2010 |
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52 | Alfredo A. Kalaitzis, Pei Gao, Antti Honkela, and Neil D. Lawrence. gptk: Gaussian Processes Tool-Kit. CRAN, December 2010. Computer program. |
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51 | 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, 11:3235–3268, Nov 2010. |
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50 | Miika-Petteri Matikainen and Antti Honkela. tigreBrowser: Gene expression model browser for results from tigre R package, September 2010. Computer program. |
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49 | Antti Honkela, Pei Gao, Jonatan Ropponen, Magnus Rattray, and Neil D. Lawrence. tigre: Transcription factor Inference through Gaussian process Reconstruction of Expression. BioConductor 2.6, April 2010. Computer program. |
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48 | Antti Honkela, Charles Girardot, E Hilary Gustafson, Ya-Hsin Liu, Eileen E M Furlong, Neil D Lawrence, and Magnus Rattray. Model-based method for transcription factor target identification with limited data. Proc Natl Acad Sci U S A, 107(17):7793–7798, Apr 2010. |
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47 | Antti Honkela, Marta Milo, Matthew Holley, Magnus Rattray, and Neil D. Lawrence. Ranking of gene regulators through differential equations and Gaussian processes. In Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2010), pages 154–159, Kittilä, Finland, 2010. |
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46 | Veli Peltola and Antti Honkela. Variational inference and learning for non-linear state-space models with state-dependent observation noise. In Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2010), pages 190–195, Kittilä, Finland, 2010. |
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45 | Samuel Kaski, David J. Miller, Erkki Oja, and Antti Honkela, editors. Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2010). IEEE, Piscataway, NJ, 2010. |
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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, Atlanta, Georgia, June 2009. |
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43 | Antti Honkela, Neil D. Lawrence, and Magnus Rattray. Identifying targets of transcriptionally regulated transcription factors using dynamical models. In 17th Annual International Conference on Intelligent Systems for Molecular Biology (ISMB) & 8th European Conference on Computational Biology (ECCB), Stockholm, Sweden, 2009. |
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42 | Antti Honkela. Identifying targets of transcriptionally regulated transcription factors using dynamical models. In Mathematical and Statistical Aspects of Molecular Biology: 19th Annual MASAMB Workshop, Imperial College London, UK, 2009. |
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2008 |
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41 | 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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40 | 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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39 | Pei Gao, Antti Honkela, Magnus Rattray, and Neil D. Lawrence. Gaussian process modelling of latent chemical species: applications to inferring transcription factor activities. Bioinformatics, 24(16):i70–i75, 2008. In Proceedings of ECCB 2008. |
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38 | Antti Honkela, Jeremias Seppä, and Esa Alhoniemi. Agglomerative independent variable group analysis. Neurocomputing, 71(7–9):1311–1320, 2008. |
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37 | 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), volume 4985 of Lecture Notes in Computer Science, pages 305–314, Kitakyushu, Japan, 2008. Springer-Verlag, Berlin. |
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2007 |
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36 | Antti Honkela, Jeremias Seppä, and Esa Alhoniemi. Agglomerative independent variable group analysis. In Proc. 15th European Symposium on Artificial Neural Networks (ESANN 2007), pages 55–60, Bruges, Belgium, 2007. |
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35 | Antti Honkela, Harri Valpola, Alexander Ilin, and Juha Karhunen. Blind separation of nonlinear mixtures by variational Bayesian learning. Digital Signal Processing, 17(5):914–934, 2007. |
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34 | 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. |
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33 | 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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32 | Esa Alhoniemi, Antti Honkela, Krista Lagus, Jeremias Seppä, Paul Wagner, and Harri Valpola. Compact modeling of data using independent variable group analysis. IEEE Transactions on Neural Networks, 18(6):1762–1776, 2007. |
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31 | Matti Tornio, Antti Honkela, and Juha Karhunen. Time series prediction with variational Bayesian nonlinear state-space models. In Proceedings of the European Symposium on Time Series Prediction (ESTSP 2007), pages 11–19, Espoo, Finland, 2007. |
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2006 |
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30 | 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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29 | Esa Alhoniemi, Antti Honkela, Krista Lagus, Jeremias Seppä, Paul Wagner, and Harri Valpola. Compact modeling of data using independent variable group analysis. Technical Report E3, Helsinki University of Technology, Publications in Computer and Information Science, Espoo, Finland, 2006. |
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28 | Antti Honkela. Distributed Bayes Blocks for variational Bayesian learning. In Conference on High Performance Computing for Statistical Inference, Dublin, Ireland, 2006. |
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27 | 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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26 | 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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25 | Janne Nikkilä, Antti Honkela, and Samuel Kaski. Exploring the independence of gene regulatory modules. In Juho Rousu, Samuel Kaski, and Esko Ukkonen, editors, Proc. Workshop on Probabilistic Modeling and Machine Learning in Structural and Systems Biology, pages 131–136, Tuusula, Finland, 2006. |
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24 | Harri Valpola and Antti Honkela. Hyperparameter adaptation in variational Bayes for the gamma distribution. Technical Report E6, Helsinki University of Technology, Publications in Computer and Information Science, Espoo, Finland, 2006. |
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2005 |
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23 | 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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22 | Krista Lagus, Esa Alhoniemi, Jeremias Seppä, Antti Honkela, and Paul Wagner. Independent variable group analysis in learning compact representations for data. In Timo Honkela, Ville Könönen, Matti Pöllä, and Olli Simula, editors, Proceedings of the International and Interdisciplinary Conference on Adaptive Knowledge Representation and Reasoning (AKRR'05), pages 49–56, Espoo, Finland, June 2005. |
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21 | Antti Honkela and Harri Valpola. Unsupervised variational Bayesian learning of nonlinear models. In L. Saul, Y. Weiss, and L. Bottou, editors, Advances in Neural Information Processing Systems 17, pages 593–600. MIT Press, Cambridge, MA, USA, 2005. Poster. |
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20 | Antti Honkela, Tomas Östman, and Ricardo Vigário. Empirical evidence of the linear nature of magnetoencephalograms. In Proc. 13th European Symposium on Artificial Neural Networks (ESANN 2005), pages 285–290, Bruges, Belgium, 2005. |
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19 | Antti Honkela. Advances in Variational Bayesian Nonlinear Blind Source Separation. PhD thesis, Helsinki University of Technology, Espoo, Finland, 2005. |
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2004 |
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18 | Antti Honkela. Approximating nonlinear transformations of probability distributions for nonlinear independent component analysis. In Proc. 2004 IEEE Int. Joint Conf. on Neural Networks (IJCNN 2004), pages 2169–2174, Budapest, Hungary, 2004. Poster. |
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17 | Antti Honkela and Harri Valpola. Variational learning and bits-back coding: an information-theoretic view to Bayesian learning. IEEE Transactions on Neural Networks, 15(4):800–810, 2004. |
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16 | Antti Honkela, Stefan Harmeling, Leo Lundqvist, and Harri Valpola. Using kernel PCA for initialisation of variational Bayesian nonlinear blind source separation method. In Carlos G. Puntonet and Alberto Prieto, editors, Proc. of the 5th Int. Conf. on Independent Component Analysis and Blind Signal Separation (ICA 2004), volume 3195 of Lecture Notes in Computer Science, pages 790–797, Granada, Spain, 2004. Springer-Verlag, Berlin. Poster. |
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15 | Alexander Ilin and Antti Honkela. Postnonlinear independent component analysis by variational Bayesian learning. In Carlos G. Puntonet and Alberto Prieto, editors, Proc. of the Fifth Int. Conf. on Independent Component Analysis and Blind Signal Separation (ICA 2004), volume 3195 of Lecture Notes in Computer Science, pages 766–773, Granada, Spain, 2004. Springer-Verlag, Berlin. |
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2003 |
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14 | Antti Honkela and Harri Valpola. On-line variational Bayesian learning. In Proc. of the 4th Int. Symp. on Independent Component Analysis and Blind Signal Separation (ICA2003), pages 803–808, Nara, Japan, 2003. Poster. |
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13 | Antti Honkela, Harri Valpola, and Juha Karhunen. Accelerating cyclic update algorithms for parameter estimation by pattern searches. Neural Processing Letters, 17(2):191–203, 2003. |
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12 | Harri Valpola, Erkki Oja, Alexander Ilin, Antti Honkela, and Juha Karhunen. Nonlinear blind source separation by variational Bayesian learning. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, E86-A(3):532–541, 2003. |
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11 | Vesa Siivola and Antti Honkela. A state-space method for language modeling. In IEEE Workshop on Automatic Speech Recognition and Understanding, pages 548–553, 2003. Poster. |
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10 | Harri Valpola, Antti Honkela, Markus Harva, Alexander Ilin, Tapani Raiko, and Tomas Östman. Bayes Blocks software library, 2003. |
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2002 |
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9 | Harri Valpola, Antti Honkela, and Juha Karhunen. An ensemble learning approach to nonlinear dynamic blind source separation using state-space models. In Proc. Int. Joint Conf. on Neural Networks (IJCNN'02), pages 460–465, Honolulu, Hawaii, USA, 2002. |
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8 | Antti Honkela. Speeding up cyclic update schemes by pattern searches. In Proc. of the 9th Int. Conf. on Neural Information Processing (ICONIP'02), pages 512–516, Singapore, 2002. |
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7 | Harri Valpola, Antti Honkela, and Xavier Giannakopoulos. Nonlinear dynamical factor analysis Matlab package, 2002. |
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2001 |
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6 | Antti Honkela. Nonlinear switching state-space models. Master's thesis, Helsinki University of Technology, Espoo, 2001. |
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5 | Harri Valpola, Antti Honkela, and Juha Karhunen. Nonlinear static and dynamic blind source separation using ensemble learning. In Proc. Int. Joint Conf. on Neural Networks (IJCNN'01), pages 2750–2755, Washington D.C., USA, 2001. |
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4 | Antti Honkela and Juha Karhunen. An ensemble learning approach to nonlinear independent component analysis. In Proc. European Conf. on Circuit Theory and Design (ECCTD'01), pages I–41–44, Espoo, Finland, 2001. |
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2000 |
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3 | Antti Honkela, Harri Valpola, and Xavier Giannakopoulos. Nonlinear factor analysis Matlab package, 2000. |
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2 | Harri Lappalainen and Antti Honkela. Bayesian nonlinear independent component analysis by multi-layer perceptrons. In M. Girolami, editor, Advances in Independent Component Analysis, pages 93–121. Springer-Verlag, Berlin, 2000. |
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1 | Harri Valpola, Xavier Giannakopoulos, Antti Honkela, and Juha Karhunen. Nonlinear independent component analysis using ensemble learning: Experiments and discussion. In Proc. Int. Workshop on Independent Component Analysis and Blind Signal Separation (ICA2000), pages 351–356, Helsinki, Finland, 2000. |
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