Tapani Raiko
Tapani Raiko
Principal research scientist, Apple
Verified email at - Homepage
Cited by
Cited by
Semi-supervised learning with ladder networks
A Rasmus, M Berglund, M Honkala, H Valpola, T Raiko
Advances in neural information processing systems 28, 2015
Deep Learning Made Easier by Linear Transformations in Perceptrons
T Raiko, H Valpola, Y LeCun
Conference on Artificial Intelligence and Statistics (AISTATS 2012), 2012
Pushing stochastic gradient towards second-order methods–backpropagation learning with transformations in nonlinearities
T Vatanen, T Raiko, H Valpola, Y LeCun
Neural Information Processing: 20th International Conference, ICONIP 2013 …, 2013
Ladder variational autoencoders
CK Sønderby, T Raiko, L Maaløe, SK Sønderby, O Winther
Advances in neural information processing systems 29, 2016
Practical approaches to principal component analysis in the presence of missing values
A Ilin, T Raiko
The Journal of Machine Learning Research 11, 1957-2000, 2010
Improved learning of Gaussian-Bernoulli restricted Boltzmann machines
KH Cho, A Ilin, T Raiko
Artificial Neural Networks and Machine Learning–ICANN 2011: 21st …, 2011
Scalable gradient-based tuning of continuous regularization hyperparameters
J Luketina, M Berglund, K Greff, T Raiko
International conference on machine learning, 2952-2960, 2016
Gaussian-Bernoulli Deep Boltzmann Machine
KH Cho, T Raiko, A Ilin
NIPS 2011 Workshop on Deep Learning and Unsupervised Feature Learning, 2011
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 28, 2015
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
Approximate Riemannian conjugate gradient learning for fixed-form variational Bayes
A Honkela, T Raiko, M Kuusela, M Tornio, J Karhunen
The Journal of Machine Learning Research 11, 3235-3268, 2010
Techniques for learning binary stochastic feedforward neural networks
T Raiko, M Berglund, G Alain, L Dinh
arXiv preprint arXiv:1406.2989, 2014
Logical hidden markov models
K Kersting, L De Raedt, T Raiko
Journal of Artificial Intelligence Research 25, 425-456, 2006
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
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
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
Dopelearning: A computational approach to rap lyrics generation
E Malmi, P Takala, H Toivonen, T Raiko, A Gionis
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge …, 2016
Zenrobotics recycler–robotic sorting using machine learning
TJ Lukka, T Tossavainen, JV Kujala, T Raiko
Proceedings of the International Conference on Sensor-Based Sorting (SBS), 1, 2014
Unsupervised deep learning: A short review
J Karhunen, T Raiko, KH Cho
Advances in independent component analysis and learning machines, 125-142, 2015
Enhanced gradient for training restricted Boltzmann machines
KH Cho, T Raiko, A Ilin
Neural computation 25 (3), 805-831, 2013
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