Tapani Raiko

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	All	Since 2016
Citations	4382	3138
h-index	28	21
i10-index	53	34

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Alexander IlinAalto UniversityVerified email at aalto.fi
Harri ValpolaThe Curious AI CompanyVerified email at cai.fi
Juha KarhunenProfessori, Aalto YliopistoVerified email at aalto.fi
Kyunghyun ChoNew York UniversityVerified email at nyu.edu
Antti HonkelaAssociate Professor, University of HelsinkiVerified email at helsinki.fi
Luc De Raedtprofessor, KU LeuvenVerified email at cs.kuleuven.be
Kristian KerstingProfessor of Artificial Intelligence and Machine Learning, hessian.ai, TU DarmstadtVerified email at cs.tu-darmstadt.de
Mikko HonkalaNokia Bell LabsVerified email at nokia.com
Ole WintherBiology, Univ of Copenhagen, Genomic Medicine, Rigshospitalet and Technical University of DenmarkVerified email at bio.ku.dk
Casper Kaae SønderbyResearch Scientist, Google BrainVerified email at google.com
Søren Kaae SønderbyVerified email at bio.ku.dk
Lars MaaløeCo-Founder & CTO at Corti | Adj. Assoc. Professor of Machine LearningVerified email at corti.ai
Tommi VatanenSenior Research fellow, Liggins Institute, University of AucklandVerified email at broadinstitute.org
Mikael KuuselaCarnegie Mellon UniversityVerified email at andrew.cmu.edu
Yann LeCunChief AI Scientist at Facebook & Silver Professor at the Courant Institute, New York UniversityVerified email at cs.nyu.edu
Eric MalmiResearch Scientist, Google ZürichVerified email at google.com
Timo HonkelaUniversity of HelsinkiVerified email at helsinki.fi
Krista LagusResearcher at University of HelsinkiVerified email at helsinki.fi
Leo KärkkäinenNokia Bell LabsVerified email at aalto.fi
Pyry TakalaAalto UniversityVerified email at aalto.fi

Tapani Raiko

Principal research scientist, Apple

Verified email at apple.com - Homepage

deep learning machine learning reinforcement learning robotics planning


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Semi-supervised learning with ladder networks A Rasmus, H Valpola, M Honkala, M Berglund, T Raiko arXiv preprint arXiv:1507.02672, 2015	972	2015
Ladder variational autoencoders CK Sønderby, T Raiko, L Maaløe, SK Sønderby, O Winther arXiv preprint arXiv:1602.02282, 2016	543*	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	384	2010
Improved learning of Gaussian-Bernoulli restricted Boltzmann machines KH Cho, A Ilin, T Raiko International conference on artificial neural networks, 10-17, 2011	231	2011
Deep Learning Made Easier by Linear Transformations in Perceptrons T Raiko, H Valpola, Y LeCun Conference on Artificial Intelligence and Statistics (AISTATS 2012), 2012	187	2012
Gaussian-Bernoulli Deep Boltzmann Machine KH Cho, T Raiko, A Ilin NIPS 2011 Workshop on Deep Learning and Unsupervised Feature Learning, 2011	130	2011
Logical hidden markov models K Kersting, L De Raedt, T Raiko Journal of Artificial Intelligence Research 25, 425-456, 2006	121	2006
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	106	2010
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	104	2010
Techniques for learning binary stochastic feedforward neural networks T Raiko, M Berglund, G Alain, L Dinh arXiv preprint arXiv:1406.2989, 2014	102	2014
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	95	2007
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	91	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	78	2016
Enhanced gradient for training restricted Boltzmann machines KH Cho, T Raiko, A Ilin Neural computation 25 (3), 805-831, 2013	71	2013
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	67	2015
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	59	2016
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	59	2015
Iterative neural autoregressive distribution estimator (NADE-k) T Raiko, L Yao, K Cho, Y Bengio arXiv preprint arXiv:1406.1485, 2014	51	2014
Building Blocks for Variational Bayesian Learning of Latent Variable Models. T Raiko, H Valpola, M Harva, J Karhunen Journal of Machine Learning Research 8 (1), 2007	47	2007
Natural Conjugate Gradient in Variational Inference A Honkela, M Tornio, T Raiko, J Karhunen Neural Information Processing, 305-314, 2008	46	2008

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