NIPS*94 Workshop CFP

[Andrew Back]

 We are organizing the following workshop for NIPS*94. The aim of this 
 one-day workshop will be to discuss issues of nonlinear signal processing
 using neural network models, specifically those which are in-between the 
 usual MLP and fully recurrent network architectures.

 The intended audience is for those applying neural networks to signal 
 processing problems, and active signal processing (but not necessarily 
 neural network) researchers.

 There is room for contributed talks. If you would like to give a short
 paper or a brief presentation of your work, please send a few details to 
 the organizers. Presenters of short papers will be allocated 15-20 mins. 
 For those who would like to contribute on an informal basis, yet be able 
 to have the opportunity to speak, 5 mins `soap-box' sessions will be.
 available. Ample time will be allocated for informal discussion. 

 We would also like to hear from others interested in attending the workshop.

 
 Andrew Back
 Department of Electrical and Computer Engineering
 University of Queensland
 Brisbane. 4072
 AUSTRALIA

 Ph:  +61 7 365 3965
 Fax: +61 7 365 4999
 email: back at elec.uq.oz.au

=============================================================================


                 C A L L    F O R    P A P E R S


                        NIPS*94 Workshop:


     "Neural Network Architectures with Time Delay Connections 
      for Nonlinear Signal Processing: Theory and Applications"


             Organizers: Andrew D. Back and Eric A. Wan


 Nonlinear signal processing methods using neural network models are a 
 topic of recent interest in the various application areas. Recurrent 
 networks offer a potentially rich and powerful modelling capability 
 though may suffer from some problems in training. On the other hand, 
 simpler network structures which have an overall feedforward structure,
 but draw more strongly on linear signal processing approaches have been
 proposed. The resulting structures can be viewed as a nonlinear 
 generalizations of linear filters. 

 It is clear that relatively little is known about how to understand the 
 various architectures in a signal processing context. For the most part
 we are able to do simulations, but proving the capabilities of the network
 architectures is much more difficult. It appears that they offer a convenient
 NARMA modelling framework, but many aspects of the models are yet to be
 considered. 

 This workshop is aimed at addressing some of the issues that arise when 
 adopting a nonlinear signal processing methodology, in particular, those 
 employing some form of time delay connections and limited recurrent 
 connections.

 Issues that may be of interest are:

 * Representational capabilities for various network structures

 * Methods of analysis - what methods from linear signal
   processing theory can be extended to these neural network 
   architectures ? What methods from analysis of nonlinear systems can be 
   used for these networks ?

 * What advantages are there in using locally recurrent connections
   within networks  as opposed to globally recurrent connections ?
   (e.g. Frasconi-Gori-Soda networks vs Williams-Zipser/Robinson networks).

 * Learning algorithms - what difficulties are encountered and what methods 
   can be applied to overcome them?

 * What types of problems or data are the different models best suited for. 

 * Given a set of time series data, what model should be selected on the basis 
   of  the observed data ? What tests can be applied to a particular data set 
   to determine what type of model should be used ? 

 * What issues need to be resolved in order for these 
   models to be confidently applied to a given problem/data-set ?

 * Successes and failures of networks on practical problems and data sets.

 * Comparisons between the methods and results that have been established by 
   various researchers. 

 * Theoretical issues which still need to be addressed, (e.g. approximation
   capabilities, convergence, stability, and computational complexity) 

 * New network architectures


Aim:
---

 At the workshop we intend to consolidate some of the theoretical and 
 practical results of current research.  We also hope to identify open 
 issues which should be addressed in on-going work.


Format:
------

 The workshop will be a one day workshop and it is planned to have a number 
 of short presentations of either 15 mins or 5 mins (`soap-box' sessions). 
 In this way a number of people will be able to speak in some detail, while 
 others can simply raise issues they feel are important.  

 As an outcome of the workshop it is intended that there should be a report 
 summarizing where we are at in this research area, and goals for future work.


 Contributions will be welcomed and details of proposed talks should be sent
 to Andrew Back as soon as possible. 

              
   Andrew D. Back*,  Eric A. Wan** 

  *Department of Electrical and Computer Engineering,
   University of Queensland, St. Lucia, Queensland  4072. Australia.
   Ph:  +61 7 365 3965
   Fax: +61 7 365 4999
   back at elec.uq.oz.au

 **Department of Electrical Engineering and Applied Physics
   Oregon Graduate Institute of Science and Technology
   P.O. Box 91000, Portland, Oregon, 97291. USA.
   Ph:  (503) 690 1164
   Fax: (503) 690 1406
   ericwan at eeap.ogi.edu