Tapani Raiko
Tapani Raiko
Principal research scientist, Apple
Verified email at apple.com - Homepage
TitleCited byYear
Semi-supervised learning with ladder networks
A Rasmus, M Berglund, M Honkala, H Valpola, T Raiko
Advances in neural information processing systems, 3546-3554, 2015
6192015
Practical approaches to principal component analysis in the presence of missing values
A Ilin, T Raiko
Journal of Machine Learning Research 11 (Jul), 1957-2000, 2010
3152010
Ladder variational autoencoders
CK Sønderby, T Raiko, L Maaløe, SK Sønderby, O Winther
Advances in neural information processing systems, 3738-3746, 2016
307*2016
Improved learning of Gaussian-Bernoulli restricted Boltzmann machines
KH Cho, A Ilin, T Raiko
International conference on artificial neural networks, 10-17, 2011
1902011
Deep Learning Made Easier by Linear Transformations in Perceptrons
T Raiko, H Valpola, Y LeCun
Conference on Artificial Intelligence and Statistics (AISTATS 2012), 2012
1502012
Logical hidden markov models
K Kersting, L De Raedt, T Raiko
Journal of Artificial Intelligence Research 25, 425-456, 2006
1132006
Gaussian-Bernoulli Deep Boltzmann Machine
KH Cho, T Raiko, A Ilin
NIPS 2011 Workshop on Deep Learning and Unsupervised Feature Learning, 2011
972011
Approximate Riemannian conjugate gradient learning for fixed-form variational Bayes
A Honkela, T Raiko, M Kuusela, M Tornio, J Karhunen
Journal of Machine Learning Research 11 (Nov), 3235-3268, 2010
892010
Parallel tempering is efficient for learning restricted Boltzmann machines
KH Cho, T Raiko, A Ilin
The 2010 international joint conference on neural networks (ijcnn), 1-8, 2010
862010
Principal component analysis for large scale problems with lots of missing values
T Raiko, A Ilin, J Karhunen
European Conference on Machine Learning, 691-698, 2007
852007
Enhanced gradient and adaptive learning rate for training restricted Boltzmann machines
KH Cho, T Raiko, A Ilin
Proceedings of the 28th International Conference on Machine Learning (ICML …, 2011
792011
Techniques for learning binary stochastic feedforward neural networks
T Raiko, M Berglund, G Alain, L Dinh
arXiv preprint arXiv:1406.2989, 2014
732014
Enhanced gradient for training restricted Boltzmann machines
KH Cho, T Raiko, A Ilin
Neural computation 25 (3), 805-831, 2013
642013
Self-organization and missing values in SOM and GTM
T Vatanen, M Osmala, T Raiko, K Lagus, M Sysi-Aho, M Orešič, T Honkela, ...
Neurocomputing 147, 60-70, 2015
512015
Scalable gradient-based tuning of continuous regularization hyperparameters
J Luketina, M Berglund, K Greff, T Raiko
International conference on machine learning, 2952-2960, 2016
472016
Towards discovering structural signatures of protein folds based on logical hidden markov models
K Kersting, T Raiko, S Kramer, L De Raedt
Biocomputing 2003, 192-203, 2002
452002
Bidirectional recurrent neural networks as generative models
M Berglund, T Raiko, M Honkala, L Kärkkäinen, A Vetek, JT Karhunen
Advances in Neural Information Processing Systems, 856-864, 2015
442015
Iterative neural autoregressive distribution estimator nade-k
T Raiko, Y Li, K Cho, Y Bengio
Advances in neural information processing systems, 325-333, 2014
432014
Building blocks for variational Bayesian learning of latent variable models
T Raiko, H Valpola, M Harva, J Karhunen
Journal of Machine Learning Research 8 (Jan), 155-201, 2007
422007
Natural Conjugate Gradient in Variational Inference
A Honkela, M Tornio, T Raiko, J Karhunen
Neural Information Processing, 305-314, 2008
382008
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Articles 1–20