Tapani Raiko

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Harri ValpolaSystem 2 AIVerified email at system2ai.com
Juha KarhunenProfessori, Aalto YliopistoVerified email at aalto.fi
Alexander IlinAalto UniversityVerified email at aalto.fi
Kyunghyun ChoNew York University, GenentechVerified email at nyu.edu
Tommi VatanenAssociate Professor, University of HelsinkiVerified email at helsinki.fi
Antti HonkelaProfessor, University of HelsinkiVerified email at helsinki.fi
Yann LeCunChief AI Scientist at Facebook & Silver Professor at the Courant Institute, New York UniversityVerified email at cs.nyu.edu
Mikko HonkalaNokia Bell LabsVerified email at nokia.com
Kristian KerstingProfessor of AI & ML, TU Darmstadt, Co-Director hessian.ai, DFKI, Germany, CLAIRE & ELLISVerified email at cs.tu-darmstadt.de
Ole WintherBiology, Univ of Copenhagen, Genomic Medicine, Rigshospitalet and Technical University of DenmarkVerified email at bio.ku.dk
Casper Kaae SønderbyFounder@twig.energy, ML/DL Research ScientistVerified email at twig.energy
Søren Kaae SønderbyVerified email at twig.energy
Lars MaaløeCo-Founder & CTO @ Corti | Adj. Assoc. Professor of Machine Learning @ DTUVerified email at corti.ai
Luc De Raedtprofessor, KU Leuven & guestprofessor, Örebro UniversityVerified email at cs.kuleuven.be
Mikael KuuselaCarnegie Mellon UniversityVerified email at andrew.cmu.edu
Eric MalmiResearch Scientist, Google ZürichVerified email at google.com
Timo HonkelaUniversity of HelsinkiVerified email at helsinki.fi
Leo KärkkäinenUniversity of Eastern FinlandVerified email at uef.fi
Pyry TakalaAalto UniversityVerified email at aalto.fi
Jaakko LuttinenAalto 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, M Berglund, M Honkala, H Valpola, T Raiko Advances in neural information processing systems 28, 2015	1902	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	1482	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	1262	2013
Ladder variational autoencoders CK Sønderby, T Raiko, L Maaløe, SK Sønderby, O Winther Advances in neural information processing systems 29, 2016	1097*	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	550	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	274	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	200	2016
Gaussian-Bernoulli Deep Boltzmann Machine KH Cho, T Raiko, A Ilin NIPS 2011 Workshop on Deep Learning and Unsupervised Feature Learning, 2011	190	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	143	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	138	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	134	2010
Techniques for learning binary stochastic feedforward neural networks T Raiko, M Berglund, G Alain, L Dinh arXiv preprint arXiv:1406.2989, 2014	133	2014
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	129	2015
Logical hidden markov models K Kersting, L De Raedt, T Raiko Journal of Artificial Intelligence Research 25, 425-456, 2006	127	2006
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	111	2016
Unsupervised deep learning: A short review J Karhunen, T Raiko, KH Cho Advances in independent component analysis and learning machines, 125-142, 2015	110	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	109	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	107	2007
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	105	2014
Enhanced gradient for training restricted Boltzmann machines KH Cho, T Raiko, A Ilin Neural computation 25 (3), 805-831, 2013	75	2013

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