Harri Valpola

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Tapani RaikoPrincipal research scientist, AppleDirección de correo verificada de apple.com
Juha KarhunenProfessori, Aalto YliopistoDirección de correo verificada de aalto.fi
Antti HonkelaProfessor, University of HelsinkiDirección de correo verificada de helsinki.fi
Erkki OjaProfessor of Computer Science and Engineering, Aalto UniversityDirección de correo verificada de aalto.fi
Alexander IlinAalto UniversityDirección de correo verificada de aalto.fi
Yann LeCunChief AI Scientist at Facebook & Silver Professor at the Courant Institute, New York UniversityDirección de correo verificada de cs.nyu.edu
Mikko HonkalaNokia Bell LabsDirección de correo verificada de nokia.com
Tommi VatanenAssociate Professor, University of HelsinkiDirección de correo verificada de helsinki.fi
Samuel KaskiProfessor, Aalto University and University of ManchesterDirección de correo verificada de aalto.fi
Klaus GreffResearch Scientist at Google BrainDirección de correo verificada de usi.ch
Rinu BoneyMachine Learning Engineer at AppleDirección de correo verificada de apple.com
Juergen SchmidhuberKing Abdullah University of Science and Technology / The Swiss AI Lab, IDSIA / University of LuganoDirección de correo verificada de kaust.edu.sa
Krista LagusProfessor of Digital Social Science, University of HelsinkiDirección de correo verificada de helsinki.fi
Isabeau Prémont-SchwarzElement AIDirección de correo verificada de elementai.com
Kaj SotalaDirección de correo verificada de foundational-research.org
Esa AlhoniemiSenior Data Scientist, Inoi OyDirección de correo verificada de inoi.fi
Norman Di PaloGoogle DeepMindDirección de correo verificada de deepmind.com
Stefan HarmelingProfessor at Technische Universität DortmundDirección de correo verificada de tu-dortmund.de
Sirkka-Liisa Jamsa-JounelaAalto UniversityDirección de correo verificada de saunalahti.fi
Paul WagnerAalto UniversityDirección de correo verificada de aalto.fi

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Harri Valpola

System 2 AI

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Machine learning


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
Weight-averaged consistency targets improve semi-supervised deep learning results A Tarvainen, H Valpola arXiv preprint arXiv:1703.01780, 2017	6373*	2017
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results A Tarvainen, H Valpola Advances in neural information processing systems, 1195-1204, 2017	6370	2017
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results A Tarvainen, H Valpola Advances in neural information processing systems, 1195-1204, 2017	6370	2017
Semi-Supervised Learning with Ladder Networks A Rasmus, H Valpola, M Honkala, M Berglund, T Raiko arXiv preprint arXiv:1507.02672, 2015	2001	2015
Deep learning made easier by linear transformations in perceptrons T Raiko, H Valpola, Y LeCun Artificial Intelligence and Statistics, 924-932, 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
Ensemble learning H Lappalainen, J Miskin Advances in Independent Component Analysis, 75-92, 2000	347*	2000
Bayesian non-linear independent component analysis by multi-layer perceptrons H Lappalainen, A Honkela Advances in independent component analysis, 93-121, 2000	271*	2000
Denoising Source Separation. J Särelä, H Valpola Journal of Machine Learning Research 6 (3), 2005	250	2005
From neural PCA to deep unsupervised learning H Valpola Advances in Independent Component Analysis and Learning Machines, 143-171, 2015	242	2015
Self-organized formation of various invariant-feature filters in the adaptive-subspace SOM T Kohonen, S Kaski, H Lappalainen Neural computation 9 (6), 1321-1344, 1997	230	1997
An unsupervised ensemble learning method for nonlinear dynamic state-space models H Valpola, J Karhunen Neural computation 14 (11), 2647-2692, 2002	178	2002
Tagger: Deep unsupervised perceptual grouping K Greff, A Rasmus, M Berglund, T Hao, H Valpola, J Schmidhuber Advances in Neural Information Processing Systems 29, 4484-4492, 2016	177	2016
Variational learning and bits-back coding: an information-theoretic view to Bayesian learning A Honkela, H Valpola IEEE Transactions on Neural Networks 15 (4), 800-810, 2004	95	2004
Method for the selection of physical objects in a robot system H Valpola, T Lukka US Patent 9,050,719, 2015	94	2015
Method for the selection of physical objects in a robot system H Valpola, T Lukka US Patent 9,050,719, 2015	94	2015
Ensemble learning for independent component analysis H Lappalainen Proc. Int. Workshop on Independent Component Analysis and Signal Separation …, 1999	76	1999
Unsupervised variational Bayesian learning of nonlinear models A Honkela, H Valpola Advances in neural information processing systems 17, 593-600, 2004	72	2004
Hierarchical models of variance sources H Valpola, M Harva, J Karhunen Signal Processing 84 (2), 267-282, 2004	64	2004
On-line variational Bayesian learning A Honkela, H Valpola 4th International Symposium on Independent Component Analysis and Blind …, 2003	63	2003

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