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Neuron Digest Volume 02 Number 27
NEURON Digest Thu Nov 5 08:19:28 CST 1987 - Volume 2 / Issue 27
Today's Topics:
need info
Books WANTED.
Six Generation Computing
Product Review
Time-averaging of neural events/firings
Shift-Invariant Neural Nets for Speech Recognition
Names for Neuromorphic Systems
Contest to name etc... (several)
Workshop on Neural Computers
Neural Networks Applied Optics Issue
----------------------------------------------------------------------
Date: 28 Oct 87 14:50:39 GMT
From: Duc Tran <dgis!duc@lll-tis.arpa>
Subject: need info
HELP !! HELP !!!
I need to get a hold of the following conference proceedings:
Conference on Neural Networking for Computing,
Snowbird, Utah (1986)
If anybody can provide me pointers to where I can get it, I would
be appreciated very much !!
Duc Tran
duc@dgis
uunet!dgis!duc
tel: 703-998-4647
------------------------------
Date: 28 Oct 87 20:38:28 GMT
From: John Eckrich <astroatc!johne@speedy.wisc.edu>
Subject: Books WANTED.
I am relatively new to neural networks/architectures and am interested in
learning more. I would greatly appreciate any assistance you could provide
in helping me in this endeavor. If you know of some good books, articles, or
journals, etc. please send me some E-mail.
10Q in advance.
-------------------------------------------------------------------------
Jonathan Eckrich | (rutgers, ames)!uwvax!astroatc!johne
Astronautics Technology Center | ihnp4!nicmad!astroatc!johne
Madison, WI | (608) 221-9001
------------------------------
Date: 3 Nov 87 15:13:25 PST (Tuesday)
Subject: Six Generation Computing
From: Michael_R._Emran.OsbuSouth@xerox.com
Can anybody out there direct me to find update information about Six-
Generation Computing and the Japanese progress after the 1984's
Proposals?
I have a presentation next week for my Expert System course.
All comments, info(s), or leads to any article is appreciated in
advance.
Mike
------------------------------
Date: Wed, 28 Oct 87 12:53:26 CST
From: mnorton@rca.com
Subject: product review
I recently saw a presentation by Nestor on their Neural Network-based
recognition systems. Fellow reader might be interested to known that
they claim to have fielded 12 of their Handwriting Recognition Systems
and that to the best of their knowledge (and mine), this is the first
commercial application of a neural network.
The presentation include a demonstration of handwriting recognition on
a Toshiba Labtop. Future products will include object recognition from
photographs (target recognition from aerial photography) and 3-D solid
recognition. I suspect they are far ahead of any competition in terms
of producting net-based products.
The network model they use is a feedforward, three-layer, perceptron-like
network which they call RCE (Reduced Coulomb Energy). It was mentioned
that a paper might be included in the March 1988 issue of IEEE Computer
(Special Issue on Neural Networks) which describes their model formally.
Mark J. Norton
RCA Advanced Technology Laboratories, AI Lab
mnorton%henry@RCA.COM
------------------------------
Date: Thu, 29 Oct 87 15:53:23 est
From: Michael Cohen <mike@bucasb.bu.edu>
Subject: Time-averaging of neural events/firings
We never did here at the Center for Adaptive Systems.
Our architectures are far more general. You should
look at a general bibliography.
Michael Cohen ---- Center for Adaptive Systems
Boston University (617-353-7857)
Email: mike@bucasb.bu.edu
Smail: Michael Cohen
Center for Adaptive System
Department of Mathematics, Boston University
111 Cummington Street
Boston, Mass 02215
------------------------------
Date: Fri, 30 Oct 87 20:31:32+0900
From: kddlab!atr-la.atr.junet!waibel@uunet.UU.NET (Alex Waibel)
Subject: Shift-Invariant Neural Nets for Speech Recognition
A few weeks ago, there was a discussion on AI-list, about connectionist
(neural) networks being afflicted by an inability to handle shifted patterns.
Indeed, shift-invariance is of critical importance to applications such as
speech recognition. Without it a speech recognition system has to rely
on precise segmentation and in practice reliable errorfree segmentation
cannot be achieved. For this reason, methods such as dynamic time warping
and now Hidden Markov Models have been very successful and achieved high
recognition performace. Standard neural nets have done well in speech
so far, but due to this lack of shift-invariance (as discussed on AI-list
a number of these nets have been limping along in comparison to these other
techniques.
Recently, we have implemented a time-delay neural network (TDNN) here at
ATR, Japan, and demonstrate that it is shift invariant. We have applied
it to speech and compared it to the best of our Hidden Markov Models. The
results show, that its error rate is four times better than the best of our
Hidden Markov Models.
The abstract of our report follows:
Phoneme Recognition Using Time-Delay Neural Networks
A. Waibel, T. Hanazawa, G. Hinton^, K. Shikano, K.Lang*
ATR Interpreting Telephony Research Laboratories
Abstract
In this paper we present a Time Delay Neural Network (TDNN) approach
to phoneme recognition which is characterized by two important
properties: 1.) Using a 3 layer arrangement of simple computing
units, a hierarchy can be constructed that allows for the formation
of arbitrary nonlinear decision surfaces. The TDNN learns these
decision surfaces automatically using error backpropagation.
2.) The time-delay arrangement enables the network to discover
acoustic-phonetic features and the temporal relationships between
them independent of position in time and hence not blurred by
temporal shifts in the input.
As a recognition task, the speaker-dependent recognition of the
phonemes "B", "D", and "G" in varying phonetic contexts was chosen.
For comparison, several discrete Hidden Markov Models (HMM) were
trained to perform the same task. Performance evaluation over 1946
testing tokens from three speakers showed that the TDNN achieves a
recognition rate of 98.5 % correct while the rate obtained by the
best of our HMMs was only 93.7 %. Closer inspection reveals that
the network "invented" well-known acoustic-phonetic features (e.g.,
F2-rise, F2-fall, vowel-onset) as useful abstractions. It also
developed alternate internal representations to link different
acoustic realizations to the same concept.
^ University of Toronto
* Carnegie-Mellon University
For copies please write or contact:
Dr. Alex Waibel
ATR Interpreting Telephony Research Laboratories
Twin 21 MID Tower, 2-1-61 Shiromi, Higashi-ku
Osaka, 540, Japan
phone: +81-6-949-1830
Please send Email to my net-address at Carnegie-Mellon University:
ahw@CAD.CS.CMU.EDU
------------------------------
Date: Fri 30 Oct 87 09:17:25-PST
From: Ken Laws <LAWS@iu.ai.sri.com>
Subject: Names for Neuromorphic Systems
I'll vote for adaptive networks. I'm not sure that fits constraint
relaxation via Hopfield networks or hill climbing with stochastic
annealing, but it fits better than any of the other suggested terms.
(I'm in the camp that sees no relation to simulation of neurons, other
than the coincidence that biological neural networks have some
capabilities that we would like to understand and then surpass.)
-- Ken
------------------------------
Date: Fri, 30 Oct 87 11:10:09 CST
From: im4u!rutgers!m.cs.uiuc.edu!matheus (Chris J. Matheus)
Subject: Re: NEURON Digest - V2 / #26
This summer I picked up the following name for computer simulated
neural networks:
"Artificial Neural Systems"
Unfortunately, I cannot identify the originator of the term. I simply
recall hearing it used in a few presentations and reading it occasionally
in papers. Other than being a bit long to say (it can be shortened to
ANS's: "anzes"), the name seems appropriate in the way it captures the
general flavor of this field of research. But this matter is not going
to be decided by a simple vote. Rather, it will depend upon what
name(s) end up being adopted in the literature and accepted by the
scientific community at large.
------------------------------------------------------------------------------
Christopher J. Matheus usenet: {ihnp4, convex, philabs}!uiucdcs!matheus
Inductive Learning Group arpa: matheus@a.cs.uiuc.edu
University of Illinois csnet: matheus@uiuc.csnet
Urbana, IL 61801 phone: (217) 333-3965
------------------------------------------------------------------------------
------------------------------
Date: Fri, 30 Oct 87 11:33:39 PST
From: Dr Josef Skrzypek <skrzypek@cs.ucla.edu>
Subject: Contest
How about
NEURONIA -- field of euphoric neuro-builders
------------------------------
Date: Fri, 30 Oct 87 21:41:59 PST
From: Dr Jacques J Vidal <vidal@cs.ucla.edu>
Subject: Contest to name etc...
I have used - Neuromimetic Systems
- Networks
to designate artificial neural nets,
plus "Neuromimetics" and, (in french), "Neuroinformatique"
(Neuroinfomatics??) to refer to the whole field.
However "Artificial Neural Networks" (ANNs) saeems OK and should
appease the neuron modeling purists.
PDP should be avoided. It apply just as well to models of
computation that have almost no neuronal flavor.
------------------------------
Date: 4 November 87, 11:05 CET
From: ECKMILLE%DD0RUD81.BITNET@wiscvm.wisc.edu
Subject: Workshop on Neural Computers
Dear Fellow Scientist,
The proceedings of the NATO-Workshop (ARW) on NEURAL COMPUTERS
in Neuss/Duesseldorf - 28.September - 2. October 1987 -
will be published as:
NEURAL COMPUTERS
R. Eckmiller and C. v.d. Malsburg (eds.)
at Springer-Verlag, Heidelberg
The Book will be distributed in January 1988.
During the Pre-Publication Sale you have the opportunity to order
one or more copies for only $ 25 (25 US Dollars) if you send me
the exact mailing address and a check before 10 December 1987.
The official price as of January 1988 will be about $100.
Please note that this book includes "Author Index",
"Subject Index",
and "Collection of References from all Contributions".
The List of Contributers and the Table of Contents are enclosed
for your information.
Sincerely yours,
Rolf Eckmiller, Ph.D.
Department of Biophysics
Universitaetsstr.1
D-4000 Duesseldorf, FRG
Tel.(211)311-4540
cut here-------------------and mail this slip--------------------------------
I order _____ copies of the book NEURAL COMPUTERS for $ 25 each
during the pre-publication sale (payment before 10 December 87).
Please send the copies upon delivery (Jan.1988) to the following
address:________________________________________________________
________________________________________________________________
I enclose a check for ___US $ or transfer ___DM (corresponding to
___US $) to your account: No. 626 171 at SPARDA Bank Wuppertal,
(Account Holder: Rolf Eckmiller, Ph.D.), Bankleitzahl 330 605 92.
Signature_______________________ Date_______________1987
NEURAL COMPUTERS
R. Eckmiller and C. v.d. Malsburg, eds.
Springer-Verlag, Heidelberg (January 1988)
LIST OF CONTRIBUTORS
Akers, Lex A. (USA)
Aleksander, Igor (UK)
de Almeida, Luis B. (PORTUGAL)
Anderson, Dana Z. (USA)
Anninos, Photios (GREECE)
Arbib, Michael A. (USA)
Atlan, H. (ISRAEL)
Barhen, J. (USA)
Beroule, Dominique (FRANCE)
Berthoz, Alain (FRANCE)
Bienenstock, Elie (FRANCE)
Bilbro, G.L. (USA)
Buhmann, J (W.GERMANY)
Caianiello, Eduardo R. (ITALY)
Carnevali, P. (ITALY)
Cotterill, Rodney M. J. (DENMARK)
Daunicht, Wolfgang (W.GERMANY)
Dress, William (USA)
Dreyfus, Gerard (FRANCE)
Droulez, J. (FRANCE)
Eckmiller, Rolf (W.GERMANY)
Feldman, Jerome A. (USA)
Ferry, D.K. (USA)
Fukushima, Kunihiko (JAPAN)
Gardner, E. (UK)
Garth, Simon (UK)
Ginosar, R. (ISRAEL)
Graf, H.P. (USA)
Grondin, R.O. (USA)
Gulyas, B. (BELGIUM)
Guyon, I. (FRANCE)
Hancock, P.J.B. (UK)
Hartmann, Georg (W.GERMANY)
Hecht-Nielsen, Robert (USA)
Hertz, John (DENMARK)
Hoffmann, Klaus-Peter (W.GERMANY)
Huberman, Bernardo A. (USA)
Iverson, L. (CANADA)
Jorgensen, C.C. (USA)
Koch, Christof (USA)
Koenderink, Jan J. (NETHERLAND)
Kohonen, Teuvo (FINLAND)
Korn, Axel (W.GERMANY)
Mackie, Stuart (USA)
Mallot, Hanspeter (W.GERMANY)
v. d. Malsburg, Christoph (W.GERMANY)
Marinaro, M. (ITALY)
May, David (UK)
Moller P. (DENMARK)
Moore, Will R. (UK)
Negrini, R. (ITALY)
Nylen, M. (DENMARK)
Orban, Guy (BELGIUM)
Palm, Guenther (W.GERMANY)
Patarnello, Stefano (ITALY)
Pellionisz, Andras J. (USA)
Personnaz, L. (FRANCE)
Phillips, William A. (UK)
Reece, M. (UK)
Ritter, Helge (W.GERMANY)
Sami, M.G. (ITALY)
Scarabottolo, N. (ITALY)
Schulten, Klaus (W.GERMANY)
Schwartz, D.B. (USA)
v. Seelen, Werner (W.GERMANY)
Sejnowski, Terrence J. (USA)
Shepherd, Roger (UK)
Singer, Wolf (W.GERMANY)
Smith, L.S. (UK)
Snyder, Wesley (USA)
Stefanelli Renato (ITALY)
Stroud, N. (UK)
Tagliaferri, R. (ITALY)
Torras, Carme (SPAIN)
Treleaven, Philip (UK)
Walker, M.R. (USA)
Wallace, David J. (UK)
Weisbuch, Gerard (FRANCE)
White, Mark (USA)
Willson, N.J. (UK)
Zeevi, Joshua Y. (ISRAEL)
Zucker, Steven (CANADA)
Zuse, Konrad (W.GERMANY)
------------------------------
Date: Wed, 4 Nov 87 18:10 EDT
From: MIKE%BUCASA.BITNET@wiscvm.wisc.edu
Subject: Neural Networks Applied Optics Issue
NEURAL NETWORKS: A special issue of Applied Optics
December 1, 1987 (vol. 26, no. 23)
Guest editors: Gail A. Carpenter and Stephen Grossberg
The Applied Optics special issue on neural networks brings together a
selection of research articles concerning both biological models of brain and
behavior and technological models for implementation in government and
industrial applications. Many of the articles analyze problems about pattern
recognition and image processing, notably those classes of problems for which
adaptive, massively parallel, fault-tolerant solutions are needed, and for
which neural networks provide solutions in the form of architectures that will
run in real-time when realized in hardware.
The articles are grouped into several topics: adaptive pattern recognition
models, image processing models, robotics models, optical implementations,
electronic implementations, and opto-electronic implementations. Each type of
neural network model is typically specialized to solve a variety of problems.
Models of back propagation, simulated annealing, competitive learning, adaptive
resonance, and associative map formation are found in a number of articles.
Each of the articles may thus be appreciated on several levels, from the
development of general modeling ideas, through the mathematical and
computational analysis of specialized model types, to the detailed explanation
of biological data or the fabrication of hardware. The table of contents
follows.
Single copies of this special issue are available from the Optical Society
of America, at $18/copy. Orders may be placed by returning the form below, or
by calling (202) 223-8130 (ask for Jeana Macleod).
-------------------------------------------------------------------------------
Please send ____ copies of the Applied Optics special issue on neural networks
(vol. 26, no. 23) to:
NAME: __________________________________________________
ADDRESS: _______________________________________________
_______________________________________________
_______________________________________________
TELEPHONE(S):___________________________________________
TOTAL COST: $ ____________ $18/copy, including domestic or foreign surface
postage (+ $10/copy for air mail outside U.S.)
PAYMENT: _____ Check enclosed (payable to Optical Society of America, or OSA)
or _____ Credit card: American Express ____ VISA ____ MasterCard ____
Account number __________________________________
Expiration date _________________________________
Signature (required)
____________________________
SEND TO: Optical Society of America
Publications Department
1816 Jefferson Place NW Or call: (202) 223-8130 (Jeana Macleod)
Washington, DC 20036 USA (credit cards)
_______________________________________________________________________________
NEURAL NETWORKS: A special issue of Applied Optics
December 1, 1987 (vol. 26, no. 23)
Guest editors: Gail A. Carpenter and Stephen Grossberg
TABLE OF CONTENTS
ADAPTIVE PATTERN RECOGNITION MODELS
Teuvo Kohonen. Adaptive, associative, and self-organizing functions in
neural computing
Gail A. Carpenter and Stephen Grossberg. ART 2: Self-organization of
stable category recognition codes for analog input patterns
Jean-Paul Banquet and Stephen Grossberg. Probing cognitive processes
through the structure of event-related potentials during learning: An
experimental and theoretical analysis
Bart Kosko. Adaptive bidirectional associative memories
T.W. Ryan, C.L. Winter, and C.J. Turner. Dynamic control of an artificial
neural system: The Property Inheritance Network
C. Lee Giles and Tom Maxwell. Learning and generalization in high order
neural networks: An overview
Robert Hecht-Nielsen. Counterpropagation networks
Kunihiko Fukushima. A neural network model for selective attention in
visual pattern recognition and associative recall
IMAGE PROCESSING MODELS
Michael H. Brill, Doreen W. Bergeron, and William W. Stoner. Retinal
model with adaptive contrast sensitivity and resolution
Daniel Kersten, Alice J. O'Toole, Margaret E. Sereno, David C. Knill, and
James A. Anderson. Associative learning of scene parameters from images
ROBOTICS MODELS
Jacob Barhen, N. Toomarian, and V. Protopopescu. Optimization of the
computational load of a hypercube supercomputer onboard a mobile robot
Stephen Grossberg and Daniel S. Levine. Neural dynamics of attentionally
modulated Pavlovian conditioning: Blocking, inter-stimulus interval, and
secondary reinforcement
OPTICAL IMPLEMENTATIONS
Dana Z. Anderson and Diana M. Lininger. Dynamic optical interconnects:
Volume holograms and optical two-port operators
Arthur D. Fisher, W.L. Lippincott, and John N. Lee. Optical implementations
of associative networks with versatile adaptive learning capabilities
Clark C. Guest and Robert Te Kolste. Designs and devices for optical
bidirectional associative memories
Kelvin Wagner and Demetri Psaltis. Multilayer optical learning networks
ELECTRONIC IMPLEMENTATIONS
Larry D. Jackel, Hans P. Graf, and R.E. Howard. Electronic neural-network
chips
Larry D. Jackel, R.E. Howard, John S. Denker, W. Hubbard, and S.A. Solla.
Building a hierarchy with neural networks: An example - image vector
quantization
A.P. Thakoor, A. Moopenn, John Lambe, and Satish K. Khanna. Electronic
hardware implementations of neural networks
OPTO-ELECTRONIC IMPLEMENTATIONS
Nabil H. Farhat. Opto-electronic analogs of self-programming neural nets:
Architectures and methodologies for implementing fast stochastic learning
by simulated annealing
Yuri Owechko. Opto-electronic resonator neural networks
(Please Post this to Your Mailing List)
------------------------------
End of NEURON-Digest
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