Logical Hidden Markov Models (Extendes abstract). - Kristian Kersting; Tapani Raiko; Luc De Raedt (Probabilistic Graphical Models, 2002)

Towards Discovering Structural Signatures of Protein Folds Based on Logical Hidden Markov Models. - Kristian Kersting; Tapani Raiko; Stefan Kramer 001; Luc De Raedt (Pacific Symposium on Biocomputing, 2003)

Partially observed values. - Tapani Raiko (IJCNN, 2004)

Nonlinear Relational Markov Networks with an Application to the Game of Go. - Tapani Raiko (ICANN (2), 2005)

Learning nonlinear state-space models for control. - Tapani Raiko; Matti Tornio (IJCNN, 2005)

Bayes Blocks: An Implementation of the Variational Bayesian Building Blocks Framework. - Markus Harva; Tapani Raiko; Antti Honkela; Harri Valpola; Juha Karhunen (UAI, 2005)

"Say EM" for Selecting Probabilistic Models for Logical Sequences. - Kristian Kersting; Tapani Raiko (UAI, 2005)

State Inference in Variational Bayesian Nonlinear State-Space Models. - Tapani Raiko; Matti Tornio; Antti Honkela; Juha Karhunen (ICA, 2006)

Logical Hidden Markov Models. - Kristian Kersting; Luc De Raedt; Tapani Raiko (J. Artif. Intell. Res., 2006)

Bayesian inference in nonlinear and relational latent variable models ; Bayesiläinen päättely epälineaarisissa ja rakenteisissa piilomuuttujamalleissa. - Tapani Raiko (2006)

Principal Component Analysis for Large Scale Problems with Lots of Missing Values. - Tapani Raiko; Alexander Ilin; Juha Karhunen (ECML, 2007)

Natural Conjugate Gradient in Variational Inference. - Antti Honkela; Matti Tornio; Tapani Raiko; Juha Karhunen (ICONIP (2), 2007)

Principal Component Analysis for Sparse High-Dimensional Data. - Tapani Raiko; Alexander Ilin; Juha Karhunen (ICONIP (1), 2007)

Building Blocks for Variational Bayesian Learning of Latent Variable Models. - Tapani Raiko; Harri Valpola; Markus Harva; Juha Karhunen (J. Mach. Learn. Res., 2007)

Transformations for variational factor analysis to speed up learning. - Jaakko Luttinen; Alexander Ilin; Tapani Raiko (ESANN, 2009)

A gradient-based algorithm competitive with variational Bayesian EM for mixture of Gaussians. - Mikael Kuusela; Tapani Raiko; Antti Honkela; Juha Karhunen (IJCNN, 2009)

Variational Bayesian learning of nonlinear hidden state-space models for model predictive control. - Tapani Raiko; Matti Tornio (Neurocomputing, 2009)

Parallel tempering is efficient for learning restricted Boltzmann machines. - KyungHyun Cho; Tapani Raiko; Alexander Ilin (IJCNN, 2010)

Extending Self-Organizing Maps with uncertainty information of probabilistic PCA. - Dusan Sovilj; Tapani Raiko; Erkki Oja (IJCNN, 2010)

Document classification utilising ontologies and relations between documents. - Katariina Nyberg; Tapani Raiko; Teemu Tiinanen; Eero Hyvönen (MLG@KDD, 2010)

Approximate Riemannian Conjugate Gradient Learning for Fixed-Form Variational Bayes. - Antti Honkela; Tapani Raiko; Mikael Kuusela; Matti Tornio; Juha Karhunen (J. Mach. Learn. Res., 2010)

Practical Approaches to Principal Component Analysis in the Presence of Missing Values. - Alexander Ilin; Tapani Raiko (J. Mach. Learn. Res., 2010)

Improved Learning of Gaussian-Bernoulli Restricted Boltzmann Machines. - KyungHyun Cho; Alexander Ilin; Tapani Raiko (ICANN (1), 2011)

Enhanced Gradient and Adaptive Learning Rate for Training Restricted Boltzmann Machines. - KyungHyun Cho; Tapani Raiko; Alexander Ilin (ICML, 2011)

Missing-Feature Reconstruction With a Bounded Nonlinear State-Space Model. - Ulpu Remes; Kalle J. Palomäki; Tapani Raiko; Antti Honkela; Mikko Kurimo (IEEE Signal Process. Lett., 2011)

Towards Generalizing the Success of Monte-Carlo Tree Search beyond the Game of Go. - António Gusmão; Tapani Raiko (ECAI, 2012)

Learning Deep Belief Networks from Non-stationary Streams. - Roberto Calandra; Tapani Raiko; Marc Peter Deisenroth; Federico Montesino-Pouzols (ICANN (2), 2012)

Tikhonov-Type Regularization for Restricted Boltzmann Machines. - KyungHyun Cho; Alexander Ilin; Tapani Raiko (ICANN (1), 2012)

Gated Boltzmann Machine in Texture Modeling. - Tele Hao; Tapani Raiko; Alexander Ilin; Juha Karhunen (ICANN (2), 2012)

Hybrid Bilinear and Trilinear Models for Exploratory Analysis of Three-Way Poisson Counts. - Juha Raitio; Tapani Raiko; Timo Honkela (ICANN (2), 2012)

Semi-supervised detection of collective anomalies with an application in high energy particle physics. - Tommi Vatanen; Mikael Kuusela; Eric Malmi; Tapani Raiko; Timo Aaltonen; Yoshikazu Nagai (IJCNN, 2012)

Controlling Self-Organization and Handling Missing Values in SOM and GTM. - Tommi Vatanen; Ilari T. Nieminen; Timo Honkela; Tapani Raiko; Krista Lagus (WSOM, 2012)

Machine learning for signal processing 2010. - Jaakko Peltonen; Tapani Raiko; Samuel Kaski (Neurocomputing, 2012)

Deep Learning Made Easier by Linear Transformations in Perceptrons. - Tapani Raiko; Harri Valpola; Yann LeCun (AISTATS, 2012)

A Two-Stage Pretraining Algorithm for Deep Boltzmann Machines. - KyungHyun Cho; Tapani Raiko; Alexander Ilin; Juha Karhunen (ICANN, 2013)

Gaussian-Bernoulli restricted Boltzmann machines and automatic feature extraction for noise robust missing data mask estimation. - Sami Keronen; KyungHyun Cho; Tapani Raiko; Alexander Ilin; Kalle J. Palomäki (ICASSP, 2013)

Two-Layer Contractive Encodings with Shortcuts for Semi-supervised Learning. - Hannes Schulz; KyungHyun Cho; Tapani Raiko; Sven Behnke (ICONIP (1), 2013)

Variational Bayesian PCA versus k-NN on a Very Sparse Reddit Voting Dataset. - Jussa Klapuri; Ilari T. Nieminen; Tapani Raiko; Krista Lagus (IDA, 2013)

Gaussian-Bernoulli deep Boltzmann machine. - KyungHyun Cho; Tapani Raiko; Alexander Ilin (IJCNN, 2013)

Pushing Stochastic Gradient towards Second-Order Methods – Backpropagation Learning with Transformations in Nonlinearities - Tommi Vatanen; Tapani Raiko; Harri Valpola; Yann LeCun (ICLR (Workshop Poster), 2013)

Enhanced Gradient for Training Restricted Boltzmann Machines. - KyungHyun Cho; Tapani Raiko; Alexander Ilin (Neural Comput., 2013)

Iterative Neural Autoregressive Distribution Estimator NADE-k. - Tapani Raiko; Li Yao; KyungHyun Cho; Yoshua Bengio (NIPS, 2014)

Linear State-Space Model with Time-Varying Dynamics. - Jaakko Luttinen; Tapani Raiko; Alexander Ilin (ECML/PKDD (2), 2014)

Stochastic Gradient Estimate Variance in Contrastive Divergence and Persistent Contrastive Divergence. - Mathias Berglund; Tapani Raiko (ICLR (Workshop Poster), 2014)

Bidirectional Recurrent Neural Networks as Generative Models. - Mathias Berglund; Tapani Raiko; Mikko Honkala; Leo Kärkkäinen; Akos Vetek; Juha Karhunen (NIPS, 2015)

Semi-supervised Learning with Ladder Networks. - Antti Rasmus; Mathias Berglund; Mikko Honkala; Harri Valpola; Tapani Raiko (NIPS, 2015)

Bidirectional Recurrent Neural Networks as Generative Models - Reconstructing Gaps in Time Series. - Mathias Berglund; Tapani Raiko; Mikko Honkala; Leo Kärkkäinen; Akos Vetek; Juha Karhunen (CoRR, 2015)

Techniques for Learning Binary Stochastic Feedforward Neural Networks. - Tapani Raiko; Mathias Berglund; Guillaume Alain; Laurent Dinh (ICLR (Poster), 2015)

Denoising autoencoder with modulated lateral connections learns invariant representations of natural images. - Antti Rasmus; Tapani Raiko; Harri Valpola (ICLR (Workshop), 2015)

Semi-Supervised Learning with Ladder Network. - Antti Rasmus; Harri Valpola; Mikko Honkala; Mathias Berglund; Tapani Raiko (CoRR, 2015)

Lateral Connections in Denoising Autoencoders Support Supervised Learning. - Antti Rasmus; Harri Valpola; Tapani Raiko (CoRR, 2015)

Self-organization and missing values in SOM and GTM. - Tommi Vatanen; Maria Osmala; Tapani Raiko; Krista Lagus; Marko Sysi-Aho; Matej Oresic; Timo Honkela; Harri Lähdesmäki (Neurocomputing, 2015)

Measuring the usefulness of hidden units in Boltzmann machines with mutual information. - Mathias Berglund; Tapani Raiko; KyungHyun Cho (Neural Networks, 2015)

Two-layer contractive encodings for learning stable nonlinear features. - Hannes Schulz; KyungHyun Cho; Tapani Raiko; Sven Behnke (Neural Networks, 2015)

Semi-supervised Domain Adaptation for Weakly Labeled Semantic Video Object Segmentation. - Huiling Wang; Tapani Raiko; Lasse Lensu; Tinghuai Wang; Juha Karhunen (ACCV (1), 2016)

Scalable Gradient-Based Tuning of Continuous Regularization Hyperparameters. - Jelena Luketina; Tapani Raiko; Mathias Berglund; Klaus Greff (ICML, 2016)

DopeLearning: A Computational Approach to Rap Lyrics Generation. - Eric Malmi; Pyry Takala; Hannu Toivonen; Tapani Raiko; Aristides Gionis (KDD, 2016)

Ladder Variational Autoencoders. - Casper Kaae Sønderby; Tapani Raiko; Lars Maaløe; Søren Kaae Sønderby; Ole Winther (NIPS, 2016)

A Character-Word Compositional Neural Language Model for Finnish. - Matti Lankinen; Hannes Heikinheimo; Pyry Takala; Tapani Raiko; Juha Karhunen (CoRR, 2016)

How to Train Deep Variational Autoencoders and Probabilistic Ladder Networks. - Casper Kaae Sønderby; Tapani Raiko; Lars Maaløe; Søren Kaae Sønderby; Ole Winther (CoRR, 2016)