neuron-request@HPLMS2.HPL.HP.COM ("Neuron-Digest Moderator Peter Marvit") (10/10/90)
Neuron Digest Tuesday, 9 Oct 1990
Volume 6 : Issue 59
Today's Topics:
NIPS*90 WORKSHOP PRELIMINARY PROGRAM
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Subject: NIPS*90 WORKSHOP PRELIMINARY PROGRAM
From: jose@learning.siemens.com (Steve Hanson)
Date: Wed, 26 Sep 90 15:17:02 -0400
NIPS*90 WORKSHOP PRELIMINARY PROGRAM
____________________________________________________________
! POST CONFERENCE WORKSHOPS AT KEYSTONE !
! THURSDAY, NOVEMBER 29 - SATURDAY, DECEMBER 2, 1990 !
!____________________________________________________________!
Dear Collegue,
I am pleased to send you a preliminary description of workshops to
be held during our annual "NIPS Post-conference Workshops". Among the
many workshop proposals we have received we believe to have selected a
program of central topics that we hope will cover most of your
interests and concerns. As you know from previous years, our NIPS
Post-conference Workshops are an opportunity for scientists actively
working in the field to gather in an informal setting and to discuss
current issues in Neural Information Processing.
The Post-conference workshops will meet in Keystone, right after the
IEEE conference on Neural Information Processing Systems, on November
30 and December 1. You should be receiving an advance program, travel
information and registration forms for both NIPS and the
Post-conference workshops. Please use these forms to register for
both events. Please also indicate which of the workshop topics below
you may be most interested in attending. Your preferences are in no
way binding or limiting you to any particular workshop but will help
us in allocating suitable meeting rooms and minimizing overlap between
workshop sessions. Please mark your three most prefered workshops
(1,2 and 3) on the corresponding form in your registration package.
I look forward to seeing you soon at NIPS and its Post-conference
Workshops. Please don't hesitate to contact me with any questions you
may have about the workshops in general (phone: 412-268-7676, email
at: waibel@cs.cmu.edu.). Should you like to discuss a specific
workshop, please also feel free to contact the individual workshop
leaders listed below.
Sincerely yours,
Alex Waibel
NIPS Workshop Program Chairman
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15217
----------------------------------------------------------------------
Thursday, November 29, 1990
5:00 PM: Registration and Reception at Keystone
Friday, November 30, 1990
7:30 - 9:30 AM: Small Group Workshops
4:30 - 6:30 PM: Small Group Workshops
7:30 - 10:30 PM: Banquet and Plenary Discussion
Saturday, December 1, 1990
7:30 - 9:30 AM: Small Group Workshops
4:30 - 6:30 PM: Small Group Workshops
6:30 - 7:15 PM: Plenary Discussion, Summaries
7:30 - 11:00 PM: Fondue Dinner, MountainTop Restaurant
----------------------------------------------------------------------
NIPS '90 WORKSHOP DESCRIPTIONS
Workshop Program Coordinator: ALEX WAIBEL
Carnegie Mellon University
phone: 412-268-7676
E-mail: waibel@cs.cmu.edu
1. OSCILLATIONS IN CORTICAL SYSTEMS
Ernst Niebur
Computation and Neural Systems
Caltech 216-76
Pasadena, CA 91125
Phone: (818) 356-6885
Bitnet: ernst@caltech
Internet: ernst@aurel.cns.caltech.edu
40-60 Hz oscillations have long been reported in the rat and rabbit
olfactory bulb and cortex on the basis of single-and multi-unit
recordings as well as EEG activity. Periodicities in eye movement
reaction times as well as oscillations in the auditory evoked
potential in response to single click or a series of clicks all
support a 30-50 Hz framework for aspects of cortical activity and
possibly cortical function. Recently, highly synchronized, stimulus
specific oscillations in the 35-85 Hz range were observed in areas 17,
18 and PMLS of anesthetized as well as awake cats. Neurons with
similar orientation tuning up to 10 mm apart and even across the
vertical meridian (i.e. in different hemispheres) show phase-locked
oscillations.
The functional importance of these oscillations as well as the
underlying mechanisms are a matter of debate. For the time being, the
field is characterized by relatively few experiments and a certain
abundance of theories, some coming from biology, some from the neural
network community, and also from physics (coupled oscillators have
been discussed by physicists in many circumstances). This workshop
will be an opportunity to bring together experimentalists and
theoreticians.
2. BIOLOGICAL SONAR
Herbert L. Roitblat
Department of Psychology
University of Hawaii
2430 Campus Road
Honolulu, HI 96822
Phone: (808) 956-6727
E-mail: herbert@uh.cc.uk.uhcc.hawaii.edu
Patrick W. B. Moore & Paul E. Nachtigall
Naval Ocean Systems Center
Hawaii Laboratory
P. O. Box 997
Kailua, Hawaii, 96734
Phone: (808) 257-5256 & (808) 257-1648
E-mail: pmoor@nosc.mil & nachtig@nosc.mil
Several species, most notably bats and dolphins, are known to use
biological sonar to obtain information about the world around them.
These animals have evolved solutions to severe processing problems
that can be exploited in the development of artificial signal
processing mechanisms, including intelligent sonar and radar. We call
the process of using biological studies to inform the design of
artificial systems "biomimetics" because the artificial systems are
designed as mimics of biological ones. This workshop will consider
the neural network and neurobiological models of animal signal
processing that have recently been advanced. It will attempt to
integrate studies of dolphins, bats, and other species with
computational models. The workshop will be of interest to
investigators of biological signal processing as well as those
interested in the development or artificial signal processing systems.
3. NETWORK DYNAMICS
Richard Rohwer
Centre for Speech Technology Research
Edinburgh University
80, South Bridge
Edinburgh EH1 1HN
Scotland
Phone: (44 or 0) (31) 225-8883 x261
E-mail: rr%ed.eusip@nsfnet-relay.ac.uk
The 1990 network dynamics workshop is to consist of mini-presentations
to nucleate discussions about temporal patterns in real and model
neural networks, much as was done in 1989. The subject area includes
the description, interpretation and engineering design of these
patterns. An effort will be made to arrange presentations on
different specific subjects than were discussed last year, although
priority will be given to new developments in any area. An issue of
particular interest is the functional or cognitive significance of
temporal patterns. Another is the diversity of temporal behaviour
produced by specific classes of models, with implications for the
evaluation of biological models and the selection of models for
engineering. Training algorithms are also of interest.
4. CONSTRUCTIVE AND DESTRUCTIVE LEARNING ALGORITHMS
Scott E. Fahlman
School of Computer Science
Carnegie-Mellon University
Pittsburgh, PA 15213
Phone: (412) 268-257
Internet: fahlman@cs.cmu.edu
Most existing neural network learning algorithms work by adjusting
connection weights in a fixed network. Recently we have seen the
emergence of new learning algorithms that alter the network's topology
as they learn. Some of these algorithms start with excess connections
and remove any that are not needed; others start with a sparse network
and add hidden units as needed, sometimes in multiple layers. The
user is relieved of the burden of guessing in advance what network
topology will best fit a given problem. In addition, many of these
algorithms claim improved learning speed and generalization.
In this workshop we will review what is known about the relationship
between network topology, expressive power, learning speed, and
generalization. Then we will examine a number of constructive and
destructive algorithms, attempting to identify the strengths and
weaknesses of each. Finally, we will look at open questions and
possible future developments.
5. COMPARISONS BETWEEN NEURAL NETWORKS AND DECISION TREES
Lorien Y. Pratt
Computer Science Department
Rutgers University
New Brunswick, NJ 08903
Phone: (201) 932-4634
E-mail: pratt@paul.rutgers.edu
Steven W. Norton
Siemens Corporate Research, Inc.
755 College Road East
Princeton, NJ 08540
Phone: (609) 734-3365
E-mail: nortonD @learning.siemens.com
The fields of Neural Networks and Machine Learning have evolved
separately in many ways. However, close examination of multilayer
perceptron learning algorithms (such as Back-Propagation) and decision
tree induction methods (such as ID3 and CART) reveals that there is
considerable convergence between these subfields. They address
similar problem classes (inductive classifier learning) and can be
characterized by a common representational formalism of hyperplane
decision regions. Furthermore, topical subjects within both fields
are related, from minimal trees and brain-damaged nets to incremental
learning.
In this workshop, invited speakers from the Neural Network and
Machine Learning communities (including Les Atlas and Tom Dietterich)
will discuss their empirical and theoretical comparisons of the two
areas. In a discussion period, we'll then compare and contrast them
along the dimensions of representation, learning, and performance
algorithms. We'll debate the ``strong convergence hypothesis'' that
these two research areas are really studying the same problem.
6. GENETIC ALGORITHMS
David Ackley
MRE-2B324
Bellcore
445, South St.
Morristown, NJ 07962-1910
Phone: (201) 829-5216
E-mail: ackley@bellcore.com
"Genetic algorithms" are optimization and adaptation techniques that
employ an evolving population of candidate solutions. "Recombination
operators" exchange information between individuals, creating a global
search strategy quite different from --- and in some ways
complementary to --- the gradient-based techniques popular in neural
network learning. The first segment of this workshop will survey the
theory and practice of genetic algorithms, and then focus on the
growing body of research efforts that combine genetic algorithms and
neural networks. Depending on the participants' interests and
backgrounds, possible discussion topics range from "So what's all
this, then?" to "How should networks best be represented as genes?" to
"Is the increased schema disruption inherent in uniform crossover a
feature or a bug?"
As natural neurons provide inspiration for artificial neural
networks, and natural selection provides inspiration for genetic
algorithms, other aspects of natural life can provide useful
inspirations for studies in "artificial life". In artificial worlds
simulated on computers, experiments can be performed whose natural
world analogues would be inconvenient or impossible for reasons of
duration, expense, danger, observability, or ethics. Interactions
between genetic evolution and neural learning can be studied over many
hundreds of generations. The consensual development of simple,
need-based lexicons among tribes of artificial beings can be observed.
The second segment of this workshop will survey several such "alife"
research projects. A discussion of prospects and problems for this
new, interdisciplinary area will close the workshop.
7. IMPLEMENTATIONS OF NEURAL NETWORKS ON DIGITAL, MASSIVELY
PARALLEL COMPUTERS
S. Y. Kung and K. Wojtek Przytula
Hughes Research Laboratories, RL69
3011 Malibu Canyon Road
Malibu, California 90265
Phone: (213) 317-5892
E-mail: wojtek@csfvax.hac.com
Implementations of neural networks span a full spectrum from software
realizations on general-purpose computers to strictly special-purpose
hardware realizations. Implementations on programmable, parallel
machines, which are to be discussed during the workshop, constitute a
compromise between the two extremes. The architectures of these
machines reflect the structure of neural network models better than do
those of sequential machines, thus resulting in higher processing
speed. The programmability provides more flexibility than is
available in specialized hardware implementations and opens a way for
realization of various models, including future modifications, on a
single machine. We will discuss the degree to which the architectures
of the machines should mimic the structure of the neural network
models versus the degree of the match to be obtained by
programmability.
8. VLSI NEURAL NETWORKS
Jim Burr
Starlab Stanford University
Stanford, CA 94305
Phone: (415) 723-4087 (office)
(415) 725-0480 (lab)
(415) 574-4655 (home)
E-mail: burr@mojave.standford.edu
This one day workshop will address the latest advances in VLSI
implementations of neural nets. How successful have implementations
been so far? Are dedicated neurochips being used in real applications?
What algorithms have been implemented? Which ones have not been? Why
not? How important is on chip learning? How much arithmetic precision
is necessary? Which is more important, capacity or performance? What
are the issues in constructing very large networks? What are the
technology scaling limits? Any new technology developments?
Several invited speakers will address these and other questions from
various points of view in discussing their current research. We will
try to gain better insight into the strengths and limitations of
dedicated hardware solutions.
9. OPTICAL NEURAL NETWORKS
Kristina Johnson
University of Colorado, Boulder
Campus Box 425
Boulder, CO 80309
Phone: (303) 492-1835
E-mail: kris%fred@boulder.colorado.edu
kris@boulder.colorado.edu
This workshop will address issues in the implementation of neural
networks in parallel optical hardware including issues in
scaleability, speed, bipolar neurons and weights, influence of
component characteristics on system performance and methods for
all-optical learning. The goal of the workshop will be to identify
algorithms and applications or neural networks that are particularly
suited for optoelectronic implementation. Novel device and systems
that can implement neural processes will also be highlighted, such as
recent advances in custome VLSI/ modulator technology.
10. NEURAL NETWORKS IN MUSIC
Samir I. Sayegh
Department of Physics
Purdue University
Fort Wayne, IN 46805-1499
Phone: (219) 481-6157
E-mail: sayegh@ed.ecn.purdue.edu
The workshop is to address aspects of perception, composition and
motor skill performance in different aspects of music and their
modeling using Neural Networks.
Although music applications can be dismissed as not "technical," the
topic is of great importance precisely because most of the knowledge
involved occurs at a preverbal cognitive level. In music, as in
neural networks, teaching by example is predominant.
The recent surge in interest is indicated by the increasing number
of presentations at major conferences and the publication of special
issues of the Computer Music Journal (Fall89, Winter90) dedicated to
Neural Networks and Connectionism. A special volume, with
contributions from and additions to these articles is being edited.
A large spectrum of applications as well as mature and refined
developments will be represented at the workshop. These include pitch
perception, composition, quantization of musical time, performance,
chord classification and fully developed systems.
11. SPEECH RECOGNITION
Nelson Morgan and John Bridle
International Computer Science Institute
1947 Center Street, Suite 600
Berkeley, CA 94704
Phone: (415) 643-9153
E-mail: morgan@icsib4.berkeley.edu
In the early, heady days of the most recent neural-network revival,
many devotees felt that undifferentiated masses of simple neural
models could solve any classification problem. More recently, it has
been widely accepted that constraints on connections, structure, and
sometimes the form of the input representation are necessary for good
performance in complex domains such as speech.
In the workshop, we will discuss perspectives on this issue. The
emphasis will be on the value and risk of using application-specific
knowledge to constrain network topologies and to integrate ANN
algorithms into systems which recognize speech.
12. NATURAL LANGUAGE PROCESSING
Robert Allen
MRE-2A367
Bellcore
445, South St.
Morristown, NJ 07962-1910
Phone: (201) 829-4315
E-mail: rba@flash.bellcore.comp
It is possible to imagine fully integrated network-based speech
processing systems, massive connectionist knowledge bases, and swarms
of communicating connectionist agents. Indeed, networks have many
properties which seem to make them suitable for pro- cessing natural
language. Activations can readily integrate con- text ranging from
phoneme interactions, to reference, to seman- tics and pragmatics.
Likewise temporal processing may accommo- date syntax and learning in
networks can model acquisition. Nonetheless even some basic issues
stir controversy such as: the ability to handle context-free grammars,
compositionality, the need for rule-like behavior, and generating
past-tense verb forms. This workshop will consider both conceptual
and practical limitations in bridging the gap between existing
demonstrations and potential applications.
13. HAND-PRINTED CHARACTER RECOGNITION
Gale Martin & Jay Pittman,
MCC, 3500 Balcones Center Drive, Austin, Texas 78759
Phone: (512) 338-3334, 338-3363,
E-mail: galem@mcc.com, pittman@mcc.com
Over the past several years backpropagation techniques have successfully been
applied to isolated hand-printed character recognition. This workshop will
consider what has been learned and where the field is headed next. The issues
to be addressed include the following: 1) Black art issues (what works, what
doesn't, what matters), 2) What are the current important research problems
& approaches (segmentation, incorporation of higher level constraints, very
large symbol sets)? 3) How can the field foster collaborations and
comparisons of techniques? The format will include very brief talks by
interested participants and subsequent discussion periods. The target
audience includes those interested in and/or working on handwriting
recognition and related visual recognition problems. Individuals interested
in giving brief talks are invited to contact Gale or Jay at the above addresses.
14. NN PROCESSING TECHNIQUES TO REAL WORLD MACHINE VISION PROBLEMS
Murali M. Menon & Paul J. Kolodzy
MIT Lincoln Laboratory
Lexington, MA 02173-9108
Phone: (617) 863-5500
E-mail: Menon@LL.LL.MIT.EDU
Kolodzy@LL.LL.MIT.EDU
Our proposed workshop will discuss the application of neural networks
to vision applications, including, but not limited to, image
restoration and pattern recognition. Participants will present their
specific applications for discussion to highlight the relevant issues.
Since the discussions will be driven by actual applications, we will
place an emphasis on the advantages of using neural networks at the
system level in addition to the individual processing steps.
To focus the discussions in the workshop we plan to present the
following applications: a medical screening system using neural
networks for identification of Pap smears, a description of a software
and hardware implementation of a neural network for multi-scale edge
extraction, a large scale software implementation of Grossberg's
Boundary Contour System (BCS), and a Markov Random Field (MRF) based
image restoration system.
The proposed workshop invites participation from researchers in
machine vision, neural network modeling, pattern recognition and
biological vision.
15. INTEGRATION OF ARTIFICIAL NEURAL NETWORKS WITH EXISTING
TECHNOLOGIES EXPERT SYSTEMS AND DATABASE MANAGEMENT SYSTEMS
Veena Ambardar
15251 Kelbrook Dr
Houston, TX 77062
Phone: (713) 663-2264
E-mail: veena@shell.uucp
Integration of Artificial Neural Networks (ANNs) with Expert Systems
(ES) & Database Management Systems (DBMS) is already being researched
in several ways such as : a) extraction of rules from a given neural
network b) developme- nt of hybrid systems using both ANNs & ESs & c)
information retrieval & query processing using ANNs etc. Possible
benefits of this integration are a) facilit- ated acquisition of
knowledge bases b) automated extension of existing expert systems(
without knowing the rules explicitly) c) better understanding of the
dynamics of ANNs d) reducing the size of the existing expert systems
by using threshold logic functions e) better retrieval of information
& queries embedded with partial cues or noise & f) facilitated
preprocessing of data before it is fed to ANNs etc. The workshop
shall focus on this integration with the following specific questions
in perspective : a) the benefits from this integration b) theoretical
& practical issues those ought to be addressed for this bridging c)
current status of research efforts & commercial packages in this
direction d) discussion of the different techniques for the extraction
of rules from a given ANN and if they could be extended to Hopfield &
ART etc. e) different ways those could be used to improve upon the
accuracy rate for the processing of the query with partial cues/noise
using neural nets f) future directions for both researchers and
vendors. Both researchers & commercial vendors would be invited.
16. BIOPHYSICS
J. J. Atick
Institute for Advanced Study
Princeton, NY 08540
Phone: 609-734-8000
B. Bialek
Department of Physiology
University of California at Berkeley
Berkeley, CA 94720
E-mail: bialek@candide.berkeley.edu
The workshop will proceed through organized discussion with as
minimal formal presentation as possible. The discussion will focus on
the development of new theoretical ideas in neurocomputing that could
be tested with real biological experiments. The workshop will review
some of the recent progress, discuss its implications and try to come
up with new directions to pursue. The emphasis will be on sensory
systems where the signal processing problems involved could be sharply
defined and the performance well qualified. Some of the specific
questions that the workshop will address are:
- Are there theories of what the nervous system should
compute?
- Can we rate the observed performance of the nervous system
on some absolute scale? How optimal are its computational
strategies?
- What is the nature of the encoding of information in the
nervous system? Is the encoding for trigger features or is
there general purpose encoding? To what extent is the
information distributed?
- How universal are the computations involved? Could the same
theoretical principle account for the variety of neural
computations observed in the as well as different species.
- What is the role of adaptation and what is its precise
computational formulation? What is kept constant in the
process of adaptation?
- Are there critical experimental tests of recent theories in
neurocomputing.
Participants who have done work related to the theme of this
workshop are encouraged to provide preprints of their work for general
distribution.
17. ASSOCIATIVE LEARNING RULES
David Willshaw
University of Edinburgh
Centre for Cognitive Science
2 Buccleuch Place
Edinburgh EH8 9LW
Phone: 031 667 1011 ext 6252
E-mail: David@uk.ac.ed.cns
<Abstract Unavailable>
18. RATIONALES FOR, AND TRADEOFFS AMONG VARIOUS NODE FUNCTION SETS
J. Stephen Judd
Siemens Corporate Research, Inc.
755 College Road East
Princeton, NJ 08540
Phone: (609) 734-6500
E-mail: Judd@learning.siemens.com
Linear threshold functions and sigmoid functions have become very
standard node functions for our neural network studies, but the
reasons for using them are not very well founded. Are there other
types of functions that might be more justifiable? or work better? or
make learning more tractable? This workshop will explore various
issues that might help answer such questions. Come hear the experts
tell us what matters and what doesn't matter; then tell the experts
what *really* matters.
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End of Neuron Digest [Volume 6 Issue 59]
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