HDTenEightyP/NetNews huggingface dataset:
From: sethb@fid.morgan.com (Seth Breidbart)
Subject: Re: measuring complexity (was Re: Kolmogorov Complexity)
Organization: Morgan Stanley & Co., New York, NY
Date: Fri, 14 Aug 1992 17:07:28 GMT

In article <1992Aug13.033835.4492@linus.mitre.org>
crawford@church.mitre.org (Randy Crawford) writes:

>That begs a question.  Are all universal turing machines of indeterminate 
>power?  Or must they be considered turing machines because they _may_ be 
>configured as turing machines, even though they also may not be so configured?
>If they _are_ still turing machines, then once again, why does computibility 
>theory become uninteresting once a turing machine is passed instructions?

A Universal Turing Machine is a Turing Machine that, on input <i,j>,
returns the same result as Turing Machine i does on input j.
Therefore, Universal Turing Machines are a subset of Turing Machines.
There can be Universal Machines that start out more powerful than
Turing Machines but this particular one is programmed to act like a
Universal Turing Machine.

Seth		sethb@fid.morgan.com
From: arms@cs.UAlberta.CA (Bill Armstrong)
Subject: Re: Reducing Training time vs Generalisation
Organization: University of Alberta, Edmonton, Canada
Date: Sat, 22 Aug 1992 21:05:21 GMT

sef@sef-pmax.slisp.cs.cmu.edu writes:

>    From: arms@cs.UAlberta.CA (Bill Armstrong)
> 
>    Now let's see what it takes to get lazy evaluation: first of
>    all, I think you would have to insist that the sign of all weights on
>    an element be positive, and all signals in the net too.  Otherwise in
>    forming a weighted sum of inputs, you can not be sure you are on one
>    side of the sharp threshold or not until you have evaluated all inputs
>    (not lazy!).  I think the signals would have to be bounded too.
>    I think this would be OK.  ALNs are still faster, because they don't
>    do arithmetic, but ALNs don't have as powerful nodes.
>    
>For lazy evalaution, the inputs and hidden unit values would have to be
>bounded, but I think you could use bipolar weights.  Sort the weights for
>each unit by magnitude.  Evaluate subtrees in order, biggest weight first.
>Give up when no combination of remaining weights times the input limits
>could pull the total back across threshold.

>In fact, you wouldn't have to replace the sigmoids with thresholds.  Divide
>the sgmoid into three regions: saturated-on, saturated-off, and in-between.
>If you find yourself in one of the saturated regions and no combination of
>other inputs can pull the net back into the in-between region, stop
>evaluating and flush the remaining sub-tress.

I agree with everything you have said.  It becomes clear that the
non-saturated portion of the sigmoid is the costly part of evaluation,
doesn't it?

>    One argument for going whole hog into ALNs is that you don't have to
>    train using sigmoids, then risk damaging the result of learning by
>    going to sharp thresholds.  If there were a training procedure for
>    networks of the above kind of node with a sharp threshold, that would
>    be very promising.  I thought backprop required differentiability to
>    work though.

>It does.  The Perceptron learning rule (and variants such as Gallant's
>pocket algorithm) can train sharp-threshold units, but not multiple layers.
>    

OK, then you have to admit that since an ALN is a multi-layer
perceptron, and since the ALN training procedure works, and since this
fact has been in the scientific and patent literature for twenty
years, it is about time that people revise their thinking: multilayer
perceptrons *can* be trained.

Scientists, for whatever reason, seem to prefer to downplay the fact.

Now, maybe if I could walk on water ...

Thanks for your comments.

Bill
--
***************************************************
Prof. William W. Armstrong, Computing Science Dept.
University of Alberta; Edmonton, Alberta, Canada T6G 2H1
arms@cs.ualberta.ca Tel(403)492 2374 FAX 492 1071
Newsgroups: comp.ai.neural-nets
From: uds@wsu-eng.eng.wayne.edu (Seetamraju Udaybhaskar)
Subject: Re: Questions on the RCE method
Keywords: rce method, scofield (1988)
Reply-To: uds@wsu-eng.eng.wayne.edu (Seetamraju Udaybhaskar)
Organization: Wayne State University, Detroit
Date: Tue, 18 Aug 1992 21:25:31 GMT

In article <66306@hydra.gatech.EDU> gt3451a@prism.gatech.EDU (PETERSON,RICHARD JOHN) writes:
>I am trying to implement the RCE method described by Scofield
>
>Unfortunately he gives little details about the way he implemented
>the simple XOR exemple and I'm not even able to do that. I don't
>know how he chose the number of layers and the number of outputs
>for each layer. I don't know either how he did the learning, i.e

I heard that they just announced their method in the
literature, but have kept their technique a secret (after
having patented it).  I also heard that they had gone
commercial with it

So my guess is that you wont knwo anything about it.  Even if
you do, beware of patent-infringement suits...


Seetamraju Udaya Bhaskar Sarma
(email : seetam @ ece7 . eng . wayne . edu)

Or am I confusing this RCE net with something similar ?
From: key@blacksea.csc.com (Gerry Key)
Newsgroups: comp.ai.neural-nets
Subject: Question: Neural Nets and Data Verification - Reposted
Keywords: Neural Networks NN Data Verification Environmental
Date: 12 Aug 92 17:16:37 GMT
Distribution: usa
Organization: Naval Ocean Systems Center, San Diego, CA

Sorry about that last posting ... it got garbled somewhere along the line.   
Here's what it should have said: 

I am a biologist and not a regular reader of this newsgroup.  Please   
respond directly via email.  I will post a summary to comp.ai.neural-nets  
if the responses warrant.  I poked around in the FAQs before posting this  
inquiry, but didn't find anything that clearly addressed my question.   

I recently saw (but did not read and now cannot find) an article on using  
neural network technology to verify large data matrices.  Is anyone  
familiar with this technology and can you give me some background  
information, product names, etc.?  

The application I have in mind is environmental survey data - everything    
from fish counts to water chemistry to sediment grain size.  Environmental  
databases, or the data matrices derived from them, tend to be large and  
complex, and checking them is tedious work and therefore error-prone.  

I'm not so much concerned with numerical outliers that should be  
identified and evaluated during routine statistical analysis.  Rather, I'm  
looking for a tool that can check for internal consistency - "quality  
control", if you will.  I'd also like a tool that can be used by the  
researcher (rather than requiring a programmer) and one that can be taught  
to recognize similar patterns in different data.  I'm not interested in  
conducting neural network research, so am looking for a commercial,  
off-the-shelf (COTS) product with at least nominal vendor support.    

A simple-minded example of the application I have in mind would be if the  
variable measured during a survey was salinity, then the units associated  
with that measurement should be parts-per-thousand, g/Kg, or Meq/L, but  
not pH, degrees Celsius, or number per square meter.  Similarly, the  
method used to count fish might be a visual transect, beach seine, or a  
fish trap, but not a pH meter, gas chromatograph or sediment sieve.  

I recognize that to some extent these tests can be performed by integrity  
checks available through the load module of an RDBMS.  However, the  
"rules" why some measurements need closer examination can be quite complex  
and subtle and not easily expressed as if-then-else logic.    

The data will come from a variety of sources, some instrumented and some  
not, but all will ultimately wind up in an SQL database.  Thus, a tool  
that could be run against such a database would be ideal.   

Any suggestions/pointers would be greatly appreciated.     

--Gerry Key 

Computer Sciences Corporation 
Internet:               key@csc.com 
FAX:                    619.225.2502 
Voice:                  619.225.2504
From: aruna@bcrka401.bnr.ca 
Subject: ANN '93: Information required
Originator: aruna@bcrka401
Organization: Bell-Northern Research, Ottawa, Canada
Date: Tue, 18 Aug 1992 19:48:28 GMT

I would like to get the details on submitting papers to the IEE Third 
International Conference on Artificial Neural Networks (ANN '93) to be held at
Brighton, UK in May '93. Specifically I would like to know the format of
submission, to whom the submissions be sent (address) and submission deadlines.

If anybody know the deatils please share it on the net.

Thanks

Arun Achyuthan

[Standard disclaimers apply]
From: vassilis@sun.pcl.ac.uk (Vassilis Konstantinou)
Newsgroups: comp.ai.neural-nets
Subject: Lecturer post in AI
Date: 14 Aug 92 11:08:41 GMT
Organization: Polytechnic of Central London


             LECTURER POST IN ARTIFICIAL INTELLIGENCE

             AI DIVISION - SCHOOL OF COMPUTER SCIENCE
		  The University of Westminster


Applications are invited for a Lecturer post in AI to support the
expanding AI  Division  in  our  school.  The  division currently
supports, a B.Sc. in AI, an M.Sc. in Cognitive  Science,  and  an
M.Sc. in Knowledge Engineering. The division also includes a very
active AI Research Group.

The  successful  applicant  is  expected  to  contribute  in  the
teaching of the  above  courses  in  one or more of the following
areas:

     a)   Distributed AI/KBS
     b)   Machine Learning
     c)   Scheduling
     d)   AI-related languages (PROLOG, LISP etc)
     e)   Human Computer Interaction
     f)   Natural Language Understanding
     g)   Alternative Logics (Modal, Temporal etc)
     h)   Computer Vision 

The successful applicant is also  expected  to  contribute  in the
research activities of the AI Research Group.

Applicants for this post must possess:

      1)  Postgraduate qualifications (preferably at Ph.D. level)
          in an AI related area.

      2)  Research experience in an AI related area.

Teaching experience will be an advantage.

The post is  temporary  for  one  year  (full-time)  and  will be
reviewed at the end of the one year period.

To apply send an extensive C.V. to:

        V. Konstantinou
        Head of AI Division
        School of Computer Science
	University of Westminster
        115 New Cavendish Str
        London W1M 8JS, UK.

        Tel: 071-911-5000 ext 3599/3598
        Fax: 071-911-5089
        Email:  vassilis@uk.ac.sun.pcl (JANET)
                vassilis@pcl.sun.ac.uk (Internet)


The deadline for applications is the 30th of August 1992.
-- 
--
V. Konstantinou
Head of A.I. Research Group
School of Computer Science, The Polytechnic of Central London
From: gt3451a@prism.gatech.EDU (PETERSON,RICHARD JOHN)
Newsgroups: comp.ai.neural-nets
Subject: Questions on the RCE method
Keywords: rce method, scofield (1988)
Date: 18 Aug 92 18:46:45 GMT
Organization: Georgia Institute of Technology


I am trying to implement the RCE method described by Scofield
in his paper "Learning internal representations in the coulomb
energy network" ICNN vol I, 271-275 (July 1988).

Unfortunately he gives little details about the way he implemented
the simple XOR exemple and I'm not even able to do that. I don't
know how he chose the number of layers and the number of outputs
for each layer. I don't know either how he did the learning, i.e
the way he presented the different patterns and the class he associated
to each pattern.

If anybody can give me more details about this implementation I would
be very glad to hear from him.

Thanks a lot.

Herve Marzio

Please address you answers to : qscsihm@prism.gatech.edu

-- 
PETERSON,RICHARD JOHN
Georgia Institute of Technology, Atlanta Georgia, 30332
uucp: ...!{allegra,amd,hplabs,seismo,ut-ngp}!gatech!prism!gt3451a
ARPA: gt3451a@prism.gatech.edu
Newsgroups: comp.ai.neural-nets,sci.physics,sci.electronics,fj.sci.physics
From: wolf@sun1.ruf.uni-freiburg.de (Klaus H.Wolf)
Subject: Germany only: Who does R&D in neural nets ?
Organization: Rechenzentrum der Universitaet Freiburg, Deutschland
Date: Thu, 20 Aug 92 17:20:44 GMT

Is there a summary of institutions which have research in neural nets, 
neurocomputing, optical neurocomputers, hardware implementations ... ?

Please send a mail if you are working with it. I'll summarize. 

-- 
Klaus H.Wolf
wolf@sun1.ruf.uni-freiburg.de
Newsgroups: comp.ai.neural-nets
From: FM010201@ysub.ysu.edu
Subject: looking for somebody's experience with hect-nelson NN board
Organization: Youngstown State University
Date: Thu, 13 Aug 1992 23:53:14 GMT

I am training a neural net on a sun workstation by using the back propagation a
lgorithm. The training data set has 31 inputs and 37 outputs with 50 as hidden
nodes in the hidden layer. The training data set consist of 500 pairs and it ha
s taken about two weeks of time to reach to a desirable accuracy. I am interest
ed in finding out about somebody's experience with the accelerator boards which
 improve the performance of back propgation for speed? what is the general spee
d up in the performance. I also tried radial based function nets. All neu
ral net formulations seems to work very well on small demo problems ,however, o
n large realistic problems training time is exorbitant? any thoughts and sugges
on this. Thanks in advance.
Alam    INTERNET fm010201@ysub.ysu.edu
From: fsistler@okra.age.lsu.edu (Fred Sistler)
Newsgroups: comp.ai.neural-nets
Subject: robotics
Keywords: robotics trajectory manipulator path
Date: 12 Aug 92 14:48:22 GMT
Distribution: usa
Organization: Louisiana State University
Nntp-Posting-Host-[nntpd-10724]: okra.age.lsu.edu

I am looking for references on the use of neural-nets in trajectory
planning of robotic manipulators.  I would also be interested in any
other uses of neural nets as applied to robotics.

Apparently I have been looking in the wrong places, but so far I can't
come up with much of anything.

Any suggestions?

Thanks for your help.

Fred Sistler
From: black@breeze.rsre.mod.uk (John Black)
Newsgroups: comp.ai.neural-nets
Subject: Re: Metrics in Self Organizing Maps
Summary: Stopping criteria for self organising maps
Date: 12 Aug 92 14:57:51 GMT
Organization: Defence Research Agency

In article <1992Aug6.145942.4330@vu-vlsi.vill.edu> raman@vu-vlsi.vill.edu (Rajasekhar Raman) writes:
>
>Secondly, is there any simple criterion by which we can decide when to stop the
>learning process of the self organizing network. All the books seem to be very
>vague about this.
>

 The stopping criteria can be based on the relative change in coding error at 
 two points in time during the training. The coding error can be calculated in
 two ways

 1) Find the total coding errors summed over all input vectors. This is incon-
    venient for continuous training algorithms, but is for the equivalent batch
    algorithm (for every continuous training algorithm there is an equivalent
    batch training algorithm - a variation on the famous LBG for vector quant-
    isers - see Steve Luttrell's paper in IEEE trans on neural networks Vol 1
    no. 2 pages 229 - 232)

 2) Compute a leaky bucket error measure. Use the coding error naturally gener-
    ated when coding. This error is added to a pre-existing error, EP using one of the following schemes, where E is the error generated by coding.

   EP = (1-f)EP + fE 

  or
  
   EP = (1-f)EP + E  

 where f is a number between zero and one.

   The stopping criterion is then, where TE is total error generated by method 1 or 2 above and PTE is the previous value of TE

    PTE - TE 
    --------
       TE

      between zero and some smallish vale eg 0.01

  The condition to be greter than zero is necessary because the TE can increase    between measurements for continuos training algorithms.


			John Black

===============================================================================
janet:   jvb@uk.mod.hermes                   | all opinions are my own and do  
Internet: jvb%hermes.mod.uk@uk.mod.hermes    |reflect those of my employer
From: arms@cs.UAlberta.CA (Bill Armstrong)
Subject: Re: Network Inversion
Organization: University of Alberta, Edmonton, Canada
Date: Sat, 22 Aug 1992 22:33:07 GMT

tjochem+@CS.CMU.EDU (Todd Jochem) writes:

>I'm looking for references to network inversion papers. The basic idea
>I'm interested in is presenting the network's output with a signal and
>by back-propagating this signal though the layer(s), recreating an input
>which could have created the applied output signal.

This is intractable for a general multi-layer perceptron.

Proof: We have to show it for the special case of ALNs, which are
trees of nodes realizing AND, OR, LEFT and RIGHT functions, with
leaves connected to input bits and complements.  The same input bit or
complement may be sent to many leaves.  It will be enough to show it
for a two-layer tree, with ORs feeding into an AND.  Back-propagating
a 1 signal is just CNF-satisfiability, and is NP-complete.

Comment: the difficulty arises because back-propagating signals often
converge at the same node, either at the inputs (ALNs) or in hidden
layers (MLPs), and they are likely to have contradictory values when
they do.

--
***************************************************
Prof. William W. Armstrong, Computing Science Dept.
University of Alberta; Edmonton, Alberta, Canada T6G 2H1
arms@cs.ualberta.ca Tel(403)492 2374 FAX 492 1071
Newsgroups: comp.ai.neural-nets
From: delusion@casbah.acns.nwu.edu (Albert Schmezer)
Subject: Neural Networks, Artificial Life, and CAs...
Organization: Northwestern University, Evanston Illinois.
Date: Thu, 20 Aug 1992 16:11:53 GMT

	Hello. I am a high school student entering my senior year. I have been
recently interested in the fields of Artificial Life and Artificial
Intelligence. I am aware of the differences between the fields, and also their
similarities. I would like to know how I can bridge the gap between my current
understanding of neural-networks (meager... I know about it through many
Discover articles, a Steven Levy book, and hearsay) to the thorough
understanding of many in this newsgroup (Turing methods? RCE Method?). Which
books/articles can I read to gain more understanding on this topic area?

	Also, through Steven Levy's book "Artificial Life", I have become
interested in the field of Cellular Automata, both 1-D and 2-D... and I have
reconstructed a version of the famous "Life" program in TurboPascal. Is there
a newsgroup on Usenet that deals with CAs? or A-Life in general? 

	Sorry to take up your time, but perhaps I will concentrate in this
field as I enter college and onward....

<<delusion
From: peter@gbg.infolog.se (Peter Nordin)
Subject: Re: measuring complexity (was Re: Kolmogorov Complexity)
Organization: Infologics AB
Date: Fri, 14 Aug 1992 09:25:45 GMT

I'm interested in the use of Kolmogorov complexity in Machine Learning and AI in general.
Is there anyone who has good pointers to literature, conferences, mailing lists or alike.

Thanks

Peter Nordin
Infologics AB
peter@gbg.infolog.se

Xref: sparky comp.ai.neural-nets:3206 soc.culture.nordic:5120
From: fwilson@bass.bu.edu (Frank Wilson)
Newsgroups: comp.ai.neural-nets,soc.culture.nordic
Subject: Swedish language neural network books / articles
Date: 17 Aug 92 00:40:08 GMT
Followup-To: comp.ai.neural-nets
Organization: Boston University

I need to collect a few references to journal articles
or books WRITTEN IN SWEDISH dealing with neural networks
or related fields such as neurobiology, speech recognition,
vision, learning.  Since even Scandinavian journals tend
to publish only in English these days, I have been having
difficulty finding anything suitable.  Are there any 
technical journals published in these fields which contain
Swedish language articles, or any postgraduate level books
in Swedish on these subjects ?  

Any suggestions (by email to fwilson@bass.bu.edu) would be
greatly appreciated.  

Tack f|r hj{lpa !
--

--------------------------------------------------------------------
 Frank Wilson                | email : fwilson@bass.bu.edu
 Cognitive & Neural Systems, | phone : 508-263-5935
Newsgroups: comp.ai.neural-nets
From: arms@cs.UAlberta.CA (Bill Armstrong)
Subject: Re: ALN's FAIL ON 1-BIT ERRORS ! DISASTER IMMINENT ?
Organization: University of Alberta, Edmonton, Canada
Date: Sat, 22 Aug 1992 21:47:12 GMT

henrik@mpci.llnl.gov (Henrik Klagges) writes:

>[Just to retaliate a little bit against Bill's nn-bashing 8-)]

Fair enough.

>The Neuronal Enquirer, 8/20/2002

>ALN's FAIL TERMINALLY ON SINGLE BIT ERRORS ! SCIENTISTS PARALYZED !
>According to informed sources, ALN's have a much hyped feature, 
>lazy evaluation that allows the switchoff of subtrees of any size
>by a single logic gate ! Pathological information or single bit 
>failures could easily disable the major parts of the ALN, thus 
>loosing all the recognition capabilities of these subtrees.

>Numerous rumours indicate major impacts have already happened. The

...

>Cheers, Henrik
>IBM Research Division physics group Munich
>massively parallel group at Lawrence Livermore

From the same source:

These were the last words of Henrik Klagges, member of a massively
parallel group at Lawrence Livermore, ... before he realized that what
he was saying of ALNs applied to any program with an "if-statement".
As a result, a group is now lobbying for stricter bit-control, in the
form of legislation that would require any "if" statement in any
program used by the Federal Government to be executed by at least
three massively parallel computers with over 80% American-made
components.  In a surprising about-face, the Vice-President is
supporting the proposed legislation, in a bid to "keep US computers
busy, and show those lazy foreigners that it's not the value of the
bit that counts, but how you compute it".

--
***************************************************
Prof. William W. Armstrong, Computing Science Dept.
University of Alberta; Edmonton, Alberta, Canada T6G 2H1
arms@cs.ualberta.ca Tel(403)492 2374 FAX 492 1071
Newsgroups: comp.ai.neural-nets
From: henrik@mpci.llnl.gov (Henrik Klagges)
Newsgroups: comp.ai.neural-nets
Subject: Re: Reducing Training time vs Generalisation
Date: 20 Aug 92 20:34:07 GMT

arms@cs.UAlberta.CA (Bill Armstrong) writes:

>You have a lot more experience than I do with sigmoid type nets, so
>what you have just said is extremely significant, in that you are

Correct. It is a very interesting finding. Same happened during 
our experiments.

>coming closer all the time to a logical net.  If you are able to
>replace sigmoids with sharp thresholds, and not change the output of
>the net significantly, then you are really using threshold *logic*
>nets.  

Well, if this replaceability is there ... would be great ! Don't 
think so, though. I'd need a few more experiments on that. 
An inital look suggests that the weights & sigmoids cannot easily
(straightforwardly) replaced with 'gates'.

Cheers, Henrik
IBM Research Division Physics Group Munich
Massively Parallel Group at Lawrence Livermore

Newsgroups: comp.ai.neural-nets,comp.ai,comp.compression,comp.theory