Tapani Raiko

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Harri ValpolaAppleVerified email at apple.com
Alexander IlinAalto UniversityVerified email at aalto.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
Mikko HonkalaNokia Bell LabsVerified email at nokia.com
Kristian KerstingProfessor of AI & Machine Learning, Co-Director of hessian.ai, TU Darmstadt, GermanyVerified email at cs.tu-darmstadt.de
Luc De Raedtprofessor, KU Leuven & guestprofessor, Örebro UniversityVerified email at cs.kuleuven.be
Ole WintherBiology, Univ of Copenhagen, Genomic Medicine, Rigshospitalet and Technical University of DenmarkVerified email at bio.ku.dk
Casper Kaae SønderbyResearch ScientistVerified 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 auckland.ac.nz
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	1228	2015
Ladder variational autoencoders CK Sønderby, T Raiko, L Maaløe, SK Sønderby, O Winther Advances in neural information processing systems 29, 3738-3746, 2016	676*	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	437	2010
Improved learning of Gaussian-Bernoulli restricted Boltzmann machines KH Cho, A Ilin, T Raiko International conference on artificial neural networks, 10-17, 2011	247	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	209	2012
Gaussian-Bernoulli Deep Boltzmann Machine KH Cho, T Raiko, A Ilin NIPS 2011 Workshop on Deep Learning and Unsupervised Feature Learning, 2011	151	2011
Logical hidden markov models K Kersting, L De Raedt, T Raiko Journal of Artificial Intelligence Research 25, 425-456, 2006	124	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	119	2010
Techniques for learning binary stochastic feedforward neural networks T Raiko, M Berglund, G Alain, L Dinh arXiv preprint arXiv:1406.2989, 2014	112	2014
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	111	2010
Scalable gradient-based tuning of continuous regularization hyperparameters J Luketina, M Berglund, K Greff, T Raiko International conference on machine learning, 2952-2960, 2016	109	2016
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	98	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	98	2007
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, 856-864, 2015	82	2015
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	81	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	79	2016
Enhanced gradient for training restricted Boltzmann machines KH Cho, T Raiko, A Ilin Neural computation 25 (3), 805-831, 2013	72	2013
Iterative neural autoregressive distribution estimator (NADE-k) T Raiko, L Yao, K Cho, Y Bengio arXiv preprint arXiv:1406.1485, 2014	56	2014
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), 1-8, 2014	54	2014
Natural Conjugate Gradient in Variational Inference A Honkela, M Tornio, T Raiko, J Karhunen Neural Information Processing, 305-314, 2008	53	2008

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