to appear |
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151 | Mikael Kuusela, Tommi Vatanen, Eric Malmi, Tapani Raiko, Timo Aaltonen, and Yoshikazu Nagai. Semi-supervised anomaly detection - towards model-independent searches of new physics. Journal of Physics: Conference Series, to appear. |
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submitted for publication |
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150 | Tommi Vatanen, Mikael Kuusela, Eric Malmi, Tapani Raiko, Timo Aaltonen, and Yoshikazu Nagai. Semi-supervised detection of collective anomalies with an application in high energy particle physics. In Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2012, submitted for publication. |
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2011 |
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149 | József Hegedüs, Yoan Miche, Alexander Ilin, and Amaury Lendasse. Methodology for behavioral-based malware analysis and detection using random projections and k-nearest neighbors classifiers. In 7th International Conference on Computational Intelligence and Security (CIS2011) TEST, pages 1016 – 1023, Sanya, China, December 2011. |
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148 | KyungHyun Cho, Tapani Raiko, and Alexander Ilin. Gaussian-bernoulli deep boltzmann machine. In NIPS 2011 Workshop on Deep Learning and Unsupervised Feature Learning, Sierra Nevada, Spain, December 2011. |
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147 | 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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146 | József Hegedüs, Yoan Miche, Alexander Ilin, and Amaury Lendasse. Random projection method for scalable malware classification. In 14th International Symposium on Recent Advances in Intrusion Detection, California, USA, September 2011. |
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145 | 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, June 2011. |
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144 | 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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143 | 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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142 | B.H. Menze, K. Van Leemput, A. Honkela, E. Konukoglu, M.-A. Weber, N. Ayache, and P. Golland. A generative model for the image-based modeling of tumor growth. In Lecture Notes in Computer Science, pages 735–742, 2011. Proceedings of IPMI 2011, July 3-8, 2011, Irsee, Germany. |
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141 | M.R. Sabuncu and K. Van Leemput. The relevance voxel machine (RVoxM): A bayesian method for image-based prediction. In Lecture Notes in Computer Science, pages 99–106, 2011. Proceedings of MICCAI 2011, September 18-22, 2009, Toronto, Canada. |
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140 | B.J. Hanseeuw, K. Van Leemput, M. Kavec, C. Grandin, X. Seron, and A. Ivanoiu. Mild cognitive impairment: differential atrophy in the hippocampal subfields. American Journal of NeuroRadiology, 32(9):1658–1661, 2011. |
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139 | Mikael Kuusela, Eric Malmi, Risto Orava, and Tommi Vatanen. Soft classification of diffractive interactions at the LHC. AIP Conference Proceedings, 1350(1):111–114, 2011. |
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138 | 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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137 | 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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136 | 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, 2011. |
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2010 |
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135 | 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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134 | 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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133 | R. Kivisaari, P. Rapeli, K. Van Leemput, S. Kähkönen, V. Puuskari, O. Jokela, and T. Autti. Cerebral measurements and their correlation with the onset age and the duration of opioid abuse. Journal of Opioid Management, 6(6):423–429, November/December 2010. |
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132 | M.R. Sabuncu, B.T.T. Yeo, K. Van Leemput, B. Fischl, and P. Golland. A generative model for image segmentation based on label fusion. IEEE Transactions on Medical Imaging, 29(10):1714–1729, October 2010. |
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131 | T. Riklin-Raviv, K. Van Leemput, B.H. Menze, W.M. Wells, and P. Golland. Segmentation of image ensembles via latent atlases. Medical Image Analysis, 14(5):654–665, October 2010. |
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130 | 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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129 | T. Saukkonen, S. Heikkinen, A. Hakkarainen, A.M. Häkkinen, K. Van Leemput, M. Lipsanen-Nyman, and N. Lundbom. Association of intramyocellular, intraperitoneal and liver fat with glucose tolerance in severely obese adolescents. European Journal of Endocrinology, 163(3):413–419, September 2010. |
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128 | Tommi Vatanen, Mikael Kuusela, Eric Malmi, and Risto Orava. Anomaly search with density estimation and EM algorithm. 6th International Summer School on Pattern Recognition, ISSPR 2010, September 2010. Poster. |
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127 | 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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126 | Dusan Sovilj, Tapani Raiko, and Erkki Oja. Extending self-organizing maps with uncertainty information of probabilistic pca. In IJCNN 2010, pages 1915–1921, Barcelona, Spain, July 18-23 2010. |
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125 | 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 3246 – 3253, Barcelona, Spain, July 2010. |
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124 | 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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123 | 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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122 | 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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121 | 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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120 | 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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119 | Heikki Järvinen, Petri Räisänen, MarkoLaine, Johanna Tamminen, Alexander Ilin, Erkki Oja, Antti Solonen, and Heikki Haario. Estimation of ECHAM5 climate model closure parameters with adaptive MCMC. Atmospheric Chemistry and Physics Discussion, 10:11951–11973, 2010. |
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118 | Jaakko Luttinen and Alexander Ilin. Transformations in variational Bayesian factor analysis to speed up learning. Neurocomputing, 73:1093–1102, 2010. |
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117 | Christian Jutten, Massoud Babaie-Zadeh, and Juha Karhunen. Chapter 14: Nonlinear mixtures. In Christian Jutten and Pierre Comon, editors, Handbook of Blind Source Separation, Independent Component Analysis and Applications, pages 549–592. Academic Press, 2010. |
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116 | B.H. Menze, K. Van Leemput, D. Lashkari, M.A. Weber, N. Ayache, and P. Golland. A generative model for brain tumor segmentation in multi-modal images. In Lecture Notes in Computer Science, pages 151–159, 2010. Proceedings of MICCAI2010, September 20-24, 2010, Beijing, China. |
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115 | Mikael Kuusela, Eric Malmi, Tommi Vatanen, Risto Orava, Timo Aaltonen, and Yoshikazu Nagai. Detection of new physics using density estimation based anomaly search. CDF/DOC/EXOTIC/CDFR/10227 (Internal note), 2010. |
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114 | Mikael Kuusela, Jerry W. Lämsä, Eric Malmi, Petteri Mehtälä, and Risto Orava. Multivariate techniques for identifying diffractive interactions at the LHC. International Journal of Modern Physics A, 25(8):1615–1647, 2010. |
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113 | Heikki Järvinen, Petri Räisänen, Marko Laine, Johanna Tamminen, Alexander Ilin, Erkki Oja, Antti Solonen, and Heikki Haario. Estimation of ECHAM5 climate model closure parameters with adaptive MCMC. Atmospheric Chemistry and Physics Discussion, 10:11951–11973, 2010. |
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112 | Mikael Kuusela, Eric Malmi, Risto Orava, and Tommi Vatanen. Soft classification of diffractive interactions at the LHC. In Proceedings of Diffraction 2010. American Institute of Physics, 2010. |
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111 | 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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2009 |
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110 | Jaakko Luttinen. Gaussian-process factor analysis for modeling spatio-temporal data. Master's thesis, Helsinki University of Technology, December 2009. |
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109 | 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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108 | 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, Grenoble, France, September 2009. |
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107 | M.K. Mannerkoski, H.J. Heiskala, K. Van Leemput, L.E. Åberg, R. Raininko, J. Hämäläinen, and T.H. Autti. Children and adolescents with learning and intellectual disabilities and familial need for full-time special-education show regional brain alterations: A voxel-based morphometry study. Pediatric Research, 66(3):306–311, September 2009. |
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106 | B. Fischl, A.A. Stevens, N. Rajendran, B.T.T. Yeo, D.N. Greve, K. Van Leemput, J. Polimeni, S. Kakunoori, R.L. Buckner, J.L. Pacheco, D.H. Salat, J. Melcher, M.P. Frosch, B.T. Hyman, B.R. Rosen P.E. Grant, A.J.W. van der Kouwe, G.C. Wiggins, L.L.Wald, and J.C. Augustinack. Predicting the location of entorhinal cortex from mri. NeuroImage, 47(1):8–17, August 2009. |
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105 | Alexander Ilin and Alexey Kaplan. Bayesian PCA for reconstruction of historical sea surface temperatures. In Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN 2009), pages 1322–1327, Atlanta, USA, June 2009. |
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104 | K. Van Leemput. Encoding probabilistic brain atlases using Bayesian inference. IEEE Transactions on Medical Imaging, 28(6):822–837, June 2009. |
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103 | 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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102 | Jaakko Luttinen, Alexander Ilin, and Tapani Raiko. Transformations for variational factor analysis to speed up learning. In Proceedings of the 14th European Symposium on Artificial Neural Networks (ESANN 2009), pages 77–82, Bruges, Belgium, April 2009. |
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101 | Jaakko Luttinen, Alexander Ilin, and Juha Karhunen. Bayesian robust PCA for incomplete data. In Proceedings of the 8th International Conference on Independent Component Analysis and Blind Signal Separation (ICA 2009), pages 66–73, Paraty, Brazil, March 2009. |
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100 | Tapani Raiko. Sudoku ihmisen ja koneen ratkaisemana. Arpakannus, magazine of the Finnish Artificial Intelligence Society, 1, February 2009. |
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99 | 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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98 | 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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97 | Jaakko Luttinen and Alexander Ilin. Variational Gaussian-process factor analysis for modeling spatio-temporal data. In Advances in Neural Information Processing Systems 22, pages 1177–1185. MIT Press, Cambridge, MA, USA, 2009. |
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96 | K. Van Leemput, A. Bakkour, T. Benner, G. Wiggins, L.L. Wald, J. Augustinack, B.C. Dickerson, P. Golland, and B. Fischl. Automated segmentation of hippocampal subfields from ultra-high resolution in vivo MRI. Hippocampus, 19:549–557, 2009. |
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95 | T. Riklin Raviv, K. Van Leemput, W.M. Wells, and P. Golland. Joint segmentation of image ensembles via latent atlases. In Lecture Notes in Computer Science, volume 5761, pages 272–280, 2009. Proceedings of MICCAI2009, September 20-14, 2009, London, UK. |
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94 | M.R. Sabuncu, B.T.T. Yeo, T. Vercauteren, K. Van Leemput, and P. Golland. Asymmetric image-template registration. In Lecture Notes in Computer Science, volume 5761, pages 565–573, 2009. Proceedings of MICCAI2009, September 20-14, 2009, London, UK. |
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93 | M.R. Sabuncu, B.T.T. Yeo, K. Van Leemput, B. Fischl, and P. Golland. Supervised nonparametric image parcellation. In Lecture Notes in Computer Science, volume 5762, pages 1075–1083, 2009. Proceedings of MICCAI2009, September 20-14, 2009, London, UK. |
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92 | M.R. Sabuncu, B.T.T. Yeo, K. Van Leemput, B. Fischl, and P. Golland. Nonparametric mixture models for supervised image parcellation. In Proceedings of the MICCAI 2009 Workshop on Probabilistic Models for Medical Image Analysis (PMMIA 2009), pages 301–313, 2009. September 20, 2009, London, UK. |
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91 | T. Riklin Raviv, B. Menze, K. Van Leemput, B. Stieltjes, M.A. Weber, N. Ayache, W.M. Wells, and P. Golland. Joint segmentation via patient-specific latent anatomy model. In Proceedings of the MICCAI 2009 Workshop on Probabilistic Models for Medical Image Analysis (PMMIA 2009), pages 244–255, 2009. September 20, 2009, London, UK. |
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90 | Mikael Kuusela. Algorithms for variational learning of mixture of Gaussians, 2009. Bachelor's thesis. |
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2008 |
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89 | 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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88 | 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, Department of Information and Computer Science, Espoo, Finland, June 2008. |
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87 | 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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86 | Jaakko Hollmén and Tapani Raiko. Learning mixture models — courseware for finite mixture distributions of multivariate Bernoulli distributions. In Stéphanie Jacquemont and Colin de la Higuera, editors, In Proceedings of Teaching Machine Learning — workshop on open problems and new directions, Saint-Étienne, France, May 2008. |
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85 | 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 Stéphanie Jacquemont and Colin de la Higuera, editors, In Proceedings of Teaching Machine Learning — workshop on open problems and new directions, May 2008. Saint-Étienne, France. |
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84 | 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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83 | 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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82 | Antti Honkela, Jeremias Seppä, and Esa Alhoniemi. Agglomerative independent variable group analysis. Neurocomputing, 71(7–9):1311–1320, 2008. |
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81 | 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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80 | 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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79 | 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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78 | 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 2008, May 6-7, Sait-Étienne, France, 2008. Papers at http://labh-curien.univ-st-etienne.fr/informatique/tml08/programme.php. |
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2007 |
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77 | 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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76 | 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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75 | 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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74 | 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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73 | 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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72 | 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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71 | 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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70 | Alexander Ilin, Harri Valpola, and Erkki Oja. Finding interesting climate phenomena by exploratory statistical techniques. In Proceedings of the Fifth Conference on Artificial Intelligence Applications to Environmental Science as part of the 87th Annual Meeting of the American Meteorological Society, San Antonio, TX, USA, January 2007. |
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69 | Juha Karhunen and Tomas Ukkonen. Extending ICA for finding jointly dependent components from two related data sets. Neurocomputing, 70(16-18):2969–2979, 2007. |
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68 | 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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2006 |
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67 | 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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66 | Alexander Ilin, Harri Valpola, and Erkki Oja. Extraction of components with structured variance. In Proceedings of the IEEE World Congress on Computational Intelligence (WCCI 2006), pages 10528–10535, Vancouver, Canada, July 2006. |
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65 | Alexander Ilin. Independent dynamics subspace analysis. In Proceedings of the 14th European Symposium on Artificial Neural Networks (ESANN 2006), pages 345–350, Bruges, Belgium, April 2006. |
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64 | 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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63 | Sergey Borisov, Alexander Ilin, Ricardo Vigário, and Erkki Oja. Comparison of bss methods for the detection of  -activity components in eeg. In Proceedings of the 6th International Conference on Independent Component Analysis and Blind Signal Separation (ICA 2006), pages 430–437, Charleston, South Carolina, USA, March 2006. |
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62 | 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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61 | 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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60 | 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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59 | 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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58 | Alexander Ilin, Harri Valpola, and Erkki Oja. Exploratory analysis of climate data using source separation methods. Neural Networks, 19(2):155–167, 2006. |
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57 | Sergey Borisov, Alexander Ilin, Ricardo Vigário, and Erkki Oja. Comparison of bss methods for the detection of ıt lpha-activity components in eeg. In ICA, pages 430–437, 2006. |
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56 | Alexander Ilin, Harri Valpola, and Erkki Oja. Extraction of components with structured variance. In IJCNN, pages 5110–5117, 2006. |
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55 | Alexander Ilin, Harri Valpola, and Erkki Oja. Exploratory analysis of climate data using source separation methods. Neural Networks, 19(2):155–167, 2006. |
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54 | K. Raju, Tapani Ristaniemi, Juha Karhunen, and Erkki Oja. Jammer suppression in ds-cdma arrays using independent component analysis. IEEE Transactions on Wireless Communications, 5(1):77–82, 2006. |
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53 | Tapani Raiko. Bayesian Inference in Nonlinear and Relational Latent Variable Models. PhD thesis, Helsinki University of Technology, Espoo, Finland, 2006. |
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52 | 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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51 | 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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50 | Janne Nikkilä, Antti Honkela, and Samuel Kaski. Exploring the independence of gene regulatory modules. In Juho Rousu, Samuel Kaski, and Esko Ukkonen, editors, Proceedings of Probabilistic Modeling and Machine Learning in Structural and Systems Biology (PMSB 2006), pages 131–136, Helsinki, Finland, 2006. Poster. |
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2005 |
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49 | Alexander Ilin and Harri Valpola. On the effect of the form of the posterior approximation in variational learning of ICA models. Neural Processing Letters, 22(2):183–204, October 2005. |
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48 | Alexander Ilin and Harri Valpola. Frequency-based separation of climate signals. In Proceedings of the 9th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD 2005), pages 519–526, Porto, Portugal, October 2005. |
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47 | 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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46 | Alexander Ilin, Harri Valpola, and Erkki Oja. Semiblind source separation of climate data detects El Ni no as the component with the highest interannual variability. In Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN 2005), pages 1722–1727, Montréal, Québec, Canada, August 2005. |
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45 | 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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44 | 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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43 | Sergey Borisov, Alexander Ilin, Ricardo Vigário, and A. Kaplan. Source localization of low- and high-amplitude alpha activity: A segmental and DSS analysis. In Proceedings of the 11th Annual Meeting of Organization for Human Brain Mapping, Toronto, Canada, June 2005. |
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42 | 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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41 | 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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40 | 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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39 | Antti Honkela. Advances in Variational Bayesian Nonlinear Blind Source Separation. PhD thesis, Helsinki University of Technology, Espoo, Finland, 2005. |
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38 | 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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37 | Alexander Ilin and Antti Honkela. Post-nonlinear independent component analysis by variational Bayesian learning. In Proceedings of the 5th International Conference on Independent Component Analysis and Blind Signal Separation (ICA 2004), pages 766–773, Granada, Spain, September 2004. |
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36 | 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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35 | Alexander Ilin, Sophie Achard, and Christian Jutten. Bayesian versus constrained structure approaches for source separation in post-nonlinear mixtures. In Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN 2004), pages 2181–2186, Budapest, Hungary, July 2004. |
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34 | Alexander Ilin, Harri Valpola, and Erkki Oja. Nonlinear dynamical factor analysis for state change detection. IEEE Transaction on Neural Networks, 15(3):559–575, May 2004. |
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33 | 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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32 | 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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31 | 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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30 | 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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29 | 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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28 | Alexander Ilin and Harri Valpola. On the effect of the form of the posterior approximation in variational learning of ICA models. In Proceedings of the 4th International Conference on Independent Component Analysis and Blind Signal Separation (ICA 2003), pages 915–920, Nara, Japan, April 2003. |
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27 | 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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26 | 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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25 | 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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24 | 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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23 | Harri Valpola, Antti Honkela, Markus Harva, Alexander Ilin, Tapani Raiko, and Tomas Östman. Bayes Blocks software library, 2003. |
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22 | 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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21 | 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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2002 |
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20 | 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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19 | 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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18 | 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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17 | 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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16 | 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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15 | 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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14 | 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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13 | Harri Valpola, Antti Honkela, and Xavier Giannakopoulos. Nonlinear dynamical factor analysis Matlab package, 2002. |
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2001 |
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12 | Antti Honkela. Nonlinear switching state-space models. Master's thesis, Helsinki University of Technology, Espoo, 2001. |
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11 | 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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10 | 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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9 | Tapani Raiko. Hierarchical nonlinear factor analysis. Master's thesis, Helsinki University of Technology, Espoo, Finland, 2001. |
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8 | 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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7 | 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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2000 |
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6 | Antti Honkela, Harri Valpola, and Xavier Giannakopoulos. Nonlinear factor analysis Matlab package, 2000. |
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5 | 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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4 | 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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1999 |
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3 | Xavier Giannakopoulos, Juha Karhunen, and Erkki Oja. An experimental comparison of neural algorithms for independent component analysis and blind separation. Int. J. Neural Syst., 9(2):99–114, 1999. |
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1998 |
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2 | Juha Karhunen, Petteri Pajunen, and Erkki Oja. The nonlinear pca criterion in blind source separation: Relations with other approaches. Neurocomputing, 22(1-3):5–20, 1998. |
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1997 |
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1 | Erkki Oja, Juha Karhunen, and Aapo Hyvärinen. From neural principal components to neural independent components. In ICANN, pages 519–528, 1997. |
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