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ORCIDTapani Raiko
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Books and Theses
- 2006
[b1]Tapani Raiko:
Bayesian inference in nonlinear and relational latent variable models ; Bayesiläinen päättely epälineaarisissa ja rakenteisissa piilomuuttujamalleissa. Aalto University, Espoo, Finland, 2006
Journal Articles
- 2015
- [j11]Tommi Vatanen
, Maria Osmala, Tapani Raiko, Krista Lagus, Marko Sysi-Aho, Matej Oresic, Timo Honkela, Harri Lähdesmäki:
Self-organization and missing values in SOM and GTM. Neurocomputing 147: 60-70 (2015) - [j10]Hannes Schulz, KyungHyun Cho, Tapani Raiko, Sven Behnke:
Two-layer contractive encodings for learning stable nonlinear features. Neural Networks 64: 4-11 (2015) - [j9]Mathias Berglund, Tapani Raiko, KyungHyun Cho:
Measuring the usefulness of hidden units in Boltzmann machines with mutual information. Neural Networks 64: 12-18 (2015) - 2013
- [j8]KyungHyun Cho, Tapani Raiko, Alexander Ilin:
Enhanced Gradient for Training Restricted Boltzmann Machines. Neural Comput. 25(3): 805-831 (2013) - 2012
- [j7]Jaakko Peltonen, Tapani Raiko, Samuel Kaski:
Machine learning for signal processing 2010. Neurocomputing 80: 1-2 (2012) - 2011
- [j6]Ulpu Remes, Kalle J. Palomäki, Tapani Raiko, Antti Honkela, Mikko Kurimo:
Missing-Feature Reconstruction With a Bounded Nonlinear State-Space Model. IEEE Signal Process. Lett. 18(10): 563-566 (2011) - 2010
- [j5]Alexander Ilin, Tapani Raiko:
Practical Approaches to Principal Component Analysis in the Presence of Missing Values. J. Mach. Learn. Res. 11: 1957-2000 (2010) - [j4]Antti Honkela, Tapani Raiko, Mikael Kuusela, Matti Tornio, Juha Karhunen:
Approximate Riemannian Conjugate Gradient Learning for Fixed-Form Variational Bayes. J. Mach. Learn. Res. 11: 3235-3268 (2010) - 2009
- [j3]Tapani Raiko, Matti Tornio:
Variational Bayesian learning of nonlinear hidden state-space models for model predictive control. Neurocomputing 72(16-18): 3704-3712 (2009) - 2007
- [j2]Tapani Raiko, Harri Valpola, Markus Harva, Juha Karhunen:
Building Blocks for Variational Bayesian Learning of Latent Variable Models. J. Mach. Learn. Res. 8: 155-201 (2007) - 2006
- [j1]Kristian Kersting, Luc De Raedt, Tapani Raiko:
Logical Hidden Markov Models. J. Artif. Intell. Res. 25: 425-456 (2006)
Conference and Workshop Papers
- 2016
- [c43]Huiling Wang, Tapani Raiko, Lasse Lensu, Tinghuai Wang, Juha Karhunen:
Semi-supervised Domain Adaptation for Weakly Labeled Semantic Video Object Segmentation. ACCV (1) 2016: 163-179 - [c42]Jelena Luketina, Tapani Raiko, Mathias Berglund, Klaus Greff:
Scalable Gradient-Based Tuning of Continuous Regularization Hyperparameters. ICML 2016: 2952-2960 - [c41]Eric Malmi, Pyry Takala, Hannu Toivonen, Tapani Raiko, Aristides Gionis:
DopeLearning: A Computational Approach to Rap Lyrics Generation. KDD 2016: 195-204 - [c40]Casper Kaae Sønderby, Tapani Raiko, Lars Maaløe, Søren Kaae Sønderby, Ole Winther:
Ladder Variational Autoencoders. NIPS 2016: 3738-3746 - 2015
- [c39]Mathias Berglund, Tapani Raiko, Mikko Honkala, Leo Kärkkäinen, Akos Vetek, Juha Karhunen:
Bidirectional Recurrent Neural Networks as Generative Models. NIPS 2015: 856-864 - [c38]Antti Rasmus, Mathias Berglund, Mikko Honkala, Harri Valpola, Tapani Raiko:
Semi-supervised Learning with Ladder Networks. NIPS 2015: 3546-3554 - [c37]Tapani Raiko, Mathias Berglund, Guillaume Alain, Laurent Dinh:
Techniques for Learning Binary Stochastic Feedforward Neural Networks. ICLR (Poster) 2015 - [c36]Antti Rasmus, Tapani Raiko, Harri Valpola:
Denoising autoencoder with modulated lateral connections learns invariant representations of natural images. ICLR (Workshop) 2015 - 2014
- [c35]Tapani Raiko, Li Yao, KyungHyun Cho, Yoshua Bengio:
Iterative Neural Autoregressive Distribution Estimator NADE-k. NIPS 2014: 325-333 - [c34]Jaakko Luttinen, Tapani Raiko, Alexander Ilin:
Linear State-Space Model with Time-Varying Dynamics. ECML/PKDD (2) 2014: 338-353 - [c33]Mathias Berglund, Tapani Raiko:
Stochastic Gradient Estimate Variance in Contrastive Divergence and Persistent Contrastive Divergence. ICLR (Workshop Poster) 2014 - 2013
- [c32]KyungHyun Cho, Tapani Raiko, Alexander Ilin, Juha Karhunen:
A Two-Stage Pretraining Algorithm for Deep Boltzmann Machines. ICANN 2013: 106-113 - [c31]Sami Keronen, KyungHyun Cho, Tapani Raiko, Alexander Ilin, Kalle J. Palomäki:
Gaussian-Bernoulli restricted Boltzmann machines and automatic feature extraction for noise robust missing data mask estimation. ICASSP 2013: 6729-6733 - [c30]Tommi Vatanen, Tapani Raiko, Harri Valpola, Yann LeCun:
Pushing Stochastic Gradient towards Second-Order Methods - Backpropagation Learning with Transformations in Nonlinearities. ICONIP (1) 2013: 442-449 - [c29]Hannes Schulz, KyungHyun Cho, Tapani Raiko, Sven Behnke:
Two-Layer Contractive Encodings with Shortcuts for Semi-supervised Learning. ICONIP (1) 2013: 450-457 - [c28]Mathias Berglund, Tapani Raiko, KyungHyun Cho:
Measuring the Usefulness of Hidden Units in Boltzmann Machines with Mutual Information. ICONIP (1) 2013: 482-489 - [c27]Jussa Klapuri, Ilari T. Nieminen, Tapani Raiko, Krista Lagus:
Variational Bayesian PCA versus k-NN on a Very Sparse Reddit Voting Dataset. IDA 2013: 249-260 - [c26]KyungHyun Cho, Tapani Raiko, Alexander Ilin:
Gaussian-Bernoulli deep Boltzmann machine. IJCNN 2013: 1-7 - [c25]Tommi Vatanen, Tapani Raiko, Harri Valpola, Yann LeCun:
Pushing Stochastic Gradient towards Second-Order Methods -- Backpropagation Learning with Transformations in Nonlinearities. ICLR (Workshop Poster) 2013 - 2012
- [c24]António Gusmão, Tapani Raiko:
Towards Generalizing the Success of Monte-Carlo Tree Search beyond the Game of Go. ECAI 2012: 384-389 - [c23]KyungHyun Cho, Alexander Ilin, Tapani Raiko:
Tikhonov-Type Regularization for Restricted Boltzmann Machines. ICANN (1) 2012: 81-88 - [c22]Tele Hao, Tapani Raiko, Alexander Ilin, Juha Karhunen:
Gated Boltzmann Machine in Texture Modeling. ICANN (2) 2012: 124-131 - [c21]Roberto Calandra, Tapani Raiko, Marc Peter Deisenroth, Federico Montesino-Pouzols:
Learning Deep Belief Networks from Non-stationary Streams. ICANN (2) 2012: 379-386 - [c20]Juha Raitio, Tapani Raiko, Timo Honkela:
Hybrid Bilinear and Trilinear Models for Exploratory Analysis of Three-Way Poisson Counts. ICANN (2) 2012: 475-482 - [c19]Tommi Vatanen, Mikael Kuusela, Eric Malmi, Tapani Raiko, Timo Aaltonen, Yoshikazu Nagai:
Semi-supervised detection of collective anomalies with an application in high energy particle physics. IJCNN 2012: 1-8 - [c18]Tommi Vatanen, Ilari T. Nieminen, Timo Honkela, Tapani Raiko, Krista Lagus:
Controlling Self-Organization and Handling Missing Values in SOM and GTM. WSOM 2012: 55-64 - [c17]Tapani Raiko, Harri Valpola, Yann LeCun:
Deep Learning Made Easier by Linear Transformations in Perceptrons. AISTATS 2012: 924-932 - 2011
- [c16]KyungHyun Cho, Alexander Ilin, Tapani Raiko:
Improved Learning of Gaussian-Bernoulli Restricted Boltzmann Machines. ICANN (1) 2011: 10-17 - [c15]KyungHyun Cho, Tapani Raiko, Alexander Ilin:
Enhanced Gradient and Adaptive Learning Rate for Training Restricted Boltzmann Machines. ICML 2011: 105-112 - 2010
- [c14]KyungHyun Cho, Tapani Raiko, Alexander Ilin:
Parallel tempering is efficient for learning restricted Boltzmann machines. IJCNN 2010: 1-8 - [c13]Dusan Sovilj, Tapani Raiko, Erkki Oja:
Extending Self-Organizing Maps with uncertainty information of probabilistic PCA. IJCNN 2010: 1-7 - [c12]Katariina Nyberg, Tapani Raiko, Teemu Tiinanen, Eero Hyvönen:
Document classification utilising ontologies and relations between documents. MLG@KDD 2010: 86-93 - 2009
- [c11]Jaakko Luttinen, Alexander Ilin, Tapani Raiko:
Transformations for variational factor analysis to speed up learning. ESANN 2009 - [c10]Mikael Kuusela, Tapani Raiko, Antti Honkela, Juha Karhunen:
A gradient-based algorithm competitive with variational Bayesian EM for mixture of Gaussians. IJCNN 2009: 1688-1695 - 2007
- [c9]Tapani Raiko, Alexander Ilin, Juha Karhunen:
Principal Component Analysis for Large Scale Problems with Lots of Missing Values. ECML 2007: 691-698 - [c8]Antti Honkela, Matti Tornio, Tapani Raiko, Juha Karhunen:
Natural Conjugate Gradient in Variational Inference. ICONIP (2) 2007: 305-314 - [c7]Tapani Raiko, Alexander Ilin, Juha Karhunen:
Principal Component Analysis for Sparse High-Dimensional Data. ICONIP (1) 2007: 566-575 - 2006
- [c6]Tapani Raiko, Matti Tornio, Antti Honkela, Juha Karhunen:
State Inference in Variational Bayesian Nonlinear State-Space Models. ICA 2006: 222-229 - 2005
- [c5]Tapani Raiko:
Nonlinear Relational Markov Networks with an Application to the Game of Go. ICANN (2) 2005: 989-996 - [c4]Markus Harva, Tapani Raiko, Antti Honkela, Harri Valpola, Juha Karhunen:
Bayes Blocks: An Implementation of the Variational Bayesian Building Blocks Framework. UAI 2005: 259-266 - [c3]Kristian Kersting, Tapani Raiko:
"Say EM" for Selecting Probabilistic Models for Logical Sequences. UAI 2005: 300-307 - 2003
- [c2]Kristian Kersting, Tapani Raiko, Stefan Kramer, Luc De Raedt:
Towards Discovering Structural Signatures of Protein Folds Based on Logical Hidden Markov Models. Pacific Symposium on Biocomputing 2003: 192-203 - 2002
- [c1]Kristian Kersting, Tapani Raiko, Luc De Raedt:
Logical Hidden Markov Models (Extendes abstract). Probabilistic Graphical Models 2002
Informal and Other Publications
- 2016
- [i12]Casper Kaae Sønderby, Tapani Raiko, Lars Maaløe, Søren Kaae Sønderby, Ole Winther:
How to Train Deep Variational Autoencoders and Probabilistic Ladder Networks. CoRR abs/1602.02282 (2016) - [i11]Huiling Wang, Tapani Raiko, Lasse Lensu, Tinghuai Wang, Juha Karhunen:
Semi-Supervised Domain Adaptation for Weakly Labeled Semantic Video Object Segmentation. CoRR abs/1606.02280 (2016) - [i10]Matti Lankinen, Hannes Heikinheimo, Pyry Takala, Tapani Raiko, Juha Karhunen:
A Character-Word Compositional Neural Language Model for Finnish. CoRR abs/1612.03266 (2016) - 2015
- [i9]Mathias Berglund, Tapani Raiko, Mikko Honkala, Leo Kärkkäinen, Akos Vetek, Juha Karhunen:
Bidirectional Recurrent Neural Networks as Generative Models - Reconstructing Gaps in Time Series. CoRR abs/1504.01575 (2015) - [i8]Antti Rasmus, Harri Valpola, Tapani Raiko:
Lateral Connections in Denoising Autoencoders Support Supervised Learning. CoRR abs/1504.08215 (2015) - [i7]Eric Malmi, Pyry Takala, Hannu Toivonen, Tapani Raiko, Aristides Gionis:
DopeLearning: A Computational Approach to Rap Lyrics Generation. CoRR abs/1505.04771 (2015) - [i6]Antti Rasmus, Harri Valpola, Mikko Honkala, Mathias Berglund, Tapani Raiko:
Semi-Supervised Learning with Ladder Network. CoRR abs/1507.02672 (2015) - [i5]Jelena Luketina, Mathias Berglund, Klaus Greff, Tapani Raiko:
Scalable Gradient-Based Tuning of Continuous Regularization Hyperparameters. CoRR abs/1511.06727 (2015) - 2014
- [i4]Tapani Raiko, Li Yao, Kyunghyun Cho, Yoshua Bengio:
Iterative Neural Autoregressive Distribution Estimator (NADE-k). CoRR abs/1406.1485 (2014) - 2012
- [i3]Kristian Kersting, Tapani Raiko:
'Say EM' for Selecting Probabilistic Models for Logical Sequences. CoRR abs/1207.1353 (2012) - [i2]Markus Harva, Tapani Raiko, Antti Honkela, Harri Valpola, Juha Karhunen:
Bayes Blocks: An Implementation of the Variational Bayesian Building Blocks Framework. CoRR abs/1207.1380 (2012) - 2011
- [i1]Luc De Raedt, Kristian Kersting, Tapani Raiko:
Logical Hidden Markov Models. CoRR abs/1109.2148 (2011)
Coauthor Index
aka: KyungHyun Cho
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