Obserwuj
Harri Valpola
Harri Valpola
System 2 AI Ltd
Zweryfikowany adres z system2ai.com - Strona główna
Tytuł
Cytowane przez
Cytowane przez
Rok
Weight-averaged consistency targets improve semi-supervised deep learning results
A Tarvainen, H Valpola
arXiv preprint arXiv:1703.01780, 2017
4262*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
42552017
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
42552017
Semi-Supervised Learning with Ladder Networks
A Rasmus, H Valpola, M Honkala, M Berglund, T Raiko
arXiv preprint arXiv:1507.02672, 2015
16922015
Deep learning made easier by linear transformations in perceptrons
T Raiko, H Valpola, Y LeCun
Artificial Intelligence and Statistics, 924-932, 2012
14532012
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
12552013
Ensemble learning
H Lappalainen, J Miskin
Advances in Independent Component Analysis, 75-92, 2000
342*2000
Bayesian non-linear independent component analysis by multi-layer perceptrons
H Lappalainen, A Honkela
Advances in independent component analysis, 93-121, 2000
265*2000
Denoising Source Separation.
J Särelä, H Valpola
Journal of Machine Learning Research 6 (3), 2005
2302005
From neural PCA to deep unsupervised learning
H Valpola
Advances in Independent Component Analysis and Learning Machines, 143-171, 2015
2222015
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
2191997
An unsupervised ensemble learning method for nonlinear dynamic state-space models
H Valpola, J Karhunen
Neural computation 14 (11), 2647-2692, 2002
1752002
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
1572016
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
862004
Method for the selection of physical objects in a robot system
H Valpola, T Lukka
US Patent 9,050,719, 2015
832015
Method for the selection of physical objects in a robot system
H Valpola, T Lukka
US Patent 9,050,719, 2015
832015
Ensemble learning for independent component analysis
H Lappalainen
Proc. Int. Workshop on Independent Component Analysis and Signal Separation …, 1999
771999
Unsupervised variational Bayesian learning of nonlinear models
A Honkela, H Valpola
Advances in neural information processing systems 17, 593-600, 2004
682004
Hierarchical models of variance sources
H Valpola, M Harva, J Karhunen
Signal Processing 84 (2), 267-282, 2004
612004
On-line variational Bayesian learning
A Honkela, H Valpola
4th International Symposium on Independent Component Analysis and Blind …, 2003
592003
Nie można teraz wykonać tej operacji. Spróbuj ponownie później.
Prace 1–20