on Neural Networks (IJCNN 2012), Brisbane, Australia, 10-15 June, 2012.

  24. T. Raiko, H. Valpola, and Y. LeCun.
    Deep Learning Made Easier by Linear Transformations in Perceptrons.
    In the proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2012), JMLR W&CP, volume 22, pages 924-932, La Palma, Canary Islands, April 21-23, 2012.
    slides

  25. 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.

  26. 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, June, 2011.
    The original publication available at springerlink.com

  27. 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.

  28. 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.

  29. 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.

  30. 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.

  31. 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.

  32. 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.

  33. 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.

  34. 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

  35. 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.

  36. 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

  37. T. Raiko, M. Tornio, A. Honkela, and J. Karhunen.
    State Inference in Variational Bayesian Nonlinear State-Space Models
    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

  38. 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

  39. 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

  40. 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

  41. 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

  42. 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

  43. 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

  44. 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

  45. 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

  46. 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

  47. 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

  48. 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

  49. 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

  50. 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. M. Lankinen, H. Heikinheimo, P. Takala, T. Raiko, and Juha Karhunen.
    A Character-Word Compositional Neural Language Model for Finnish.
    Available as arXiv:1612.03266 [cs.CL], Dec 2016.

  2. T. Raiko, J.N. Alanko, L. Pesola, and N. Viljanmaa.
    Go-tekoäly - Ihminen taipui konetta vastaan jälleen yhdessä lautapelissä.
    Skrolli magazine 2, 2016.

  3. M. Abbas*, J. Kivinen*, T. Raiko*.
    Understanding regularization by virtual adversarial training, ladder networks and others.
    In the workshop track of the International Conference on Learning Representations (ICLR), Puerto Rico, May 2016.
    * Authors contributed equally.

  4. T. Raiko.
    Towards Super-Human Artificial Intelligence in Go by Further Improvements of AlphaGo.
    Unpublished manuscript, February, 2016.

  5. A. Rasmus, H. Valpola, and T. Raiko
    Lateral Connections in Denoising Autoencoders Support Supervised Learning.
    In the ICML'15 Deep Learning Workshop, Lille, France, July, 2015.
    Preprint available as arXiv:1504.08215 [cs.LG], April 2015.

  6. A. Rasmus, T. Raiko, and H. Valpola
    Denoising autoencoder with modulated lateral connections learns invariant representations of natural images.
    In the workshop track of the International Conference on Learning Representations (ICLR 2015), San Diago, California, May, 2015.
    Preprint available as arXiv:1412.7210 [cs.NE], December 2014.

  7. M. Berglund and T. Raiko
    Stochastic Gradient Estimate Variance in Contrastive Divergence and Persistent Contrastive Divergence.
    In the workshop track of the International Conference on Learning Representations (ICLR 2014), Banff, Canada, April 14-16, 2014.
    Preprint available as arXiv:1312.6002 [cs.NE], December 2013.

  8. T. Lukka, T. Tossavainen, J. Kujala, and T. Raiko.
    ZenRobotics Recycler - Sensor and Recognition System for Robotic Sorting using Machine Learning.
    In the proceedings of the International Conference on Sensor-Based Sorting (SBS), Aachen, March 11-13, 2014.

  9. T. Raiko.
    Jonglöörauksen matematiikka (in Finnish).
    In Arpakannus 1/2013, magazine of the Finnish Artificial Intelligence Society, May 2013.

  10. K. Cho, T. Raiko, A. Ilin, and J. Karhunen.
    A Two-stage Pretraining Algorithm for Deep Boltzmann Machines.
    In the Deep Learning and Unsupervised Feature Learning Workshop at NIPS 2012.

  11. K. Cho, T. Raiko, and J. Karhunen.
    Advances in Training Restricted Boltzmann Machines (Abstract).
    In the Eleventh International Symposium on Intelligent Data Analysis (IDA), October, 2012.

  12. A. Gusmão and T. Raiko.
    Reinforcement Learning in Real-Time Strategy Games (Extended Abstract).
    In the proceedings of the Yhdistetyt tietojenkäsittelyn päivät (YTP 2012), Helsinki, Finland, May 28-29, 2012.

  13. J. Karhunen, T. Raiko, A. Ilin, A. Honkela, J. Luttinen, and K. Cho.
    Chapter 2: Bayesian learning of latent variable models.
    In the Biennial Report (2010-2011) of the Adaptive Informatics Research Centre, Aalto University School of Science, May 2012.

  14. 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.

  15. 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.

  16. 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 publication series SCIENCE + TECHNOLOGY 22/2011.

  17. T. Raiko, K. Cho, and A. Ilin.
    Derivations of the Enhanced Gradient for the Boltzmann Machine.
    Technical report, Aalto University publication series SCIENCE + TECHNOLOGY 20/2011.

  18. 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.

  19. 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.

  20. 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

  21. 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.

  22. J. Karhunen, A. Honkela, T. Raiko, A. Ilin, K. Van Leemput, J. Luttinen, M. Tornio, and M. Harva.
    Chapter 2: Bayesian learning of latent variable models.
    In the Biennial Report (2008-2009) of the Adaptive Informatics Research Centre, Aalto University School of Science and Technology, April 2010.

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

  24. 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.
    Slides, Executables

  25. 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

  26. 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.

  27. 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

  28. 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

  29. J. Karhunen, A. Honkela, T. Raiko, M. Harva, A. Ilin, M. Tornio, and H. Valpola.
    Chapter 2: Bayesian learning of latent variable models.
    In the Biennial Report (2006-2007) of the Laboratory of Computer and Information Science, Helsinki University of Technology, April 2008.

  30. 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

  31. 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

  32. 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

  33. 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

  34. J. Karhunen, A. Honkela, A. Ilin, T. Raiko, M. Harva, H. Valpola, and E. Oja.
    Chapter 4: Variational Bayesian learning of generative models.
    In the Biennial Report (2004-2005) of the Laboratory of Computer and Information Science, Helsinki University of Technology, April 2006.

  35. 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

  36. H. Valpola, A. Honkela, A. Ilin, T. Raiko, M. Harva, T. Östman, J. Karhunen, and E. Oja.
    Chapter 3: Variational Bayesian learning of generative models.
    In the Biennial Report (2002-2003) of the Laboratory of Computer and Information Science, Helsinki University of Technology, February 2004.

  37. 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

  38. 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

  39. 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

  40. 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

  41. 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

  42. 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

  43. E. Oja, J. Karhunen, A. Hyvärinen, P. Pajunen, R. Vigario, H. Valpola, J. Särelä, E. Bingham, M. Inki, A. Honkela, T. Raiko, K. Raju, A. Ilin, R. Cristescu, S. Mãlãroiu, K. Kiviluoto, and M. Ilmoniemi.
    Chapter 2: Independent component analysis and blind source separation.
    In the Biennial Report (2000-2001) of the Laboratory of Computer and Information Science, Helsinki University of Technology, April 2002.

  44. E. Oja, J. Karhunen, A. Hyvärinen, P. Pajunen, R. Vigario, H. Valpola, J. Särelä, E. Bingham, M. Inki, A. Honkela, T. Raiko, K. Raju, A. Ilin, R. Cristescu, S. Mãlãroiu, K. Kiviluoto, M. Ilmoniemi, A. Kabán, and M. Funaro.
    Chapter 3: Applications of independent component analysis.
    In the Biennial Report (2000-2001) of the Laboratory of Computer and Information Science, Helsinki University of Technology, April 2002.

  45. H. Valpola, A. Honkela, J. Karhunen, T. Raiko, X. Giannakopoulos, A. Ilin, and E. Oja.
    Chapter 4: Bayesian ensemble learning of generative models.
    In the Biennial Report (2000-2001) of the Laboratory of Computer and Information Science, Helsinki University of Technology, April 2002.