From: neuron-request@CATTELL.PSYCH.UPENN.EDU ("Neuron-Digest Moderator")
Newsgroups: comp.ai.neural-nets
Subject: Neuron Digest V10 #24 (jobs + discussion + software)
Date: 27 Dec 92 23:27:29 GMT
Reply-To: "Neuron-Request" <neuron-request@cattell.psych.upenn.edu>
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Organization: University of Pennsylvania

Neuron Digest   Sunday, 27 Dec 1992
                Volume 10 : Issue 24

Today's Topics:
         Doctoral Program in Philosophy-Psychology-Neuroscience
                         some information needed
                             Job Opportunity
                       Follow-up on product guide
         Very Fast Simulated Reannealing (VFSR) v6.35 in Netlib
                          NIPS workshop summary


Send submissions, questions, address maintenance, and requests for old
issues to "neuron-request@cattell.psych.upenn.edu". The ftp archives are
available from cattell.psych.upenn.edu (130.91.68.31). Back issues
requested by mail will eventually be sent, but may take a while.

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

Subject: Doctoral Program in Philosophy-Psychology-Neuroscience
From:    Andy Clark <andycl@syma.sussex.ac.uk>
Date:    Tue, 15 Dec 92 16:43:25 +0000



       First Announcement of a New Doctoral Programme in


             PHILOSOPHY-NEUROSCIENCE-PSYCHOLOGY

                             at

             Washington University in St. Louis



The Philosophy-Neuroscience-Psychology (PNP) program offers a unique
opportunity to combine advanced philosophical studies with in-depth work
in Neuroscience or Psychology. In addition to meeting the usual
requirements for a Doctorate in Philosophy, students will spend one year
working in Neuroscience or Psychology. The Neuroscience option will draw
on the resources of the Washington University School of Medicine which is
an internationally acknowledged center of excellence in neuroscientific
research. The initiative will also employ several new PNP related
Philosophy faculty and post-doctoral fellows.


Students admitted to the PNP program will embark upon a five-year course
of study designed to fulfill all the requirements for the Ph.D. in
philosophy, including an academic year studying neuroscience at
Washington University's School of Medicine or psychology in the
Department of Psychology.  Finally, each PNP student will write a
dissertation jointly directed by a philosopher and a faculty member from
either the medical school or the psychology department.

THE FACULTY

Roger F. Gibson, Ph.D., Missouri, Professor and Chair:
  Philosophy of Language, Epistemology, Quine

Robert B. Barrett, Ph.D., Johns Hopkins, Professor:
  Pragmatism, Renaissance Science, Philosophy of Social
  Science, Analytic Philosophy.

Andy Clark, Ph.D., Stirling, Visiting Professor (1993-6) and
Acting Director of PNP:
  Philosophy of Cognitive Science, Philosophy of Mind,
  Philosophy of Language, Connectionism.

J. Claude Evans, Ph.D., SUNY-Stony Brook, Associate Pro-
  fessor:  Modern Philosophy, Contemporary Continental
  Philosophy, Phenomenology, Analytic Philosophy, Social and
  Political Theory.

Marilyn A. Friedman, Ph.D., Western Ontario, Associate
  Professor:  Ethics, Social Philosophy, Feminist Theory.

William H. Gass, Ph.D., Cornell, Distinguished University
  Professor of the Humanities:  Philosophy of Literature,
  Photography, Architecture.

Lucian W. Krukowski, Ph.D., Washington University, Pro-
  fessor:  20th Century Aesthetics, Philosophy of Art,
  18th and 19th Century Philosophy, Kant, Hegel,
  Schopenhauer.

Josefa Toribio Mateas, Ph.D., Complutense University,
  Assistant Professor:  Philosophy of Language, Philosophy
  of Mind.

Larry May, Ph.D., New School for Social Research, Pro-
  fessor:  Social and Political Philosophy, Philosophy of
  Law, Moral and Legal Responsibility.

Stanley L. Paulson, Ph.D., Wisconsin, J.D., Harvard, Pro-
  fessor:  Philosophy of Law.

Mark Rollins, Ph.D., Columbia, Assistant Professor:
  Philosophy of Mind, Epistemology, Philosophy of Science,
  Neuroscience.

Jerome P. Schiller, Ph.D., Harvard, Professor:  Ancient
  Philosophy, Plato, Aristotle.

Joyce Trebilcot, Ph.D., California at Santa Barbara, Associ-
  ate Professor:  Feminist Philosophy.

Joseph S. Ullian, Ph.D., Harvard, Professor:  Logic, Philos-
  ophy of Mathematics, Philosophy of Language.

Richard A. Watson, Ph.D., Iowa, Professor:  Modern Philoso-
  phy, Descartes, Historical Sciences.

Carl P. Wellman, Ph.D., Harvard, Hortense and Tobias Lewin
  Professor in the Humanities:  Ethics, Philosophy of Law,
  Legal and Moral Rights.

EMERITI

Richard H. Popkin, Ph.D., Columbia:  History of Ideas,
  Jewish Intellectual History.

Alfred J. Stenner, Ph.D., Michigan State:  Philosophy of
  Science, Epistemology, Philosophy of Language.

FINANCIAL SUPPORT

Students admitted to the Philosophy-Neuroscience-Psychology (PNP) program
are eligible for five years of full financial support at competitive
rates in the presence of satisfactory academic progress.

APPLICATIONS

Application for admission to the Graduate School should be made to:
                    Chair, Graduate Admissions
                    Department of Philosophy
                    Washington University
                    Campus Box 1073
                    One Brookings Drive
                    St. Louis, MO 63130-4899

Washington University encourages and gives full consideration to all
applicants for admission and financial aid without regard to race, color,
national origin, handicap, sex, or religious creed.  Services for
students with hearing, visual, orthopedic, learning, or other
disabilities are coordinated through the office of the Assistant Dean for
Special Services.


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

Subject: some information needed
From:    Antonio Villani <ANTONIO%IVRUNIV.bitnet@ICINECA.CINECA.IT>
Organization: "Information Center - Verona University - Italy"
Date:    Tue, 15 Dec 92 16:56:46 -0100

I'm looking for any kind of information about 'avalanche network' and
'neural networks for prediction' applied to dynamic signal processing.
Can someone help me?  Thanks in advance

Antonio Villani
antonio@ivruniv.bitnet


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

Subject: Job Opportunity
From:    Marwan Jabri <marwan@ee.su.oz.au>
Date:    Thu, 17 Dec 92 10:19:55 +1100

The University of Sydney
Department of Electrical Engineering
Systems Engineering and Design Automation Laboratory

Girling Watson Research Fellowship
Reference No. 51/12


Applications are invited for a Girling Watson Research Fellowship at
Sydney University Electrical Engineering. The applicant should have a
strong research and development experience, preferably with a background
in one or more of the following areas: machine intelligence and
connectionist architectures, microelectronics, pattern recognition and
classification.

The Fellow will work with the Systems Engineering and Design Automation
Laboratory (SEDAL), one of the largest laboratories at Sydney University
Electrical Engineering. The Fellow will join a group of 18 people (8 staff
and 10 postgraduate students). SEDAL currently has projects on pattern
recognition for implantable devices, VLSI implementation of connectionist
architectures, time series prediction, knowledge integration and
continuous learning, and VLSI computer aided design. The Research Fellow
position is aimed at:

o       contributing to the research program
o       helping with the supervision of postgraduate students
o       supporting some management aspects of SEDAL
o       providing occasional teaching support

Applicants should have either a PhD or an equivalent industry research and
development experience.  The appointment is available for a period of
three years, subject to satisfactory progress.

Salary is in the range of Research Fellow: A$39,463 to A$48,688.


Applications quoting the reference number 51/12 can be sent to:

        The Staff Office
        The University of Sydney
        NSW 2006
        AUSTRALIA



For further information contact

        Dr. M. Jabri, 
        Tel: (+61-2) 692-2240, 
        Fax: (+61-2) 660-1228, 
        Email: marwan@sedal.su.oz.au


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

Subject: Follow-up on product guide
From:    eric@sunlight.llnl.gov (Eric Keto)
Date:    Thu, 17 Dec 92 17:01:36 -0800

>> Thanks for posting my question about neural net product reviews. I
>> received a response with the information that there is a recent
>> review in the July-August 1992 PC AI magazine.
> 
>Could you write up a little note about what you found and send it to
>neuron@cattell... I'm sure others would like to know also.
 
OK, I finally got this magazine. 

Here is your note:

In the July-August 1992 issue of PC-AI magazine there is a "4th Annual
Product Guide" which includes "information on products in a number of AI
areas" including neural nets.  The list of products is quite long, 13
pages of tiny type, and the descriptions are quite brief: product name,
vendor, 20 words or so on the description, requirements, price. This is
certainly not a critical review, but it is an extensive list.

Eric Keto (eric@sunlight.llnl.gov)


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

Subject: Very Fast Simulated Reannealing (VFSR) v6.35 in Netlib
From:    Lester Ingber <ingber@alumni.cco.caltech.edu>
Date:    Fri, 18 Dec 92 05:47:07 -0800

                Very Fast Simulated Reannealing (VFSR) v6.35
 
Netlib requested an early update, and VFSR v6.35 is now in Netlib
and soon will be updated in Statlib.  The code is stable, and is
being used widely.  The changes to date typically correct typos and
account for some problems encountered on particular machines.
 
                NETLIB
Interactive:
        ftp research.att.com
        [login as netlib, your_login_name as password]
        cd opt
        binary
        get vfsr.Z
Email:
        mail netlib@research.att.com
        send vfsr from opt
 
                STATLIB
Interactive:
        ftp lib.stat.cmu.edu
        [login as statlib, your_login_name as password]
        cd general
        get vfsr
Email:
        mail statlib@lib.stat.cmu.edu
        send vfsr from general
 
                EXCERPT FROM README
2.  Background and Context
 
     VFSR  was  developed  in  1987 to deal with the necessity of
performing adaptive global optimization on multivariate nonlinear
stochastic  systems[2].   VFSR was recoded and applied to several
complex systems, in combat analysis[3],  finance[4],  and  neuro-
science[5].   A  comparison  has  shown  VFSR to be superior to a
standard genetic algorithm simulation on a suite of standard test
problems[6],  and  VFSR  has  been  examined  in the context of a
review of methods of simulated annealing[7].  A project comparing
standard  Boltzmann  annealing  with "fast" Cauchy annealing with
VFSR has concluded that VFSR is a superior algorithm[8].  A paper
has  indicated how this technique can be enhanced by combining it
with some other powerful algorithms[9].


|| Prof. Lester Ingber                         [10ATT]0-700-L-INGBER ||
|| Lester Ingber Research                        Fax: 0-700-4-INGBER ||
|| P.O. Box 857                           Voice Mail: 1-800-VMAIL-LI ||
|| McLean, VA  22101                EMail: ingber@alumni.caltech.edu ||


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

Subject: NIPS workshop summary
From:    "Scott A. Markel x2683" <sam@sarnoff.com>
Date:    Wed, 16 Dec 92 15:57:30 -0500


[[ Editor's Note: Since I did not go to NIPS, I greatly appreciated this
summary.  I hope (and urge) that readers will contribute their own
summaries of future conferecnes and workshops. -PM ]] 

                            NIPS 92 Workshop Summary
                            ========================

                Computational Issues in Neural Network Training
                ===============================================

Main focus: Optimization algorithms used in training neural networks
- ----------

Organizers: Scott Markel and Roger Crane
- ----------

This was a one day workshop exploring the use of optimization algorithms, such
as back-propagation, conjugate gradient, and sequential quadratic programming,
in neural network training.  Approximately 20-25 people participated in the
workshop.  About two thirds of the participants used some flavor of back
propagation as their algorithm of choice, with the other third using conjugate
gradient, sequential quadratic programming, or something else.  I would guess
that participants were split about 60-40 between industry and the academic
community.


The workshop consisted of lots of discussion and the following presentations:

Introduction
- ------------
Scott Markel (David Sarnoff Research Center - smarkel@sarnoff.com)

I opened by saying that Roger and I are mathematicians and started
looking at neural network training problems when neural net researchers
were experiencing difficulties with back-propagation.  We think there are
some wonderfully advanced and robust implementations of classical
algorithms developed by the mathematical optimization community that are
not being exploited by the neural network community.  This is due largely
to a lack of interaction between the two communities.  This workshop was
set up to address that issue.  In July we organized a similar workshop
for applied mathematicians at SIAM '92 in Los Angeles.


Optimization Overview
- ---------------------
Roger Crane (David Sarnoff Research Center - rcrane@sarnoff.com)

Roger gave a very brief, but broad, historical overview of optimization
algorithm research and development in the mathematical community.  He
showed a time line starting with gradient descent in the 1950's and
progressing to sequential quadratic programming (SQP) in the 1970's and
1980's.  SQP is the current state of the art optimization algorithm for
constrained optimization.  It's a second order method that solves a
sequence of quadratic approximation problems.  SQP is quite frugal with
function evaluations and handles both linear and nonlinear constraints.
Roger stressed the robustness of algorithms found in commercial packages
(e.g. NAG library) and that reinventing the wheel was usually not a good
thing to do since many subtleties will be missed.  A good reference for
this material is

Practical Optimization
Gill, P. E., Murray, W., and Wright, M. H.
Academic Press: London and New York
1981

Roger's overview generated a lot of discussion.  Most of it centered
around the fact that second order methods involve using the Hessian, or
an approximation to it, and that this is impractical for large problems
(> 500-1000 parameters).  Participants also commented that the
mathematical optimization community has not yet fully realized this and
that stochastic optimization techniques are needed for these large
problems.  All classical methods are inherently deterministic and work
only for "batch" training.


SQP on a Test Problem
- ---------------------
Scott Markel (David Sarnoff Research Center - smarkel@sarnoff.com)

I followed Roger's presentation with a short set of slides showing actual
convergence of a neural network training problem where SQP was the
training algorithm.  Most of the workshop participants had not seen this
kind of convergence before.  Yann Le Cun noted that with such sharp
convergence generalization would probably be pretty bad.  I noted that
sharp convergence was necessary if one was trying to do something like
count local minima, where generaization is not an issue.


In Defense of Gradient Descent
- ------------------------------
Barak Pearlmutter (Oregon Graduate Institute - bap@merlot.cse.ogi.edu)

By this point back propagation and its many flavors had been well
defended from the audience.  Barak's presentation captured the main
points in a clarifying manner.  He gave examples of real application
neural networks with thousands, millions, and billions of connections.
This underscored the need for stochastic optimization techniques.  Barak
also made some general remarks about the characteristics of error
surfaces.  Some earlier work by Barak on gradient descent and second
order momentum can be found in the NIPS-4 proceedings (p. 887).  A strong
plea was made by Barak, and echoed by the other participants, for fair
comparisons between training methods.  Fair comparisons are rare, but
much needed.


Very Fast Simulated Reannealing
- -------------------------------
Bruce Rosen (University of Texas at San Antonio - rosen@ringer.cs.utsa.edu)

This presentation focused on a new optimization technique called Very
Fast Simulated Reannealing (VFSR), which is faster than Boltzmann
Annealing (BA) and Fast (Cauchy) Annealing (FA).  Unlike back
propagation, which Bruce considers mostly a method for pattern
association/classification/generalization, simulated annealing methods
are perhaps best used for functional optimization. He presented some
results on this work, showing a comparison of Very Fast Simulated
Reannealing to GA for function optimization and some recent work on
function optimization with BA, FA, and VFSR.

Bruce's (and Lester Ingber's) code is available from netlib -

Interactive:
        ftp research.att.com
        [login as netlib, your_login_name as password]
        cd opt
        binary
        get vfsr.Z
Email:
        mail netlib@research.att.com
        send vfsr from opt
        
Contact Bruce (rosen@ringer.cs.utsa.edu) or Lester
(ingber@alumni.cco.caltech.edu) for further information.


General Comments
- ----------------
Yann Le Cun (AT&T Bell Labs - yann@neural.att.com)

I asked Yann to summarize some of the comments he and others had been
making during the morning session.  Even though we didn't give him much
time to prepare, he nicely outlined the main points.  These included

- - large problems require stochastic methods
- - the mathematical community hasn't yet addressed the needs of the neural
  network community
- - neural network researchers are using second order information in a variety of
  ways, but are definitely exploring uncharted territory
- - symmetric sigmoids are necessary; [0,1] sigmoids cause scaling problems
  (Roger commented that classical methods would accommodate this)


Cascade Correlation and Greedy Learning
- ---------------------------------------
Scott Fahlman (Carnegie Mellon University - scott.fahlman@cs.cmu.edu)

Scott's presentation started with a description of QuickProp.  This
algorithm was developed in an attempt to address the slowness of back
propagation.  QuickProp uses second order information ala modified Newton
method.  This was yet another example of neural network researchers
seeing no other alternative but to do their own algorithm development.
Scott then described Cascade Correlation.  CasCor and CasCor2 are greedy
learning algorithms.  They build the network, putting each new node in
its own layer, in response to the remaining error.  The newest node is
trained to deal with the largest remaining error component.  Papers on
QuickProp, CasCor, and Recurrent CasCor can be found in the neuroprose
archive (see fahlman.quickprop-tr.ps.Z, fahlman.cascor-tr.ps.Z, and
fahlman.rcc.ps.Z).


Comments on Training Issues
- ---------------------------
Gary Kuhn (Siemens Corporate Research - gmk@learning.siemens.com)

Gary presented

1. a procedure for training with stochastic conjugate gradient.
   (G. Kuhn and N. Herzberg, Some Variations on Training of Recurrent Networks,
    in R. Mammone & Y. Zeevi, eds, Neural Networks: Theory and Applications,
    New York, Academic Press, 1991, p 233-244.)

2. a sensitivity analysis that led to a change in the architecture of a speech
   recognizer and to further, joint optimization of the classifier and its 
   input features.  (G. Kuhn, Joint Optimization of Classifier and Feature
   Space in Speech Recognition, IJCNN '92, IV:709-714.)

He related Scott Fahlmans' interest in sensitivity to Yann Le Cun's emphasis on
trainability, by showing how a sensitivity analysis led to improved
trainability.


Active Exemplar Selection
- -------------------------
Mark Plutowski (University of California - San Diego - pluto@cs.ucsd.edu)

Mark gave a quick recap of his NIPS poster on choosing a concise subset
for training.  Fitting these exemplars results in the entire set being
fit as well as desired.  This method has only been used on noise free
problems, but looks promising.  Scott Fahlman expressed the opinion that
exploiting the training data was the remaining frontier in neural network
research.


Final Summary
- -------------
 Incremental, stochastic methods are required for training large networks.
Robust, readily available implementations of classical algorithms can be
used for training modest sized networks and are especially effective
research tools for investigating mathematical issues, e.g. estimating the
number of local minima.


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

End of Neuron Digest [Volume 10 Issue 24]
*****************************************