Tapani Raiko

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Harri ValpolaSystem 2 AIEmail yang diverifikasi di system2ai.com
Juha KarhunenProfessori, Aalto YliopistoEmail yang diverifikasi di aalto.fi
Alexander IlinAalto UniversityEmail yang diverifikasi di aalto.fi
Kyunghyun ChoNew York University, GenentechEmail yang diverifikasi di nyu.edu
Tommi VatanenAssociate Professor, University of HelsinkiEmail yang diverifikasi di helsinki.fi
Antti HonkelaProfessor, University of HelsinkiEmail yang diverifikasi di helsinki.fi
Yann LeCunChief AI Scientist at Facebook & Silver Professor at the Courant Institute, New York UniversityEmail yang diverifikasi di cs.nyu.edu
Mikko HonkalaNokia Bell LabsEmail yang diverifikasi di nokia.com
Kristian KerstingProfessor of AI & ML, Technical University of Darmstadt, Co-Director hessian.ai, DFKI, CAIRNE, ELLISEmail yang diverifikasi di cs.tu-darmstadt.de
Ole WintherBiology, Univ of Copenhagen, Genomic Medicine, Rigshospitalet and Technical University of DenmarkEmail yang diverifikasi di bio.ku.dk
Casper Kaae Sønderby[email protected], ML/DL Research ScientistEmail yang diverifikasi di twig.energy
Søren Kaae SønderbyEmail yang diverifikasi di twig.energy
Lars MaaløeCo-Founder & CTO @ Corti | Adj. Assoc. Professor of Machine Learning @ DTUEmail yang diverifikasi di corti.ai
Luc De Raedtprofessor, KU Leuven & guestprofessor, Örebro UniversityEmail yang diverifikasi di cs.kuleuven.be
Mikael KuuselaCarnegie Mellon UniversityEmail yang diverifikasi di andrew.cmu.edu
Eric MalmiStaff Research Scientist, Google DeepMindEmail yang diverifikasi di google.com
Timo HonkelaUniversity of HelsinkiEmail yang diverifikasi di helsinki.fi
Leo KärkkäinenUniversity of Eastern FinlandEmail yang diverifikasi di uef.fi
Pyry TakalaAalto UniversityEmail yang diverifikasi di aalto.fi
Jaakko LuttinenAalto UniversityEmail yang diverifikasi di aalto.fi

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Tapani Raiko

Principal research scientist, Apple

Email yang diverifikasi di apple.com - Beranda

deep learning machine learning reinforcement learning robotics planning


Judul Urutkan menurut kutipan Urutkan menurut tahun Urutkan menurut judul	Dikutip oleh Dikutip oleh	Tahun
Semi-supervised learning with ladder networks A Rasmus, M Berglund, M Honkala, H Valpola, T Raiko Advances in neural information processing systems 28, 2015	2011	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	1490	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	1264	2013
Ladder variational autoencoders CK Sønderby, T Raiko, L Maaløe, SK Sønderby, O Winther Advances in neural information processing systems 29, 2016	1155*	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	563	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	279	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	210	2016
Gaussian-Bernoulli Deep Boltzmann Machine KH Cho, T Raiko, A Ilin NIPS 2011 Workshop on Deep Learning and Unsupervised Feature Learning, 2011	193	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	148	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	141	2010
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	137	2015
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	137	2010
Techniques for learning binary stochastic feedforward neural networks T Raiko, M Berglund, G Alain, L Dinh arXiv preprint arXiv:1406.2989, 2014	134	2014
Logical hidden markov models K Kersting, L De Raedt, T Raiko Journal of Artificial Intelligence Research 25, 425-456, 2006	128	2006
Unsupervised deep learning: A short review J Karhunen, T Raiko, KH Cho Advances in independent component analysis and learning machines, 125-142, 2015	127	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	117	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	113	2014
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
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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