Tapani Raiko

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Bayesian learning of latent variable models

Juha Karhunen, Antti Honkela, Tapani Raiko, 2008 .

Bayes Blocks: An Implementation of the Variational Bayesian Building Blocks Framework pdf

Juha Karhunen, Antti Honkela, Tapani Raiko, 2005, UAI.

Variational Bayesian PCA versus k-NN on a Very Sparse Reddit Voting Dataset

Tapani Raiko, Jussa Klapuri, Ilari T. Nieminen, 2013, IDA.

Understanding Regularization by Virtual Adversarial Training, Ladder Networks and Others

Tapani Raiko, Mudassar Abbas, Jyri Kivinen, 2016, ICLR 2016.

Proceedings of the Ninth Scandinavian Conference on Artificial Intelligence (SCAI 2006)

Tapani Raiko, Harri Valpola, Timo Honkela, 2006 .

Two-Layer Contractive Encodings with Shortcuts for Semi-supervised Learning

Sven Behnke, Tapani Raiko, Kyunghyun Cho, 2013, ICONIP.

State Inference in Variational Bayesian Nonlinear State-Space Models

Juha Karhunen, Antti Honkela, Tapani Raiko, 2006, ICA.

Machine learning for signal processing 2010

Tapani Raiko, Samuel Kaski, Jaakko Peltonen, 2012, Neurocomputing.

Researcher's guide to Bayes Blocks library

Antti Honkela, Tapani Raiko, Markus Harva, 2006 .

Missing Values in Nonlinear Factor Analysis

Tapani Raiko, Harri Valpola, T. Raiko, 2001 .

Learning Deep Belief Networks from Non-stationary Streams

Tapani Raiko, Marc Peter Deisenroth, Roberto Calandra, 2012, ICANN.

Advances in Independent Component Analysis and Learning Machines

Juha Karhunen, Tapani Raiko, Kyunghyun Cho, 2015 .

The International Conference on Artificial Neural Networks and Machine Learning - ICANN 2013

Juha Karhunen, Tapani Raiko, Alexander Ilin, 2013 .

Classification Using Machine Learning and Ontologies

Tapani Raiko, Katariina Nyberg, T. Raiko, 2011 .

Building Blocks for Variational Bayesian Learning of Latent Variable Models

Juha Karhunen, Tapani Raiko, Markus Harva, 2007, J. Mach. Learn. Res..

International Conference on Learning Representations (ICLR)

Tapani Raiko, Mudassar Abbas, Jyri Kivinen, 2016 .

Chapter 7 – Unsupervised deep learning: A short review

Juha Karhunen, Tapani Raiko, Kyunghyun Cho, 2015 .

Enhanced Gradient and Adaptive Learning Rate for Training Restricted Boltzmann Machines

Tapani Raiko, Alexander Ilin, Kyunghyun Cho, 2011, ICML.

Bayesian Learning of Logical Hidden Markov Models

Juha Karhunen, Tapani Raiko, Kristian Kersting, 2002 .

Oscillatory neural network for image segmentation with biased competition for attention.

Tapani Raiko, Harri Valpola, T. Raiko, 2011, Advances in experimental medicine and biology.

Deep Learning Made Easier by Linear Transformations in Perceptrons

Tapani Raiko, Harri Valpola, Yann LeCun, 2012, AISTATS.

Tikhonov-Type Regularization for Restricted Boltzmann Machines

Tapani Raiko, Alexander Ilin, Kyunghyun Cho, 2012, ICANN.

Constructing Graphical Models for Bayesian Ensemble Learning from Simple Building Blocks

Juha Karhunen, Tapani Raiko, Harri Valpola, 2002 .

Natural Conjugate Gradient in Variational Inference

Juha Karhunen, Antti Honkela, Tapani Raiko, 2007, ICONIP.

The International Conference on Machine Learning (ICML 2011)

Tapani Raiko, Alexander Ilin, Kyunghyun Cho, 2011 .

Enhanced Gradient for Learning Boltzmann Machines

Tapani Raiko, Alexander Ilin, Kyunghyun Cho, 2011 .

Unsupervised deep learning

Juha Karhunen, Tapani Raiko, Kyunghyun Cho, 2015 .

Deep learning and Boltzmann machines

Tapani Raiko, Alexander Ilin, Kyunghyun Cho, 2012 .

AI and Machine Consciousness, Proceedings of the 13th Finnish Artificial Intelligence Conference (STeP 2008)

Tapani Raiko, Pentti Haikonen, Jaakko J. Väyrynen, 2008 .

Transformations for variational factor analysis to speed up learning

Tapani Raiko, Alexander Ilin, Jaakko Luttinen, 2009, ESANN.

Developments in Artificial Intelligence and the Semantic Web - Proceedings of the 12th Finnish AI Conference STeP 2006.Finland, October 26-27, 2006

Tapani Raiko, Eero Hyvönen, Kim Viljanen, 2006 .

Macadamia: Master's Programme in Machine Learning and Data Mining

Erkki Oja, Juha Karhunen, Antti Honkela, 2008 .

A Character-Word Compositional Neural Language Model for Finnish pdf

Juha Karhunen, Tapani Raiko, Hannes Heikinheimo, 2016, ArXiv.

Two-layer contractive encodings for learning stable nonlinear features

Sven Behnke, Tapani Raiko, Kyunghyun Cho, 2015, Neural Networks.

The NIPS workshop on Deep Learning and Unsupervised Feature Learning

Tapani Raiko, Harri Valpola, Yann LeCun, 2011 .

Missing Values in Hierarchical Nonlinear Factor Analysis

Juha Karhunen, Tapani Raiko, Harri Valpola, 2003 .

Nonlinear Relational Markov Networks with an Application to the Game of Go

Tapani Raiko, 2005, ICANN.

Fixed-Background EM Algorithm for Semi-Supervised Anomaly Detection

Tapani Raiko, Tommi Vatanen, Eric Malmi, 2011 .

ICANN Artificial Neural Networks and Machine Learning - ICANN 2012

Tapani Raiko, Roberto Calandra, Montesino Pouzols, 2012 .

Towards Discovering Structural Signatures of Protein Folds Based on Logical Hidden Markov Models

Luc De Raedt, Tapani Raiko, Kristian Kersting, 2003, Pacific Symposium on Biocomputing.

A gradient-based algorithm competitive with variational Bayesian EM for mixture of Gaussians

Juha Karhunen, Antti Honkela, Tapani Raiko, 2009, 2009 International Joint Conference on Neural Networks.

Bayesian Inference in Nonlinear and Relational Latent Variable Models

Tapani Raiko, T. Raiko, 2006 .

Semi-supervised detection of collective anomalies with an application in high energy particle physics

Tapani Raiko, Tommi Vatanen, Timo Aaltonen, 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

Two-Layer Contractive Encodings with Linear Transformation of Perceptrons for Semi-Supervised Learning

Sven Behnke, Tapani Raiko, Kyunghyun Cho, 2013 .

33rd International Conference on Machine Learning, ICML 2016

Tapani Raiko, Klaus Greff, Jelena Luketina, 2016, International Conference on Machine Learning.

"Say EM" for Selecting Probabilistic Models for Logical Sequences

Tapani Raiko, Kristian Kersting, K. Kersting, 2005, UAI.

Principal Component Analysis for Sparse High-Dimensional Data

Juha Karhunen, Tapani Raiko, Alexander Ilin, 2007, ICONIP.

Linear State-Space Model with Time-Varying Dynamics

Tapani Raiko, Alexander Ilin, Jaakko Luttinen, 2014, ECML/PKDD.

Principal Component Analysis for Large Scale Problems with Lots of Missing Values

Juha Karhunen, Tapani Raiko, Alexander Ilin, 2007, ECML.

Extending Self-Organizing Maps with uncertainty information of probabilistic PCA

Erkki Oja, Tapani Raiko, Dusan Sovilj, 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

Document classification utilising ontologies and relations between documents

Tapani Raiko, Eero Hyvönen, Katariina Nyberg, 2010, MLG '10.

Semi-supervised Learning with Ladder Networks

Tapani Raiko, Harri Valpola, Mikko Honkala, 2015, NIPS.

Computer Vision – ACCV 2018

Juha Karhunen, Tapani Raiko, Tinghuai Wang, 2018, Lecture Notes in Computer Science.

A Structural GEM for Learning Logical Hidden Markov Models

Tapani Raiko, Kristian Kersting, L. De Raedt, 2003 .

Binary principal component analysis in the Netflix collaborative filtering task

Tapani Raiko, Alexander Ilin, Laszlo Kozma, 2009, 2009 IEEE International Workshop on Machine Learning for Signal Processing.

Stochastic gradient estimate variance in contrastive divergence and persistent contrastive divergence pdf

Tapani Raiko, Mathias Berglund, 2016, ESANN.

Semi-supervised Learning with Ladder Networks pdf

Tapani Raiko, Harri Valpola, Mikko Honkala, 2015, NIPS.

Advances in Training Restricted Boltzmann Machines

Juha Karhunen, Tapani Raiko, Kyunghyun Cho, 2012 .

Hybrid Bilinear and Trilinear Models for Exploratory Analysis of Three-Way Poisson Counts

Tapani Raiko, Timo Honkela, Juha Raitio, 2012, ICANN.

Neural Information Processing (ICONIP 2013)

Tapani Raiko, Harri Valpola, Yann LeCun, 2013 .

Gated Boltzmann Machine in Texture Modeling

Juha Karhunen, Tapani Raiko, Alexander Ilin, 2012, ICANN.

European conference on machine learning and knowledge discovery in databases

Tapani Raiko, Alexander Ilin, Jaakko Luttinen, 2014 .

Iterative Neural Autoregressive Distribution Estimator NADE-k

Tapani Raiko, Li Yao, Yoshua Bengio, 2014, NIPS.

Variational Bayesian Approach for Nonlinear Identification and Control

Tapani Raiko, Matti Tornio, 2006 .

Enhanced Gradient for Training Restricted Boltzmann Machines

Tapani Raiko, Alexander Ilin, Kyunghyun Cho, 2013, Neural Computation.

ICANN International Conference on Artificial Neural Networks (ICANN 2012) ICANN, Lausanne, Switzerland, September 11-14, 2012

Tapani Raiko, Alexander Ilin, Kyunghyun Cho, 2012 .

Semi-supervised Domain Adaptation for Weakly Labeled Semantic Video Object Segmentation pdf

Juha Karhunen, Tapani Raiko, Tinghuai Wang, 2016, ACCV.

Semi-supervised anomaly detection – towards model-independent searches of new physics

Tapani Raiko, Tommi Vatanen, Timo Aaltonen, 2011, 1112.3329.

NIPS 2011 Workshop on Deep Learning and Unsupervised Feature Learning

Tapani Raiko, Alexander Ilin, Kyunghyun Cho, 2011 .

DopeLearning: A Computational Approach to Rap Lyrics Generation

Tapani Raiko, Aristides Gionis, Hannu Toivonen, 2015, KDD.

BINARY PRINCIPAL COMPONENTANALYSIS IN THE NETFLIX COLLABORATIVEFILTERING TASK

Tapani Raiko, László Kozma, 2009 .

Self-organization and missing values in SOM and GTM

Matej Oresic, Tapani Raiko, Timo Honkela, 2015, Neurocomputing.

Measuring the Usefulness of Hidden Units in Boltzmann Machines with Mutual Information

Tapani Raiko, Kyunghyun Cho, Mathias Berglund, 2013, ICONIP.

The Learning Workshop

Tapani Raiko, Alexander Ilin, Kyunghyun Cho, 2011 .

A Two-Stage Pretraining Algorithm for Deep Boltzmann Machines

Juha Karhunen, Tapani Raiko, Alexander Ilin, 2013, ICANN.

Controlling Self-Organization and Handling Missing Values in SOM and GTM

Tapani Raiko, Timo Honkela, Tommi Vatanen, 2012, WSOM.

Denoising autoencoder with modulated lateral connections learns invariant representations of natural images pdf

Tapani Raiko, Harri Valpola, Antti Rasmus, 2015, ICLR.

The International Conference on Artificial Neural Networks (ICANN 2011)

Tapani Raiko, Alexander Ilin, Kyunghyun Cho, 2011 .

How to Train Deep Variational Autoencoders and Probabilistic Ladder Networks pdf

Ole Winther, Tapani Raiko, Casper Kaae Sønderby, 2016, ICML 2016.

Bidirectional Recurrent Neural Networks as Generative Models - Reconstructing Gaps in Time Series pdf

Juha Karhunen, Tapani Raiko, Mikko Honkala, 2015, ArXiv.

The 2012 International Joint Conference on Neural Networks (IJCNN), Brisbane, Australia, June 10- 15, 2012

Tapani Raiko, Tommi Vatanen, Eric Malmi, 2012 .

Bidirectional Recurrent Neural Networks as Generative Models

Juha Karhunen, Tapani Raiko, Mikko Honkala, 2015, NIPS.

Bayes Blocks Software Library

Antti Honkela, Tapani Raiko, Markus Harva, 2003 .

Variational Bayesian learning of nonlinear hidden state-space models for model predictive control

Tapani Raiko, Matti Tornio, T. Raiko, 2009, Neurocomputing.

Lateral Connections in Denoising Autoencoders Support Supervised Learning pdf

Tapani Raiko, Harri Valpola, Antti Rasmus, 2015, ArXiv.

The Go-Playing Program Called Go81

Tapani Raiko, 2004 .

Logical Hidden Markov Models (Extendes abstract)

Luc De Raedt, Tapani Raiko, Kristian Kersting, 2002, Probabilistic Graphical Models.

How to Pretrain Deep Boltzmann Machines in Two Stages

Juha Karhunen, Tapani Raiko, Alexander Ilin, 2015 .

Towards Generalizing the Success of Monte-Carlo Tree Search beyond the Game of Go

Tapani Raiko, António Gusmão, 2012, ECAI.

Neurocomputing, Special Issue on Machine Learning for Signal Processing 2010, 80:1-128

Tapani Raiko, Samuel Kaski, Jaakko Peltonen, 2012 .

Pushing Stochastic Gradient towards Second-Order Methods -- Backpropagation Learning with Transformations in Nonlinearities

Tapani Raiko, Harri Valpola, Yann LeCun, 2013, ICLR.

Parallel tempering is efficient for learning restricted Boltzmann machines

Tapani Raiko, Alexander Ilin, Kyunghyun Cho, 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

Approximate Riemannian Conjugate Gradient Learning for Fixed-Form Variational Bayes

Juha Karhunen, Antti Honkela, Tapani Raiko, 2010, J. Mach. Learn. Res..

Gaussian-Bernoulli restricted Boltzmann machines and automatic feature extraction for noise robust missing data mask estimation

Tapani Raiko, Alexander Ilin, Kyunghyun Cho, 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

The International Conference on Neural Information Processing - ICONIP 2013

Sven Behnke, Tapani Raiko, Kyunghyun Cho, 2013 .

ZenRobotics Recycler - Robotic Sorting using Machine Learning

Tapani Raiko, Janne V. Kujala, Timo Tossavainen, 2014 .

Scalable Gradient-Based Tuning of Continuous Regularization Hyperparameters

Tapani Raiko, Klaus Greff, Jelena Luketina, 2015, ICML.

Missing-Feature Reconstruction With a Bounded Nonlinear State-Space Model

Mikko Kurimo, Antti Honkela, Tapani Raiko, 2011, IEEE Signal Processing Letters.

Gaussian-Bernoulli deep Boltzmann machine

Tapani Raiko, Alexander Ilin, Kyunghyun Cho, 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

Measuring the usefulness of hidden units in Boltzmann machines with mutual information

Tapani Raiko, Kyunghyun Cho, Mathias Berglund, 2015, Neural Networks.

Ladder Variational Autoencoders

Ole Winther, Tapani Raiko, Casper Kaae Sønderby, 2016, NIPS.

Improved Learning of Gaussian-Bernoulli Restricted Boltzmann Machines

Tapani Raiko, Alexander Ilin, Kyunghyun Cho, 2011, ICANN.

Tkk Reports in Information and Computer Science Practical Approaches to Principal Component Analysis in the Presence of Missing Values Tkk Reports in Information and Computer Science Practical Approaches to Principal Component Analysis in the Presence of Missing Values

Tapani Raiko, Alexander Ilin, T. Raiko, 2022 .

Techniques for Learning Binary Stochastic Feedforward Neural Networks pdf

Tapani Raiko, Laurent Dinh, Guillaume Alain, 2014, ICLR.

NIPS 2012 Workshop on Deep Learning and Unsupervised Feature Learning, Lake Tahoe, Usa, December 8, 2012

Juha Karhunen, Tapani Raiko, Alexander Ilin, 2012 .

Learning mixture models - courseware for finite mixture distributions of multivariate Bernoulli distributions

Tapani Raiko, Jaakko Hollmén, 2008 .