NIPS*96 Workshop - Blind Signal Processing

[Andrew Back]

Dear colleagues,

Please find attached, the schedule for the NIPS*96 workshop on
Blind Signal Processing. Further information, including abstracts 
and some papers are available on the workshop WWW homepage:  

 http://www.bip.riken.go.jp/absl/back/nips96ws/nips96ws.html


Andrzej Cichocki
Andrew Back

----------------------------------------------------------------------------

                              NIPS*96 Workshop

               Blind Signal Processing and Their Applications

                 (Neural Information Processing Approaches)

                            Saturday Dec 7, 1996
                         Snowmaas (Aspen), Colorado

Workshop Organizers:
Andrzej Cichocki
Andrew D. Back
Brain Information Processing Group
Frontier Research Program RIKEN,
The Institute of Physical and Chemical Research
Hirosawa 2-1, Wako-shi, Saitama, 351-01, Japan
Phone: +81-48-462-1111 ext: 6733
Fax: +81-48-462-4633
Email: cia at hare.riken.go.jp back at zoo.riken.go.jp

     Blind Signal Processing is an emerging area of research in neural
     networks and image/signal processing with many potential
     applications. It originated in France in the late 80's and since
     then there has continued to be a strong and growing interest in
     the field. Blind signal processing problems can be classified into
     three areas: (1) blind signal separation of sources and/or
     independent component analysis (ICA), (2) blind channel
     identification and (3) blind deconvolution and blind equalization.
     These areas will be addressed in this workshop. See the objectives
     below for further details.


----------------------------------------------------------------------------

                                 Objectives

     The main objectives of this workshop are to:

     Give presentations by experts in the field on the state of the art
     in this exciting area of research.

     Compare the performance of recently developed adaptive
     un-supervised learning algorithms for neural networks.

     Discuss issues surrounding prospective applications and the
     suitability of current neural network models. Hence we seek to
     provide a forum for better understanding current limitations of
     neural network models.

     Examine issues surrounding local, online adaptive learning
     algorithms and their robustness and biologically plausibility or
     justification.

     Discuss issues concerning effective computer simulation programs.

     Discuss open problems and perspectives for future research in this
     area.

     Especially, we intend to discuss the following items:

     1. Criteria for blind separation and blind deconvolution problems
     (both for time and frequency domain approaches)

     2. Natural (or relative) gradient approach to blind signal
     processing.

     3. Neural networks for blind separation of time delayed and
     convolved signals.

     4. On line adaptive learning algorithms for blind signal
     processing with variable learning rate (learning of learning
     rate).

     5.Open problems, e.g. dynamic on-line determination of number of
     sources (more sources than sensors), influence of noise,
     robustness of algorithms, stability, convergence, identifiability,
     non-causal, non-stationary dynamic systems .

     6. Applications in different areas of science and engineering,
     e.g., non-invasive medical diagnosis (EEG, ECG),
     telecommunication, voice recognition problems, image processing
     and enhancement.

----------------------------------------------------------------------------

                             Workshop Schedule

7:30-7:50
     A Review of Blind Signal Processing: Results and Open Issues
     Andrzej Cichocki and Andrew Back
     Brain Information Processing Group,
     Frontier Research Program RIKEN, Japan


7:50-8:10
     Natural Gradient in Blind Separation and Deconvolution - Information
     Geometrical Approach
     Schun-ichi Amari
     Brain Information Processing Group,
     Frontier Research Program RIKEN, Japan


8:10-8:30
     Entropic Contrasts for Blind Source Separation
     Jean-Francois Cardoso
     Ecole Nationale Superieure des Telecommunications, Paris, France


8:30-8:40
     Coffee Break/Discussion Time

8:40-9:00
     Several Theorems on Information Theoretic Independent Component
     Analysis
     Lei Xu. J. Ruan and Shun-ichi Amari
     The Chinese University of Hong Kong, Hong Kong
     Brain Information Processing Group, FRP, Riken, Japan


9:00-9:20
     From Neural PCA to Neural ICA
     Erkki Oja, Juha Karhunen and Aapo Hyvarinen
     Helsinki University of Technology, Finland


9:20-9:40
     Local Adaptive Algorithms and their Convergence Analysis for
     Decorrelation and Blind Equalization/Deconvolution
     Scott Douglas and Andrzej Cichocki
     Department of EE, University of Utah, USA
     FRP Riken, Japan


9:40-10:00
     Negentropy and Kurtosis as Projection Pursuit Indices Provide
     Generalized ICA Algorithms
     Mark Girolami and Colin Fyfe
     The University of Paisley, Scotland


10:00-10:15
     Bussgang Methods for Separation of Multipath Mixtures
     Russel Lambert
     Dept of Electrical Engineering
     University of South California, USA


10:15-10:30
     Discussion Time


4:00-4:20
     Blind Signal Separation by Output Decorrelation
     Dominic C.B. Chan, Simon J. Goodsil and Peter J.W. Rayner
     University of Cambridge, United Kingdom


4:20-4:40
     Temporal Decorrelation Using Teacher Forcing Anti-Hebbian Learning and
     its Application in Adaptive Blind Source Separation
     Jose C. Principe, Chuan Wang, and Hsiao-Chun Wu
     University of Florida, USA


4:40-5:00
     A Direct Adaptive Blind Equalizer for Multi-Channel Transmission
     Seungjin Choi and Ruey-wen Liu
     University of Notre Dame, USA


5:00-5:10
     Coffee Break/Discussion Time


5:10:5:30
     IIR Filters for Blind Deconvolution Using Information Maximization
     Kari Torkkola
     Motorola Phoenix Corporate Research, USA


5:30-5:40
     Information Maximization and Independent Component Analysis: Is there a
     difference ?
     D. Obradovic and G. Deco
     Siemens AG,
     Coporate Research and Development, Germany


5:40-5:50
     Convergence Properties of Cichocki's Extension of the Herault-Jutten
     Source Separation Neural Network
     Yannick Deville
     Laboratoires d'Electronique Philips S.A.S. (LEP) France


5:50-6:10
     Independent Component Analysis of EEG and ERP Data
     Tzyy-Ping Jung, Scott Makeig, Anthony J. Bell and Terrence J. Sejnowski
     Computational Neurobiology Laboratory
     The Salk Institute, CNL, USA


6:10-6:20
     Blind separation of delayed and convolved sources - the problem
     Tony Bell and Te-Won Lee
     Computational Neurobiology Laboratory
     The Salk Institute, CNL, USA


6:20-6:30
     Information Back-propagation for Blind Separation of Sources from
     Non-linear Mixture
     Howard H. Yang, Shun-ichi Amari and Andrzej Cichocki
     Brain Information Processing Group, FRP, RIKEN, Japan


6:30-7:00
     Discussion Time

--