Suomeksi / This page is also available in Finnish

I'm now (again) working at ICS. See my new workpage. During 1.11.2004-30.11.2010, I worked in LCE (another lab at HUT). Have a look at my old workpage.

- Unsupervised learning (theoretical, practical and biological aspects)
- Bayesian modelling

In the past, I have applied ensemble learning for several generative models, in addition to ICA and nonlinear factor analysis for instance for sparse coding. Related Matlab packages are available at the Bayes-group homepage.

H. Valpola. (2004)

Behaviourally meaningful representations from normalisation and
context-guided denoising, AI Lab technical report, University of
Zurich.

H. Valpola and J. Särelä. (2004)

Accurate, fast and stable denoising source separation algorithms.
In Proceedings of the 5th International Conference on Independent
Component Analysis and Blind Signal Separation, ICA 2004, Granada,
Spain, pp. 65-72.

A. Honkela,
S. Harmeling,
L. Lundqvist and H. Valpola. (2004)

Using kernel PCA for initialisation of variational
Bayesian nonlinear blind source separation method.
In Proceedings of the 5th International Conference on Independent
Component Analysis and Blind Signal Separation, ICA 2004, Granada,
Spain, pp. 65-72.

A. Honkela and H. Valpola. (2004)

Variational Learning and Bits-Back Coding: an Information Theoretic
View to Bayesian Learning,
IEEE Transactions on Neural Networks,
15(4), pp. 800-810.

A. Ilin, H. Valpola and
E. Oja. (2004)

Nonlinear Dynamical Factor Analysis for State Change Detection,
IEEE Transactions on Neural Networks,
15(3), pp. 559-575.

H. Valpola, M. Harva and
J. Karhunen. (2004)

Hierarchical Models of Variance Sources,
Signal Processing, 84(2), pp. 267-282.

T. Raiko, H. Valpola,
T. Östman and J. Karhunen.
(2003)

Missing Values in
Hierarchical Nonlinear Factor Analysis. In Proceedings of
ICANN/ICONIP'03, Istanbul, Turkey, pp. 185-189.

Paper: Post Script
version,
PDF version

Poster: Post Script version,
PDF version

H. Valpola, M. Harva and
J. Karhunen. (2003)

Hierarchical Models of Variance Sources. In Proceedings of
the 4th International Symposium on Independent Component Analysis
and Blind Signal Separation, ICA 2003, Nara, Japan, pp. 83-88.

PDF version (627 kb)

H. Valpola, T. Östman and
J. Karhunen. (2003)

Nonlinear Independent Factor Analysis by Hierarchical Models. In
Proceedings of the 4th International Symposium on Independent
Component Analysis and Blind Signal Separation, ICA 2003, Nara, Japan,
pp. 257-262.

PDF version (1 Mb)

A. Honkela and H. Valpola. (2003)

On-line Variational Bayesian Learning. In Proceedings of
the 4th International Symposium on Independent Component Analysis
and Blind Signal Separation, ICA 2003, Nara, Japan, pp. 803-808.

PostScript version (160 kB),
PDF version (140 kB)

A. Ilin and H. Valpola. (2003)

On the Effect of the Form of the Posterior Approximation in
Variational Learning of ICA Models. In Proceedings of the 4th
International Symposium on Independent Component Analysis and Blind
Signal Separation, ICA 2003, Nara, Japan, pp. 915-920.

Post Script version,
PDF version

A. Honkela, H. Valpola and
J. Karhunen. (2003)

Accelerating Cyclic Update Algorithms for Parameter Estimation by
Pattern Searches,
Neural
Processing Letters, 17(2), pp. 191-203.

M. Funaro, E. Oja and H. Valpola. (2003)

Independent component analysis for artefact separation in astrophysical
images,
Neural
Networks, 16(3-4), pp. 469-478.

PDF version available at
ElsevierComputerScience

H. Valpola, E. Oja,
A. Ilin,
A. Honkela and
J. Karhunen. (2003)

Nonlinear blind source separation by variational Bayesian learning,
IEICE Trans. on Fundamentals
of Electronics, Communications, and Computer Sciences, E86-A(3),
pp. 532-541.

PDF
version

H. Valpola, A. Honkela, and
J. Karhunen. (2002)

An Ensemble Learning Approach to Nonlinear Dynamic Blind Source
Separation Using State-Space Models.
In Proceedings of the International Joint Conference on Neural
Networks (IJCNN'02), Honolulu, Hawaii, USA, pp. 460-465.

Post Script version (241 kB),
PDF version (308 kB)

H. Valpola and J. Karhunen. (2002)

An Unsupervised Ensemble Learning Method for Nonlinear Dynamic
State-Space Models, Neural
Computation 14(11), pp. 2647-2692, MIT Press.

Post Script version (654 kb) and
PDF version (937 kb)

Matlab code available at the Bayes-group
homepage.

H. Valpola, T. Raiko and
J. Karhunen. (2001)

Building Blocks for Hierarchical Latent
Variable Models.
In Proceedings of the 3rd International Conference on Independent
Component Analysis and Blind Signal Separation,
ICA 2001, San Diego,
California, USA, pp. 716-721.

Post Script version (48 kb)

A. Iline, H. Valpola and
E. Oja. (2001)
Detecting Process State Changes by Nonlinear Blind Source Separation.
In Proceedings of the 3rd International Conference on Independent
Component Analysis and Blind Signal Separation,
ICA 2001, San Diego,
California, USA, pp. 710-715.

Post Script version (152 kb) and
PDF version (465 kb)

J. Särelä, H. Valpola, R. Vigário and E. Oja. (2001) Dynamical Factor Analysis of Rhythmic Magnetoencephalographic Activity. In Proceedings of the 3rd International Conference on Independent Component Analysis and Blind Signal Separation, ICA 2001, San Diego, California, USA, pp. 457-462.

M. Funaro, E. Oja and H. Valpola. (2001) Artefact Detection in Astrophysical Image Data Using Independent Component Analysis. In Proceedings of the 3rd International Conference on Independent Component Analysis and Blind Signal Separation, ICA 2001, San Diego, California, USA, pp. 49-54.

T. Raiko and H. Valpola. (2001)
Missing Values in Nonlinear Factor Analysis. In Proceedings of the
8th International Conference on Neural Information Processing,
ICONIP 2001,
Shanghai, China, pp. 822-827.

Post Script version
(292 kb)

K. Lagus,
E. Alhoniemi
and H. Valpola. (2001)
Independent Variable Group Analysis. In Proceedings of
International Conference on Artificial Neural Networks -
ICANN 2001,
Vienna, Austria, pp. 203-210.

Post Script version
(69 kb)

H. Valpola, A. Honkela and J. Karhunen. (2001) Nonlinear Static and Dynamic Blind Source Separation Using Ensemble Learning. In Proc. of the Int. Joint Conf. on Neural Networks, IJCNN'01, Washington D.C., USA, pp. 2750-2755.

H. Valpola. (2001) Learning hidden dynamical systems. Learning workshop, Snowbird, Utah, USA.

X. Giannakopoulos and
H. Valpola. (2001) Nonlinear Dynamical Factor Analysis. In
Proceedings of The Twentieth International Workshop on Bayesian
Inference and Maximum Entropy Methods in Science and Engineering, MaxEnt 2000, Paris,
France, pp. 305-317.

Post Script version (422 kb)

H. Valpola. (2000) Bayesian
Ensemble Learning for Nonlinear Factor Analysis. PhD thesis,
Helsinki University of Technology, Espoo. Published in Acta
Polytechnica Scandinavica, Mathematics and Computing Series No. 108.

Post Script version
(1.5 Mb)

H. Valpola. (2000) Unsupervised Learning of
Nonlinear Dynamic State-Space Models, Publications in Computer and
Information Science A59, Helsinki University of Technology, Espoo,
Finland.

Post Script version (341 kb)

H. Valpola, X. Giannakopoulos, A. Honkela and J. Karhunen. (2000) Nonlinear
Independent Component Analysis Using Ensemble Learning: Experiments
and Discussion. In Proceedings of the Second International
Workshop on Independent Component Analysis and Blind Signal
Separation, ICA 2000, Helsinki, Finland,
pp. 351-356.

Post Script version
(386 kb)

H. Valpola. (2000) Nonlinear Independent
Component Analysis Using Ensemble Learning: Theory. In
Proceedings of the Second International Workshop on Independent
Component Analysis and Blind Signal Separation, ICA 2000, Helsinki, Finland, pp. 251-256.

Post Script version (63 kb)

H. Valpola and P. Pajunen. (2000) Fast Algorithms for Bayesian Independent Component
Analysis. In Proceedings of the Second International Workshop on
Independent Component Analysis and Blind Signal Separation, ICA 2000, Helsinki, Finland, pp. 233-237.

Post Script version (167 kb)

(Note: Lappalainen = nowadays Valpola.)

H. Lappalainen, A. Honkela,
X. Giannakopoulos and
J. Karhunen. (2000) Nonlinear Source Separation
Using Ensemble Learning and MLP Networks, to appear in Proceedings
of the Symposium 2000
on Adaptive Systems for Signal Processing, Communications, and Control,
AS-SPCC, Lake Louise, Alberta, Canada, October 1-4.

Post Script version (367 kb)

H. Lappalainen and A. Honkela. (2000) Bayesian Nonlinear Independent Component Analysis by
Multi-Layer Perceptrons. In M. Girolami, editor, *Advances in Independent
Component Analysis*, pp. 93-121, Springer-Verlag.

Post Script version (420 kb)

H. Lappalainen and J. W. Miskin. (2000)
Ensemble Learning. In M. Girolami, editor, *Advances in
Independent Component Analysis*, pp. 75-92, Springer-Verlag.

Post Script version (127 kb)

H. Lappalainen. (1999) Fast Fixed-Point Algorithms for Bayesian
Blind Source Separation, Publications in Computer and Information
Science A56, Helsinki University of Technology, Espoo, Finland.

Post Script version (277 kb)

H. Lappalainen,
X. Giannakopoulos and
J. Karhunen. (1999)
Nonlinear Blind Source Separation Using Ensemble Learning.
Presented at
Snowbird Learning Workshop.

Post Script version (38 kb)

H. Lappalainen and
X. Giannakopoulos. (1999)
Multi-Layer Perceptrons as Nonlinear Generative
Models for Unsupervised Learning: a Bayesian Treatment. In Proceedings
of the ICANN'99, Edinburgh,
Scotland, pp. 19-24.

Post Script version (126 kb)

H. Lappalainen. (1999) Ensemble Learning for
Independent Component Analysis. In Proceedings of the First International
Workshop on Independent Component Analysis and Blind Signal Separation,
ICA'99, Aussois, France, pp. 7-12.

Post Script version (90 kb)

H. Lappalainen. (1998)
Using an MDL-based cost function with neural
networks. In Proceedings of the IJCNN'98,
Anchorage, pp. 2384-2389.

Comments,
Post Script version (63 kb)

T. Kohonen,
S. Kaski, and
H. Lappalainen. (1997)
Self-organized formation of various invariant-feature filters in the
Adaptive-Subspace SOM. Neural Computation, 9(6), pp. 1321-1344.

Post Script version (246 kb)

T. Kohonen, S. Kaski, H. Lappalainen, and J. Salojärvi. (1997) The Adaptive-Subspace Self-Organizing Map (ASSOM). Workshop on Self-Organizing Maps (WSOM'97), Espoo, Finland, pp. 191-196.

H. Lappalainen. (1996)
Soft multiple winners for sparse feature extraction.
In Proceedings of the ICNN'96,
Washington, pp. 207-210.

Post Script version (196 kb)

S. Laine, H. Lappalainen, and
S.-L. Jämsä-Jounela. (1995)
On-line determination of ore type using cluster analysis
and neural networks. Minerals Engineering, 8(6), pp. 637-648.

harri.valpola at tkk.fi

Monday, 17-Jan-2011 15:56:32 EET

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