
H. Schulz, K. Cho, T. Raiko, and S. Behnke.
TwoLayer Contractive Encodings for Learning Stable Nonlinear Features.
In Neural Networks journal, volume 64, pages 411, special issue on Deep Learning of Representations,
Yoshua Bengio and Honglak Lee editors,
Elsevier, April 2015.

M. Berglund, T. Raiko, K. Cho.
Measuring the Usefulness of Hidden Units in Boltzmann Machines with Mutual Information.
In Neural Networks journal, volume 64, pages 1218, special issue on Deep Learning of Representations,
Yoshua Bengio and Honglak Lee editors,
Elsevier, April 2015.

T. Vatanen, M. Osmala, T. Raiko, K. Lagus, M. SysiAho, M. Oresic, T. Honkela, H. Lähdesmäki.
SelfOrganization and Missing Values in SOM and GTM.
In Neurocomputing
journal, volume 147, pages 6070, January 2015.

K. Cho,
T. Raiko,
and A. Ilin.
Enhanced Gradient for Training Restricted Boltzmann Machines.
In Neural Computation, Vol. 25, No. 3, Pages 805831, March 2013.

M. Kuusela, E. Malmi, R. Orava, T.
Raiko, T. Vatanen.
SemiSupervised Anomaly Detection  Towards ModelIndependent Searches of
New Physics.
In the Journal of Physics: Conference Series (JPCS), volume 368, pages 19, June 2012.

U. Remes, K. Palomäki, T. Raiko, A. Honkela, and M. Kurimo
MissingFeature Reconstruction with a Bounded Nonlinear StateSpace Model.
In IEEE Signal Processing Letters, vol 18, issue 10, pp. 563566, October 2011.

A. Honkela^{*}, T. Raiko^{*}, M. Kuusela,
M. Tornio, and J. Karhunen.
Approximate Riemannian Conjugate Gradient Learning for FixedForm Variational Bayes.
In the Journal of Machine Learning Research (JMLR), 11(Nov):32353268, 2010.
* These authors contributed equally.

A. Ilin and T. Raiko.
Practical Approaches to Principal Component
Analysis in the Presence of Missing Values.
In the Journal of Machine
Learning Research (JMLR), volume 11, pages 19572000, July 2010.
Related: Matlab
package for PCA with missing values.

T. Raiko and M. Tornio.
Variational Bayesian learning of nonlinear hidden statespace models for
model predictive control.
In Neurocomputing, volume 72, issues 1618, pages 37043712, October 2009.

T. Raiko, H. Valpola, M. Harva,
and J. Karhunen.
Building Blocks for Variational Bayesian Learning of Latent Variable
Models.
In the Journal of Machine
Learning Research (JMLR), volume 8, pages 155201, January 2007.
Related: Bayes Blocks software
library

K. Kersting,
L. De Raedt,
and T. Raiko.
Logical Hidden Markov
Models.
In the Journal of Artificial Intelligence
Research (JAIR), volume 25, pages 425456, April 2006.

J. Karhunen, T. Raiko, and K. Cho.
Chapter 7:
Unsupervised Deep Learning: A Short Survey.
In Advances in Independent Component Analysis and Learning Machines, Ella Bingham, Samuel Kaski, Jorma Laaksonen, and Jouko Lampinen editors, Academic Press, May 2015.

K. Cho, T. Raiko, A. Ilin, and J. Karhunen.
Chapter 10: How to Pretrain Deep Boltzmann Machines in Two Stages.
In Artificial Neural Networks, Methods and Applications in Bio/Neuroinformatics, Springer Series in Bio/Neuroinformatics, Volume 4, Petia
KoprinkovaHristova, Valeri Mladenov, Nikola Kasabov editors,
isbn:9873319099026, pages 201219, Springer, 2015.
draft

T. Raiko and H. Valpola.
Chapter 7:
Oscillatory Neural Network for Image Segmentation with Biased Competition
for Attention.
In From Brains to Systems: BrainInspired Cognitive Systems 2010 (ISBN 9781461401636), Advances in Experimental Medicine and Biology, Volume 718, pages 7586,
Springer New York, 2011.
The original publication available at springerlink.com

T. Raiko.
Bayesian Inference in Nonlinear and Relational Latent Variable
Models.
Doctoral thesis, Helsinki University of Technology, Espoo,
1st December, 2006.
Thesis info

T. Raiko.
Hierarchical Nonlinear Factor Analysis.
Master's thesis, Helsinki University of Technology, Espoo, 2001.

J. Peltonen, T. Raiko,
S. Kaski editors.
Special Issue on Machine Learning for Signal Processing 2010.
Neurocomputing journal, Volume 80, March, 2012.

T. Raiko, P.
Haikonen,
and J. Väyrynen
editors.
AI and Machine Consciousness.
Proceedings of the 13th Finnish Artificial Intelligence Conference
(STeP 2008),
Helsinki University of Technology, Espoo, Finland, August 2022, 2008.

T. Honkela, T. Raiko, J. Kortela, and H. Valpola editors.
Proceedings of the Ninth Scandinavian Conference on Artificial
Intelligence (SCAI 2006), Espoo, Finland, October 2527,
2006.

E. Hyvönen, T. Kauppinen,
J. Kortela,
M. Laukkanen, T. Raiko, and K. Viljanen editors.
New Developments in Artificial Intelligence and the Semantic Web.
Proceedings of the 12th Finnish Artificial Intelligence Conference
(STeP
2006),
Helsinki University of Technology, Espoo, Finland, October 2627, 2006.

C. K. Sønderby, T. Raiko, L. Maaløe, S. K. Sønderby, O. Winther.
Ladder Variational Autoencoders.
In Conference on Neural Information Processing Systems (NIPS), Barcelona, Spain, December 2016.
Preprint available as arXiv:1602.02282 [stat.ML], February 2016.

H. Wang, T. Raiko, L. Lensu, T. Wang, and J. Karhunen.
SemiSupervised Domain Adaptation for Weakly Labeled Semantic Video Object Segmentation.
In Asian Conference on Computer Vision (ACCV), Taipei, Taiwan, November 2016.
Preprint available as arXiv:1606.02280 [cs.CV], June 2016.

E. Malmi, P. Takala, H. Toivonen, T. Raiko, and A. Gionis.
DopeLearning: A Computational Approach to Rap Lyrics Generation.
In the ACM KDD Conference on Knowledge Discovery and Data Mining, San Francisco, California, August, 2016.
Preprint available as arXiv:1505.04771 [cs.LG], May 2015.

J. Luketina, M. Berglund, K. Greff, and T. Raiko.
Scalable GradientBased
Tuning of Continuous Regularization Hyperparameters
In the International Conference on Machine Learning (ICML), New York, June, 2016.
Also presented in the workshop track of the International Conference on Learning Representations (ICLR), Puerto Rico, May 2016.
Preprint available as arXiv:1511.06727 [cs.LG], Nov 2015.

A. Rasmus, H. Valpola, M. Honkala, M. Berglund, and T. Raiko.
SemiSupervised Learning with Ladder Networks.
Advances in Neural Information Processing Systems 28 (NIPS 2015), pages 35323540, December 2015.
Extended version available as arXiv:1507.02672 [cs.NE], July 2015.

M. Berglund, T. Raiko, M. Honkala, L. Kärkkäinen, A. Vetek, J. Karhunen.
Bidirectional Recurrent Neural Networks as Generative Models.
Advances in Neural Information Processing Systems 28 (NIPS 2015), pages 856864, December 2015.
Preprint available as arXiv:1504.01575 [cs.LG], April 2015.

T. Raiko, M. Berglund, G. Alain, and L. Dinh.
Techniques for Learning
Binary Stochastic Feedforward Neural Networks.
In the International Conference on Learning Representations (ICLR 2015), San Diego, May, 2015.
ArXiv preprint, arXiv:1406.2989 [stat.ML], June 2014.
Note: Also presented in NIPS 2014 Workshop on Deep Learning and Representation Learning.

T. Raiko, L. Yao, K. Cho, and Y. Bengio.
Iterative Neural Autoregressive Distribution Estimator (NADEk).
In Advances in Neural Information Processing Systems 27 (NIPS 2014), Montreal, December, 2014.
Code

J. Luttinen, T. Raiko, and A. Ilin.
Linear StateSpace Model with TimeVarying Dynamics.
In Machine Learning and Knowledge Discovery in Databases (ECML),
Lecture Notes in Computer Science, Volume 8725, pp 338353,
September 2014.

M. Berglund, T. Raiko, and K. Cho.
Measuring the Usefulness of Hidden Units in Boltzmann Machines with Mutual Information.
In Lecture Notes in Computer Science, volume 8226, Neural Information Processing (ICONIP 2013), Special Session on Deep Learning and Related
Technologies, pages 482489, Springer, Heidelberg, November 2013.

H. Schulz, K. Cho, T. Raiko, and S. Behnke.
TwoLayer Contractive Encodings with Linear Transformation of Perceptrons for SemiSupervised Learning.
In Lecture Notes in Computer Science, volume 8226, Neural Information Processing (ICONIP 2013), Special Session on Deep Learning and Related
Technologies, pages 450457, Springer, Heidelberg, November 2013.

T. Vatanen, T. Raiko, H. Valpola, and Y. LeCun.
Pushing Stochastic Gradient towards SecondOrder Methods  Backpropagation Learning with Transformations in
Nonlinearities.
In Lecture Notes in Computer Science, volume 8226, Neural Information Processing (ICONIP 2013), Special Session on Deep Learning and Related
Technologies, pages 442449, Springer, Heidelberg, November 2013.
Note: Also presented in the workshop of the International Conference on Learning Representations (ICLR 2013), Scottsdale, Arizona, May, 2013.

J. Klapuri, I. Nieminen,
T. Raiko, and K. Lagus.
Variational Bayesian PCA versus kNN on a Very Sparse Reddit Voting Dataset.
In Lecture Notes in Computer Science, volume 8207, Advances in Intelligent Data Analysis XII, pages 249260,
Springer, October 2013.

K. Cho, T. Raiko,
A. Ilin, and
J. Karhunen.
A TwoStage Pretraining Algorithm for Deep Boltzmann Machines.
In Artificial Neural Networks and Machine Learning  ICANN 2013, Lecture
Notes in Computer Science, September 2013.

K. Cho, T. Raiko, and
A. Ilin.
GaussianBernoulli Deep Boltzmann Machine.
In the proceedings of the IEEE International Joint Conference
on Neural Networks (IJCNN 2013), Dallas, Texax, August, 2013.

S. Keronen, K. Cho, T. Raiko, A. Ilin, and K. Palomäki.
GaussianBernoulli restricted Boltzmann machines and automatic feature
extraction for noise robust missing data mask estimation.
In the proceedings of the IEEE International Conference on
Acoustics,
Speech, and Signal Processing (ICASSP 2013), Vancouver, Canada, 2631 May,
2013.

T. Vatanen,
I.T. Nieminen,
T. Honkela, T. Raiko, and
K. Lagus.
Controlling SelfOrganization and Handling Missing Values in SOM and GTM.
In the proceedings of the 9th Workshop on SelfOrganizing Maps (WSOM
2012), Santiago, Chile, 1214 December, 2012.

J. Raitio, T. Raiko, and
T. Honkela.
Hybrid Bilinear and Trilinear Models for Exploratory Analysis of ThreeWay Poisson Counts.
In
Artificial Neural Networks and Machine Learning  ICANN 2012, Lecture Notes in Computer Science, volume 7553, pages 475482,
September 2012.

R. Calandra,
T. Raiko,
F. Montesino Pouzols, and
M. P. Deisenroth.
Learning Deep Belief Networks from NonStationary Streams.
In
Artificial Neural Networks and Machine Learning  ICANN 2012, Lecture Notes in Computer Science, volume 7553,
pages 379386,
September 2012.

T. Hao,
T. Raiko,
A. Ilin, and
J. Karhunen.
Gated Boltzmann Machine in Texture Modeling.
In Artificial Neural Networks and Machine Learning  ICANN 2012, Lecture Notes in Computer Science, volume 7553, pages 124131,
September 2012.

K. Cho,
A. Ilin, and
T. Raiko.
TikhonovType Regularization for Restricted Boltzmann Machines.
In
Artificial Neural Networks and Machine Learning  ICANN 2012, Lecture Notes in Computer Science, volume 7552, pages 8188,
September 2012.

A. Gusmão and T. Raiko.
Towards Generalizing the Success of
MonteCarlo Tree Search beyond the Game of Go.
In Frontiers in Artificial Intelligence and Applications, vol.
242, Proceedings of the European Conference on Artificial
Intelligence (ECAI 2012), pages 384389, IOS Press,
August 2012.

T. Vatanen, M. Kuusela, E. Malmi, T. Raiko, T. Aaltonen, and Y. Nagai.
SemiSupervised Detection of Collective Anomalies with an Application in High Energy Particle Physics.
In the proceedings of the International Joint Conference on Neural
Networks (IJCNN 2012), Brisbane, Australia, 1015 June, 2012.

T. Raiko, H. Valpola, and Y. LeCun.
Deep Learning Made Easier by Linear Transformations in Perceptrons.
In the proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2012), JMLR W&CP, volume 22, pages 924932,
La Palma, Canary Islands, April 2123, 2012.
slides

K. Cho, T. Raiko, and A. Ilin.
Enhanced Gradient and Adaptive Learning Rate for Training Restricted
Boltzmann Machines.
In the proceedings of the International Conference on Machine Learning (ICML 2011), Bellevue, Washington, USA, June 28July 2, 2011.
See also a technical report for derivations and proofs.

K. Cho, A. Ilin, and T. Raiko.
Improved Learning of GaussianBernoulli
Restricted Boltzmann Machines.
In Lecture Notes in Computer Science, Volume 6791, Artificial Neural Networks and Machine Learning  ICANN 2011, pages 1017, June, 2011.
The original publication available at springerlink.com

V. Lämsä and T. Raiko.
Novelty Detection by Nonlinear Factor Analysis for Structural Health Monitoring.
In the IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2010), Kittilä, Finland, Aug 29Sep 1, 2010.
 K. Nyberg, T. Raiko, T. Tiinanen, and E. Hyvönen.
Document Classification Utilising Ontologies and Relations between Documents.
In the Eighth Workshop on Mining and Learning with Graphs (MLG 2010),
Washington, USA, July 2425, 2010.

K. Cho, T. Raiko, and A. Ilin.
Parallel Tempering is Efficient for Learning Restricted Boltzmann Machines.
In the proceedings of the International Joint Conference on Neural
Networks (IJCNN 2010), Barcelona, Spain, 1823 July, 2010.

D. Sovilj, T. Raiko,
and E. Oja.
Extending SelfOrganizing Maps with Uncertainty Information of Probabilistic PCA.
In the proceedings of the International Joint Conference on Neural
Networks (IJCNN 2010), Barcelona, Spain, 1823 July, 2010.

L. Kozma, A. Ilin, and T.
Raiko.
Binary Principal Component Analysis in the Netflix Collaborative
Filtering Task.
In the proceedings of the IEEE International Workshop on
Machine Learning for Signal Processing (MLSP 2009), Grenoble, France, 24
September, 2009.

M. Kuusela, T. Raiko, A. Honkela, and J. Karhunen.
A GradientBased Algorithm Competitive with Variational Bayesian EM for
Mixture of Gaussians.
In the proceedings of
the International Joint Conference on Neural Networks (IJCNN 2009),
Atlanta, Georgia, June 1519, 2009.

J. Luttinen,
A. Ilin,
and T. Raiko.
Linear Transformations in the Variational Factor Analysis Subspace for
Speeding up Learning.
In the proceedings of
the European Symposium on Artificial Neural Networks 
Advances in Computational Intelligence and Learning (ESANN 2009),
Bruges, Belgium, 2224 April, 2009.

T. Raiko, A. Ilin, and J. Karhunen.
Principal Component Analysis for Sparse HighDimensional Data.
In Lecture Notes in Computer Science, Volume 4984, Neural Information Processing (ICONIP 2007), pages
566575, 2008.
The original publication available at springerlink.com
Related: Software package

A. Honkela, M. Tornio, T. Raiko, and J. Karhunen.
Natural Conjugate Gradient in Variational Inference.
In Lecture Notes in Computer Science, Volume 4985, Neural Information Processing (ICONIP 2007), pages 305314, 2008.
The original publication available at springerlink.com
Errata: The denominator of Equation (14) should have k1 twice instead of k1 and k.

T. Raiko, A. Ilin, and J. Karhunen.
Principal Component Analysis for Large Scale Problems with Lots of Missing Values.
In J. Kok et al. (Eds.), Lecture Notes in Artificial Intelligence, vol.
4701, SpringerVerlag,
proceedings of the 18th European Conference on Machine Learning (ECML 2007), pages 691698, 2007.
The original publication is available at springerlink.com
Related: Software package

T. Raiko, M. Tornio,
A.
Honkela, and J.
Karhunen.
State Inference in Variational Bayesian Nonlinear StateSpace Models
In Lecture Notes in Computer Science, Volume 3889, Independent Component Analysis and Blind Signal Separation (ICA 2006), pages 222229,
2006.
The original publication is available at springerlink.com

T. Raiko.
Nonlinear Relational Markov Networks with an Application to the
Game of Go.
In Lecture Notes in Computer Science, 2005, Volume 3697, Artificial Neural Networks: Formal Models and Their Applications (ICANN 2005), pages 989996, 2005.
The original publication is available at springerlink.com

A. Honkela, M. Harva, T. Raiko, H. Valpola, J. 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.
PDF

A. Honkela, M. Tornio, T. Raiko.
Variational Bayes for
ContinuousTime Nonlinear StateSpace Models.
In NIPS*2006
Workshop
on Dynamical Systems, Stochastic Processes and Bayesian
Inference,
Whistler, B.C., Canada, 2006.
PDF

T. Raiko.
Higher Order Statistics in Playout Analysis.
In the proceedings of the
Scandinavian Conference on Artificial intelligence (SCAI 2006),
pages 189195,
Espoo, Finland, October 2527, 2006.
PS,
PDF

M. Tornio and T.
Raiko.
Variational Bayesian Approach for Nonlinear Identification and
Control.
In the proceedings of the IFAC Workshop on
Nonlinear Model Predictive Control for Fast Systems (NMPC FS06), pages
4146,
Grenoble, France, October 911, 2006.
PS,
PDF

T. Raiko and M. Tornio.
Learning Nonlinear StateSpace Models for Control.
In the proceedings of the International Joint Conference on
Neural Networks, IJCNN 2005, pages 815820, Montreal, Canada, July 31August 4,
2005.
Paper: PS,
PDF,
Poster: PS,
PDF

K. Kersting and T. Raiko.
'Say EM' for Selecting Probabilistic Models for Logical Sequences.
In the proceedings of the
21st Conference on Uncertainty in Artificial Intelligence, UAI 2005, pages 300307,
Edinburgh, Scotland, July 2629, 2005.
PS, PDF

M. Harva, T. Raiko, A. Honkela, H. Valpola, and J. Karhunen.
Bayes Blocks: An Implementation of the Variational Bayesian Building Blocks Framework.
In the proceedings of the
21st Conference on Uncertainty in Artificial Intelligence, UAI 2005, pages 259266,
Edinburgh, Scotland, July 2629, 2005.
PS,
PDF

T. Raiko.
Partially Observed Values.
In the proceedings of the International Joint Conference on
Neural
Networks, IJCNN 2004, pages 28252830, Budapest, Hungary, July 2529, 2004.
Paper: PS, PDF,
poster: PS, PDF

T. Raiko, H. Valpola, T. Östman, and J. Karhunen.
Missing Values in Hierarchical
Nonlinear Factor Analysis.
In the proceedings of the International Conference on Artificial
Neural Networks and Neural Information Processing, ICANN/ICONIP 2003, pages 185189,
Istanbul, Turkey, June 2629, 2003.
Paper: PS, PDF, poster: PS,
PDF

K. Kersting, T. Raiko, S. Kramer, and L. De Raedt.
Towards Discovering
Structural Signatures of Protein Folds based on Logical Hidden Markov
Models.
In the proceedings of the Pacific Symposium on
Biocomputing, PSB2003, pages 192203, Kauai, Hawaii, January 37, 2003.
PS,
PDF

H. Valpola, T. Raiko, and J. Karhunen.
Building blocks for hierarchical latent variable models.
In the proceedings of the 3rd International Conference on
Independent Component Analysis and Blind Signal Separation, ICA
2001, pages 716721, San
Diego, California, USA, December 912, 2001.
PS, PDF

T. Raiko and H. Valpola.
Missing values in nonlinear factor
analysis.
In the proceedings of the 8th International Conference on Neural
Information Processing, ICONIP 2001, pages 822827, Shanghai, China, November
1418, 2001.
PS,
PDF

M. Lankinen, H. Heikinheimo, P. Takala, T. Raiko, and Juha Karhunen.
A CharacterWord Compositional Neural Language Model for Finnish.
Available as arXiv:1612.03266 [cs.CL], Dec 2016.

T. Raiko, J.N. Alanko, L. Pesola, and N. Viljanmaa.
Gotekoäly  Ihminen taipui konetta vastaan jälleen yhdessä lautapelissä.
Skrolli magazine 2, 2016.

M. Abbas^{*}, J. Kivinen^{*}, T. Raiko^{*}.
Understanding regularization by virtual adversarial training, ladder networks and others.
In the workshop track of the International Conference on Learning Representations (ICLR), Puerto Rico, May 2016.
* Authors contributed equally.

T. Raiko.
Towards SuperHuman Artificial Intelligence in Go by
Further Improvements of AlphaGo.
Unpublished manuscript, February, 2016.

A. Rasmus, H. Valpola, and T. Raiko
Lateral Connections in Denoising Autoencoders Support Supervised Learning.
In the ICML'15 Deep Learning Workshop, Lille, France, July, 2015.
Preprint available as arXiv:1504.08215 [cs.LG], April 2015.

A. Rasmus, T. Raiko, and H. Valpola
Denoising autoencoder with modulated
lateral connections learns invariant
representations of natural images.
In the workshop track of the International Conference on Learning
Representations (ICLR 2015), San Diago, California, May, 2015.
Preprint available as arXiv:1412.7210 [cs.NE], December 2014.

M. Berglund and T. Raiko
Stochastic Gradient Estimate Variance in Contrastive Divergence and Persistent Contrastive Divergence.
In the workshop track of the International Conference on Learning
Representations (ICLR 2014), Banff, Canada, April 1416, 2014.
Preprint available as arXiv:1312.6002 [cs.NE], December 2013.

T. Lukka, T. Tossavainen, J. Kujala, and T. Raiko.
ZenRobotics Recycler  Sensor and Recognition System for Robotic Sorting
using Machine Learning.
In the proceedings of the International Conference on
SensorBased Sorting (SBS), Aachen, March 1113, 2014.

T. Raiko.
Jonglöörauksen matematiikka (in Finnish).
In Arpakannus 1/2013, magazine of the Finnish Artificial
Intelligence Society, May 2013.

K. Cho, T. Raiko, A. Ilin, and J. Karhunen.
A Twostage Pretraining Algorithm for Deep Boltzmann Machines.
In the Deep Learning and Unsupervised Feature Learning Workshop at NIPS
2012.

K. Cho, T. Raiko, and J. Karhunen.
Advances in Training Restricted Boltzmann Machines (Abstract).
In the Eleventh International Symposium on Intelligent Data Analysis (IDA), October, 2012.

A. Gusmão and T. Raiko.
Reinforcement Learning in RealTime Strategy Games (Extended
Abstract).
In the proceedings of the Yhdistetyt tietojenkäsittelyn
päivät (YTP 2012), Helsinki, Finland, May 2829, 2012.

J. Karhunen, T. Raiko, A. Ilin, A. Honkela, J. Luttinen, and K. Cho.
Chapter 2: Bayesian learning of latent variable models.
In the Biennial Report (20102011) of the Adaptive Informatics Research Centre, Aalto University School of Science, May 2012.

T. Raiko, H. Valpola, and Y. LeCun.
Deep Learning Made Easier by Linear Transformations in Perceptrons.
In the NIPS 2011 Workshop on Deep Learning and Unsupervised Feature Learning, Granada, Spain, December 16, 2011.

K. Cho, T. Raiko, and A. Ilin
GaussianBernoulli Deep Boltzmann Machine.
In the NIPS 2011 Workshop on Deep Learning and Unsupervised Feature Learning, Granada, Spain, December 16, 2011.

T. Vatanen, M. Kuusela, E. Malmi, T. Raiko, T. Aaltonen, and
Y. Nagai.
FixedBackground EM Algorithm for
SemiSupervised Anomaly
Detection.
Technical report, Aalto University publication series SCIENCE +
TECHNOLOGY 22/2011.

T. Raiko, K. Cho, and A. Ilin.
Derivations of the Enhanced Gradient for the
Boltzmann Machine.
Technical report, Aalto University publication series SCIENCE +
TECHNOLOGY 20/2011.

T. Raiko, K. Cho, and A. Ilin.
Enhanced Gradient for Learning Boltzmann
Machines (Abstract).
In the Learning Workshop, Fort Lauderdale, Florida, April, 2011.
See also the poster, the ICML 2011 paper, and the technical report on the topic.

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 the proceedings of the 14th Finnish Artificial Intelligence Conference
(STeP 2010), Helsinki, Finland, August 2010.
Slides (PDF)
Received the best paper award.

T. Raiko and H. Valpola.
Oscillatory Neural Network for Image Segmentation with Biased Competition
for Attention.
In the Brain Inspired Cognitive Systems (BICS 2010) symposium, Madrid,
Spain, 1416 July, 2010.
See also the book chapter and slides

T. Raiko, A. Ilin, N. Korsakova, E. Oja, and H. Valpola.
Drifting Linear Dynamics (Abstract).
Presented at the 13th International Conference on Artificial Intelligence and Statistics (AISTATS 2010), Chia Laguna Resort, Sardinia, Italy, May 1315, 2010.

J. Karhunen, A. Honkela, T. Raiko, A. Ilin, K. Van Leemput, J. Luttinen, M. Tornio, and M. Harva.
Chapter 2: Bayesian learning of latent variable models.
In the Biennial Report (20082009) of the Adaptive Informatics Research Centre, Aalto University School of Science and Technology, April 2010.

T. Raiko.
Sudoku ihmisen ja koneen ratkaisemana (in Finnish).
Arpakannus 1/2009, magazine of the Finnish Artificial Intelligence Society, February, 2009.

T. Raiko and J. Peltonen.
Application of UCT Search to the Connection Games of Hex, Y, *Star, and Renkula!
Proc. of the Finnish Artificial Intelligence Conference (STeP 2008), Espoo, Finland, August 2008.
Slides,
Executables

A. Ilin and T. Raiko.
Practical Approaches to Principal Component Analysis in the Presence of Missing Values.
Technical report TKKICSR6, Helsinki University of Technology, June 2008.
Related: Software package, JMLR paper

A. Honkela, M. Harva, T. Raiko, J. Karhunen
Variational Inference and Learning for
ContinuousTime Nonlinear StateSpace Models.
Proc. of PASCAL 2008 Workshop on
Approximate Inference in Stochastic Processes and Dynamical Systems,
Cumberland Lodge, UK, May 2729, 2008.

T. Raiko, K. Puolamäki, J. Karhunen, J.
Hollmén, A.
Honkela,
S. Kaski, H. Mannila, E. Oja, and O. Simula
Macadamia: Master's Programme in Machine
Learning
and Data Mining
Proc. of Teaching Machine Learning: Workshop on open problems and new
directions, SaintEtienne, France, May 68, 2008
PDF

J. Hollmén and T.
Raiko
Learning mixture models  courseware for
finite
mixtures of Bernoulli
distributions
Proc. of Teaching Machine Learning: Workshop on open problems and new
directions, SaintEtienne, France, May 68, 2008
PDF

J. Karhunen, A. Honkela, T. Raiko, M. Harva, A. Ilin, M. Tornio, and H. Valpola.
Chapter 2: Bayesian learning of latent variable models.
In the Biennial Report (20062007) of the Laboratory of Computer and Information Science, Helsinki University of Technology, April 2008.
 T. Raiko.
Higher Order Statistics in Playout Analysis (Extended Abstract).
In the proceedings of the
5th International Workshop on Mining and Learning with Graphs, MLG'07, Firenze, Italy, August 13, 2007.
PDF

A. Honkela,
M. Tornio,
T. Raiko, and
J. Karhunen.
Natural Conjugate Gradient in
Variational Inference.
Publications in Computer and Information Science E10, Helsinki
University of Technology, Espoo, Finland, March 2007.
PDF

A. Honkela,
M. Tornio,
T. Raiko, and
J. Karhunen.
Natural Conjugate Gradient in Variational Inference (Abstract).
The Learning Workshop, San Juan, Puerto Rico, March 1922
2007.
PDF, PS

T. Raiko, H. Valpola, M. Harva,
and J. Karhunen.
Building Blocks for Variational Bayesian
Learning of Latent Variable Models.
Publications in Computer and Information Science E4, Helsinki
University of Technology, Espoo, Finland, April 2006.
PDF

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