HDTenEightyP/NetNews huggingface dataset:
From: albert@sedal.su.OZ.AU (Albert Hirawan)
Subject: MUME DOS version distribution: unzip with unzip 5.0 or pkunzip 1.93a
Followup-To: comp.ai.neural-nets
Keywords: Multi-module, MUME, DOS, zip
Organization: SEDAL -- Sydney University Electrical Engineering
Date: Tue, 12 Jan 1993 22:20:13 GMT

A number of people had problems in unzipping the MUME distribution for DOS.
The zip file was created with zip 1.9 which is compatible with pkzip 1.93a which
is incompatible with pkunzip 1.10. Therefore, it should be unzipped with
unzip 5.0 or pkunzip 1.93a.

Unzip 5.0 is available on may ftp sites, including archie's.

Following is an extract from zip 1.9 manual page:

...
     zip version 1.9 is compatible with pkzip 1.93a.   Note  that
     pkunzip 1.10 cannot extract files produced by pkzip 1.93a or
     zip 1.9. You must use pkunzip 1.93a or unzip 5.0 to  extract
     them.
...

Apologies to the problems caused.




--------------------------------------------------------------------------------
Albert Hirawan
S.E.D.A.L. (Systems Engineering and Design Automation Laboratory)
Department of Electrical Engineering
University of Sydney
N.S.W. 2006
AUSTRALIA

Internet: albert@sedal.su.OZ.AU
--------------------------------------------------------------------------------
From: sjain@calsci (Sandeep Jain)
Newsgroups: comp.ai.neural-nets
Subject: Schools doing Neural Networks
Date: 12 Jan 93 01:17:55 GMT
Reply-To: calsci!sjain@gvgpsa.gvg.tek.com (Sandeep Jain)
Followup-To: comp.ai.neural-nets
Organization: California Scientific Software
News-Software: FSUUCP 1.1 Release 5

Here are some institutions doing work in neural networks:
(The department names may not be exactly correct)

-- Boston University
      Center for Adaptive Systems

-- University of Pennsylvania
      Department of Computer Science, Department of Electrical Engineering

-- California Institute of Technology
      Department of Computation and Neural Systems

-- University of Illinois at Urbana-Champaign
      Beckman Institute ( this is more of a research center than a department;
      I'm told some amount of neural network research is done here )

-- Brown University

-- University of Southern California

-- AT & T, Bell Labs
      Usually, a PhD is required to get in here. It helps if you can walk
      on water.

    A few these institutions have established separate departments to
    study neural systems. Typically, neural network activity is scattered
    across deparments of Electrical Engineering, Computer Science, Physics,
    Mathematics, and Neurobiology. Each department gives the work its own
    special flavor, and the degree of interaction between departments
    (said to be a healthy thing in an inter-disciplinary field) varies
    with institution.

    The trick is to get in touch with SOMEONE at the institution who's
    familiar with neural networks. He or she will know who's doing what,
    where. The Computer Science and Electrical Engineering departments are
    a good place to begin hunting.


                                                            Sandeep Jain
From: cartabia@ghost.dsi.unimi.it (giovanni cartabia)
Newsgroups: comp.ai.neural-nets
Subject: recurrent neural network
Date: 12 Jan 93 14:50:07 GMT
Organization: Computer Science Dep. - Milan University

I am a C.S. student wich works on neural nets (boltzmann machines) but
I heared somethink about recurrent neural networks. Can anyone suggest
me some GOOD papers about the argument?

Thanks in advance.
Joe Cartabia
Dipartimento di Scienze dell'informazione
Universita' di Milano. Italy.

e-mail: cartabia@ghost.dsi.unimi.it
From: timh@meaddata.com (Tim Humphrey)
Newsgroups: comp.ai.neural-nets
Subject: Richard Granger's olfactory cortex simulation
Date: 10 Jan 1993 02:39:26 GMT
Organization: Mead Data Central, Dayton OH
NNTP-Posting-Host: neuron.meaddata.com

Richard Granger working with Gary Lynch at the University of California at
Irvine have developed simulations of the olfactory cortex.  Does anyone
know if there is a public domain version of this, or know of any technical
papers that describe their work.

Tim Humphrey
timh@meaddata.com
From: cscon143@uoft02.utoledo.edu
Newsgroups: comp.ai.neural-nets
Subject: Re: will Cascade Correlation work in stochastic mode?dir
Date: 7 Jan 93 15:03:20 EST
Distribution: comp.ai.neural-nets
Organization: University of Toledo, Computer Services

In article <1993Jan6.182726.29899@cs.brown.edu>, ra@cs.brown.edu (Ronny Ashar) writes:
> Hi
> 
> I've been using standard backprop and on-line training, i.e., the algorithm can sample a single randomly generated example at a time. Backprop performs well
> when given about 50,000 such examples( thats a small subset of the domain).
> 
> However, I would prefer a more robust algorithm. I was looking at Fahlman's 
> Cascade Correlation. My impression is that Cascor needs epoch training only;
> it could be modified to work in stochastic mode, but, in that case, it will 
> end up creating huge nets with redundant units. Is that correct?
> 
> Have people been using Cascade Correlation in stochastic mode? I'll appreciate
> getting comments/suggestions.
> 
> Ronny
> 
> 
-- 

	In the proceedings of COGANN '92 held in Baltimore, there are
	two articles on Genetic Cascade Correlation.  It's not exactly
	purely stochastic but you might find it interesting.  It just
	happens to be what I am working on for my thesis too.

					- Erik Mayer 
___
  Internet: cscon143@uoft02.utoledo.edu    
  Bitnet: cscon143@uoft02.BITNET                 

Newsgroups: comp.ai.neural-nets
From: moeller@iw.uni-hannover.de (Henning Moeller)
Subject: Q: feature maps
Reply-To: moeller@iw.uni-hannover.de
Organization: Institut fuer Werkstoffkunde, Uni Hannover
Date: Tue, 12 Jan 1993 15:55:29 GMT


Hi,
We like to develop a control-system for a dynamic system based on feature maps (Kohonen).
Now we have two questions:
 1. Is it necessary to normalize the sensoric signals, if they are of a 
    different kind (position/orientation)?
 2. What`s about the correctness concerning the estimation of the Jacobi 
    matrix in both cases?

Thanks in advance,
                  Henning





-----------------------------------------------------------------------	
|  Henning Moeller	         |  Tel: INT-49-511-762-3906   	      |
|  Universitaet Hannover         |  Fax: INT-49-511-762-5245	      |
|  Institut fuer Werkstoffkunde  |  Email: moeller@iw.uni-hannover.de |
|  Fachgruppe Unterwassertechnik |		   		      |
|  Appelstr. 11a  	         |                                    |
|W-3000 Hannover, GERMANY        |           			      |
-----------------------------------------------------------------------
From: nagle@netcom.com (John Nagle)
Newsgroups: comp.ai.neural-nets
Subject: Re: Neraul cockroach
Date: 12 Jan 93 23:57:41 GMT
Article-I.D.: netcom.1993Jan12.235741.8860
Organization: Netcom - Online Communication Services  (408 241-9760 guest)

treason@gnu.ai.mit.edu (Act of Treason) writes:

>I retrieved a program off of the funic archive a while back called
>nervous.lzh and found it rather interesting.  Its a neural simulation of a
>cockroach for msdos.  It allows building of neurons  and connetions with
>moderation and such, but there was one problem.  The neurons are listed
>as acronyms, and by themselves mean nothing.  I have threaded through the 
>various acronyms and deciphered what they were due to the connections and
>functionality, but alas, I still am stumped on a few.  I would really appreciateif someone sent me a list if such a thing exists, and if so, I will modify the source to include a description of what the neuron is for reference of the
>individual wishing to modify its parameters.  

       Sounds like you have the MS-DOS version of Beer's cockroach
simulator.  That program was written by
Pat and Greg Williams, Rt. 1, Box 302, Gravel Switch, KY 40328, 
606-332-7606, and they will send you a source disk for a small fee.
The theory is based on the ideas of Randall D. Beer, Department of 
Computer Engineering and Science, Case Western Reserve University, 
Cleveland, Ohio, as found in his INTELLIGENCE AS ADAPTIVE BEHAVIOR: AN 
EXPERIMENT IN COMPUTATIONAL NEUROETHOLOGY, Academic Press, 1990 (ISBN 
0-12-084730-2; to order, you can phone A.P. toll-free at 1-800-321-5068). 
That book will explain what each neuron does.

					John Nagle

       
Newsgroups: comp.ai.neural-nets
From: fdm@engr.ucf.edu (Frederic D. McKenzie (Rick))
Subject: LECUN digit recognition pseudo-newton method
Organization: engineering, University of Central Florida, Orlando
Distribution: na
Date: Sun, 10 Jan 1993 22:53:16 GMT

Hello,

I'm looking for code that implements the pseudo-newton backprop method
used by LECUN for digit recognition.
I have implemented the architecture and used a continuous backprop 
which works; but, there is something wrong with the way I'm implementing
the double partial of the cost function for the pseudo-newton method.

Can anyone help me?

Please email at:
fdm@engr.ucf.edu

Thanks
Rick McKenzie

Newsgroups: comp.ai.neural-nets
From: sef@sef-pmax.slisp.cs.cmu.edu
Subject: Re: some basic questions
Organization: School of Computer Science, Carnegie Mellon
Date: Tue, 5 Jan 1993 18:38:55 GMT


    From: danz+@CENTRO.SOAR.CS.CMU.EDU (Dan Zhu)
    
          - Is symmetric sigmoid function (-0.5, 0.5) or (-1, 1) always
            better than the asymmetric one (0, 1)? Any reference? 

Almost always.  In my quickprop paper (citation below) I explore this on a
few simple learning tasks.  Asymmetric sigmoid learned faster on simple
encoder tasks, but I view that as an unusual feature of that problem.
There's a paper by Stornetta and Hubermann in the 1987 IEEE ICNN that
claims superiority for symmetric sigmoids, and a nice paper in NIPS 3 by
leCun, Kanter, and Solla presents some theory that may explain why
symmetric sigmoids work better.

          - Shall I do this kind of scaling with the input representation also? 

Your first layer of weights should be able to learn to do the sacling for
you.  However, some researchers report better and much faster results if
you pre-scale the inputs so that all are in the same range.  I suspect that
this is a trivial difference for fast, fairly robust learning algorithms
and a more important difference for slower algorithms that may tend to get
stuck.

          - What would be a good cut point to test the network from time to time
            to avoid the overtraining? 

The usual practice is to use a separate training and validation set.
Periodically, you run the validation set through the partially trained net,
and you stop (and maybe revert) when you see the generalization starting to
get worse.  How often you check this depends on your goals: checking
frequently is more expensive, but stops you at closer to the right place.

          - I remember I read something like "three layer (with one hidden layer)
            is sufficient for the generalization of the network...". Could anyone
            give me any pointer to the exact reference for it?

I don't have the references handy.  There have been various results along
this line by Cybenko, Hal White, Sontag, and others.

          - Also, is there any new reference about the clue for selecting 
            the range of "hidden nodes", "learning rate", "momentum" and 
            judgement about the "initial weight"?

There's no new magic that I know of that would allow you to choose
parameters and network topology just by looking at the data set (at least
for non-toy problems).  If you want a near-optimal result, you have to
adjust the learning parameters and network topology dynamically.

-- Scott

===========================================================================
Scott E. Fahlman			Internet:  sef+@cs.cmu.edu
Senior Research Scientist		Phone:     412 268-2575
School of Computer Science              Fax:       412 681-5739
Carnegie Mellon University		Latitude:  40:26:33 N
5000 Forbes Avenue			Longitude: 79:56:48 W
Pittsburgh, PA 15213
===========================================================================
From: lordSnooty@cup.portal.com (Andrew - Palfreyman)
Newsgroups: comp.ai.neural-nets
Subject: Re: Biologically Plausible Dynamic Artificial Neural Networks
Date: Thu,  7 Jan 93 15:08:05 PST
Organization: The Portal System (TM)
Distribution: world
  <1993Jan6.224107.27144@rhrk.uni-kl.de>

Ulf R. Andrick   <andrick@rhrk.uni-kl.de> writes:
: Biologically Plausible Artificial Neural Network sounds to me
: a bit like an oxymoron. I tend to consider any `Artificial Neural 
: Networks' as not biologically plausible. 

Unless you mean this modulo current technology, you are clearly wrong.

In the computing power limit, anything can be simulated to exquisite
(d.h. beliebig) levels of detail, provided that the same level of detail
is verifiably present in the real system you seek to model.
 --------------------------------------------------------------------------
| lord snooty @the giant |  inceptus clamor frustratur hiantes             |
| poisoned electric head |              andrew_-_palfreyman@cup.portal.com |
  --------------------------------------------------------------------------
From: demers@cs.ucsd.edu (David DeMers)
Newsgroups: comp.ai.neural-nets
Subject: Re: Can Anyone tell me when and where the next NIPS will be ?
Keywords: NIPS
Date: 9 Jan 93 04:47:26 GMT
Distribution: usa
Organization: =CSE Dept., U.C. San Diego

In article <1993Jan9.031714.16302@EE.Stanford.EDU> hadar@boreas.Stanford.EDU (Hadar Avi-Itzhak) writes:
>Can anyone give me the info for the next NIPS ? Thanks in advance.

Based on extrapolation from the past, NIPS will be the first
week of December, with abstracts due in May (my guess is the 22d)
Gerry Tesauro is the general chair, I believe, and will have more
for us all on or about the time the proceedings from NIPS*5 come
out...

Dave

-- 
Dave DeMers			 ddemers@UCSD   demers@cs.ucsd.edu
Computer Science & Engineering	C-014		demers%cs@ucsd.bitnet
UC San Diego					...!ucsd!cs!demers
La Jolla, CA 92093-0114	(619) 534-0688, or -8187, FAX: (619) 534-7029
Newsgroups: comp.ai.neural-nets,soc.college.grad
From: ledwards@leland.Stanford.EDU (Laurence James Edwards)
Subject: Re: Schools strong in neural nets? (was Request: Neural-Nets-classes in US graduate schools)
Organization: DSG, Stanford University, CA 94305, USA
Date: Sun, 10 Jan 93 09:57:49 GMT

In article <QfHp_3G00WBMQ2W3Im@andrew.cmu.edu>, fc0v+@andrew.cmu.edu (Franklin Chang) writes:
|> >	Stanford		Psychology
	    Rumelhart (probably are more but I don't know em)
|> >	MIT			Brain and Cognitive Sciences
|> >	UCSD			Cognitive Sciences
|> >	Carnegie Mellon		Psychology and Computer Science
|>         Jay McClelland
|>         Johnathan Cohen
|>         Mark Johnson
|> >	Brown			Cognitive and Linguistic Sciences
|> >	UC Irvine		Computer and Information Sciences
|> >	Pittsburgh		Psychology and Computer Sciences
|> >	Boston University	Neural Systems (or something like that)
|> >	University of CO	Computer Science
|> >	Oregon Grad Insti	Computer Science
|> >	Indiana			Psychology and Computer Science
|> 
|> Could people also put down people that are doing neural networks
|> research in these departments?  I've filled in CMU to the best of my
|> knowledge.

Stanford's EE dept also offers a course on neural nets, which is a little
heavier on the math and lighter on the cognitive psychology than Rumelhart's
course. I don't know any names of people doing NN research in EE though. If
you're interests are a little broader than NN's Stanford also has a good
program in Cognitive Psych.

Larry Edwards
From: pikoehn@cip.informatik.uni-erlangen.de (Philipp Koehn)
Newsgroups: comp.ai.neural-nets
Subject: SUMMARY&REQUEST: US univ. with NN courses
Date: Thu, 7 Jan 1993 17:03:52 GMT
Organization: CSD., University of Erlangen
NNTP-Posting-Host: faui00i.informatik.uni-erlangen.de



I've got about twenty replies to my request for US universities, that have 
graduate schools, that offer courses about neural nets. My informations so far:

Boulder, Col.
  offers two or three courses that deal with NN, for instance "connectionst     
  approach to AI",
  Mozer and Smellensky teach,
  estimated annual costs are $19'000,
  Deadline for internation students for the Fall Term is Dec 1st

Bloomington, Indiana
  is very strong in cognitive science (Hofstaedter is prof.) and offers 
  courses about NN

University of Calif., Los Angelos
  is recommended by some replicants (no, that's not the right word, repliers?)
  Deadline is Jan 15.

Caltech
  is recommended
  highly competitive (6-10 ppl per year)
  tuition and books are $15000, but help is easy to get
  Deadline is Jan 15 ("but they are understanding")

Southern Methodis University (near Dallas, Tx.)
  offers two NN courses, one more CS, one more EE

Washington State         
  offers an NN course, held by two competent persons
  the Univ. is near Univ. of Idaho, therefor switching Univ. is easy

Corvallis, Oregon
  offers a NN course
  estimated annual costs are $13300
  Deadline is May (or later)

----

Please mail further informations (or comments, because all the listed 
data is NOT official data).



---
 @@@                    Philipp K"ohn - FAU Erlangen (Germany)
@   @ 
  @ @                NIE WIEDER FASCHISMUS ! - NIE WIEDER KRIEG !
 @@@                 
  @                                 peace, man!
Newsgroups: comp.ai.neural-nets
From: brumleve@iboga (Dan Brumleve)
Subject: Simple program
Organization: University of Illinois at Urbana
Date: Sat, 9 Jan 1993 01:44:34 GMT

I'm wondering if anyone has (or knows where I can pick up) a simple neural-net
program written in C.  I know next to nothing about neural-nets, but I've
always been intrigued by AI, and I thought I'd have a look at them.  Just
something that demonstrates a neural net's capabilities would be good.
Send replies by mail please.

Thanks.
-Dan
Newsgroups: comp.ai.neural-nets,soc.college.grad
From: cbrooks@pdx234.NoSubdomain.NoDomain (Clark Brooks)
Subject: Re: Schools strong in neural nets? (was Request: Neural-Nets-classes in US graduate schools)
Reply-To: $USER@ichips.intel.com
Organization: Microprocessor Division 6, PDX, Intel
Date: Mon, 11 Jan 1993 21:07:54 GMT

In article <1993Jan11.150726.4140@newssun.med.miami.edu>, dbrown@newssun.med.miami.edu (Daniel Brown) writes:
|> In article <QfHp_3G00WBMQ2W3Im@andrew.cmu.edu> fc0v+@andrew.cmu.edu (Franklin Chang) writes:
 I feel compelled to add that some research is these areaes is done at Caltech.

Dr Hopfield invented the Hopfield model.

Dr Mead has done recent work in analog VLSI implementations of neural nets.

Other faculty do both theoretical and practical work in the field, though 
I forget who and what...

Perhaps this school is too strong :-)

		-- Clark
Xref: sparky comp.ai.neural-nets:4697 comp.text.tex:14627
From: mdp@eng.cam.ac.uk (Mark Plumbley)
Newsgroups: comp.ai.neural-nets,comp.text.tex
Subject: Re: Does anyone have a LaTeX style file for IEE ANN'93 (UK) Conference?
Date: 5 Jan 93 16:58:04 GMT
Reply-To: M.Plumbley@oak.cc.kcl.ac.uk
Organization: Cambridge University Engineering Department, UK

In article <1993Jan5.141054.27697@eng.cam.ac.uk> I wrote:
>I'm looking for a LaTeX style file for the IEE (sic) ANN'93 Neural Networks
>conference being held in Brighton, UK in April this year.

I've just received a copy of Adrian Clark's IEECONF.STY from
"LISZT::elgar::black"@uk.mod.hermes, which looks like it should do just
the job.

Archie couldn't even find it, so I've asked the Aston TeX archive
people if they could install it in case anyone else is looking for it.

Mark.

--
Dr. Mark D. Plumbley                          M.Plumbley@oak.cc.kcl.ac.uk
                                                  Tel: +44 (0)71 873 2241
Centre for Neural Networks                        Fax: +44 (0)71 873 2017
Department of Mathematics/King's College London/Strand/London WC2R 2LS/UK
From: choy@skorpio.usask.ca (I am a terminator.)
Newsgroups: comp.ai.neural-nets
Subject: What do neural nets do?
Date: 4 Jan 1993 20:13:23 GMT
Organization: University of Saskatchewan, Saskatoon, Canada
Distribution: world
NNTP-Posting-Host: skorpio.usask.ca

I've seen neural nets classify information. Do they do more than this?


-- 

Henry Choy
choy@cs.usask.ca

  What rolls down stairs alone or in pairs
  Rolls over your neighbor's dog?
  What's great for a snack and fits on your back?
  It's Log, Log, Log!      -- "The Log Song", from Ren & Stimpy

  Math is tough!           -- Barbie
Newsgroups: comp.ai.neural-nets
Newsgroups: comp.ai.neural-nets
Subject: motion analysis with NN ?
From: alex@irisgt.cineca.it (Alessandro Sarti)
Date: 11 Jan 93 12:16:11 +0100
Distribution: world
Organization: CINECA  Bologna  Italy
Keywords: motion neural-network

I am looking for papers and software about motion analysis with neural networks.
I am wondering if someone could help me ... 
Thanks in advance for your help.

Here is my address
alex@irisgt.cineca.it

Alessandro Sarti

Xref: sparky comp.ai.neural-nets:4794 comp.sources.wanted:5621 comp.sys.mac.hypercard:4763
From: D_NIBBY@UNHH.UNH.EDU (Dana   Nibby)
Newsgroups: comp.ai.neural-nets,comp.sources.wanted,comp.sys.mac.hypercard
Subject: Mac neural-net software?
Date: 11 Jan 1993 14:43:38 -0600
Organization: UTexas Mail-to-News Gateway
NNTP-Posting-Host: cs.utexas.edu


 A friend of mine wants to know "Does anyone know of simple neural
network demos (for the macinotsh) that complement those w/the PD 
software, perhaps a bit more intuitive for novices?"

  He's also looking for a mac program called "LAZ Life" and a
hypercard called "Letter learner?" What FTP sites have these programs?

 PLEASE RESPOND BY E-MAIL to D_NIBBY@UNHH.UNH.EDU as I don't regularly
subscribe to this and other mac newsgroups

 Thanks,

     Dana
Newsgroups: comp.ai.neural-nets
From: sundsted@amnese.eng.uab.edu (Todd Sundsted)
Subject: MOSIS
Summary: information in MOSIS facility
Organization: University of Alabama at Birmingham
Date: Mon, 4 Jan 1993 16:26:26 GMT

I am looking for information on the MOSIS IC fabrication facility.  I believe
several ANN/VLSI researchers have used it to produce custom VLSI ANN's.
I would like any information anyone can provide... (i.e. the name of
someone at MOSIS I could contact, etc.).  Thanks a lot.

						Todd


* Todd Sundsted - sundsted@amnese.eng.uab.edu
* University of Alabama at Birmingham - Biomedical Engineering Department
*      "Let thee not eat beans."
*            - Pythagoras
From: hadar@boreas.Stanford.EDU (Hadar Avi-Itzhak)
Subject: NIPS question
Organization: DSG, Stanford University, CA 94305, USA
 
Distribution: usa
Date: Fri, 8 Jan 93 01:34:10 GMT


Newsgroups: comp.ai.neural-nets
From: evol@infko.uni-koblenz.de
Subject: Technical Report on many bp speed up techniques available
Organization: University of Koblenz, Germany
Date: Thu, 07 Jan 1993 06:10:15 GMT

Our new Technical Report "Optimization of the Backpropagation Algorithm
for Training Multilayer Perceptrons" is now available via ftp:


	ftp archive.cis.ohio-state.edu  or  ftp 128.146.8.52
	cd pub/neuroprose
	binary
	get schiff.bp_speedup.ps.Z
	quit

The report is an overview of many different backprop speedup techniques.
15 different algorithms are described in detail and compared by using
a big, very hard to solve, practical data set. Learning speed and network
classification performance with respect to the training data set and also
with respect to a testing data set are discussed.
These are the tested algorithms:

backprop
backprop (batch mode)
backprop + Learning rate calculated by Eaton and Oliver's formula
backprop + decreasing learning rate (Darken and Moody)
backprop + Learning rate adaptation for each training pattern (J. Schmidhuber)
backprop + evolutionarily learning rate adaptation (R. Salomon)
backprop + angle driven learning rate adaptation(Chan and Fallside)
Polak-Ribiere + line search (Kramer and Vincentelli)
Conj. gradient + line search (Leonard and Kramer)
backprop + learning rate adaptation by sign changes (Silva and Almeida)
SuperSAB (T. Tollenaere)
Delta-Bar-Delta (Jacobs)
RPROP (Riedmiller and Braun)
Quickprop (Fahlman)
Cascade correlation (Fahlman)



	Randolf Werner




Newsgroups: comp.ai.neural-nets