| 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 | 485 | 2010 |
| Improved learning of Gaussian-Bernoulli restricted Boltzmann machines KH Cho, A Ilin, T Raiko Artificial Neural Networks and Machine Learning–ICANN 2011: 21st …, 2011 | 262 | 2011 |
| Gaussian-bernoulli deep boltzmann machine KH Cho, T Raiko, A Ilin The 2013 International Joint Conference on Neural Networks (IJCNN), 1-7, 2013 | 177 | 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 | 122 | 2010 |
| 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 | 108 | 2011 |
| Principal component analysis for large scale problems with lots of missing values T Raiko, A Ilin, J Karhunen Machine Learning: ECML 2007: 18th European Conference on Machine Learning …, 2007 | 101 | 2007 |
| Enhanced gradient for training restricted Boltzmann machines KH Cho, T Raiko, A Ilin Neural computation 25 (3), 805-831, 2013 | 76 | 2013 |
| Variational Gaussian-process factor analysis for modeling spatio-temporal data J Luttinen, A Ilin Advances in neural information processing systems, 1177-1185, 2009 | 66 | 2009 |
| 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), 9993-10002, 2010 | 61 | 2010 |
| 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), 127-143, 2012 | 46 | 2012 |
| 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 |
| Bayesian robust PCA of incomplete data J Luttinen, A Ilin, J Karhunen Neural processing letters 36, 189-202, 2012 | 44 | 2012 |
| Recurrent ladder networks I Prémont-Schwarz, A Ilin, T Hao, A Rasmus, R Boney, H Valpola Advances in neural information processing systems 30, 2017 | 43* | 2017 |
| On the effect of the form of the posterior approximation in variational learning of ICA models A Ilin, H Valpola Neural Processing Letters 22, 183-204, 2005 | 41 | 2005 |
| Efficient Gaussian process inference for short-scale spatio-temporal modeling J Luttinen, A Ilin Artificial Intelligence and Statistics, 741-750, 2012 | 39 | 2012 |
| Principal component analysis for sparse high-dimensional data T Raiko, A Ilin, J Karhunen Neural Information Processing: 14th International Conference, ICONIP 2007 …, 2008 | 38 | 2008 |
| Methodology for behavioral-based malware analysis and detection using random projections and k-nearest neighbors classifiers J Hegedus, Y Miche, A Ilin, A Lendasse 2011 seventh international conference on computational intelligence and …, 2011 | 37 | 2011 |
| 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 | 37 | 2007 |
| Estimating model error covariance matrix parameters in extended Kalman filtering A Solonen, J Hakkarainen, A Ilin, M Abbas, A Bibov Nonlinear Processes in Geophysics 21 (5), 919-927, 2014 | 33 | 2014 |
| Transformations in variational Bayesian factor analysis to speed up learning J Luttinen, A Ilin Neurocomputing 73 (7-9), 1093-1102, 2010 | 32 | 2010 |
