neuron-request@HPLABS.HP.COM (Neuron-Digest Moderator Peter Marvit) (11/23/88)
Neuron Digest Tuesday, 22 Nov 1988 Volume 4 : Issue 28 Today's Topics: Re: Neuron Digest V4 #25 (music and nets) Refs/opinions wanted -- Neural nets & approximate reasoning RE: Relaxation Labelling Re: Refs/opinions wanted -- Neural nets & approximate reasoning Re: Neuron Digest V4 #26 Talk on ASPN at AI Forum 11-22-88 More on learning arbitrary transfer functions Free Recurrent Simulator FTPing full.tar.Z Send submissions, questions, address maintenance and requests for old issues to "neuron-request@hplabs.hp.com" or "{any backbone,uunet}!hplabs!neuron-request" ------------------------------------------------------------ Subject: Re: Neuron Digest V4 #25 (music and nets) From: Stephen Smoliar <smoliar@vaxa.isi.edu> Date: Fri, 18 Nov 88 18:14:14 -0800 I don't know if you want to publish this. I may be the only reader seeking clarification. However, it seems relevant to the general issue of what we expect to read in these digests. [[ Editor's note: I'm including this, since I believe Stephen has thoughtfully voiced many of my own concerns. I have not imposed editorial rights since delaying CLAU's message about the quality of papers at meetings. In fact, I am disposed to be lax in any restrictions. I am not fond of ad hominem arguments and implore submitters to consider their words carefully. What do you readers feel? Should I exert more control over content? On the broader issue, how can one cope with information overload... even on a relatively specialized subject as Neural Nets? On with Stephen's article... -PM ]] *************************** Given my rather intense interest in music and my burgeoning curiosity regarding interesting applications of neural nets, my attention has been drawn to the recent exchange between Jamshed Bharucha and Eliot Handelman. I, for one, read the Neuron Digest because of the second word in its name: My stack of articles to read is large enough as it is to allow for new items to get pushed on because I wish to investigate a new path of research. I find it very useful to have a service which distills recent results and helps me to decide whether or not I want to read further in the matter. In this respect, I was most grateful to Eliot Handelman for taking the trouble to review Bharucha's recent publication. When I first became aware of Bharucha's work, I immediately asked myself if his papers should go into competition with the others on my stack. On the basis of Handelman's review, I decided in the negative. My impression was that there may eventually be something in Bharucha's work which would interest me, but I certainly did not have to rush to see what he had written. More likely, I could wait until the material had undergone some refinement. Having made this conclusion, I found myself somewhat distressed at Bharucha's recent reply to Handelman. Given the current information overload, I have very little sympathy for anyone who writes "I suggest you take the time to read ALL my work, as well as the major sources I cite." Having spent my graduate school years writing for a newspaper, I have little sympathy for someone who cannot tell a story with a moderate amount of brevity in such a way that the underlying message will stand by itself. (I recently had to engage in this exercise in explaining the work of Gerald Edelman, so I appreciate that this is no easy job. Nevertheless, I tend to hold stubbornly to the creed "If you can't write it right, don't write it at all;" and, perhaps unreasonably, I expect to find this attitude shared throughout my professional community. If Bharucha cannot provide us Digest readers with a concise summary of his position as a defense against Handelman's review, I suggest that he withdraw until he can muster the words to do so. The other observation which caused me some distress was the following sentence in Bharucha's final paragraph: "It is not uncommon for musicians to read more into the claims made by psychologists and computer scientists, and probably vice versa, because of the different histories, code words and writing styles of the different fields." This danger is, indeed, very valid; and the only way it can be avoided is for the WRITER to show proper respect for the fact that his reader audience may be broader than usual. To prepare a paper on the topic of music and not assume that there will be musicians interested in it is sheer folly. My own thesis advisor was particularly aware of this and wanted to make sure that anything I wrote would be acceptable to a music professor who lacked a broad base of computer expertise. Such a professor sat on my committee, and I welcomed the challenge of his presence. If Bharucha lacks this ability to communicate without the argot of his profession, I, for one, would just as soon wait until this shortcoming has been remedied. ------------------------------ Subject: Refs/opinions wanted -- Neural nets & approximate reasoning From: bradb@ai.toronto.edu (Brad Brown) Organization: Department of Computer Science, University of Toronto Date: 18 Nov 88 06:18:08 +0000 I am working on a paper which compares symbolic and neural network approaches to approximate reasoning, including fuzzy sets, probabilities logic, and approximate solutions to problems. I would very much appreciate references and personal comments. Given current hardware technology and current neural net (NN) learning algorithms, NNs seem to have some desirable properties that symbolic systems do not, but suffer from implementation problems that prevent them from being useful or efficient in many cases. A summary of my thinking, which includes many generalizations and omits justification, follows. Neural network-based systems have advantages over symbolic systems for the following reasons. (1) For some classes of problems, NN learning algorithms are known. In these cases, "programming" a NN is often a matter of presenting it with training information and letting it learn. Symbolic systems have more known algorithms and can be applied to more problems than NNs, but constructing symbolic programs is labour intensive. The resulting programs are typically problem-specific. (2) Neural nets can adapt to changes in their environment. For instance, a financial expert system implemented as a NN could use new information to modify its performance over time to reflect changing market conditions. Symbolic systems are usually either static or require re-training on a substantial fraction of the dataset to adapt to new data. Neural nets are forgiving in their response to input. Inputs that are similar are treated similarly. In symbolic systems it is very difficult to give the system a notion of what constitutes a "similar" input so input errors or input noise are big problems for symbolic systems. (3) NNs are good at constraint problems and have the desirable property of finding good compromises when a single best solution does not exist. (4) NNs can deal with multiple sources of information. For instance, a financial system could consider inputs from both stock market information and internal company sales information, which are not causally related. The learning procedure can be expected to find weights that "weigh" different kinds of evidence and judge accordingly. Symbolic systems require extensive manual tuning to be able to effectively use multiple orthogonal sources of information. On the other hand, practical applications of NNs are held back by (1) Lack of well-understood training algorithms for many tasks. Many interesting tasks simply cannot be solved with NNs because no one knows how to train them. (2) Difficulty in running neural nets on commercially available hardware. Neural net simulations require vast CPU and memory resources so NN systems may not be cost effective compared to equivalent symbolic systems. (3) Absence of an ability to easily explain why a particular result was achieved. Because knowledge is distributed throughout the network and there is no concept of the network as a whole proceeding stepwise toward a solution, explaining results is difficult. All things considered, I am a believer in neural networks. I see them as the "natural" way to make big advances towards "human-level" intelligence, but the field is too new to be applied to many practical applications right now. Symbolic approaches draw on a more mature and complete base of experience. Nevertheless, it is very difficult to get symbolic systems to show some of the nice traits seen in neural networks, like an ability to deal with noise and approximate inputs and to produce good compromise solutions. An interesting compromise would be the integration of neural networks into symbolic reasoning systems, which has been tried with some success by at least one expert system group. ----------------------------------------------------------- Comments and criticisms on these thoughts would be greatly appreciated. References to current work on neural networks for approximate reasoning and comparisons between neural networks and symbolic processing systems would also be very much appreciated. Thank you very much for your time and thoughts. (-: Brad Brown :-) bradb@ai.toronto.edu ------------------------------ Subject: RE: Relaxation Labelling From: <EACARME%EBRUPC51.BITNET@CUNYVM.CUNY.EDU> Date: Fri, 18 Nov 88 11:35:00 +0100 I studied the relationship between Relaxation Labelling and NN in a couple of papers: "Exploring three possibilities in network design: Spontaneous node activity, node plasticity and temporal coding". In "Neural Computers", edited by R. Eckmiller and C. von der Malsburg, NATO ASI Series F, Vol.41, pp. 301-310, Springer-Verlag, 1988. "Relaxation and neural learning: Points of convergence and divergence". Journal of Parallel and Distributed Computing, to appear. It turns out that many results (convergence, consequences of symmetric compatibility/connectivity, etc.) have been proven independently in both fields. In the papers, I show how both short-term NN functioning and long-term learning can be formulated as relaxation labelling processes. You can contact me at the following address: Carme Torras Institut de Cibernetica (CSIC-UPC) Diagonal 647 08028-Barcelona SPAIN e-mail: eacarme@ebrupc51.bitnet ------------------------------ Subject: Re: Refs/opinions wanted -- Neural nets & approximate reasoning From: songw@csri.toronto.edu (Wenyi Song) Organization: University of Toronto, CSRI Date: 20 Nov 88 02:02:03 +0000 In the previous article, bradb@ai.toronto.edu (Brad Brown) writes: >... > On the other hand, practical applications of NNs are held > back by >... > (3) Absence of an ability to easily explain why a > particular result was achieved. Because knowledge is > distributed throughout the network and there is no > concept of the network as a whole proceeding stepwise > toward a solution, explaining results is difficult. It may remain difficult, if not impossible, to explain results of NN in terms of traditional symbolic processing. However this is not a drawback if you do not attempt to unify them into a grand theory of AI :-) An alternative is to explain the phenomenology in terms of the dynamics of neural networks. It seems to me that this is the correct way to go. We gain much better global predicability of information processing in neural networks by trading off controllability of local quantum steps. The Journal of Complexity devoted a special issue on neural computation this year. [[ Editor's note: Could someone give a reference to this journal and specific issue? -PM]] ------------------------------ Subject: Re: Neuron Digest V4 #26 From: ganymede!tmb@wheaties.ai.mit.edu Organization: The Moons of Jupiter (MIT Artificial Intelligence Lab) Date: Sun, 20 Nov 88 04:14:26 -0500 Valiant and friends have come up with theories of the sort you desire, but only for boolean concepts (binary y's in your notation) and learning algorithms in general, not neural nets in particular. "Graded concepts" are continuous. To my knowledge, no work has addressed the theoretical learnability of graded concepts. Before trying to come up with theoretical learnability results for neural networks, one should probably address the graded concept learning problem in general. The Valiant approach of a Probably Almost Correct (PAC) learning criterion should be applicable to graded concepts. - -- Michael R. Hall | Bell Communications Research I have been working on the problem of probably approximately correct learning of real functions of real variables with respect to classes of sample distributions, different noise models, and constraints (e.g. smoothness constraints) on the functions. Some of that work is published (in the form of an extended abstract) in the proceedings of the 1988 conference of the INNS (Thomas Breuel: "Problem Intrinsic Bounds on Sample Complexity"). The full paper corresponding to the abstract and a collection of other results are in preparation. ------------------------------ Subject: Talk on ASPN at AI Forum 11-22-88 From: aluko@Portia.Stanford.EDU (Stephen Goldschmidt) Organization: Stanford University Date: 21 Nov 88 19:05:53 +0000 I will be presenting an informal talk on an algorithm called ASPN which synthesizes a network of polynomial elements to approximate a function specified in terms of examples. The most remarkable apsect of ASPN is that it chooses the network structure and complexity based on the examples supplied. Also, the network produced is independent of the order of the examples in the knowledge base. ASPN has been successfully applied to problems in many areas, most recently in flight control and guidance law design. It is a successor to, and improvement upon the PNETTR family and also the GMDH approaches of the past 30 years. I participated both in the implementation and application of this little-known algorithm. I will be speaking on Tuesday night November 22, 1988 at 7pm at the monthly AI forum at Bldg. 202 on Hannover St. in the Stanford Industrial Park in Palo Alto. You can call my answering machine at (415)494-1748 or send e-mail to aluko@portia.stanford.edu. *-*-*- Stephen R. Goldschmidt -*-*-* ------------------------------ Subject: More on learning arbitrary transfer functions From: leary#bob%a.sdscnet@SDS.SDSC.EDU Date: Mon, 21 Nov 88 20:03:02 +0000 Recently Prof. Halbert White of the UCSD Economics Dept. gave a talk at an ACM SIGAMS meeting. I quote from the flyer announcing the talk: "Professor White will discuss his new paper which rigorously establishes that standard multilayer feedforward networks with as few as one hidden layer using arbitrary squashing functions are capable of approximating any Borel measurable (i.e. realistic) function from one Euclidean space to another to any desired degree of accuracy, provided that sufficiently many hidden units are available. In this sense, Multi-layered feedforward networks are a class of Universal Approximators." Bob Leary San Diego Supercomputer Center leary@sds.sdsc.edu ------------------------------ Subject: Free Recurrent Simulator From: Barak.Pearlmutter@F.GP.CS.CMU.EDU Date: 21 Nov 88 23:09:00 -0500 I wrote a bare bones simulator for recurrent temporally recurrent neural networks in C. It simulates a network of the sort described in "Learning State Space Trajectories in Recurrent Neural Networks", and is named "full". Full simulates only fully connected networks, uses only arrays, and has no user interface at all. It was intended to be easy to translate into other languages, to vectorize and parallelize well, etc. It vectorized fully on the convex on the first try with no source modifications. Although it is short, it is actually usable and it works well. If you wish to use full, I'm allowing access to a compressed tar file through anonymous ftp from host DOGHEN.BOLTZ.CS.CMU.EDU, user "ftpguest", password "oaklisp", file "full/full.tar.Z". Be sure to use the BINARY command, and don't use the CD command or you'll be sorry. I am not going to support full in any way, and I don't have time to mail copies out. If you don't have FTP access perhaps someone with access will post full to the usenet, and perhaps some archive server somewhere will include it. Full is copyrighted, but I'm giving people permission to use if for academic purposes. If someone were to sell a it, modified or not, I'd be really angry. ------------------------------ Subject: FTPing full.tar.Z From: Barak.Pearlmutter@F.GP.CS.CMU.EDU Date: 22 Nov 88 12:55:00 -0500 People have been having problems ftping full.tar.Z despite their avoiding the CD command. The solution is to specify the remote and local file names separately: ftp> get remote file: full/full.tar.Z local file: full.tar.Z For the curious, the problem is that when you type "get full/full.tar.Z" to ftp it tries to retrieve the file "full/full.tar.Z" from the remote host and put it in the local file "full/full.tar.Z". If the directory "full/" does not exist at your end you get an error message, and said message does not say which host the file or directory does not exist on. Sorry for the inconvenience. --Barak. ------------------------------ End of Neurons Digest *********************