Journal Articles and Thesis

• A. Solonen, J. Hakkarainen, A. Ilin, M. Abbas and A. Bibov. Estimating model error covariance matrix parameters in extended Kalman filtering. Nonlinear Processes in Geophysics, 21(5):919-927, 2014.
  
  
• K. Cho, T. Raiko, and A. Ilin. Enhanced Gradient for Training Restricted Boltzmann Machines. Neural Computation, 25(3): 805-831, 2013.
  
  
• J. Luttinen, A. Ilin and J. Karhunen. Bayesian Robust PCA of Incomplete Data. Neural Processing Letters (Springer), 36(2):189-202, 2012.
  
  
• J. Hakkarainen, A. Ilin, A. Solonen, M. Laine, H. Haario, J. Tamminen, E. Oja and H. Järvinen. On Closure Parameter Estimation in Chaotic Systems. Nonlinear Processes in Geophysics, 19(1):127-143, 2012.
  
  
• H. Järvinen, P. Räisänen, M. Laine,E. Oja, A. Solonen and H. Haario. Estimation of ECHAM5 Climate Model Closure Parameters with Adaptive MCMC. Atmospheric Chemistry and Physics Discussion, 10(5):9993-10002, 2010.
  
  
• A. Ilin and T. Raiko. Practical Approaches to Principal Component Analysis in the Presence of Missing Values. Journal of Machine Learning Research, 11(Jul):1957-2000, 2010. [Matlab toolbox]
  
  
• J. Luttinen and A. Ilin. Transformations in Variational Bayesian Factor Analysis to Speed Up Learning. Neurocomputing, 73(7-9):1093-1102, 2010.
  
  
• A. Honkela, H. Valpola, A. Ilin and J. Karhunen. Blind Separation of Nonlinear Mixtures by Variational Bayesian Learning. Digital Signal Processing, 17(2):914-934, 2007.
  
  
• A. Ilin. Advanced Source Separation Methods with Applications to Spatio-Temporal Datasets. PhD Thesis, Helsinki University of Technology, Finland, November 2006.
  
  
• A. Ilin, H. Valpola and E. Oja. Exploratory Analysis of Climate Data Using Source Separation Methods. Neural Networks, 19(2):155-167, 2006.
  
  
• A. Ilin and H. Valpola. On the Effect of the Form of the Posterior Approximation in Variational Learning of ICA Models. Neural Processing Letters (Springer), 22(2):183-204, 2005. [Matlab code]
  
  
• A. Ilin, H. Valpola and E. Oja. Nonlinear Dynamical Factor Analysis for State Change Detection. IEEE Transactions on Neural Networks, 15(3):559-575, 2004.
  
  
• H. Valpola, E. Oja, A. Ilin, A. Honkela and J. Karhunen. Nonlinear Blind Source Separation by Variational Bayesian Learning. IEICE Transactions, E86-A(3):532-541, 2003.
  

Conference papers

• J. Luttinen, T. Raiko and A. Ilin. Linear State-Space Model with Time-Varying Dynamics In Proc. of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML 2014), 2014.
  
  
• K. Cho, T. Raiko and A. Ilin. Gaussian-Bernoulli Deep Boltzmann Machines. In Proc. of the International Joint Conference on Neural Networks. (IJCNN 2013), 2013.
  
  
• S. Keronen, K. Cho, T. Raiko. A. Ilin, K. Palomäki. Gaussian-Bernoulli restricted Boltzmann machines and automatic feature extraction for noise robust missing data mask estimation. In Proc. of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013), 2013.
  
  
• T. Hao, T. Raiko, A. Ilin and J. Jarhunen. Gated Boltzmann Machine in Texture Modeling. In Proc. of the International Conference on Artificial Neural Networks and Machine Learning (ICANN 2012), LNCS 7553:124-131, 2012.
  
  
• K. Cho, A. Ilin and T. Raiko. Tikhonov-Type Regularization for Restricted Boltzmann Machines. In Proc. of the International Conference on Artificial Neural Networks and Machine Learning (ICANN 2012), LNCS 7552:81-88, 2012.
  
  
• J. Luttinen and A. Ilin. Efficient Gaussian Process Inference for Short-Scale Spatio-Temporal Modeling. In Proc. of the 15th International Conference on Artificial Intelligence and Statistics (AISTATS 2012), JMLR W&CP 22:741-750, 2012.
  
  
• J. Hegedüs, Y. Miche, A. Ilin and A. Lendasse. Methodology for Behavioral-based Malware Analysis and Detection using Random Projections and K-Nearest Neighbors Classifiers. In Proc. of the 7th International Conference on Computational Intelligence and Security (CIS2011), 2011.
  
  
• K. Cho, T. Raiko and A. Ilin. Enhanced Gradient and Adaptive Learning Rate for Training Restricted Boltzmann Machines. In Proc. of the International conference on machine learning (ICML 2011), pp. 105-112, 2011.
  
  
• K. Cho, A. Ilin and T. Raiko. Improved Learning of Gaussian-Bernoulli Restricted Boltzmann Machines. In Proc. of the International Conference on Artificial Neural Networks (ICANN 2011), LNCS 6791:10-17, 2011.
  
  
• K. Cho, T. Raiko and A. Ilin. Parallel Tempering is Efficient for Learning Restricted Boltzmann Machines. In Proc. of the International Joint Conference on Neural Networks (IJCNN 2010), 2010.
  
  
• J. Luttinen and A. Ilin. Variational Gaussian-Process Factor Analysis for Modeling Spatio-Temporal Data. In Proc. of the 23rd Annual Conference on Neural Information Processing Systems (NIPS 2009), 2009.
  
  
• L. Kozma, A. Ilin and T. Raiko. Binary Principal Component Analysis in the Netflix Collaborative Filtering Task. In Proc. of the IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2009), 2009.
  
  
• A. Ilin and A. Kaplan. Bayesian PCA for Reconstruction of Historical Sea Surface Temperatures. In Proc. of the Int. Joint Conf. on Neural Networks (IJCNN 2009), pp. 1322-1327, 2009.
  
  
• J. Luttinen, A. Ilin and T. Raiko. Transformations for Variational Factor Analysis to Speed up Learning. In Proc. of the 17th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning (ESANN 2009), pp. 77-82, 2009.
  
  
• J. Luttinen, A. Ilin and J. Karhunen. Bayesian Robust PCA for Incomplete Data. [Springer] In Proc. of the 8th Int. Conf. on Independent Component Analysis and Signal Separation (ICA 2009), pp. 66-73, 2009.
  
  
• T. Raiko, A. Ilin and J. Karhunen. Principal Component Analysis for Sparse High-Dimensional Data. [Springer] In Proc. of the 14th International Conference on Neural Information Processing (ICONIP 2007), pp. 566-575, 2007.
  
  
• T. Raiko, A. Ilin and J. Karhunen. Principal Component Analysis for Large Scale Problems with Lots of Missing Values. [Springer] In Proc. of the 18th European Conference on Machine Learning (ECML 2007), pp. 691-698, 2007.
  
  
• A. Ilin, H. Valpola and E. Oja. Finding Interesting Climate Phenomena by Exploratory Statistical Techniques. In Proc. of the Fifth Conference on Artificial Intelligence Applications to Environmental Science, as part of the 87th Annual Meeting of the American Meteorological Society, 2007. Best student presentation.
  
  
• A. Ilin, H. Valpola and E. Oja. Extraction of Components with Structured Variance. In Proc. of the IEEE World Congress on Computational Intelligence (WCCI 2006), pp. 10528-10535, 2006.
  
  
• A. Ilin. Independent Dynamics Subspace Analysis. In Proc. of the 14th European Symposium on Artificial Neural Networks (ESANN 2006), pp. 345-350, 2006.
  
  
• S. Borisov, A. Ilin, R. Vigário, E. Oja. Comparison of BSS Methods for the Detection of α-Activity Components in EEG. In Proc. of the 6th Int. Conf. on Independent Component Analysis and Blind Signal Separation (ICA 2006), pp. 430-437, 2006.
  
  
• A. Ilin and H. Valpola. Frequency-Based Separation of Climate Signals. [Springer] In Proc. of the 9th European Conf. on Principles and Practice of Knowledge Discovery in Databases (PKDD 2005), pp. 519-526, 2005.
  
  
• A. Ilin, H. Valpola and E. Oja. Semiblind Source Separation of Climate Data Detects El Niño as the Component with the Highest Interannual Variability. In Proc. of the Int. Joint Conf. on Neural Networks (IJCNN 2005), pp. 1722-1727, 2005.
  
  
• A. Ilin and A. Honkela. Post-Nonlinear Independent Component Analysis by Variational Bayesian Learning. [Springer] In Proc. of the 5th Int. Conf. on Independent Component Analysis and Blind Signal Separation (ICA 2004), pp. 766-773, 2004.
  
  
• A. Ilin, S. Achard and C. Jutten. Bayesian versus Constrained Structure Approaches for Source Separation in Post-Nonlinear Mixtures. In Proc. of the Int. Joint Conf. on Neural Networks (IJCNN 2004), pp. 2181-2186, 2004.
  
  
• A. Ilin and H. Valpola. On the Effect of the Form of the Posterior Approximation in Variational Learning of ICA Models. In Proc. of the 4th Int. Conf. on Independent Component Analysis and Blind Signal Separation (ICA 2003), pp. 915-920, 2003.
  
  
• A. Ilin, H. Valpola and E. Oja. Detecting Process State Changes by Nonlinear Blind Source Separation. In Proc. of the 3rd Int. Conf. on Independent Component Analysis and Blind Signal Separation (ICA 2001), pp. 704-709, 2001.
  

Other publications

• J. Hegedüs, Y. Miche, A. Ilin and A. Lendasse. Random Projection Method for Scalable Malware Classification. In Proc. of the 14th International Symposium on Recent Advances in Intrusion Detection (RAID 2011), 2011.
  
  
• T. Raiko, A. Ilin, N. Korsakova, E. Oja, and H. Valpola. Drifting Linear Dynamics. In the 13th International Conference on Artificial Intelligence and Statistics (AISTATS 2010), 2010.
  
  
• L. De Alba, A. Ilin, and T. Raiko. Comparison of Variational Bayes and Gibbs Sampling in Reconstruction of Missing Values with Probabilistic Principal Component Analysis. In Proc. of the 14th Finnish Artificial Intelligence Conference (STeP 2010), Helsinki, Finland, 2010. Best paper award.
  
  
• S. Borisov, A. Ilin, R. Vigário and A. Kaplan. Source Localization of Low- and High-Amplitude Alpha Activity: A Segmental and DSS Analysis. In Proc. of the 11th Annual Meeting of Organization for Human Brain Mapping, 2005.
  
  

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