Harri Valpola
Harri Valpola
The Curious AI Company
Verified email at cai.fi - Homepage
Title
Cited by
Cited by
Year
Semi-Supervised Learning with Ladder Networks
A Rasmus, H Valpola, M Honkala, M Berglund, T Raiko
arXiv preprint arXiv:1507.02672, 2015
9082015
Weight-averaged consistency targets improve semi-supervised deep learning results
A Tarvainen, H Valpola
arXiv preprint arXiv:1703.01780, 2017
733*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
7332017
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
7322017
Ensemble learning
H Lappalainen, J Miskin
Advances in Independent Component Analysis, 75-92, 2000
319*2000
Bayesian non-linear independent component analysis by multi-layer perceptrons
H Lappalainen, A Honkela
Advances in independent component analysis, 93-121, 2000
248*2000
Denoising Source Separation.
J Särelä, H Valpola
Journal of Machine Learning Research 6 (3), 2005
2022005
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
1951997
Deep learning made easier by linear transformations in perceptrons
T Raiko, H Valpola, Y LeCun
Artificial Intelligence and Statistics, 924-932, 2012
1822012
An unsupervised ensemble learning method for nonlinear dynamic state-space models
H Valpola, J Karhunen
Neural computation 14 (11), 2647-2692, 2002
1672002
From neural PCA to deep unsupervised learning
H Valpola
Advances in Independent Component Analysis and Learning Machines, 143-171, 2015
1472015
Ensemble learning for independent component analysis
H Lappalainen
Proc. Int. Workshop on Independent Component Analysis and Signal Separation …, 1999
751999
Nonlinear independent component analysis using ensemble learning: Experiments and discussion
H Valpola, X Giannakopoulos, A Honkela, J Karhunen
Proc. Int. Workshop on Independent Component Analysis and Blind Signal …, 2000
692000
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
622004
Tagger: Deep unsupervised perceptual grouping
K Greff, A Rasmus, M Berglund, T Hao, H Valpola, J Schmidhuber
Advances in Neural Information Processing Systems, 4484-4492, 2016
612016
Unsupervised variational Bayesian learning of nonlinear models
A Honkela, H Valpola
Advances in neural information processing systems, 593-600, 2005
602005
Hierarchical models of variance sources
H Valpola, M Harva, J Karhunen
Signal Processing 84 (2), 267-282, 2004
582004
Bayesian ensemble learning for nonlinear factor analysis
H Valpola
Finnish Academy of Technology, 2000
552000
On-line variational Bayesian learning
A Honkela, H Valpola
4th International Symposium on Independent Component Analysis and Blind …, 2003
492003
Building blocks for variational Bayesian learning of latent variable models
T Raiko, H Valpola, M Harva, J Karhunen
Journal of Machine Learning Research 8 (Jan), 155-201, 2007
462007
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