2012 |
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62 | KyungHyun Cho, Tapani Raiko, Alexander Ilin, and Juha Karhunen. A two-stage pretraining algorithm for deep boltzmann machines. In NIPS 2012 Workshop on Deep Learning and Unsupervised Feature Learning, page Note: to appear, 2012. |
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2011 |
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61 | KyungHyun Cho, Tapani Raiko, and Alexander Ilin. Gaussian-Bernoulli deep Boltzmann machine. In Proceedings of the NIPS workshop on Deep Learning and Unsupervised Feature Learning, Sierra Nevada, Spain, December 2011. |
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60 | Tapani Raiko, Harri Valpola, and Yann LeCun. Deep learning made easier by linear transformations in perceptrons. In Proceedings of the NIPS workshop on Deep Learning and Unsupervised Feature Learning, Sierra Nevada, Spain, December 2011. |
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59 | KyungHyun Cho, Alexander Ilin, and Tapani Raiko. Improved learning of gaussian-bernoulli restricted boltzmann machines. In Proceedings of the International Conference on Artificial Neural Networks (ICANN 2011), pages 10–17, Espoo, Finland, June 2011. |
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58 | Tapani Raiko, KyungHyun Cho, and Alexander Ilin. Enhanced gradient for learning boltzmann machines (abstract). In The Learning Workshop, Fort Lauderdale, Florida, April 2011. |
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57 | Tapani Raiko, KyungHyun Cho, and Alexander Ilin. Derivations of the enhanced gradient for the Boltzmann machine. Technical Report TKK-ICS-R37, Aalto University, TKK Reports in Information and Computer Science, Espoo, Finland, 2011. |
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56 | KyungHyun Cho, Tapani Raiko, and Alexander Ilin. Enhanced gradient and adaptive learning rate for training restricted boltzmann machines. In Proceedings of the International conference on machine learning (ICML 2011), Bellevue, Washington, USA, 2011. |
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55 | Tapani Raiko and Harri Valpola. Chapter 7: Oscillatory neural network for image segmentation with biased competition for attention. In From Brains to Systems: Brain-Inspired Cognitive Systems 2010, volume 718 of Advances in Experimental Medicine and Biology, pages 75–86. Springer New York, 2011. |
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54 | Tommi Vatanen, Mikael Kuusela, Eric Malmi, Tapani Raiko, Timo Aaltonen, and Yoshikazu Nagai. Fixed-background EM algorithm for semi-supervised anomaly detection. Technical report, Aalto University School of Science, Espoo, Finland, 2011. |
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2010 |
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53 | Antti Honkela, Tapani Raiko, Mikael Kuusela, Matti Tornio, and Juha Karhunen. Approximate riemannian conjugate gradient learning for fixed-form variational bayes. Journal of Machine Learning Research (JMLR), 11:3235–3268, November 2010. |
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52 | Ville Lämsä and Tapani Raiko. Novelty detection by nonlinear factor analysis for structural health monitoring. In IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2010), pages 468–473, Kittilä, Finland, August 2010. |
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51 | Alexander Ilin and Tapani Raiko. Practical approaches to principal component analysis in the presence of missing values. Journal of Machine Learning Research (JMLR), 11:1957–2000, July 2010. |
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50 | Katariina Nyberg, Tapani Raiko, Teemu Tiinanen, and Eero Hyvönen. Document classification utilising ontologies and relations between documents. In Proceedings of the Eighth Workshop on Mining and Learning with Graphs (MLG 2010), Washington DC, USA, July 2010. |
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49 | Tapani Raiko and Harri Valpola. Oscillatory neural network for image segmentation with biased competition for attention. In Proceedings of the Brain Inspired Cognitive Systems (BICS 2010) symposium, Madrid, Spain, July 2010. |
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48 | KyungHyun Cho, Tapani Raiko, and Alexander Ilin. Parallel tempering is efficient for learning restricted boltzmann machines. In Proceedings of the International Joint Conference on Neural Networks (IJCNN 2010), pages 1–8, Barcelona, Spain, July 2010. |
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47 | Dusan Sovilj, Tapani Raiko, and Erkki Oja. Extending self-organizing maps with uncertainty information of probabilistic pca. In Proceedings of the International Joint Conference on Neural Networks (IJCNN 2010), pages 1–7, Barcelona, Spain, July 2010. |
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2009 |
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46 | Tapani Raiko and Matti Tornio. Variational bayesian learning of nonlinear hidden state-space models for model predictive control. Neurocomputing, 72:3704–3712, October 2009. |
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45 | Laszlo Kozma, Alexander Ilin, and Tapani Raiko. Binary principal component analysis in the netflix collaborative filtering task. In Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2009), pages 1–6, Grenoble, France, September 2009. |
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44 | Mikael Kuusela, Tapani Raiko, Antti Honkela, and Juha Karhunen. A gradient-based algorithm competitive with variational bayesian EM for mixture of gaussians. In Proceedings of the International Joint Conference on Neural Networks, IJCNN 2009, pages 1688–1695, Atlanta, Georgia, June 2009. |
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43 | Jaakko Luttinen, Alexander Ilin, and Tapani Raiko. Linear transformations in the variational factor analysis subspace for speeding up learning. In Proceedings of the European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning (ESANN 2009), Bruges, Belgium, April 2009. |
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42 | Tapani Raiko. Sudoku ihmisen ja koneen ratkaisemana. Arpakannus, magazine of the Finnish Artificial Intelligence Society, 1, February 2009. |
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2008 |
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41 | Tapani Raiko, Pentti Haikonen, and Jaakko Väyrynen editors, editors. AI and Machine Consciousness, Proceedings of the 13th Finnish Artificial Intelligence Conference (STeP 2008). Finnish Artificial Intelligence Society, Espoo, Finland, August 2008. |
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40 | Tapani Raiko and Jaakko Peltonen. Application of uct search to the connection games of hex, y, *star, and renkula!. In Proc. of the Finnish Artificial Intelligence Conference (STeP 2008), pages 89–93, Espoo, Finland, August 2008. |
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39 | Antti Honkela, Markus Harva, Tapani Raiko, and Juha Karhunen. Variational inference and learning for continuous-time nonlinear state-space models. In Proc. of PASCAL 2008 Workshop on Approximate Inference in Stochastic Processes and Dynamical Systems, Cumberland Lodge, UK, May 2008. |
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38 | Jaakko Hollmén and Tapani Raiko. Learning mixture models - courseware for finite mixtures of Bernoulli distributions. In Teaching Machine Learning: Workshop on open problems and new directions, Saint-Étienne, France, May 2008. |
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37 | Tapani Raiko, Kai Puolamäki, Juha Karhunen, Jaakko Hollmén, Antti Honkela, Samuel Kaski, Heikki Mannila, Erkki Oja, and Olli Simula. Macadamia: Master's programme in machine learning and data mining. In Teaching Machine Learning: Workshop on open problems and new directions, Saint-Étienne, France, May 2008. |
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36 | Alexander Ilin and Tapani Raiko. Practical approaches to principal component analysis in the presence of missing values. Technical Report TKK-ICS-R6, Helsinki University of Technology, TKK reports in information and computer science, Espoo, Finland, 2008. |
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35 | Tapani Raiko, Alexander Ilin, and Juha Karhunen. Principal component analysis for sparse high-dimensional data. In Proceedings of the 14th International Conference on Neural Information Processing (ICONIP 2007), pages 566–575, Kitakyushu, Japan, 2008. |
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34 | Antti Honkela, Matti Tornio, Tapani Raiko, and Juha Karhunen. Natural conjugate gradient in variational inference. In Proceedings of the 14th International Conference on Neural Information Processing (ICONIP 2007), pages 305–314, Kitakyushu, Japan, 2008. |
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2007 |
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33 | Tapani Raiko, Alexander Ilin, and Juha Karhunen. Principal component analysis for large scale problems with lots of missing values. In Proceedings of the 18th European Conference on Machine Learning (ECML 2007), volume 4701 of Lecture Notes in Artificial Intelligence, pages 691–698, Warsaw, Poland, September 2007. Springer-Verlag, Berlin. |
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32 | Tapani Raiko. Higher order statistics in play-out analysis (extended abstract). In Proceedings of the 5th International Workshop on Mining and Learning with Graphs, MLG'07, Firenze, Italy, August 2007. |
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31 | Tapani Raiko, Harri Valpola, Markus Harva, and Juha Karhunen. Building blocks for variational Bayesian learning of latent variable models. Journal of Machine Learning Research, 8(Jan):155–201, January 2007. |
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30 | Antti Honkela, Matti Tornio, Tapani Raiko, and Juha Karhunen. Natural conjugate gradient in variational inference. Technical Report E10, Helsinki University of Technology, Publications in Computer and Information Science, Espoo, Finland, 2007. Available at http://www.cis.hut.fi/Publications/. |
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29 | Antti Honkela, Matti Tornio, Tapani Raiko, and Juha Karhunen. Natural conjugate gradient in variational inference (abstract). In The Learning Workshop, San Juan, Puerto Rico, 2007. |
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2006 |
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28 | Tapani Raiko. Higher order statistics in play-out analysis. In T. Honkela, T. Raiko, J. Kortela, and H. Valpola, editors, Proceedings of the Ninth Scandinavian Conference on Artificial Intelligence (SCAI 2006), pages 189–195, Espoo, Finland, October 2006. |
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27 | Timo Honkela, Tapani Raiko, Jukka Kortela, and Harri Valpola, editors. Proceedings of the Ninth Scandinavian Conference on Artificial Intelligence (SCAI 2006). Finnish Artificial Intelligence Society, Espoo, Finland, October 2006. |
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26 | Eero Hyvönen, Tomi Kauppinen, Jukka Kortela, Mikko Laukkanen, Tapani Raiko, and Kim Viljanen, editors. New Developments in Artificial Intelligence and the Semantic Web, Proceedings of the 12th Finnish Artificial Intelligence Conference (STeP 2006). Finnish Artificial Intelligence Society, Espoo, Finland, October 2006. |
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25 | Kristian Kersting, Luc De Raedt, and Tapani Raiko. Logical hidden Markov models. Journal of Artificial Intelligence Research, 25:425–456, April 2006. |
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24 | Tapani Raiko, Matti Tornio, Antti Honkela, and Juha Karhunen. State inference in variational Bayesian nonlinear state-space models. In Proceedings of the 6th International Conference on Independent Component Analysis and Blind Source Separation (ICA 2006), pages 222–229, Charleston, South Carolina, USA, March 2006. |
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23 | Tapani Raiko. Bayesian Inference in Nonlinear and Relational Latent Variable Models. PhD thesis, Helsinki University of Technology, Espoo, Finland, 2006. |
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22 | Tapani Raiko, Harri Valpola, Markus Harva, and Juha Karhunen. Building blocks for variational Bayesian learning of latent variable models. Technical Report E4, Helsinki University of Technology, Publications in Computer and Information Science, Espoo, Finland, 2006. Available at http://www.cis.hut.fi/Publications/. |
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21 | Antti Honkela, Markus Harva, Tapani Raiko, Harri Valpola, and Juha 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. |
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20 | Antti Honkela, Matti Tornio, and Tapani 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. |
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19 | Matti Tornio and Tapani Raiko. Variational Bayesian approach for nonlinear identification and control. In Proceedings of the IFAC Workshop on Nonlinear Model Predictive Control for Fast Systems, NMPC FS06, pages 41–46, Grenoble, France, 2006. |
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2005 |
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18 | Tapani Raiko. Nonlinear relational Markov networks with an application to the game of Go. In Proceedings of the International Conference on Artificial Neural Networks (ICANN 2005), pages 989–996, Warsaw, Poland, September 2005. |
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17 | Markus Harva, Tapani Raiko, Antti Honkela, Harri Valpola, and Juha Karhunen. Bayes Blocks: An implementation of the variational Bayesian building blocks framework. In Proc. of the 21st Conf. on Uncertainty in Artificial Intelligence (UAI 2005), pages 259–266, Edinburgh, Scotland, July 2005. |
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16 | Kristian Kersting and Tapani Raiko. 'say EM' for selecting probabilistic models for logical sequences. In Proceedings of the 21st Conference on Uncertainty in Artificial Intelligence, UAI 2005, pages 300–307, Edinburgh, Scotland, July 2005. |
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15 | Tapani Raiko and Matti Tornio. Learning nonlinear state-space models for control. In Proc. Int. Joint Conf. on Neural Networks (IJCNN'05), pages 815–820, Montreal, Canada, 2005. |
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2004 |
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14 | Tapani Raiko. The go-playing program called Go81. In Proceedings of the Finnish Artificial Intelligence Conference (STeP 2004), pages 197–206, Helsinki, Finland, September 2004. |
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13 | Tapani Raiko. Partially observed values. In Proc. Int. Joint Conf. on Neural Networks (IJCNN'04), pages 2825–2830, Budapest, Hungary, 2004. |
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2003 |
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12 | Kristian Kersting, Tapani Raiko, and Luc 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. |
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11 | Kristian Kersting, Tapani Raiko, Stefan Kramer, and Luc De Raedt. Towards discovering structural signatures of protein folds based on logical hidden markov models. In Proceedings of the Pacific Symposium on Biocomputing, PSB-2003, pages 192–203, Kauai, Hawaii, January 2003. |
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10 | Tapani Raiko, Harri Valpola, Tomas Östman, and Juha Karhunen. Missing values in hierarchical nonlinear factor analysis. In Proc. of the Int. Conf. on Artificial Neural Networks and Neural Information Processing (ICANN/ICONIP 2003), pages 185–189, Istanbul, Turkey, 2003. |
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9 | Harri Valpola, Antti Honkela, Markus Harva, Alexander Ilin, Tapani Raiko, and Tomas Östman. Bayes Blocks software library. http://www.cis.hut.fi/projects/bayes/software/, 2003. |
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2002 |
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8 | Tapani Raiko, Kristian Kersting, Juha Karhunen, and Luc De Raedt. Bayesian learning of logical hidden markov models. In Proceedings of the Finnish Artificial Intelligence Conference, STeP 2002, pages 64–71, Oulu, Finland, December 2002. |
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7 | Kristian Kersting, Tapani Raiko, and Luc De Raedt. Logical hidden markov models (extended abstract). In Proceedings of the First European Workshop on Graphical Models, PGM-02, pages 99–107, Cuenca, Spain, November 2002. |
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6 | Kristian Kersting, Tapani Raiko, Stefan Kramer, and Luc De Raedt. Towards discovering structural signatures of protein folds based on logical hidden markov models (extended abstract). In Work-in-Progress Reports of the Twelfth International Conference on Inductive Logic Programming (ILP -2002), Sydney, Australia, July 2002. |
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5 | Kristian Kersting, Tapani Raiko, Stefan Kramer, and Luc De Raedt. Towards discovering structural signatures of protein folds based on logical hidden markov models. Technical Report 175, Institute for Computer Science, University of Freiburg, Germany, June 2002. |
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4 | Harri Valpola, Tapani Raiko, and Juha Karhunen. Constructing graphical models for bayesian ensemble learning from simple building blocks (abstract). In The Learning Workshop, Snowbird, Utah, April 2002. |
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2001 |
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3 | Tapani Raiko. Hierarchical nonlinear factor analysis. Master's thesis, Helsinki University of Technology, Espoo, Finland, 2001. |
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2 | Tapani Raiko and Harri Valpola. Missing values in nonlinear factor analysis. In Proc. of the 8th Int. Conf. on Neural Information Processing (ICONIP'01), pages 822–827, Shanghai, 2001. |
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1 | Harri Valpola, Tapani Raiko, and Juha Karhunen. Building blocks for hierarchical latent variable models. In Proc. 3rd Int. Conf. on Independent Component Analysis and Signal Separation (ICA2001), pages 710–715, San Diego, USA, 2001. |
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