NIPS*95 Workshop on Neural Networks for Signal Processing

[Bill Horne]

		       **** FINAL SCHEDULE ****

			   NIPS*95 Workshop

		Neural Networks for Signal Processing

			  Friday Dec 1, 1995
	       Marriott Vail Mountain Resort, Colorado



			      ORGANIZERS

        Andrew D. Back             C. Lee Giles and Bill G. Horne
   University of Queensland            NEC Research Institute
     back at elec.uq.edu.au         {giles,horne}@research.nj.nec.com




			    WORKSHOP AIMS

Nonlinear signal processing methods using neural network models form a
topic of some recent interest.  A common goal is for neural network
models to outperform traditional linear and nonlinear models.  Many
researchers are interested in understanding, analysing and improving
the performance of these nonlinear models by drawing from the well
established base of linear systems theory and existing knowledge in
other areas.  How can this be best achieved?

In the context of neural network models, a variety of methods have
been proposed for capturing the time-dependence of signals.  A common
approach is to use recurrent connections or time-delays within the
network structure. On the other hand, many signal processing
techniques have been well developed over the last few decades.
Recently, a strong interest has developed in understanding how better
signal processing techniques can be developed by considering these
different approaches.

A major aim of this workshop is to obtain a better understanding of
how well this development is proceeding.  For example, the different
model structures raise the question, "how suitable are the various
neural networks for signal processing problems?".  The success of some
neural network models in signal processing problems indicate that they
form a class of potentially powerful modeling methods, yet relatively
little is understood about these architectures in the context of
signal processing.

As an outcome of the workshop it is intended that there should be a
summary of current progress and goals for future work in this research
area.



			  SCHEDULE OF TALKS


		    ** Session 1 - Speech Focus **


7:30-7:40: Introduction

7:40-8:10: Herve Bourlard, ICSI and Faculte Polytechnique de Mons,
"Hybrid use of hidden Markov models and neural networks for improving
state-of-the-art speech recognition systems"

8:10-8:40: John Hogden, Los Alamos National Laboratory, "A maximum
likelihood approach to estimating speech articulator positions from
speech acoustics"

8:40-9:10: Shun-ichi Amari, A.Cichocki and H. Yang, RIKEN, "Blind
separation of signals - Information geometric point of view"

9:10-9:30: Discussion


	      ** Session 2 - Recurrent Network Focus **


4:30-4:40: Introduction

4:40-5:10: Andrew Back, University of Queensland, "Issues in signal
processing relevant to dynamic neural networks"

5:10-5:40: John Steele and Aaron Gordon, Colorado School of Mines,
"Hierarchies of recurrent neural networks for signal interpretation
with applications"

5:40-6:10: Stephen Piche, Pavilion Technologies, "Discrete Event
Recurrent Neural Networks"

6:10-6:30: Open Forum, discussion time.


For more information about the workshop see the workshop homepage:

	http://www.elec.uq.edu.au/~back/nips95ws/nips95ws.html

or contact:

Andrew D. Back
Department of Electrical and Computer Engineering,
University of Queensland,
Brisbane, Qld 4072. Australia
Ph: +61 7 365 3965
Fax: +61 7 365 4999
back at .elec.uq.edu.au

C. Lee Giles, Bill G. Horne
NEC Research Institute
4 Independence Way
Princeton, NJ 08540. USA
Ph: 609 951 2642, 2676
Fax: 609 951 2482
{giles,horne}@research.nj.nec.com



-- 
	       Bill Horne     horne at research.nj.nec.com
	   http://www.neci.nj.nec.com/homepages/horne.html
	     PHN:  (609) 951-2676     FAX: (609) 951-2482
   NEC Research Institute, 4 Independence Way, Princeton, NJ  08540