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- Anderson and Moore(1979)
-
B. Anderson and J. Moore.
Optimal Filtering.
Prentice-Hall, Englewood Cliffs, NJ, 1979.
- Attias(2001)
-
H. Attias.
ICA, graphical models and variational methods.
In S. Roberts and R. Everson, editors, Independent Component
Analysis: Principles and Practice, pages 95-112. Cambridge University
Press, 2001.
- Attias(1999)
-
H. Attias.
Independent factor analysis.
Neural Computation, 11(4):803-851, 1999.
- Attias(2000)
-
H. Attias.
A variational Bayesian framework for graphical models.
In T. Lee et al., editor, Advances in Neural Information
Processing Systems 12, pages 209-215, Cambridge, 2000. MIT Press.
- Barber and Bishop(1998)
-
D. Barber and C. Bishop.
Ensemble learning in Bayesian neural networks.
In C. Bishop, editor, Neural Networks and Machine Learning,
pages 215-237. Springer, Berlin, 1998.
- Beal(2003)
-
M. Beal.
Variational Algorithms for Approximate Bayesian Inference.
PhD thesis, University of London, UK, 2003.
- Beal and Ghahramani(2003)
-
M. Beal and Z. Ghahramani.
The variational Bayesian EM algorithm for incomplete data: with
application to scoring graphical model structures.
Bayesian Statistics 7, pages 453-464, 2003.
- Bishop(1995)
-
C. Bishop.
Neural Networks for Pattern Recognition.
Clarendon Press, 1995.
- Bishop(1999)
-
C. Bishop.
Latent variable models.
In M. Jordan, editor, Learning in Graphical Models, pages
371-403. The MIT Press, Cambridge, MA, USA, 1999.
- Cardoso(1998)
-
J.-F. Cardoso.
Multidimensional independent component analysis.
In Proc. IEEE Int. Conf. on Acoustics, Speech and Signal
Processing (ICASSP'98), pages 1941-1944, Seattle, Washington, USA, May
12-15, 1998.
- Chan et al.(2001)Chan, Lee, and Sejnowski
-
K. Chan, T.-W. Lee, and T. Sejnowski.
Variational learning of clusters of undercomplete nonsymmetric
independent components.
In Proc. Int. Conf. on Independent Component Analysis and
Signal Separation (ICA2001), pages 492-497, San Diego, USA, 2001.
- Choudrey et al.(2000)Choudrey, Penny, and Roberts
-
R. Choudrey, W. Penny, and S. Roberts.
An ensemble learning approach to independent component analysis.
In Proc. of the IEEE Workshop on Neural Networks for Signal
Processing, Sydney, Australia, December 2000, pages 435-444. IEEE Press,
2000.
- Dayan and Zemel(1995)
-
P. Dayan and R. Zemel.
Competition and multiple cause models.
Neural Computation, 7(3):565-579, 1995.
- Doucet et al.(2001)Doucet, de Freitas, and Gordon
-
A. Doucet, N. de Freitas, and N. J. Gordon.
Sequential Monte Carlo Methods in Practice.
Springer Verlag, 2001.
- Frey and Hinton(1999)
-
B. J. Frey and G. E. Hinton.
Variational learning in nonlinear Gaussian belief networks.
Neural Computation, 11(1):193-214, 1999.
- Gelman et al.(1995)Gelman, Carlin, Stern, and Rubin
-
A. Gelman, J. Carlin, H. Stern, and D. Rubin.
Bayesian Data Analysis.
Chapman & Hall/CRC Press, Boca Raton, Florida, 1995.
- Ghahramani and Beal(2001)
-
Z. Ghahramani and M. Beal.
Propagation algorithms for variational Bayesian learning.
In T. Leen, T. Dietterich, and V. Tresp, editors, Advances in
Neural Information Processing Systems 13, pages 507-513. The MIT Press,
Cambridge, MA, USA, 2001.
- Ghahramani and Hinton(1998)
-
Z. Ghahramani and G. E. Hinton.
Hierarchical non-linear factor analysis and topographic maps.
In M. I. Jordan, M. J. Kearns, and S. A. Solla, editors,
Advances in Neural Information Processing Systems 10, pages 486-492.
The MIT Press, Cambridge, MA, USA, 1998.
- Ghahramani and Roweis(1999)
-
Z. Ghahramani and S. Roweis.
Learning nonlinear dynamical systems using an EM algorithm.
In M. Kearns, S. Solla, and D. Cohn, editors, Advances in
Neural Information Processing Systems 11, pages 431-437. The MIT Press,
Cambridge, MA, USA, 1999.
- Gray et al.(2002)Gray, Fischer, Schumann, and Buntine
-
A. Gray, B. Fischer, J. Schumann, and W. Buntine.
Automatic derivation of statistical algorithms: The EM family and
beyond.
In Advances in Neural Information Processing Systems 15, 2002.
URL http://www.hiit.fi/u/buntine/nips2002.htm.
- Harva(2004)
-
M. Harva.
Hierarchical Variance Models of Image Sequences.
Helsinki Univ. of Technology, Dept. of Computer Science and Eng.,
Espoo, Finland, March 2004.
Master of Science (Dipl.Eng.) thesis. Available at http://www.cis.hut.fi/mha.
- Harva and Kabán(2005)
-
M. Harva and A. Kabán.
A variational Bayesian method for rectified factor analysis.
In Proc. 2005 IEEE International Joint Conference on Neural
Networks (IJCNN 2005), pages 185-190, Montreal, Canada, 2005.
- Harva et al.(2005)Harva, Raiko, Honkela, Valpola, and
Karhunen
-
M. Harva, T. Raiko, A. Honkela, H. Valpola, and J. Karhunen.
Bayes Blocks: An implementation of the variational Bayesian
building blocks framework.
In Proceedings of the 21st Conference on Uncertainty in
Artificial Intelligence, UAI 2005, pages 259-266, Edinburgh, Scotland, July
2005.
- Haykin(2001)
-
S. Haykin, editor.
Kalman Filtering and Neural Networks.
Wiley, New York, 2001.
- Haykin(1998)
-
S. Haykin.
Neural Networks - A Comprehensive Foundation, 2nd ed.
Prentice-Hall, 1998.
- Hinton and van Camp(1993)
-
G. E. Hinton and D. van Camp.
Keeping neural networks simple by minimizing the description length
of the weights.
In Proc. of the 6th Ann. ACM Conf. on Computational Learning
Theory, pages 5-13, Santa Cruz, CA, USA, 1993.
- Højen-Sørensen et al.(2002)Højen-Sørensen, Winther, and
Hansen
-
P. Højen-Sørensen, O. Winther, and L.K. Hansen.
Mean-field approaches to independent component analysis.
Neural Computation, 14(4):889-918, 2002.
- Honkela(2002)
-
A. Honkela.
Speeding up cyclic update schemes by pattern searches.
In Proc. of the 9th Int. Conf. on Neural Information
Processing (ICONIP'02), pages 512-516, Singapore, 2002.
- Honkela and Valpola(2004)
-
A. Honkela and H. Valpola.
Variational learning and bits-back coding: an information-theoretic
view to Bayesian learning.
IEEE Transactions on Neural Networks, 15(4):800-810, 2004.
- Honkela and Valpola(2005)
-
A. Honkela and H. Valpola.
Unsupervised variational Bayesian learning of nonlinear models.
In L. Saul, Y. Weiss, and L. Bottou, editors, Advances in
Neural Information Processing Systems 17. MIT Press, Cambridge, MA, USA,
2005.
To appear.
- Honkela et al.(2003)Honkela, Valpola, and Karhunen
-
A. Honkela, H. Valpola, and J. Karhunen.
Accelerating cyclic update algorithms for parameter estimation by
pattern searches.
Neural Processing Letters, 17(2):191-203,
2003.
- Honkela et al.(2004)Honkela, Harmeling, Lundqvist, and
Valpola
-
A. Honkela, S. Harmeling, L. Lundqvist, and H. Valpola.
Using kernel PCA for initialisation of variational Bayesian
nonlinear blind source separation method.
In C. Puntonet and A. Prieto, editors, Proc. of the Fifth
Int. Conf. on Independent Component Analysis and Blind Signal Separation
(ICA 2004), volume 3195 of Lecture Notes in Computer Science, pages
790-797, Granada, Spain, 2004. Springer-Verlag, Berlin.
- Honkela et al.(2005)Honkela, Östman, and
Vigário
-
A. Honkela, T. Östman, and R. Vigário.
Empirical evidence of the linear nature of magnetoencephalograms.
In Proc. 13th European Symposium on Artificial Neural Networks
(ESANN 2005), pages 285-290, Bruges, Belgium, 2005.
- Hyvärinen and Hoyer(2000a)
-
A. Hyvärinen and P. Hoyer.
Emergence of phase and shift invariant features by decomposition of
natural images into independent feature subspaces.
Neural Computation, 12(7):1705-1720,
2000a.
- Hyvärinen and Hoyer(2000b)
-
A. Hyvärinen and P. Hoyer.
Emergence of topography and complex cell properties from natural
images using extensions of ICA.
In S. A. Solla, T. K. Leen, and K.-R. Müller, editors, Advances
in Neural Information Processing Systems 12, pages 827-833. The MIT Press,
Cambridge, MA, USA, 2000b.
- Hyvärinen et al.(2001)Hyvärinen, Karhunen, and Oja
-
A. Hyvärinen, J. Karhunen, and E. Oja.
Independent Component Analysis.
J. Wiley, 2001.
- Ilin and Valpola(2003)
-
A. Ilin and H. Valpola.
On the effect of the form of the posterior approximation in
variational learning of ICA models.
In Proc. of the 4th Int. Symp. on Independent Component
Analysis and Blind Signal Separation (ICA2003), pages 915-920, Nara, Japan,
2003.
- Ilin et al.(2003)Ilin, Valpola, and Oja
-
A. Ilin, H. Valpola, and E. Oja.
Nonlinear dynamical factor analysis for state change detection.
IEEE Trans. on Neural Networks, 15(3):559-575, May 2003.
- Jordan(1999)
-
M. Jordan, editor.
Learning in Graphical Models.
The MIT Press, Cambridge, MA, USA, 1999.
- Jordan and Sejnowski(2001)
-
M. Jordan and T. Sejnowski, editors.
Graphical Models: Foundations of Neural Computation.
The MIT Press, Cambridge, MA, USA, 2001.
- Jordan et al.(1999)Jordan, Ghahramani, Jaakkola, and
Saul
-
M. Jordan, Z. Ghahramani, T. Jaakkola, and L. Saul.
An introduction to variational methods for graphical models.
In M. Jordan, editor, Learning in Graphical Models, pages
105-161. The MIT Press, Cambridge, MA, USA, 1999.
- Kohonen(2001)
-
T. Kohonen.
Self-Organizing Maps.
Springer, 3rd, extended edition, 2001.
- Kohonen et al.(1997)Kohonen, Kaski, and Lappalainen
-
T. Kohonen, S. Kaski, and H. Lappalainen.
Self-organized formation of various invariant-feature filters in the
Adaptive-Subspace SOM.
Neural Computation, 9(6):1321-1344, 1997.
- Lappalainen and Honkela(2000)
-
H. Lappalainen and A. Honkela.
Bayesian nonlinear independent component analysis by multi-layer
perceptrons.
In M. Girolami, editor, Advances in Independent Component
Analysis, pages 93-121. Springer-Verlag, Berlin, 2000.
- Lappalainen and Miskin(2000)
-
H. Lappalainen and J. Miskin.
Ensemble learning.
In M. Girolami, editor, Advances in Independent Component
Analysis, pages 75-92. Springer-Verlag, Berlin, 2000.
- MacKay(2001)
-
D. MacKay.
Local minima, symmetry-breaking, and model pruning in variational
free energy minimization.
Available at http://www.inference.phy.cam.ac.uk/mackay/, 2001.
- MacKay(2003)
-
D. MacKay.
Information Theory, Inference, and Learning Algorithms.
Cambridge University Press, 2003.
- MacKay(1992)
-
D. MacKay.
A practical Bayesian framework for backpropagation networks.
Neural Computation, 4(3):448-472, 1992.
- MacKay(1995)
-
D. MacKay.
Developments in probabilistic modelling with neural networks -
ensemble learning.
In Neural Networks: Artificial Intelligence and Industrial
Applications. Proc. of the 3rd Annual Symposium on Neural Networks, pages
191-198, 1995.
- MacKay(1997)
-
D. MacKay.
Ensemble learning for hidden Markov models.
Available at http://wol.ra.phy.cam.ac.uk/mackay/, 1997.
- MacKay(1999)
-
D. MacKay.
Introduction to Monte Carlo methods.
In M. Jordan, editor, Learning in Graphical Models, pages
175-204. The MIT Press, Cambridge, MA, USA, 1999.
- Minka(2001)
-
T. Minka.
Expectation propagation for approximate Bayesian inference.
In Proceedings of the 17th Conference in Uncertainty in
Artificial Intelligence, UAI 2001, pages 362-369, Seattle, Washington,
USA, 2001.
- Miskin and MacKay(2001)
-
J. Miskin and D. MacKay.
Ensemble learning for blind source separation.
In S. Roberts and R. Everson, editors, Independent Component
Analysis: Principles and Practice, pages 209-233. Cambridge University
Press, 2001.
- Murphy(2001)
-
K. Murphy.
The Bayes net toolbox for Matlab.
Computing Science and Statistics, 33:331-350, 2001.
- Murphy(1999)
-
K. Murphy.
A variational approximation for Bayesian networks with discrete and
continuous latent variables.
In Proc. of the 15th Annual Conf. on Uncertainty in
Artificial Intelligence (UAI-99), pages 457-466, Stockholm, Sweden, 1999.
- Neal(1996)
-
R. Neal.
Bayesian Learning for Neural Networks, Lecture Notes in
Statistics No. 118.
Springer-Verlag, 1996.
- Nolan et al.(2005)Nolan, Harva, Kabán, and Raychaudhury
-
L. Nolan, M. Harva, A. Kabán, and S. Raychaudhury.
A data-driven Bayesian approach to finding young stellar
populations in early-type galaxies from their UV-optical spectra.
Monthly Notices of the Royal Astronomical Society, 2005.
To appear. Available at http://www.cis.hut.fi/mha/.
- Park and Lee(2004)
-
H.-J. Park and T-W. Lee.
A hierarchical ICA method for unsupervised learning of nonlinear
dependencies in natural images.
In C. Puntonet and A. Prieto, editors, Proc. of the 5th Int. Conf. on Independent Component Analysis and Blind Signal Separation
(ICA2004), pages 1253-1261, Granada, Spain, 2004.
- Parra et al.(2001)Parra, Spence, and Sajda
-
L. Parra, C. Spence, and P. Sajda.
Higher-order statistical properties arising from the non-stationarity
of natural signals.
In T. Leen, T. Dietterich, and V. Tresp, editors, Advances in
Neural Information Processing Systems 13, pages 786-792. The MIT Press,
Cambridge, MA, USA, 2001.
- Pearl(1988)
-
J. Pearl, editor.
Probabilistic Reasoning in Intelligent Systems: Networks of
Plausible Inference.
Morgan Kaufmann Publishers, San Francisco, California, 1988.
- Pham and Cardoso(2001)
-
D.-T. Pham and J.-F. Cardoso.
Blind separation of instantaneous mixtures of nonstationary sources.
IEEE Trans. on Signal Processing, 49(9):1837-1848, 2001.
- Raiko(2004)
-
T. Raiko.
Partially observed values.
In Proc. Int. Joint Conf. on Neural Networks (IJCNN'04),
pages 2825-2830, Budapest, Hungary, 2004.
- Raiko(2005)
-
T. Raiko.
Nonlinear relational Markov networks with an application to the
game of Go.
In Proceedings of the International Conference on Artificial
Neural Networks (ICANN 2005), pages 989-996, Warsaw, Poland, September
2005.
- Raiko and Tornio(2005)
-
T. Raiko and M. Tornio.
Learning nonlinear state-space models for control.
In Proc. Int. Joint Conf. on Neural Networks (IJCNN'05),
pages 815-820, Montreal, Canada, 2005.
- Raiko et al.(2003)Raiko, Valpola, Östman, and
Karhunen
-
T. Raiko, H. Valpola, T. Östman, and J. Karhunen.
Missing values in hierarchical nonlinear factor analysis.
In Proc. of the Int. Conf. on Artificial Neural Networks and
Neural Information Processing (ICANN/ICONIP 2003), pages 185-189, Istanbul,
Turkey, 2003.
- Roberts and Everson(2001)
-
S. Roberts and R. Everson, editors.
Independent Component Analysis: Principles and Practice.
Cambridge Univ. Press, 2001.
- Roberts et al.(2004)Roberts, Roussos, and Choudrey
-
S. Roberts, E. Roussos, and R. Choudrey.
Hierarchy, priors and wavelets: structure and signal modelling using
ICA.
Signal Processing, 84(2):283-297,
February 2004.
- Rowe(2003)
-
D. Rowe.
Multivariate Bayesian Statistics: Models for Source
Separation and Signal Unmixing.
Chapman & Hall/CRC, Medical College of Wisconsin, 2003.
- Schwarz(1978)
-
G. Schwarz.
Estimating the dimension of a model.
Annals of Statistics, 6:461-464, 1978.
- Spiegelhalter et al.(1995)Spiegelhalter, Thomas, Best, and
Gilks
-
D.J. Spiegelhalter, A. Thomas, N.G. Best, and W.R. Gilks.
BUGS: Bayesian inference using Gibbs sampling, version
0.50.
Available at http://www.mrc-bsu.cam.ac.uk/bugs/, 1995.
- Valpola and Karhunen(2002)
-
H. Valpola and J. Karhunen.
An unsupervised ensemble learning method for nonlinear dynamic
state-space models.
Neural Computation, 14(11):2647-2692,
2002.
- Valpola et al.(2001)Valpola, Raiko, and Karhunen
-
H. Valpola, T. Raiko, and J. Karhunen.
Building blocks for hierarchical latent variable models.
In Proc. 3rd Int. Conf. on Independent Component Analysis
and Signal Separation (ICA2001), pages 710-715, San Diego, USA, 2001.
- Valpola et al.(2002)Valpola, Honkela, and Karhunen
-
H. Valpola, A. Honkela, and J. Karhunen.
An ensemble learning approach to nonlinear dynamic blind source
separation using state-space models.
In Proc. Int. Joint Conf. on Neural Networks (IJCNN'02),
pages 460-465, Honolulu, Hawaii, USA, 2002.
- Valpola et al.(2003a)Valpola, Honkela, Harva, Ilin,
Raiko, and Östman
-
H. Valpola, A. Honkela, M. Harva, A. Ilin, T. Raiko, and T. Östman.
Bayes Blocks software library, 2003a.
Available at http://www.cis.hut.fi/projects/bayes/software/.
- Valpola et al.(2003b)Valpola, Oja, Ilin, Honkela, and
Karhunen
-
H. Valpola, E. Oja, A. Ilin, A. Honkela, and J. Karhunen.
Nonlinear blind source separation by variational Bayesian learning.
IEICE Transactions on Fundamentals of Electronics,
Communications and Computer Sciences, E86-A(3):532-541,
2003b.
- Valpola et al.(2003c)Valpola, Östman, and
Karhunen
-
H. Valpola, T. Östman, and J. Karhunen.
Nonlinear independent factor analysis by hierarchical models.
In Proc. 4th Int. Symp. on Independent Component Analysis
and Blind Signal Separation (ICA2003), pages 257-262, Nara, Japan,
2003c.
- Valpola et al.(2004)Valpola, Harva, and Karhunen
-
H. Valpola, M. Harva, and J. Karhunen.
Hierarchical models of variance sources.
Signal Processing, 84(2):267-282, 2004.
- van Hateren and Ruderman(1998)
-
J. H. van Hateren and D. L. Ruderman.
Independent component analysis of natural image sequences yields
spatio-temporal filters similar to simple cells in primary visual cortex.
Proceedings of the Royal Society of London B, 265(1412):2315-2320, 1998.
- Vesanto et al.(1999)Vesanto, Himberg, Alhoniemi, and
Parhankangas
-
J. Vesanto, J. Himberg, E. Alhoniemi, and J. Parhankangas.
Self-organizing map in Matlab: the SOM toolbox.
In Proceedings of the Matlab DSP Conference, pages 35-40,
Espoo, Finland, November 1999.
Available at http://www.cis.hut.fi/projects/somtoolbox/.
- Wallace(1990)
-
Christopher S. Wallace.
Classification by minimum-message-length inference.
In S. G. Aki, F. Fiala, and W. W. Koczkodaj, editors, Advances
in Computing and Information - ICCI '90, volume 468 of Lecture Notes
in Computer Science, pages 72-81. Springer, Berlin, 1990.
- Winn and Bishop(2005)
-
J. Winn and C. M. Bishop.
Variational message passing.
Journal of Machine Learning Research, 6:661-694,
April 2005.
Tapani Raiko
2006-08-28