PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

EPrints submitted by Tapani Raiko

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Number of EPrints submitted by this user: 33

Bayes Blocks: An Implementation of the Variational Bayesian Building Blocks Framework
Markus Harva, Tapani Raiko, Antti Honkela, Harri Valpola and Juha Karhunen
In: 21st Conference on Uncertainty in Artificial Intelligence, UAI 2005, 26-29 Jul, 2005, Edinburgh, Scotland.

Learning Nonlinear State-Space Models for Control
Tapani Raiko and Matti Tornio
In: International Joint Conference on Neural Networks, IJCNN 2005, 31 Jul - 03 Aug 2005, Montreal, Canada.

Nonlinear Relational Markov Networks with an Application to the Game of Go
Tapani Raiko
In: 15th International Conference on Artificial Neural Networks, ICANN 2005, 11-15 Sep, 2005, Warsaw, Poland.

State Inference in Variational Bayesian Nonlinear State-Space Models
Tapani Raiko, Matti Tornio, Antti Honkela and Juha Karhunen
In: 6th International Conference on Independent Component Analysis and Blind Source Separation (ICA 2006), 5-8 Mar 2006, Charleston, South Carolina, USA.

Variational Bayesian Approach for Nonlinear Identification and Control
Matti Tornio and Tapani Raiko
In: IFAC Workshop on IFAC Workshop on Nonlinear Model Predictive Control for Fast Systems (NMPC_FS06), 9-11 Oct 2006, Grenoble, France.

Higher order statistics in play-out analysis
Tapani Raiko
In: The Ninth Scandinavian Conference on Artificial Intelligence (SCAI 2006), 25-27 Oct 2006, Espoo, Finland.

Bayesian Inference in Nonlinear and Relational Latent Variable Models
Tapani Raiko
(2006) PhD thesis, Helsinki University of Technology.

Building Blocks for Variational Bayesian Learning of Latent Variable Models
Tapani Raiko, Harri Valpola, Markus Harva and Juha Karhunen
Journal of Machine Learning Research Volume 8, pp. 155-201, 2007.

Principal Component Analysis for Large Scale Problems with Lots of Missing Values
Tapani Raiko, Alexander Ilin and Juha Karhunen
In: European Conference on Machine Learning, ECML 2007, September 17-21, 2007, Warsaw, Poland.

Natural Conjugate Gradient in Variational Inference
Antti Honkela, Matti Tornio, Tapani Raiko and Juha Karhunen
In: International Conference on Neural Information Processing (ICONIP 2007), November 13-16, 2007, Kitakyushu, Japan.

Principal Component Analysis for Sparse High-Dimensional Data
Tapani Raiko, Alexander Ilin and Juha Karhunen
In: International Conference on Neural Information Processing (ICONIP 2007), November 13-16, 2007, Kitakyushu, Japan.

Variational Bayes for Continuous-Time Nonlinear State-Space Models
Antti Honkela, Matti Tornio and Tapani Raiko
In: NIPS*2006 Workshop on Dynamical Systems, Stochastic Processes and Bayesian Inference, 2006, Whistler, B.C., Canada.

Bayes Blocks: A Python Toolbox for Variational Bayesian Learning
Antti Honkela, Markus Harva, Tapani Raiko, Harri Valpola and Juha Karhunen
In: NIPS*2006 Workshop on Machine Learning Open Source Software, 2006, Whistler, B.C., Canada.

Higher Order Statistics in Play-out Analysis
Tapani Raiko
In: International Workshop on Mining and Learning with Graphs, MLG'07, August 1-3, 2007, Firenze, Italy.

Practical Approaches to Principal Component Analysis in the Presence of Missing Values
Alexander Ilin and Tapani Raiko
(2008) Technical Report. Helsinki University of Technology, Espoo, Finland.

Variational inference and learning for continuous-time nonlinear state-space models
Antti Honkela, Markus Harva, Tapani Raiko and Juha Karhunen
In: PASCAL 2008 Workshop on Approximate Inference in Stochastic Processes and Dynamical Systems, May 27-29 2008, Cumberland Lodge, UK.

Variational Bayesian learning of nonlinear hidden state-space models for model predictive control
Tapani Raiko and Matti Tornio
Neurocomputing Volume 72, Number 16-18, pp. 3704-3712, 2009. ISSN 0925-2312

Binary Principal Component Analysis in the Netflix Collaborative Filtering Task
Laszlo Kozma, Alexander Ilin and Tapani Raiko
In: 2009 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2009), 2-4 Sep 2009, Grenoble, France.

A Gradient-Based Algorithm Competitive with Variational Bayesian EM for Mixture of Gaussians
Mikael Kuusela, Tapani Raiko, Antti Honkela and Juha Karhunen
In: International Joint Conference on Neural Networks, 14-19 June 2009, Atlanta, USA.

Transformations for Variational Factor Analysis to Speed up Learning
Jaakko Luttinen, Alexander Ilin and Tapani Raiko
In: ESANN 2009, 22-24 April 2009, Bruges, Belgium.

Sudoku ihmisen ja koneen ratkaisemana
Tapani Raiko
Arpakannus Volume 2009, Number 1, pp. 6-10, 2009. ISSN 0783-3121

Missing-Feature Reconstruction with a Bounded Nonlinear State-Space Model
Ulpu Remes, Kalle Palomäki, Tapani Raiko, Antti Honkela and Mikko Kurimo
IEEE Signal Processing Letters Volume 18, Number 10, pp. 563-566, 2011.

Oscillatory Neural Network for Image Segmentation with Biased Competition for Attention
Tapani Raiko and Harri Valpola
In: From Brains to Systems: Brain-Inspired Cognitive Systems 2010 Advances in Experimental Medicine and Biology , 718 . (2011) Springer , New York , pp. 75-86. ISBN 978-1-4614-0163-6

A Two-stage Pretraining Algorithm for Deep Boltzmann Machines
KyungHyun Cho, Tapani Raiko, Alexander Ilin and Juha Karhunen
In: NIPS 2012 Workshop on Deep Learning and Unsupervised Feature Learning, December 8, 2012, Lake Tahoe, USA.

Semi-Supervised Anomaly Detection - Towards Model-Independent Searches of New Physics
Mikael Kuusela, Eric Malmi, Risto Orava, Tapani Raiko and Tommi Vatanen
Journal of Physics: Conference Series (JPCS) Volume 368, Number 1, pp. 1-9, 2012. ISSN 1742-6596

Controlling Self-Organization and Handling Missing Values in SOM and GTM
Tommi Vatanen, Ilari T. Nieminen, Timo Honkela, Tapani Raiko and Krista Lagus
In: 9th Workshop on Self-Organizing Maps (WSOM 2012), Dec 2012, Santiago, Chile.

Hybrid Bilinear and Trilinear Models for Exploratory Analysis of Three-Way Poisson Counts
Juha Raitio, Tapani Raiko and Timo Honkela
In: Artificial Neural Networks and Machine Learning - ICANN 2012, 11-14 Sep 2012, Lausanne, Switzerland.

Gated Boltzmann Machine in Texture Modeling
Tele Hao, Tapani Raiko, Alexander Ilin and Juha Karhunen
In: Artificial Neural Networks and Machine Learning - ICANN 2012, 11-14 Sep 2012, Lausanne, Switzerland.

Tikhonov-Type Regularization for Restricted Boltzmann Machines
KyungHyun Cho, Alexander Ilin and Tapani Raiko
In: Artificial Neural Networks and Machine Learning - ICANN 2012, 11-14 Sep 2012, Lausanne, Switzerland.

Learning Deep Belief Networks from Non-Stationary Streams
Roberto Calandra, Tapani Raiko, Federico Montesino Pouzols and Mark P. Deisenroth
In: Artificial Neural Networks and Machine Learning - ICANN 2012, 11-14 Sep 2012, Lausanne, Switzerland.

Towards Generalizing the Success of Monte-Carlo Tree Search beyond the Game of Go
Antonio Gusmao and Tapani Raiko
In: European Conference on Artificial Intelligence (ECAI 2012), 27-31 Aug 2012, Montpellier, France.

Semi-Supervised Detection of Collective Anomalies with an Application in High Energy Particle Physics
Tommi Vatanen, Mikael Kuusela, Eric Malmi, Tapani Raiko, Timo Aaltonen and Y. Nagai
In: International Joint Conference on Neural Networks (IJCNN 2012), June 11-15 2012, Brisbane, Australia.

Deep learning made easier by linear transformations in perceptrons
Tapani Raiko, Harri Valpola and Yann LeCun
In: Int. Conf. on Artificial Intelligence and Statistics (AISTATS 2012), 21-23 Apr 2012, La Palma, Canary Islands, Spain.