HDTenEightyP/NetNews huggingface dataset:
Newsgroups: comp.ai.neural-nets
Subject: Info on applying NN to High speed ATM networks?
From: <PVJQC@CUNYVM.BITNET>
Date: Wednesday, 26 Aug 1992 16:10:45 EDT
Organization: City University of New York/ University Computer Center

Hi,
    I wanted to know more about applying NN techniques to the traffic
management of Very High speed Networks (ATM networks) which carry
all kinds of traffic (voice, video, images, data, etc).
I found one paper from couple of people in CAmbridge, UK.
I working towards my Ph.D. dissertation in the areas of Traffic management
of VHSNs and application of Neural-nets seems to be a good approach.
Any information would be appreciated.
Thank you very much.
Prashant Joshi
-------

         *********************************************************
         *  PRASHANT V. JOSHI                                    *
         *  COMPUTER SCIENCE DEPARTMENT                          *
         *  CITY UNIVERSITY OF NEW YORK.                         *
         *  INTERNET ADDRESS: PVJQC@CUNYVM.CUNY.EDU,             *
         *  BITNET ADDRESS  : PRASHANT@QCVAX.BITNET              *
         *                                                       *
         *********************************************************
From: hong@nttsgw.yh.ntts.co.jp (Zhang Hong)
Newsgroups: comp.ai.neural-nets
Subject: How to choose the appropriate NN model
Date: 3 Sep 92 10:16:41 GMT
Distribution: comp
Organization: NTT Software Co., Yokohama, Japan


I am looking for references on application of ART. Any information will
be appreciated. Please e-mail me directly. Thanks in advance.

						H.Zhang
--
-=-=-=-=-=-=-T=-=-=-=-=-H=-=-=-==-=A=-==-==-==N-==-==KS-==-=-===-
   HONG  ZHANG  E-mail:hong%nttsgw.yh.ntts.co.jp@uunet.uu.net
NTT SOFTWARE CORPORATION. TEL:+81 45 212 7505 FAX:+81 45 212 7509
  223-1 YAMASHITA-CHO NAKA-KU YOKOHAMA-SHI KANAGAWA 231 JAPAN
-=-=-=-A=-=-=-=-=L-=-=-=-=-=O-=-==-==-==-==-==-=-=T=-==-==-==-==-
From: maillet@tls-cs.cert.fr (Luc Maillet)
Newsgroups: comp.ai.neural-nets
Subject: apologies for tests
Date: 3 Sep 92 10:36:35 GMT
Organization: CERT Toulouse Dept of Computer Science, FRANCE


  For those who worry about the sudden increase in test articles in this
newsgroup, I offer an apology. We have a problem in posting.

								Z.
Newsgroups: comp.ai.neural-nets
From: dbg@mullian.ee.mu.oz.au (David Grayden)
Subject: TDNN training time
Summary: My TDNN trains too slow
Keywords: TDNN, BP, training time
Organization: Electrical and Electronic - University of Melbourne 
Date: Mon, 31 Aug 1992 03:10:56 GMT


I am doing some research in phoneme recognition of continuous speech.  I
have the Timit database from which I extract my training and testing sets.

At first, I have been trying to replicate somewhat the results of Alex
Waibel and the ATR group in TDNN recognition of /b/, /d/ and /g/.  However,
the training time is almost prohibitively long.  I am basing most of my
work on the two papers:

A.Waibel, T.Hanazawa, G.Hinton, K.Shikano, K.J.Lang, "Phoneme recognition
using time-delay neural networks," IEEE Trans. on Acoustics, Speech and
Signal Processing, vol.37, no.3, March 1989.

P.Haffner, A.Waibel, H.Sawai, K.Shikano, "Fast back-propagation learning
methods for large phonemic neural networks," ATR Technical Report.


In these papers, it seems to be that the bdg network can be trained in as
little as 1 minute on an Alliant super-computer.  However, I can't get it
down below 1 week for 1600 training samples.  I am working on a SPARCstation
which will explain some of the slowness, and the TIMIT English database
may be "harder to train on" than the Japanese database used above, but the
slowness of my training seems to be several orders of magnitude greater.

For comparison, I have trained a fully-interconnected neural net in the
same task.  It trains extremely fast although each epoch takes longer due
to the extra weights in the fully-interconnected setup.

The fully-interconnected network trains in 1/10 to 1/1000 the number of
iterations and thus takes less time by about the same orders of magnitude.

The TDNN (after waiting for over a week sometimes) gives recognition rates
between 60-80% on the TIMIT test set, while the fully-interconnected
network recognizes between 70-90%.

Has anyone else done work in this area on TIMIT and managed to get speeds
and results anywhere close to those of the ATR group?

Regards,
David.
-----------------------------------------------------------------
David Grayden                     | Tel:  +61 3 344 4974
Dept of Electrical Engineering    | Fax:  +61 3 344 6678
The University of Melbourne       |
Parkville VIC 3052 AUSTRALIA      | Email:dbg@mullian.ee.mu.OZ.AU
-----------------------------------------------------------------
From: hutchens@cod.nosc.mil (Chris G. Hutchens)
Subject: Info requested by job seeker.
Organization: Naval Ocean Systems Center, San Diego
Distribution: usa
Date: Thu, 3 Sep 1992 23:41:23 GMT

Hello:

     Could  somebody  give  me  the  information  about
Neural   Network  bulletin  or  periodical  that  lists
current job openings in NN field.  Thank in advance.

Sanjay Patil

(619) 553-2680
(619) 222-2371

E-mail hutchens@.cod.nosc.mil
From: fenner@engcon.marshall.ltv.com (JWFENNER)
Newsgroups: comp.ai.neural-nets
Subject: NeuSprint
Date: 25 Aug 92 15:07:01 GMT
Reply-To: fenner@engcon.UUCP (JWFENNER)
Organization: LTV MEG, Dallas, TX

Has anyone heard of and/or used a product called NeuSprint available from
Neural Computer Sciences of Hampshire, England?  I just read an article in
Electronic Engineering Times about it.  It is an add-on board for 386-based
computers that is suppose to give about 100 times faster processing of
neural networks than software alone.  What is it, a DSP or math co-processor
board, or is it something completely different?  If anyone has used it, or
happens to have the e-mail address of this company, I would appreciate hearing
about it.  Thanks!

-- 
==================================================================
= Jon Fenner (engcon!fenner@uunet.uu.net)                        =
= LTV Aerospace and Defense - Neural Network / AI Research Group =
==================================================================
Newsgroups: comp.ai.neural-nets
From: moshes@gemini.math.tau.ac.il (Sipper Moshe)
Subject: Backpropagation simulation - possible bugs to look for
Organization: School of Math & CS - Tel Aviv University , Tel Aviv , ISRAEL.
Date: Mon, 31 Aug 1992 11:50:35 GMT

I have written a simple backprop simulation
program in which the weights converge, but alas
the error rate does not....

Are there maybe some small but important pointers to watch out for ?
(weight resolution, initialization, logistic function etc' ?)

thanks in advance for any reply.

Moshe Sipper.
Newsgroups: comp.ai.neural-nets
From: siva@a.cs.okstate.edu (KAVUTURU SIVA RAMA)
Subject: search for literature on recurrent networks
Followup-To: siva@a.cs.okstate.edu
Organization: Oklahoma State University, Computer Science, Stillwater
Date: Fri, 4 Sep 92 15:56:07 GMT
Keywords: recurrent, networks

   Hai,
        I am searching for the literature on recurrent networks. If you 
        have any information regarding recurrent networks please send me.

       Thank you.
From: sassoj@aix.rpi.edu (John J. Sasso Jr.)
Newsgroups: comp.ai.neural-nets
Subject: NEED INFO ON WAVELETS & NNs
Date: 29 Aug 92 04:49:48 GMT
Organization: Rensselaer Polytechnic Institute, Troy NY


   Does anyone know of any reports on the applications of wavelets w/ NNs
in engineering problems and such?  Any info on related articles is appreciated.

                         John
From: tawfik@cwis.unomaha.edu (Ahmed Tawfik)
Subject: Integer Results Backprop
Organization: University of Nebraska at Omaha
Date: Fri, 4 Sep 1992 01:03:34 GMT

Working on a backprop ANN for prediction, I have noticed that for some value
of the learning rate ; the network produces Integer Only Results. Have you
noticed this ? Any explanations ? Any references ?

I have the output layer function f(x) = x and sigmoidal for other layers.


--Ahmed Tawfik

Graduate Student 
Computer Science
University of Nebraska -Omaha
From: peso@diku.dk (Peer Sommerlund)
Newsgroups: comp.ai.neural-nets
Subject: IEEE NNSP: New algo' for recognizing speech?
Keywords: IEEE NNSP Speech
Date: 4 Sep 92 08:44:23 GMT
Organization: Department of Computer Science, U of Copenhagen

Hi, everybody.

I'm a computer science student at Copenhagen University (DIKU) who is
facinated by neural networks. This week IEEE held a workshop on neural
networks for signal processing (NNSP) at Helsingoer here in Denmark - I just
couldn't let that opportunity go, so of course I went there :) 

About 30% of the time was devoted to speech & NN, and one of the papers I
found interesting was titled: "Unsupervised Sequence Classification" by Jorg
Kindermann and Christoph Windheuser. I'm not sure which of them were at the
workshop, but I think it was Joerg Kindermann. He presented a novel idea (as
far as I know) that made his kohonen network able to recognize speech of
varying speed(!) (up to 20%) I have never seen this done before so I was
really impressed.

He used seval layers to recognize sequences of increasing complexity. The
net at the workshop had 4 layers and was able to recognize phonemens. (He
told me that they had build a 15 layer network to recognize a specific long
German word!)

The neurons each had two inputs, one with a delay and one without. To
activate the neuron both inputs had to be active at the same time. The
neuron stays active (hysteresis?) until both inputs are inactive.


As I said before, I'm a student (haven't even got my masters yet), so please
bear with me if I have misunderstod something. What I would really like to
know if this IS a novel idea? I suspect somebody might have tried something
simmilar with TDNN. Any coments?


-- 
___________________
| Peer Sommerlund |  <-- This is my .signature file
-- 
___________________
| Peer Sommerlund |  <-- This is my .signature file
Newsgroups: comp.ai.neural-nets
From: rpatil@nmsu.edu (Raj Patil)
Subject: Nettalk--- and hardware...
Organization: Computing Research Lab
Distribution: comp.ai
Date: Wed, 2 Sep 1992 19:56:53 GMT


Does anyone know of how to implement Nettalk in hardware. I have seen a demo,
but would like to know how to go about building the system in hardware with
NN part as conventional backprop simulator, but the output to a real speech
synthesis chip.

Any help appreciated.
regards

raj patil
USDA-ARS, NMSU-CRL

Xref: sparky comp.ai.neural-nets:3356 sci.math:10723 comp.theory:1840
From: ambati@acsu.buffalo.edu (Balamural K. Ambati)
Newsgroups: comp.ai.neural-nets,sci.math,comp.theory
Subject: Kolmogorov's theorem
Date: 31 Aug 92 12:55:24 GMT
Organization: UB

Could someone post some info. on Kolmogorov's theorem on reconstruction
of multidimensional functions and information processing in neural
networks?  Also refs. if possible. Thanks.

Balamurali K. Ambati
ambati@ubunix.buffalo.edu
From: ambati@acsu.buffalo.edu (Balamural K. Ambati)
Newsgroups: comp.ai.neural-nets
Subject: Re: Kolmogorov's theorem
Date: 1 Sep 92 20:20:52 GMT
Organization: UB

lnd@cs.bu.edu (Levin) writes:

>In article <BtuMKD.14p@acsu.buffalo.edu> ambati@ (Balamural K. Ambati) writes:
>>Could someone post some info. on Kolmogorov's theorem on reconstruction
>>of multidimensional functions and information processing in neural
>>networks?  Also refs. if possible. Thanks.

>Every continuous multivariate real function can be represented as a depth-4
>composition of continuous one-variate functions and sum. This result of
>Kolmogorov and his student Arnold solves the 13-th Hilbert problem.

What implications does this have for capacities for neural networks?

Thanks.

Balamurali K. Ambati

ambati@ubunix.buffalo.edu


.
From: sunjain@matt.ksu.ksu.edu (mera bharat mahan)
Newsgroups: comp.ai.neural-nets
Subject: Travelling salesman problem: help needed
Date: 28 Aug 1992 10:41:32 -0500
Organization: Kansas State University
NNTP-Posting-Host: matt.ksu.ksu.edu

In my AI class today, my professor was explaining the Travelling Salesman
problem and the complexities involved with it.

She also mentioned that the problem is solved using Neural-Nets.

I'll appreciate if anybody can give me some info, some references as to how 
neural networks is applied to solve this problem.

thanks a lot.

sunil jain.
Newsgroups: comp.ai.neural-nets
From: chrism@col.hp.com (Chris Magnuson)
Subject: Beginner Help
Date: Thu, 3 Sep 1992 20:35:52 GMT
Organization: HP Colorado Springs Division

Can anyone point to a good intro discussion/text about nets?

Thanks in advance,
chrism@col.hp.com
From: pj_pycro@csd.brispoly.ac.uk (PJ Pycroft)
Newsgroups: comp.ai.neural-nets
Subject: Noise Recognition
Date: 26 Aug 92 20:31:17 GMT
Organization: Bristol Polytechnic, England

Hello netters,

I'm trying to set up a project on noise recognition systems, recognize some
form of noise such as siren (police, fire etc.), alarm signal or any other
warning noise which will alert a deaf person.

I'm thinking of using Signal Processing and Neural Network for detecting
above noise and will be using parallel processing such as Transputers
and DSP (perhaps, I will be using Transputers instead of DSP).

I would be grateful if you had any ideas/info' of using Signal Processing
and Neural Network (both hardware and software) for the above project - any
hints would be grateful obtained.

Where shall I get the info'/material of Richard P. Lippmann's work on sound
alerting aid for the deaf, signal processing for hearing aids and relating
info'.

I'm a profoundly deaf postgraduate student doing M.Sc. in Parallel Computing
Systems.

Thanks for your help.

Paul

--------
pj_pycro@csd.brispoly.ac.uk
From: bakker@cs.uq.oz.au (Paultje Bakker)
Newsgroups: comp.ai.neural-nets
Subject: Does anyone have bp on a Super Computer?
Date: 3 Sep 92 06:37:57 GMT
Reply-To: bakker@cs.uq.oz.au
Organization: Depts. of Computer Science & Psychology, The University of Queensland, Brisbane, Australia

Netters,
   I would like to get in contact with any researcher who has
been able to successfully implement (or is simply using) 
Rumelhart & McClelland's `bp' simulator on the Connection Machine 
Supercomputer, or the MasPar. 

I have obtained an adaptation of the bp program by Sebastian Thrun
for the Connection Machine (written in 1990) but have been unable to get
it to work properly; the code appears to have a few bugs, only some
of which I have been able to find. It does run, but the output
from parallel computation is different than that of the serial
computation.

*Please* email me if you can help. Email me also if you are interested
in the implementation I have already.

thanks,
Paul Bakker
University of Queensland
--
(paul) bakker@cs.uq.oz.au     >  "Although the Australian accent does
computer science/ psychology  >   not lend itself as a pointer to
university of qld 4072        >   intellect, ... "
terra australis               >   The (British) Sunday Telegraph, 1992
From: henrik@mpci.llnl.gov (Henrik Klagges)
Newsgroups: comp.ai.neural-nets
Subject: Re: Nettalk--- and hardware...
Date: 4 Sep 92 18:30:15 GMT
Distribution: comp.ai

rpatil@nmsu.edu (Raj Patil) writes:


>Does anyone know of how to implement Nettalk in hardware. I have seen a demo,

Buy an Adaptive Solutions CNAPS-1024 for only 55k$ ...

Cheers, Henrik
IBM Research
MPCI at Lawrence Livermore Labs

Newsgroups: comp.ai.neural-nets
From: arkell@swanee.ee.uwa.edu.au (Arkell Rasiah)
Subject: Looking for EEG/EMG data
Summary: looking for EEG/EMG data
Keywords: EEG/EMG data
Organization: Elec Eng, Univ of Western Australia
Date:  3 Sep 92 09:14:38 GMT


Hi everyone,
	I am looking for EEG, EMG or ECG data to work with. Can someone can tell me where I can download such data from or even purchase such data (eg. on CDROM).
	Thank you.

Regards Arkell.


Arkell Rasiah                                                        _--_|\
Centre for Intelligent Information Processing Systems               /      \
Dept. of Electrical & Electronic Engineering                        *_.--._/
The University of Western Australia                                       v
NEDLANDS WA 6009 Australia                 Email: arkell@swanee.ee.uwa.edu.au

Newsgroups: comp.ai.neural-nets
From: es_macar@rcvie.co.at (Gilles Macario-Rat)
Subject: Ordering
Keywords: Sequence
Reply-To: es_macar@rcvie.co.at
Organization: Alcatel ELIN Research
Date: Thu, 27 Aug 1992 11:35:37 GMT

I would like to order a set of objects according to a pre-existing list
of ordered objects.
Each object is characterized by a certain number of parameters 
(the same for all).
Does anyone know a way to compute an input for a backprop NN that gives
all the information concerning a set and that is independent of the order
of its elements ?
My intention is to train a NN with pairs . The first part of a pair should 
hold the unordered information, and the second part the order.
Thanks in advance.
From: ckt@eng.cam.ac.uk (C.K. Tham)
Newsgroups: comp.ai.neural-nets
Subject: Reinforcement Learning for Robot Control
Date: 1 Sep 92 13:10:22 GMT
Distribution: comp.ai.neural-nets
Organization: Cambridge University Engineering Department, UK


    TECHNICAL REPORT ANNOUNCEMENT

The following technical report is available via anonymous ftp:

REINFORCEMENT LEARNING FOR MULTI-LINKED MANIPULATOR CONTROL
by  Chen K. Tham & Richard W. Prager.
    (Technical Report CUED/F-INFENG/TR104)

Abstract:

We present a trajectory planning and obstacle avoidance method which uses
Reinforcement Learning to learn the appropriate real-valued torques to
apply at each joint of a simulated two-linked manipulator in order to move
the end-effector to a desired destination in the workspace. The inputs to
the controller are the joint positions and velocities which are fed
directly into a Cerebellar Model Arithmetic Computer (CMAC) (Albus,75). In
each state, the expected reward and appropriate torques for each joint are
learnt through self-experimentation using a combination of the Temporal
Difference (TD) technique (Sutton,87) and stochastic hillclimbing
(Williams,88). Actions which cause the manipulator to reach the desired
destination are rewarded whereas actions which lead to collisions with
either joint limits or obstacles are punished by an amount proportional to
the velocity before collision.  After training, the manipulator is able to
move along smooth collision-free paths from different start positions in
the workspace to the destination.

The file is in compressed Postscript format.

Procedure for obtaining the report:

unix> ftp svr-ftp.eng.cam.ac.uk
Name: anonymous
Password: (your e-mail address)
ftp> cd reports 
ftp> binary
ftp> get tham_tr104.ps.Z
ftp> quit
unix> uncompress tham_tr104.ps.Z
unix> lpr tham_tr104.ps    .. etc. .. to print

The authors welcome comments and suggestions from readers.

----------------------------------------------------------------------------
Chen K. THAM,                                 E-mail: ckt@eng.cam.ac.uk
Speech, Vision and Robotics Group,            Tel.  : +44 223 332754
Cambridge University Engineering Department,  Fax   : +44 223 332662
Trumpington Street,
Cambridge CB2 1PZ,
United Kingdom.
Newsgroups: comp.ai.neural-nets
Subject: Neural nets on CM2
From: adele@tls-cs.cert.fr (Martin Adelantado)
Date: 4 Sep 92 02:53:31 CDT

 I am researching applications of neural networks models on
Date: 3 Sep 92 10:41:38 GMT
Organization: CERT Toulouse Dept of Computer Science, FRANCE

the Connection Machine CM-2.
   Information on references, ftp sites, code, etc.. on this
subject would be greatly appreciated.

   Please email to adele@tls-cs.cert.fr

   Thanks,

           Martin ADELANTADO
Newsgroups: comp.ai.neural-nets
From: gummert@koblenz.informatik.uni-kl.de (Jochem Gummert - Hiwi Ritter)
Subject: Question: Force a constraint into a NN
Organization: University of Kaiserslautern, Germany
Date: Fri, 4 Sep 1992 11:19:05 GMT


I want to design a NN that satisfies the following constraint (for any input):

      The number of activated neurons in the input layer  equals
      the number of activated neurons in the output layer.


I tried an ordinary backprop-NN (15 input neurons, 15 output neurons ,15 hidden
neurons).  The net was trained with about 30 patterns (which, of course, satis-
fied the constraint).
Unfortunately, but not surprisingly, not all testing data behaved correct,
in reference to the constraint: Some data activated too many, some data
activated too less output neurons.

How, if at all, can I force the net to satisfy this constraint?

Any  ideas, hints, references will be greatly appreciated.
I'll summarize my email replies.

Many thanks in advance.

Jochen Gummert (gummert@informatik.uni-kl.de)
Newsgroups: comp.ai.neural-nets
Subject: Proceedings avaible for the DOSES workshop ?
From: tim@resmel.bhp.com.au (Tim Monks)
Date: 3 Sep 92 13:41:02 +1000
X-Newsreader: Tin 1.1 PL5

I am interested in obtaining the proceedings (if any was published)
from the "DOSES Workshop on Neural Networks for Statistical and
Economic Data" held December 10-11, 1990 in Dublin (and any
later workshops under the same aegis)

Has anyone heard about this workshop and its proceedings ? (I
got the reference from a paper in the San Diego IJCNN proceedings).

Thanks in advance,

Tim



--
Dr Tim Monks
Exploration Group                      | (direct)   (+61-3)566-7448
BHP Research - Melbourne Laboratories  | (switch)   (+61-3)560-7066
245 Wellington Rd, Mulgrave, 3170,     | (fax)      (+61-3)561-6709
AUSTRALIA                              | (internet) tim@resmel.bhp.com.au
From: evidal@graf.ci.uv.es (Enrique Vidal (DSIC))
Subject: ays to get bounds on values *between* test points? The application
Date: Tue, 25 Aug 1992 16:32:27 GMT
Organization: Universitat de Valencia, Spain
X-Newsreader: Tin 1.1 PL4


Organization: Doctoral student, Psychology, Carnegie Mellon, Pittsburgh, PA
Newsgroups: comp.ai.neural-nets
Date: Fri, 28 Aug 1992 02:14:37 -0400 
From: Quanfeng Wu <qw0w+@andrew.cmu.edu>
Subject: References on computational capacity of NN

Somewhere I read the notion the connectionist networks are
computationally universal, i.e., computationally equivalent to the
Turing machine. Is there any formal theoretical work proving or
disproving this notion? I would appreciate references on theoretical
work concerning computational, algorithmic, and/or informational
capacities of various kinds of networks.

--Quanfeng Wu (Psychology, Carnegie Mellon University)
Newsgroups: comp.ai.neural-nets
From: gaynier@elec.uq.oz.au (Ron Gaynier)
Subject: Williams & Zipser's simulation results
Summary: I'm looking for a "Connection Science" article
Keywords: Recurrent nets results
Organization: Prentice Centre, University of Queensland
Date: Wed, 2 Sep 1992 03:27:50 GMT

In their 1989 paper "A Learning Algorithm for Continually Running Fully 
Recurrent Neural Networks", Williams & Zipser make reference to an
article which gives a more complete description of their simulations
and results. It was a yet to be published article in "Connection Science".

Does anyone have a copy of that article? Was it ever published? Our
library only has copies of that journal dating back to 1990 and I need
their results for a paper I'm writing.

Is there an ftp site that might have an on line copy?

Any help would be greatly appreciated.

Ron Gaynier
Newsgroups: comp.ai.neural-nets