Practical approaches to principal component analysis in the presence of missing values A Ilin, T Raiko The Journal of Machine Learning Research 11, 1957-2000, 2010 | 353 | 2010 |

Improved learning of Gaussian-Bernoulli restricted Boltzmann machines KH Cho, A Ilin, T Raiko International conference on artificial neural networks, 10-17, 2011 | 206 | 2011 |

Gaussian-bernoulli deep boltzmann machine KH Cho, T Raiko, A Ilin The 2013 International Joint Conference on Neural Networks (IJCNN), 1-7, 2013 | 114 | 2013 |

Parallel tempering is efficient for learning restricted Boltzmann machines KH Cho, T Raiko, A Ilin The 2010 international joint conference on neural networks (ijcnn), 1-8, 2010 | 97 | 2010 |

Principal component analysis for large scale problems with lots of missing values T Raiko, A Ilin, J Karhunen European Conference on Machine Learning, 691-698, 2007 | 86 | 2007 |

Enhanced gradient and adaptive learning rate for training restricted Boltzmann machines KH Cho, T Raiko, A Ilin Proceedings of the 28th International Conference on Machine Learning (ICML …, 2011 | 85 | 2011 |

Enhanced gradient for training restricted Boltzmann machines KH Cho, T Raiko, A Ilin Neural computation 25 (3), 805-831, 2013 | 68 | 2013 |

Estimation of ECHAM5 climate model closure parameters with adaptive MCMC H Järvinen, P Räisänen, M Laine, J Tamminen, A Ilin, E Oja, A Solonen, ... Atmospheric Chemistry and Physics 10 (20), 9993, 2010 | 52 | 2010 |

Nonlinear blind source separation by variational Bayesian learning H Valpola, E Oja, A Ilin, A Honkela, J Karhunen IEICE Transactions on Fundamentals of Electronics, Communications and …, 2003 | 45 | 2003 |

Variational Gaussian-process factor analysis for modeling spatio-temporal data J Luttinen, A Ilin Advances in neural information processing systems, 1177-1185, 2009 | 43 | 2009 |

On the effect of the form of the posterior approximation in variational learning of ICA models A Ilin, H Valpola Neural Processing Letters 22 (2), 183-204, 2005 | 39 | 2005 |

Blind separation of nonlinear mixtures by variational Bayesian learning A Honkela, H Valpola, A Ilin, J Karhunen Digital Signal Processing 17 (5), 914-934, 2007 | 33 | 2007 |

On closure parameter estimation in chaotic systems. J Hakkarainen, A Ilin, A Solonen, M Laine, H Haario, J Tamminen, E Oja, ... Nonlinear processes in Geophysics 19 (1), 2012 | 31 | 2012 |

Nonlinear dynamical factor analysis for state change detection A Ilin, H Valpola, E Oja IEEE transactions on neural networks 15 (3), 559-575, 2004 | 31 | 2004 |

Transformations in variational Bayesian factor analysis to speed up learning J Luttinen, A Ilin Neurocomputing 73 (7-9), 1093-1102, 2010 | 28 | 2010 |

Principal component analysis for sparse high-dimensional data T Raiko, A Ilin, J Karhunen International Conference on Neural Information Processing, 566-575, 2007 | 28 | 2007 |

Recurrent ladder networks I Prémont-Schwarz, A Ilin, T Hao, A Rasmus, R Boney, H Valpola Advances in neural information processing systems, 6009-6019, 2017 | 27* | 2017 |

Binary principal component analysis in the Netflix collaborative filtering task L Kozma, A Ilin, T Raiko 2009 IEEE International Workshop on Machine Learning for Signal Processing, 1-6, 2009 | 27 | 2009 |

Efficient Gaussian process inference for short-scale spatio-temporal modeling J Luttinen, A Ilin Artificial Intelligence and Statistics, 741-750, 2012 | 25 | 2012 |

Exploratory analysis of climate data using source separation methods A Ilin, H Valpola, E Oja Neural Networks 19 (2), 155-167, 2006 | 24 | 2006 |