Harri Valpola 个人学术档案

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	总计	2019 年至今
引用	13191	7803
h 指数	30	16
i10 指数	62	23

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Tapani RaikoPrincipal research scientist, Apple在 apple.com 的电子邮件经过验证
Juha KarhunenProfessori, Aalto Yliopisto在 aalto.fi 的电子邮件经过验证
Antti HonkelaProfessor, University of Helsinki在 helsinki.fi 的电子邮件经过验证
Erkki OjaProfessor of Computer Science and Engineering, Aalto University在 aalto.fi 的电子邮件经过验证
Alexander IlinAalto University在 aalto.fi 的电子邮件经过验证
Yann LeCunChief AI Scientist at Facebook & Silver Professor at the Courant Institute, New York University在 cs.nyu.edu 的电子邮件经过验证
Antti TarvainenML engineer在 iprally.com 的电子邮件经过验证
Mikko HonkalaNokia Bell Labs在 nokia.com 的电子邮件经过验证
Tommi VatanenAssociate Professor, University of Helsinki在 helsinki.fi 的电子邮件经过验证
Samuel KaskiProfessor, Aalto University and University of Manchester在 aalto.fi 的电子邮件经过验证
Klaus GreffResearch Scientist at Google Brain在 usi.ch 的电子邮件经过验证
Rinu BoneyMachine Learning Engineer at Apple在 apple.com 的电子邮件经过验证
Juergen SchmidhuberKing Abdullah University of Science and Technology / The Swiss AI Lab, IDSIA / University of Lugano在 kaust.edu.sa 的电子邮件经过验证
Krista LagusProfessor of Digital Social Science, University of Helsinki在 helsinki.fi 的电子邮件经过验证
Isabeau Prémont-SchwarzElement AI在 elementai.com 的电子邮件经过验证
Kaj Sotala在 foundational-research.org 的电子邮件经过验证
Esa AlhoniemiSenior Data Scientist, Inoi Oy在 inoi.fi 的电子邮件经过验证
Norman Di PaloImperial College London在 deepmind.com 的电子邮件经过验证
Stefan HarmelingProfessor at Technische Universität Dortmund在 tu-dortmund.de 的电子邮件经过验证
Sirkka-Liisa Jamsa-JounelaAalto University在 saunalahti.fi 的电子邮件经过验证

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Harri Valpola

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Machine learning


标题按引用次数排序按年份排序按标题排序	引用次数引用次数	年份
Weight-averaged consistency targets improve semi-supervised deep learning results A Tarvainen, H Valpola arXiv preprint arXiv:1703.01780, 2017	5515*	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	5513	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	5513	2017
Semi-Supervised Learning with Ladder Networks A Rasmus, H Valpola, M Honkala, M Berglund, T Raiko arXiv preprint arXiv:1507.02672, 2015	1871	2015
Deep learning made easier by linear transformations in perceptrons T Raiko, H Valpola, Y LeCun Artificial Intelligence and Statistics, 924-932, 2012	1481	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
Ensemble learning H Lappalainen, J Miskin Advances in Independent Component Analysis, 75-92, 2000	343*	2000
Bayesian non-linear independent component analysis by multi-layer perceptrons H Lappalainen, A Honkela Advances in independent component analysis, 93-121, 2000	268*	2000
Denoising Source Separation. J Särelä, H Valpola Journal of Machine Learning Research 6 (3), 2005	243	2005
From neural PCA to deep unsupervised learning H Valpola Advances in Independent Component Analysis and Learning Machines, 143-171, 2015	237	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	225	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	170	2016
Method for the selection of physical objects in a robot system H Valpola, T Lukka US Patent 9,050,719, 2015	91	2015
Method for the selection of physical objects in a robot system H Valpola, T Lukka US Patent 9,050,719, 2015	91	2015
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	90	2004
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	71	2004
Hierarchical models of variance sources H Valpola, M Harva, J Karhunen Signal Processing 84 (2), 267-282, 2004	62	2004
On-line variational Bayesian learning A Honkela, H Valpola 4th International Symposium on Independent Component Analysis and Blind …, 2003	60	2003

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