1. M.S. Lewicki. Efficient coding of natural sounds. Nature Neuroscience, 5(4):356-363, 2002.
   Keywords: sounds, ICA. [Abstract] [bibtex-entry]



2. Shun-Ichi Amari. Natural Gradient Learning for Over- and Under-Complete Bases in ICA. Neural Computation, 1999.
   Keywords: ICA, statistics, neural networks. [Abstract] [Comments] [bibtex-entry]



3. Aapo Hyvärinen. Independent Component Analysis in the Presence of Gaussian Noise by Maximizing Joint Likelihood. Neurocomputing, 22:49-67, 1998.
   Keywords: ICA. [bibtex-entry]



4. A.J. Bell and T.J. Sejnowski. The 'independent components' of natural scenes are edge filters. Vision Research, (37):3327-3338, 1997.
   Keywords: information theory. [Abstract] [bibtex-entry]



5. Jean-François Cardoso. Infomax and maximum likelihood for source separation. IEEE Letters on Signal Processing, 4(4):112-114, 1997.
   Keywords: ICA, statistics, information theory, BSS. [Abstract] [bibtex-entry]



6. Jean-François Cardoso. Equivariant adaptive source separation. IEEE Trans. on S.P., 44(45):3017-3030, 1996.
   Keywords: ICA, statistics, BSS. [Abstract] [bibtex-entry]



7. S Makeig, Anthony J. Bell, Tzyy-Ping Jung, and Terrence J. Sejnowski Sejnowski. Independent component analysis of electroencephalographic data. Advances in Neural Information Processing Systems, 8:145-151, 1996.
   Keywords: ICA, Matlab. [Comments] [bibtex-entry]



8. B.A. Olshausen and D.J. Field. Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature, 381:607-609, 1996.
   Keywords: vision, ICA, information theory. [Abstract] [bibtex-entry]



9. Antony J. Bell and Terrence J. Sejnowski. An information maximisation approach to blind separation and blind deconvolution. Neural Computation, 7(6):1129-1159, 1995.
   Keywords: statistics, ICA, information theory. [Abstract] [bibtex-entry]



10. L. De Lathauwer, B. De Moor, and J. Vandewalle. Fetal Electrocardiogram Extraction by Source Subspace Separation. Proceedings IEEE SP/Athos Workshop on Higher-Order Statistics, pp 134-138, june 1995.
    Keywords: ICA, statistics, BSS. [Abstract] [Comments] [bibtex-entry]



11. J-P Nadal and N Parga. Non linear neurons in the low noise limit: a factorial code maximizes information transfer. Network, 1994.
    Keywords: ICA, statistics, neural networks, information theory. [Abstract] [Comments] [bibtex-entry]

1. F. Meinecke, A. Ziehe, M. Kawanabe, and K-R Müller. Assessing Reliability of ICA Projections - a Resampling Approach. In ICA 2001, 2001.
   Keywords: ICA, statistics, BSS. [Abstract] [Comments] [bibtex-entry]



2. R. Vollgraf and K. Obermayer. Multi Dimensional ICA to Separate Correlated Sources. In , 2001.
   Keywords: ICA, statistics, BSS. [Comments] [bibtex-entry]



3. Shun-Ichi Amari, A. Cichocki, and H. H. Yang. Recurrent Neural Networks for Blind Separation of Sources. In International Symposium on Nonlinear Theory and its Application, pages 37-42, 1995.
   Keywords: ICA, statistics, neural networks. [Comments] [bibtex-entry]

1. N. Murata. Properties of the Empirical Characteristic Function and its Application to Testing for Independence. Technical report, Waseda University, 2001.
   Keywords: ICA, statistics, BSS. [Abstract] [Comments] [bibtex-entry]

1. D. J. C. MacKay. Maximum Likelihood and Covariant Algorithms for ICA. Note: Unpublished, 1996.
   Keywords: ICA, statistics, BSS. [Abstract] [Comments] [bibtex-entry]

All the ressources below are referenced through direct links, for easier use as a library of introductory and comprehensive papers on various subjects. If a paper of yours is on this list and you do not want your homepage to be bypassed, just send me an email.

Comments and ratings are for personnal use, they do not claim to reflect academic value. It is only an indication of their relevance with respect to my own interests and the success they happened to have in the scientific community.

This document was translated from BibT_EX by bibtex2html