Received: from BUACCA by BUACCA.BU.EDU (Mailer R2.08 PTF009) with BSMTP id 6034; Mon, 02 Nov 92 23:07:47 EST Received: from noc2.dccs.upenn.edu by BUACCA.BU.EDU (IBM VM SMTP R1.2.1) with TCP; Mon, 02 Nov 92 23:07:43 EST Received: from CATTELL.PSYCH.UPENN.EDU by noc2.dccs.upenn.edu id AA19048; Mon, 2 Nov 92 23:02:56 -0500 Return-Path: Received: from LOCALHOST by cattell.psych.upenn.edu id AA17547; Mon, 2 Nov 92 22:15:22 EST Posted-Date: Mon, 02 Nov 92 22:14:55 EST From: "Neuron-Digest Moderator" To: Neuron-Distribution:; Subject: Neuron Digest V10 #12 (conferences) Reply-To: "Neuron-Request" X-Errors-To: "Neuron-Request" Organization: University of Pennsylvania Date: Mon, 02 Nov 92 22:14:55 EST Message-Id: <17534.720760495@cattell.psych.upenn.edu> Sender: marvit@cattell.psych.upenn.edu Neuron Digest Monday, 2 Nov 1992 Volume 10 : Issue 12 Today's Topics: SimTec93*WNN93*FNN93 for Registration See Below NIPS*92 Tutorials Fwd: NIPS Program errors address change & CFP submission CALL FOR PAPERS: WIRN-93 Hotel reservation deadline for NIPS workshops A distribution of a call for papers Send submissions, questions, address maintenance, and requests for old issues to "neuron-request@cattell.psych.upenn.edu". The ftp archives are available from cattell.psych.upenn.edu (130.91.68.31). Back issues requested by mail will eventually be sent, but may take a while. ---------------------------------------------------------------------- Subject: SimTec93*WNN93*FNN93 From: Mary Lou Padgett Date: Wed, 30 Sep 92 03:01:54 -0600 ***************************************************************************** ***************************************************************************** CALL FOR PAPERS SimTec93/WNN93/FNN93 in SAN FRANCISCO November 7-10, 1993 San Francisco Airport Marriott SimTec 93 1993 International Simulation Technology Conference Emerging Technologies * Simulation Applications * Aerospace Visualization, Circuit Simulation, Intelligent Programming Tools and Techniques Multimedia in Simulation Pattern Recognition, Controls, Microelectronics, Life Sciences, Management, Supercomputing, Parallel & Distributed Processing Simulation Facilities, Training Command & Control Paper competitions in Academic, Industry and Government Categories Awards Luncheon * Exhibitors Reception TOUR of NASA/AMES PANELS, DISCUSSIONS, PROFESSIONAL ACTIVITIES, STANDARDS, SOFTWARE EXCHANGES JOIN US in BEAUTIFUL SAN FRANCISCO . . . Bring your family . . . Sponsored by The Society for Computer Simulation, International Co-sponsored by NASA/JSC,GSFC and LRC in cooperation with SPIE WNN93/San Francisco Workshop on Neural Networks and Fuzzy Logic WNN is an informal conference on neural networks, fuzzy logic and related applications. Artificial networks and life sciences foundations are of interest. The meeting features workshops, standards discussions and paper contests. In addition to SimTec sponsors, the IEEE Neural Networks Council is a participating society and INNS is cooperating. Mary Lou Padgett, Auburn University; Robert Shelton, NASA/JSC; Walter J. Karplus, UCLA; Bart Kosko, USC and Paul Werbos, NSF; NASA Representative: Robert Savely, NASA/JSC FNN93: Fuzzy Neural Networks Symposium with Tutorials and Standards A collection of presentations on fuzzy logic theory and applications, neural fuzzy control and basic neural networks concepts and applications will be featured on Sunday. Anyone interested in these concepts should benefit from participating. Proposed Neural Networks and Fuzzy Logic Standards will be explained by Padgett. NASA/NETS software executable and examples will be included in this tutorial. Sponsored by SCS, co-sponsored by NASA/JSC,GSFC. SimTec93 * WNN93 * FNN93 DEADLINES: Abstracts/Draft Papers: May 1, 1993; Camera-Ready: June 30, 1993. TO SUBMIT AN ABSTRACT, PROPOSE A SESSION OR SUGGEST A TOPIC OF INTEREST . . . Contact: Mary Lou Padgett, SCS Associate VP for SimTec, Auburn University, 1165 Owens Road, Auburn, AL 36830. Phone: (205) 821-2472/3488 Fax: (205) 844-1809 Email: mpadgett@eng.auburn.edu. SCS Office: Phone: (619) 277-3888 Fax: (619) 277-3930. General Chair: Ted Lambert, Program Chair: Martin Dost; Associate Program Chair: Ralph Huntsinger, UC Chico; NASA Representatives: Robert Savely, NASA/JSC and Joseph Mica, NASA/GSFC; ESA Representative: Juan Miro Committee Includes: Bill Cameron; Paul Luker, UC Chico; Norman Pobanz, Bechtel; Stuart Schlessinger; A. Martin Wildberger; Tim Cleghorn, NASA/JSC; Robert Lea, NASA/JSC ************************************************************************ ************************************************************************ SimTec 93 1993 International Simulation Technology Conference WNN93/San Francisco & FNN93 Symposium November 7-10, 1992 San Francisco Airport Marriott If you wish to receive further information about SimTec, WNN and FNN, please, return (preferably by Email) the form printed below: NAME: AFFILIATION: ADDRESS: PHONE: FAX: EMAIL: Please send more information on registration ( ) optional tours ( ). I intend to submit a paper ( ), a tutorial ( ), an abstract only ( ). I may give a demonstration ( ) or exhibit ( ). Return to: Mary Lou Padgett, 1165 Owens Rd., Auburn, AL 36830. email: mpadgett@eng.auburn.edu ======= SCS OFFICE: (619) 277-3888 ======= SimTec 93 * WNN93/San Francisco * FNN93 Society for Computer Simulation, International P.O. Box 17900, San Diego, CA 92177 ************************************************************************ ------------------------------ Subject: for Registration See Below From: Steve Hanson Date: Mon, 05 Oct 92 08:07:30 -0500 FOR NIPS*92 REGISTRATION SEE BELOW NEURAL INFORMATION PROCESSING SYSTEMS (NIPS) -Natural and Synthetic- Monday, November 30 - Thursday, December 3, 1992 Denver, Colorado This is the sixth meeting of an inter-disciplinary conference which brings together neuroscientists, engineers, computer scientists, cognitive scientists, physicists, and mathematicians interested in all aspects of neural processing and computation. A day of tutorial presentations (Nov 30) will precede the regular session and two days of focused workshops will follow at a nearby ski area (Dec 4-5). Major categories and examples of subcategories for paper submissions are the following; Neuroscience: Studies and Analyses of Neurobiological Systems, Inhibition in cortical circuits, Signals and noise in neural computation, Theoretical Neurobiology and Neurophysics. Theory: Computational Learning Theory, Complexity Theory, Dynamical Systems, Statistical Mechanics, Probability and Statistics, Approximation Theory. Implementation and Simulation: VLSI, Optical, Software Simulators, Implementation Languages, Parallel Processor Design and Benchmarks. Algorithms and Architectures: Learning Algorithms, Constructive and Pruning Algorithms, Localized Basis Functions, Tree Structured Networks, Performance Comparisons, Recurrent Networks, Combinatorial Optimization, Genetic Algorithms. Cognitive Science & AI: Natural Language, Human Learning and Memory, Perception and Psychophysics, Symbolic Reasoning. Visual Processing: Stereopsis, Visual Motion, Recognition, Image Coding and Classification. Speech and Signal Processing: Speech Recognition, Coding, and Synthesis, Text-to-Speech, Adaptive Equalization, Nonlinear Noise Removal. Control, Navigation, and Planning: Navigation and Planning, Learning Internal Models of the World, Trajectory Planning, Robotic Motor Control, Process Control. Applications: Medical Diagnosis or Data Analysis, Financial and Economic Analysis, Timeseries Prediction, Protein Structure Prediction, Music Processing, Expert Systems. The technical program will contain plenary, contributed oral and poster presentations with no parallel sessions. All presented papers will be due (January 13, 1993) after the conference in camera-ready format and will be published by Morgan Kaufmann. FOR REGISTRATION PLEASE SEND YOUR NAME AND ADDRESS ASAP TO: NIPS*92 Registration SIEMENS Research Center 755 College Road East Princeton, NJ, 08540 NIPS*92 Organizing Committee: General Chair, Stephen J. Hanson, Siemens Research & Princeton University; Program Chair, Jack Cowan, University of Chicago; Publications Chair, Lee Giles, NEC; Publicity Chair, Davi Geiger, Siemens Research; Treasurer, Bob Allen, Bellcore; Local Arrangements, Chuck Anderson, Colorado State University; Program Co-Chairs: Andy Barto, U. Mass.; Jim Burr, Stanford U.; David Haussler, UCSC ; Alan Lapedes, Los Alamos; Bruce McNaughton, U. Arizona; Barlett Mel, JPL; Mike Mozer, U. Colorado; John Pearson, SRI; Terry Sejnowski, Salk Institute; David Touretzky, CMU; Alex Waibel, CMU; Halbert White, UCSD; Alan Yuille, Harvard U.; Tutorial Chair: Stephen Hanson, Workshop Chair: Gerry Tesauro, IBM Domestic Liasons: IEEE Liaison, Terrence Fine, Cornell; Government & Corporate Liaison, Lee Giles, NEC; Overseas Liasons: Mitsuo Kawato, ATR; Marwan Jabri, University of Sydney; Benny Lautrup, Niels Bohr Institute; John Bridle, RSRE; Andreas Meier, Simon Bolivar U. ------------------------------ Subject: NIPS*92 Tutorials From: Steve Hanson Date: Thu, 08 Oct 92 08:33:03 -0500 NIPS*92 TUTORIAL PROGRAM November 30, 1992 NEUROSCIENCE 9:30 - 11:30 ``ASPECTS OF COMPUTATION WITH REAL NEURONS'' William BIALEK NEC Research Institute 1:00 - 3:00 ``ADVANCES IN COGNITIVE NEUROSCIENCE'' William HIRST New School for Social Research 3:30 - 5:30 ``CORTICAL OSCILLATIONS: CURRENT EXPERIMENTAL AND THEORETICAL STATUS'' Christof KOCH CalTech 9:30 - 11:30 ``BIFURCATIONS IN NEURAL NETWORKS'' Bard ERMENTROUT Department of Mathematics University of Pittsburgh ARCHITECTURES, ALGORITHMS, AND THEORY 9:30 - 11:30 ``LEARNING THEORY AND NEURAL COMPUTATION'' Les VALIANT Computer Science Department Harvard University 1:00 - 3:00 ``STATISTICAL ACCURACY OF NEURAL NETWORKS'' Andrew BARRON Statistics Department University of Illinois 3:30 - 5:30 ``LEARNING AND APPROXIMATION IN NEURAL NETWORKS'' Tommy POGGIO Brain & Cognitive Science & AI Lab MIT IMPLEMENTATIONS 1:00 - 3:00 ``ELECTRONIC NEURAL NETWORKS'' Josh ALSPECTOR Bellcore All inquiries for registration to Conference, Tutorials or Workshop should go to NIPS*92 Registration SIEMENS Research Center 755 College Rd. East Princeton, NJ 08550 phone 609-734-3383 email kic@learning.siemens.com Stephen J. Hanson Learning Systems Department SIEMENS Research 755 College Rd. East Princeton, NJ 08540 ------------------------------ Subject: Fwd: NIPS Program errors From: Steve Hanson Date: Tue, 13 Oct 92 09:29:07 -0500 We have been made aware of some errors in the NIPS Program and apologize for any inconvenience this may have caused you. Please be aware of the following: The Tutorial Program is held on November 30th, 1992 (disregard incorrect date on top of page 6 in the NIPS Program booklet) The Tutorial by Josh Alspector "Electronic Neural Networks" will be held on November 30th, 1992 from 3:30-5:30 (disregard incorrect time on page 6 in the NIPS Program booklet) The dates and times as they appear on the Conference Registration form are correct. ------------------------------ Subject: address change & CFP From: xin@csadfa.cs.adfa.oz.au (Xin Yao) Organization: Dept of Computer Sci, Australian Defence Force Academy, Canberra Date: Sun, 18 Oct 92 12:27:29 -0500 =========================================================================== CALL FOR PAPERS =========================================================================== The 3rd International Conference for Young Computer Scientists (ICYCS'93) Beijing China, July 15-17,1993 Sponsored by CFF (Chinese Computer Federation) CICCST (China internatonal Conference Center for Science and Technology) Co-Sponsored by ACM, HKCS (HONG KONG), IEEE, IEICE (Japan), IPS (Japan), NZCS (NEW ZEALAND), and SCS (Singapore) This conference is mainly devoted to the computer professionals under 40 and provides them an opportunity to present their ressarch achievements and communicate with other researchers. This is the third bi-annual conference for young computer scientists, after two successful conferences in 1989 and 1991. The ICYCS'91, held at the Fragant-Hill hotel in Beijing from 18-20 July 1991, was attended by 200 delegates form 12 countries. The proceedings of the conference, published by International Academic Publishers, is about 800 pages. The focus of the ICYCS'93 is "Towards Future of Computer Science and Applications". Topics of interests include the following areas: Architectures Artificial Intelligence Artificial Neural Networks CAD & CAE & CAI & CAM & computer Graphics Computer vision & Robotics Database Foundation of Computer Science Networks & Distributed Processing Pattern Recognition & Image Processing Software Engineering & Tools, Applications The authors are requested to submit 4 copies of a full paper, not published elsewhere, up to six pages in length, in English, with related key words to one of the program Co-Chairmans indicated below. The deadline of submission is 15 November 1992. Prof. Wen Gao Prof. Jianping Wu Dept of Computer Science Dept of Computer Science Harbin Institute of Technology Tsinghua university Harbin 150006,china Beijing 100084 P. R. China P. R. China Fax: +86 451 32-1048 Fax: +86 1 256-2768 Prof. Charles X. Ling Department of Computer Science University of Western ontario London,Ontario Canada N6A 5B7 All submitted papers will be subject to peer review by three referees and all accepted papers will appear in proceedings distributed in US, Europe and other areas. IMPORTANT DATES Submission deadline: Nov. 15, 1992 Notification of acceptanceor rejection: Jan. 20, 1993 Camera-ready version on special sheets: March 15, 1993 For conference information please contact to : Dr. Yamin Li Dept of Computer Science Tsinghua University Beijing 10084 P.R. China Fax: +86 1 256-2768 For post conference tours information, please contact: Ms. Jin Fang CICCST/CAST 32 Baishiqiao Lu Beijin 100086 P.R.China Fax: +86 1 831-6091 Telex: 222337 iccst cn ------------------------------ Subject: submission From: L. Z. Wu Date: Mon, 19 Oct 92 12:13:35 +0000 Hello, Could you include the following "Call for Papers" into next Neuron Digest. Thank you! Li Wu First Announcement & Call for Papers 1993 International Conference on Neural Networks and Signal Processing (ICNNSP'93) Nov. 21-24, 1993 Guangzhou CHINA In collaboration with the IEEE Beijing Section and the National Natural Science Foundation Committee of China (NNSFC), and sponsored by the Chinese Neural Networks Council (CNNC) and the Chinese Neural Networks and Signal Processing Academic Committee of the Chinese Institute of Electronics (CIE), the ICNNSP'93 will be held in Guangzhou in 1993. Papers are solicited for technical sessions on the following topics: * Theories of Artificial Neural Networks * Artificially Intelligent Neural Networks * Associative Memory * Electronic Neurocomputers * Pattern Recognition * Image Analysis * Speech Recognition * Adaptive Resonance Theory * Supervised & Unsupervised Learning * Applications * Neural Fuzzy Systems * Neurocognition * System Identification & Spectral Estimation * Optimisation * Non-linear Filtering * Adaptive Signal Processing * Biology & Biomedical Signal Processing * Multi-dimensional Signal Processing * Radar Signal Processing * Video Signal Processing Prospective authors are invited to submit 4 copies (original and three copies) of complete paper of no more than 6 pages including figures, tables and references. Papers must be in camera-ready format on 8.5"X11" white paper with one inch margins on all four sides. Centred at the top of the first page should be the complete title, author name(s), affiliation(s) and mailing address. In the accompanying letter, the following information must be included: full title of the paper, corresponding author, technical area, presentation preferred (oral/poster), presenter, the accepted papers will be published in conference proceedings. Schedule: -------- Submission of complete paper : Mar. 15, 1993 Notification of acceptance : May 30, 1993 Advanced registration : Before Sept. 30 1993 Send paper to: ------------- Prof. Zhen-Ya He Programme Chair of ICNNSP'93 Radio Engineering Department Southeast University Nanjing 210018, P.R.CHINA Tel: 634691 Ext.2481 Fax: (8625) 714212 Organised by ------------ South China University of Technology Southeast University Beijing Institute of Electronics Conference Committee -------------------- General Chair ------------- Prof. Bing-Zheng Xu Institute of Radio Engg. & Automation South China University of Technology Guangzhou 510641 CHINA Co-chair Prof. Russel Eberhart Johns Hopkins University Applied Physics Lab. Johns Hopkins Road Laurel, MD 20723, USA Programme Chair --------------- Prof. Zhen-Ya He Radio Engineering Dept. Southeast University Nanjing 210018, CHINA Co-chair Prof. John J. Shynk University of California Santa Barbara, USA Organisation Chair ----------------- Prof. Ying-Lin Yu Institute of Radio Engg. & Automation South China University of Technology Guangzhou 510641 CHINA Co-chair Prof. Da-Fa Li Guangzhou Institute of Electronics Guangzhou, CHINA Publications Chair ------------------ Prof. Gan-Ying Luo Institute of Radio Engg. & Automation South China University of Technology Guangzhou 510641 CHINA General Secretary ----------------- Dr. Hai-Zhou Li Institute of Radio Engg. & Automation South China University of Technology Guangzhou 510641 CHINA Programme Committee ------------------- Yan-Da Li (China) K.D. Baker (UK) Zheng Bao (China) A. Ushida (Japan) Fan Jin (China) Y.T. Zhou (USA) Zhen-Ming Chai (China) W.H. Ku (USA) Bing-Zheng Xu (China) M.O. Ahmad (Canada) Ying-Lin Yu (China) H.H. Chen (USA) Qian-Sheng Cheng (China) W.C. Siu (HK) Hai-Zhou Li (China) You-An Ke (China) Da-Fa Li (China) W.K. Jenkins (USA) International Advisory Committee -------------------------------- Chair Zhen-Ming Chai (China) ----- H.K. Chen (USA) S.K. Tao (Hong Kong) H. Fujisaki (Japan) S.P. Chan (Hong Kong) S.Y. Kung (USA) A.N. Michel (USA) M.N.S. Swamy (Canada) R.W. Liu (USA) Shigeo Tsujii (Japan) F.Y. Chen (China) J.V.McCanny (UK) Y.S. Cheung (Hong Kong) P.C. Cheng (Hong Kong) H.E. Luder (Germany) ------------------------------ Subject: CALL FOR PAPERS: WIRN-93 From: RAMPO@SALERNO.INFN.IT Date: 29 Oct 92 13:30:00 +0000 ***************** CALL FOR PAPERS ***************** The 6-th Italian Workshop on Neural Nets WIRN VIETRI-93 May 12-14, 1993 Vietri Sul Mare, Salerno ITALY FIRST ANNOUNCEMENT Organizing - Scientific Committee - -------------------------------------------------- B. Apolloni (Univ. Milano) A. Bertoni ( Univ. Milano) E. R. Caianiello ( Univ. Salerno) D. D. Caviglia ( Univ. Genova) P. Campadelli ( CNR Milano) M. Ceccarelli ( Univ. Salerno - IRSIP CNR) P. Ciaccia ( Univ. Bologna) M. Frixione ( I.I.A.S.S.) G. M. Guazzo ( I.I.A.S.S.) M. Gori ( Univ. Firenze) F. Lauria ( Univ. Napoli) M. Marinaro ( Univ. Salerno) A. Negro ( Univ. Salerno) G. Orlandi ( Univ. Roma) E. Pasero ( Politecnico Torino ) A. Petrosino ( Univ. Salerno - IRSIP CNR) M. Protasi ( Univ. Roma II) S. Rampone ( Univ. Salerno - IRSIP CNR) R. Serra ( Gruppo Ferruzzi Ravenna) F. Sorbello ( Univ. Palermo) R. Stefanelli ( Politecnico Milano) L. Stringa ( IRST Trento) R. Tagliaferri ( Univ. Salerno) R. Vaccaro ( CNR Napoli) Topics - ---------------------------------------------------- Mathematical Models Architectures and Algorithms Hardware and Software Design Hybrid Systems Pattern Recognition and Signal Processing Industrial and Commercial Applications Fuzzy Tecniques for Neural Networks Schedule - ----------------------- Papers Due: January 15, 1993 Replies to Authors: March 29, 1993 Revised Papers Due: May 14, 1993 Sponsors - --------------------------------------------------------------------------- International Institute for Advanced Scientific Studies (IIASS) Dept. of Fisica Teorica, University of Salerno Dept. of Informatica e Applicazioni, University of Salerno Dept. of Scienze dell'Informazione, University of Milano Istituto per la Ricrca dei Sistemi Informatici Paralleli (IRSIP - CNR) Societa' Italiana Reti Neuroniche (SIREN) The 6-th Italian Workshop on Neural Nets (WIRN VIETRI-93) will take place in Vietri Sul Mare, Salerno ITALY, May 12-14, 1993. The conference will bring together scientists who are studying several topics related to neural networks. The three-day conference, to be held in the I.I.A.S.S., will feature both introductory tutorials and original, refereed papers, to be published by World Scientific Publishing. Papers should be 6 pages,including title, abstract, figures, tables, and bibliography. The first page should give keywords, postal and electronic mailing addresses, telephone, and FAX numbers. Submit 3 copies to the address shown. For more information, contact the Secretary of I.I.A.S.S. I.I.A.S.S Via G.Pellegrino, 19 84019 Vietri Sul Mare (SA) ITALY Tel. +39 89 761167 Fax +39 89 761189 E-Mail robtag@udsab.dia.unisa.it ***************************************************************** ------------------------------ Subject: Hotel reservation deadline for NIPS workshops From: "Gerald Tesauro (8-863-7682)" Date: Fri, 30 Oct 92 12:38:28 -0500 The NIPS 92 post-conference workshops will take place Dec. 3-5 in Vail, Colorado, at the Radisson Resort Vail. The Radisson is offering attendees a special discounted room rate of $78.00 per night, and is holding a block of rooms for us until WEDNESDAY, NOVEMBER 4. Attendees are strongly encouraged to make their hotel reservations by this date. Reservations after Nov. 4 will be on a space-available basis only. To make reservations, call the Radisson at 303-476-4444 and mention our "NIPS" group code. Gerry Tesauro NIPS 92 Workshops Chair ------------------------------ Subject: A distribution of a call for papers From: NKASABOV@gandalf.otago.ac.nz Organization: University of Otago Date: 03 Nov 92 08:58:43 +1200 FIRST CALL FOR PAPERS AND PARTICIPANTS The First New Zealand International Two-stream Conference on Artificial Neural Networks and Expert Systems- ANNES'93 November 24-26, 1993 University of Otago, Dunedin, New Zealand LETTER from the President of the New Zealand Computer Society: Dear Colleague, It has been suggested by NZCS members and members of the Expert Systems Interest Group that we should hold a conference on Expert Systems in 1993. We are now glad to invite you to participate to The First New Zealand International Two-stream Conference on Artificial Neural Networks and Expert Systems ANNES'93. The aim of the conference is to gather together scientists, industry and business representatives in order to enrich their knowledge and technological skills in developing knowledge based systems and their numerous applications. I would recommend this conference to you and urge you to attend. Yours faithfully, Philip Sallis TOPICS OF INTEREST * Artificial neural networks: models; architectures; algorithms; software tools; hardware implementations; cognitive models of the brain and their impact. * Neural networks for problem solving: handling large experimental data bases; speech-, image- and text processing; time-series prognosis; control; diagnosis, etc. * Fuzzy systems: methods; tools; software and hardware implementations; fuzzy systems for problem solving. * Expert systems: methods for representing inexact data and uncertain knowledge; approximate reasoning; tools and systems; object-oriented systems. * Hybrid systems: integrating neural networks and AI-techniques; integrating neural networks and fuzzy systems; extending existing software tools with fuzzy reasoning and neural nets. * Industrial applications of expert systems and neural networks: manufacturing; process control; quality testing; etc. * Business applications of neural networks and expert systems: Finance; Economics; Marketing; Management; Banking; etc. * Applications of neural networks and expert systems in Agriculture, Environment protection, Medicine, Geographic information systems; and other application areas. * The impact of neural networks and expert systems to the future IT development. INVITED KEYNOTE SPEAKERS Professor Takeshi Yamakawa, Department of Computer Science and Control, Kyushu Institute of Technology, Chairman of the Fuzzy Logic Systems Institute (Japan). Professor V.Rao Vemuri, Department of Applied Science, University of California, Davis (U.S.A.). CALL FOR PAPERS Papers must be received by April 30, 1993. They will be reviewed by senior researchers in the field and the authors will be informed about the decisions at the end of the review process by June 30, 1993. Final versions of the accepted papers should be submitted by 30 July 1993. A recommended size for a paper would be between 4 and 10 pages. All accepted papers will be published in the conference proceedings, which will be available at the conference for distribution to all the regular conference registrants. As the conference is a multi-disciplinary meeting the papers are required to be comprehensible to a wider rather than to a very specialised audience. Papers will be presented at the conference either in an oral or in a poster session. Please submit three (3) copies (one camera-ready original and two copies) of the paper written in English on A4-format white paper with one inch margins on all four sides, in one-column format, single-spaced, in Times or similar font of 12 points, and printed on one side of the page only. Centred at the top of the first page should be the complete title, author(s), mailing and e- mailing addresses, following by an abstract, followed by the text. TUTORIALS During the first day of the conference the following 3-hour tutorials will be organized: 1. The basics of artificial neural networks. 2. The basics of fuzzy systems. Fuzzy systems applications. 3. Neural networks for problem solving. 4. Expert systems- tools and systems. These aim at providing basic knowledge in the subject area. The tutorial fee is not included in the conference fee. Tutorial materials will be distributed among the participants. EXHIBITION Companies and university research laboratories are encouraged to exhibit their developed or distributed software and hardware products. There will be an additional fee of NZ$50 for exhibiting products at the conference. STUDENTS SESSION A postgraduate session will be organised. Postgraduate students are encouraged to submit papers to this session following the same formal requirements for paper submission. The submitted papers will be published in a separate brochure. VIDEO TRACK A video session will be organised which will allow participants to display up to 15 minute films. These should ideally cover applications of expert systems and neural networks to real problems in Commerce, Industry, Medicine, Agriculture, Government, Education, etc. SPONSORSHIP The initial sponsor of the ANNES'93 conference is the New Zealand Computer Society. REGISTRATION The registration fees to attend the conference are: Full time students: NZ$ 75.00 Academics,company representatives: NZ$300.00 One tutorial: NZ$ 100.00 A single day registration: NZ$ 150.00 An exhibition fee: NZ$50.00 A discount of 20% applies for advance registration which must be posted to the secretary before 30 July 1993. A discount of NZ$50 applies to participants who will present their accepted papers either in the oral or in the poster session. VENUE The University of Otago, Dunedin, New Zealand. ACCOMMODATION Accommodation has been booked at St Margaret's College located right on the Campus and 10 minutes from downtown Dunedin. The college offers well equipped facilities including library, sports hall, music hall and computers with E-MAIL connection. Full board (NZ$50) is available during the conference days as well as two days before or after the conference. Accommodation will be also booked for a range of hotels in the city. POSTCONFERENCE EVENTS Following the conference, delegates may like to experience the delights of Queenstown and Central Otago. A variety of options are available with travel plans able to be coordinated by the Dunedin Visitors Centre (telephone +(3)4743300, Octagon, Dunedin, New Zealand). Further information will be provided in the second call for papers. ANNES'93 CONFERENCE CONTACTS: PROGRAM AND CONFERENCE CHAIR Nikola Kasabov Tel. +(3) 479 8319, Fax. +(3) 479 8311 email: nkasabov@otago.ac.nz Department of Information Science, University of Otago, P.O.Box 56, Dunedin, New Zealand (Conference program, papers, proceedings, tutorials, reviewing, invited talks) CHAIR OF THE ORGANIZING COMMITTEE Martin Anderson Tel. +(3) 479 8315, Fax. +(3) 479 8311 email: manderson@otago.ac.nz Department of Information Science, University of Otago, P.O. Box 56, Dunedin, New Zealand (Sponsorship proposals, exhibition proposals, video track, business and industry contacts) POSTGRADUATE STUDENT SESSION Ms. Kitty Ko Tel. +(3) 479 8153, Fax. +(3) 479 8311, email: kittyko@otago.ac.nz Department of Information Science, University of Otago, P.O.Box 56, Dunedin, New Zealand ADMINISTRATIVE SECRETARY: Ms Gina Porteous Tel.+(3) 479 8180, Fax. +(3) 479 8311, email:gporteous@otago.ac.nz Department of Information Science, University of Otago, P.O. Box 56, Dunedin, New Zealand (Registration and all enquiries). DEADLINES 30 April 1993 Submission of papers. 30 June 1993 Notification of acceptance. 30 July 1993 Early registration; final papers. ----------------------------------------------------------------- ANNES'93 - The First New Zealand International Two-stream Conference on Artificial Neural Networks and Expert Systems, 24-26 November 1993, University of Otago, Dunedin, New Zealand REPLY FORM Please complete and send to the secretary: Ms. Gina Porteous Department of Information Science, University of Otago P.O. Box 56, Dunedin, New Zealand Tel. +(3) 479 8180, Fax. +(3) 479 8311, email: gporteous@otago.ac.nz Name, First name: University or company: Mail address: Fax: Phone: Email: I intend to attend the conference: I intend to submit a paper (If Yes, please give the provisional title): Please send me the program when ready: I intend to attend the tutorial(s): 1 ,2 ,3 ,4 I intend to exhibit a product (If Yes, please give details on a separate sheet ) I intend to display a video film (Give details on a separate sheet please) I intend to attend the postgraduate student session: I intend to submit a paper to the postgraduate session (Please give the provisional title): Please send the ANNES'93 First Call for Papers and Participants to the colleagues of mine at the following addresses: ------------------------------ End of Neuron Digest [Volume 10 Issue 12] ***************************************** Received: from BUACCA by BUACCA.BU.EDU (Mailer R2.08 PTF009) with BSMTP id 9770; Tue, 03 Nov 92 18:03:39 EST Received: from noc2.dccs.upenn.edu by BUACCA.BU.EDU (IBM VM SMTP R1.2.1) with TCP; Tue, 03 Nov 92 18:03:33 EST Received: from CATTELL.PSYCH.UPENN.EDU by noc2.dccs.upenn.edu id AA03964; Tue, 3 Nov 92 17:58:38 -0500 Return-Path: Received: from LOCALHOST by cattell.psych.upenn.edu id AA03558; Tue, 3 Nov 92 16:59:52 EST Posted-Date: Tue, 03 Nov 92 16:59:13 EST From: "Neuron-Digest Moderator" To: Neuron-Distribution:; Subject: Neuron Digest V10 #13 Reply-To: "Neuron-Request" X-Errors-To: "Neuron-Request" Organization: University of Pennsylvania Date: Tue, 03 Nov 92 16:59:13 EST Message-Id: <3543.720827953@cattell.psych.upenn.edu> Sender: marvit@cattell.psych.upenn.edu Neuron Digest Tuesday, 3 Nov 1992 Volume 10 : Issue 13 Today's Topics: NIPS Registration NIPS*92 NIPS*92 CONFERENCE PROGRAM Send submissions, questions, address maintenance, and requests for old issues to "neuron-request@cattell.psych.upenn.edu". The ftp archives are available from cattell.psych.upenn.edu (130.91.68.31). Back issues requested by mail will eventually be sent, but may take a while. ---------------------------------------------------------------------- Subject: NIPS Registration From: Steve Hanson Date: Mon, 02 Nov 92 08:19:18 -0500 "Register early and often..." As an election day bonus for voting.. we have extended the PREREGISTRATION DEADLINE for NIPS*92 to the End of the WEEK (Nov. 6, 1992) Please send your completed registration form to NIPS*92 Registration Siemens Research Center 755 College Rd. East Princeton, NJ 08540 Stephen J. Hanson Learning Systems Department SIEMENS Research 755 College Rd. East Princeton, NJ 08540 ------------------------------ Subject: NIPS*92 From: Steve Hanson Date: Mon, 02 Nov 92 08:39:34 -0500 NOTE that NIPS*92 is being held at new hotel for the first time this year. It will be Downtown CITY-CENTER MARIOTT in DENVER. This is a (as the name suggests) a centrally located hotel which we have a 72$ rate. You must Register this week in order to ensure this speical discount Rate. Please do so ASAP. To Make reservations call 303-297-1300 and be sure to mention you are with the NIPS*92 Group. Steve Hanson NIPS*92 General Chair Stephen J. Hanson Learning Systems Department SIEMENS Research 755 College Rd. East Princeton, NJ 08540 ------------------------------ Subject: NIPS*92 CONFERENCE PROGRAM From: Steve Hanson Date: Fri, 02 Oct 92 14:39:13 -0500 NIPS*92 Conference PROGRAM ORAL PROGRAM: Monday, November 30 After Dinner Talk: Stuart Anstis, Psychology Department., UC San Diego "I Thought I saw it move: The Psychology of Motion Perception." Tuesday, December 1 ORAL 1: COMPLEXITY, LEARNING & GENERALIZATION [8:30--9:40] 0.1.1. T. Cover, Department of Elec. Eng., Stanford University "Complexity and Generalization in Neural Networks." (Invited Talk)[8:30am] 0.1.2. N. Intrator, Center for Neural Science, Brown University "Combining Exploratory Projection Pursuit and Projection Pursuit Regression with Application to Neural Networks" [9:00am] 0.1.3. A, Stolcke & S. Omohundro, International Computer Science Institute, Berkeley, CA "Hidden Markov Model Induction by Bayesian Model." [9:20am] 0.1.4 K-Y Siu*, V. Roychowdhury%, T. Kailath+, *Department of Elec. & Computer Eng., UC Irvine, %School of Elec. Eng., Purdue University, +Information Systems Lab, Stanford University "Computing with Almost Optimal Size Neural Networks." [9:40am] ORAL 2: CONTROL, NAVIGATION & PLANNING 0.2.1. D. DeMers* & K. Kreutz-Delgado%, *Dept. of Computer Science, UC San Diego, %Dept. of Elec. & Computer Eng. & Inst. for Neural Comp., UC San Diego "Global Regularization of Inverse Kinematics for Redundant Manipulators." [10:30am] 0.2.2 A. W. Moore & C. G. Atkeson, MIT Al Lab "Memory-based Reinforcement Learning: Efficient Computation with Prioritized Sweeping." [10:50am] 0.2.3 P. Dayan* & G. E. Hinton% *CNL, The Salk Institute %Department of Computer Science, Univeristy of Toronto "Feudal Reinforcement Learning." [11:10am] 0.2.4 D. Pomerleau, School of Computer Science, CMU "Input Reconstruction Reliability Estimation." [11:30am] SPOTLIGHT 1: COMPLEXITY, LEARNING & GENERALIZATION. CONTROL, NAVIGATION & PLANNING. [11:50-11:58am] ORAL 3: VISUAL PROCESSING 0.3.1. S. Geman, Mathematics Department, Brown University "Interpretation-guided Segmentation and Recognition." (Invited Talk) [2:00pm] 0.3.2. S. Becker, Department of Computer Science, Univ. of Toronto "Learning to Categorize Objects Using Temporal Coherence." [2:30pm] 0.3.3. S. J Nowlan & T. J. Sejnowski, CNL, The Salk Institute "Filter Selection Model for Generating Visual Motion Signals for Target Tracking." [2:50pm] 0.3.4. E. Stern*, A. Aertsen%, E. Vaadia+ & S. Hochstein** *Department of Neurobiology, Hebrew University, Jerusalem %Inst. fur Neuroinformatik, Ruhr-Univ., Bochum, Germany +Department of Physiology, Hebrew University, Jerusalem ** "Stimulus Encoding by Multi-Dimensional Receptive Fields in Single Cells and Cell Populations in V1 of Awake Monkey." [3:10pm] ORAL 4: STOCHASTIC LEARNING AND ANALYSIS 0.4.1. T. K. Leen* & J. Moody % *CSE Department, Oregon Graduate Institute %Department of Computer Science, Yale University "Probability Densities and Equilibria in Stochastic i Learning." [4:00pm] 0.4.2. W. Finnoff, Siemens AG Corp. Res. & Dev., Munich, Germany "Diffusion Approximations for the Constant Learning Rate Backpropagation Algorithm and Resistance to Local Minima." [4:20pm] 0.4.3. L. Xu & A. Yuille, Division of Applied Sciences, Harvard Univ. "Self-Organization for Robust Principal Component Analysis by the Statistical Physics Approach." [4:40pm] SPOTLIGHT 2: VISUAL PROCESSING [5:00-5:12pm] SPOTLIGHT 3: STOCHASTIC LEARNING & ANALYSIS [5:15-5:35pm] Wednesday, December 2 ORAL 5: COMPUTATIONAL AND THEORETICAL NEUROBIOLOGY 0.11.1. J. Rinzel, Mathematical Research Branch, NIH "Coupling Mechanisms and Rhythmogenesis in Neuron Models." (Invited Talk) [8:30am] 0.11.2. K. Doya, M.E.T. Boyle, and A. I. Selverston Department of Biology, UC San Diego "Mapping between Neural and Physical Activities of the Lobster Gastric Mill System." [9:00am] 0.11.3. M. E. Nelson, Beckman Institute, University of Illinois "Neural Models of Adaptive Filtering Mechanisms in the Electrosensory System."9:20am] 0.11.4. N. Burgess, J. O'Keefe and M. Reece Department of Anatomy, University College, London "Using Hippocampal 'Place Cells' for Navigation, Exploiting Phase Coding." [9:40am] 0.11.5. M. A. Gluck and C. E. Myers Center for Molecular and Behaviorial Neuroscience, Rutgers Univ. "Neural Bases of Adaptive Stimulus Representations: A Computational Theory of Hippocampal-Region Function." [10:00am] ORAL 6: SPEECH AND SIGNAL PROCESSING 0.6.1. M. Cohen*, H. Franco*, N. Morgan%, D. Rumelhart+, and V. Abrash* *SRI Inst., Menlo Park, CA %ICSI, Berkeley, CA +Psychology Department, Stanford University, CA "Context-Dependent Multiple Distribution Phonetic i Modeling with MLPS." [10:50am] 0.6.2. M. Hirayama*, E. V. Bateson%, K. Honda%, Y. Koike* and M. Kawato* *ATR Human Inf. Proc. Res. Labs %ATR Auditory and Visual Perception Res. Labs., Kyoto, Japan "Physiologically Based Speech Synthesis." [11:10am] 0.6.3. W. Liu, M. H. Goldstein, Jr. and A. G. Androu, Dept. of Elec. & Comp. Eng., The Johns Hopskins University "Analog Cochlear Model for Multiresolution Speech Analysis." [11:30am] SPOTLIGHT 4: COMPUTATIONAL AND THEORETICAL NEUROBIOLOGY. [11:50am-12:02pm] SPOTLIGHT 5: SPEECH AND SIGNAL PROCESSING. [12:04-12:08pm] ORAL 7: COMPLEXITY, LEARNING & GENERALIZATION 2 0.5.1. S. Solla, AT&T Bell Labs "The Emergence of Generalization Ability in Learning Machines." (Invited Talk) [2:00pm] 0.5.2. J. Wiles* & M Ollila% *Depts. of Computer Science & Psychology, Univ. of Queensland, Australia %Vision Lab, CITRI, Dept. of Computer Science, Univ. of Melbourne, Australia "Intersecting Regions: The Key to Combinatorial Structure in Hidden Unit Space." [2:30pm] 0.5.3. T. A. Plate, Department of Computer Science, Univ. of Toronto "Holographic Recurrent Networks." [2:50pm] 0.5.4. P. Simard, Y. LeCun & J. Denker, AT& T Bell Labs "Efficient Pattern Recognition Using a New Transformation Distance." [3:10pm] SPOTLIGHT 6: COMPLEXITY, LEARNING & GENERALISATION 2 [3:30-3:42pm] ORAL 8: IMPLEMENTATIONS 0.8.1. J. Platt, J. Anderson, & D. Kirk, Synaptics, Inc., San Jose, CA "An Analog VLSI Chip for Radial Basis Functions." [4:15pm] 0.8.2. H. P. Graf, E. Cosatto, E. Sackinger, and J. Snyder, AT&T Bell Labs "A Modular System with Multiple Neural Net Chips." [4:35pm] 0.8.3. D. J. Baxter, S. Churcher, A. Hamilton, A. F. Murray, and H. M. Rackie Department of Elec. Eng., University of Edinburgh, Scotland "The Edinburgh Pulse Stream Implementation of a i Learning-Oriented Network (Epsilon) Chip." [4:55pm] SPOTLIGHT 7: COGNITIVE SCIENCE, [5:15-5-19pm] SPOTLIGHT 8: IMPLEMENTATIONS, APPLICATIONS [5:20-5:40pm] Thursday, December 3 ORAL 9: PREDICTION 0.9.1. A. Lapedes, Theory Division, Los Alamos National Laboratory "Nonparametric Neural Networks for Prediction." (Invited Talk) [8:30am] 0.9.2. M. Plutowski*, G. Cottrell%, and H. White+ *Department of Computer Science & Engineering, %Inst. for Neural Comp. and Department of Computer Science & Eng., +Inst. for Neural Comp. and Department of Economics, UCSD "Learning Mackey-Glass from 25 Examples, Plus or Minus 2." [9:00am] ORAL 10: COGNITIVE SCIENCE 0.10.1. P. Smolensky Dept. of Computer Sci. and Inst. of Cog. Sci., Univ. of Colorado, Boulder "Harmonic Grammars for Formal Languages." [9:20am] 0.10.2. D. Gentner & A. B. Markman Department of Psychology, Northwestern University "Analogy -- Watershed or Waterloo? Structural Alignment and the Development of Connectionist Models of Cognition." [9:40am] ORAL 11: APPLICATIONS 0.7.1. Dr. W. Baxt, UCSD Medical Center "The Application of the Artificial Neural Network to Clinical Decision Making." (Invited Talk) [10:30am] 0.7.2. V. Tresp*, J. Moody%, and W-R. Delong+ *Seimens AG, Central Research, Munich, Germany %Computer Science Department, Yale University +Seimens AG, Medical Eng. Group, Erlangen, Germany "Prediction and Control of the Glucose Metabolism of a Diabetic." [11:00am] 0.7.3. P. Baldi* & Y. Chavin% *JPL, Division of Biology, Cal Tech %Net-ID, Inc., and Psychology Department, Stanford University "Neural Networks for Finger Print Matching and Classification." [11:20am] 0.7.4. M. Schenkel*, H. Weismann, I. Guyon, C. Nohl, D. Henderson, B. Bosser%, and L. Jackel AT&T Bell Labs *also ETH-Zunch, %also EECS Dept., UC Berkeley "TDNN Solutions for Recognizing On-Line Natural Handwriting." [11:40am] POSTER SPOTLIGHT TALKS (4 Minute Talks) SPOTLIGHT 1: COMPLEXITY, LEARNING & GENERALIZATION 1. CONTROL, NAVIGATION & PLANNING. P&S.1.1. K-Y Siu* & V. Roychowdhury% *Department of Elec. & Comp. Eng., UC Irvine %School of Elec. Eng., Purdue University "Optimal Depth Neural Networks for Multiplication and Related Problems." P&S.1.2. T. M. Mitchell and S. B. Thrun School of Computer Science, CMU "Explanation-Based Neural Network Learning for Robot Control." SPOTLIGHT 2: VISUAL PROCESSING P&S.2.1. S. Madarasmi*, D. Kersten%, and T-C Pong* *Department of Computer Science, %Department of Psychology, University of Minnesota "Computation of Stereo Disparity for Transparent and for Opaque Surfaces." P&S.2.2. S. Ahmad and V. Tresp Siemens Research, Munich, Germany "Some Solutions to the Missing Feature Problem in Vision." P&S.2.3. J. Utans and G. Gindi Department of Elec. Eng., Yale University "Improving Convergence in Hierarchical Matching Networks for Object Recognition." SPOTLIGHT 3: STOCHASTIC LEARNING & ANALYSIS P&S.3.1. R. M. Neal, Department of Computer Science, University of Toronto "Bayesian Learning via Stochastic Dynamics." P&S.3.2. Y. Freund*, H. S. Seung%, and N. Tishby+ *Comp. and Inf. Sci., UC Santa Cruz, %Racah Inst. of Physics, and Center for Neural Comp., Hebrew Univ., Jerusalem, +Department of Comp. Sci. and Center for Neural Comp., Hebrew Univ., Jerusalem "Accelerating Learning Using Query by Committee." P&S.3.3. A. F. Murray, J. P. Edwards Department of Elec. Eng., University of Edinburgh, Scotland "Synaptic Weight Noise During MLP Learning Enhances Fault-Tolerance." P&S.3.4. D. De Mers and G. Cottrell Department of Computer Science, UC San Diego "Non-Linear Dimensionality Reduction." P&S.3.5. N. N. Schraudolph* and T. J. Sejnowski% *Computer Science & Engr. Department, UC San Diego %Computer Neurobiology Lab., The Salk Institute "Self-Stabilizing Hebbian Learning: Beyond Principal Components." SPOTLIGHT 4: COMPUTATIONAL AND THEORETICAL NEUROBIOLOGY. P&S.4.1. I. Gutterman and N. Tishby Department of Comp. Sci. and Center for Neural Computation, Hebrew University, Jerusalem "Statistical Modeling of Cell-Assemblies Activities in Prefrontal Cortex of Behaving Monkeys." P&S.4.2. R. Linsker, IBM. TJ Watson Center, Yorktown Heights "Towards Unambiguous Derivation of Receptive Fields Using a New Optimal-Encoding Criterion." P&S.4.3. O. Coenen*, T. J. Sejnowski*, and S. G. Lisberger% *Comp. Neurobiol. Lab., Howard Hughes Medical Inst., The Salk Institute, La Jolla, CA %Department of Physiology, Kick Center for Integrating Neuroscience, UCSF, CA "Biologically Plausible Learning Rules for the Vestibular-Ocular Reflex (VOR)." SPOTLIGHT 5: SPEECH AND SIGNAL PROCESSING. P&S.5.1. M. Hild and A. Waibel, School of Computer Science, CMU "Connected Letter Recognition with a Multi-State Time Delay Neural Network." SPOTLIGHT 6: COMPLEXITY, LEARNING & GENERALIZATION 2 P&S.6.1. I. Guyon*, B. Boser%, and V. Vapnik* *AT&T Bell Labs, Holmdel, NJ %EE&CS Department, UC Berkeley "Automatic Capacity Tuning of Very Large VC-Dimension Classifiers" P&S.6.2. P.Y. Simard*, Y. LeCun*, and B. Pearlmutter% *AT&T Bell Labs, Holmdel, NJ %Yale University "Local Computation of the Second Derivative Information in a Multi-Layer Network." P&S. 6.3 H. Drucker, R. Schapire & P. Simard, AT&T Bell Labs "Improving Performance in Neural Networks Using a Boosting Algorithm." SPOTLIGHT 7: COGNITIVE SCIENCE P&S.7.1. M. C. Mozer and S. Das Department of Computer Science & Inst. of Cognitive Science, Univ. of Colorado, Boulder, CO "A Connectionist Chunker that Induces the Structure of Context-Free Languages." SPOTLIGHT 8: IMPLEMENTATIONS, APPLICATIONS, P&S.5.1. J. Lazzaro*, J. Wawrzynck*, M. Mahowald%, M. Sivilotti+, D. Gillespie$ *EE &CS, UC Berkeley %Computation and Neural Sciences, Cal Tech +Computer Science, Cal. Tech. and Tanner Research, Pasadena, CA $Computer Science, Cal. Tech. and Synaptics, San Jox, CA "Silicon Auditory Processors as Computer Peripherals." P&S.5.2. C. Koch*, B. Mathur%, S-C Liu+, J. G. Harris+, J. Luo and M. Sivilotti$ *Computation and Neural Systems, Cal. Tech. %Rockwell Intl. Science Center, Thousand Oaks, CA +Al Lab, MIT $Tanner Research, Pasadena, CA "Object-Based Analog VLSI Vision Circuits." P&S.5.3. J. Alspector, R. Meir, B. Yuhas, A. Jayakumar Bellcore, Morristown, NJ "A Parallel Gradient Descent Method for Learning in Analog VLSI Neural Networks." P&S.5.4. A. C. Tsoi, D. S. C. So, and A. Sergejew Department of Elec. Eng., University of Queensland, Australia "Classification of Electroencephalogram Using Artificial Neural Networks." P&S.5.5. Y. Salu, Physics Department, and CSTEA, Howard University "Classification of Satelite Multi-Spectral Image Data by the Binary Diamond Neural Network." NIPS '92 FINAL POSTER SESSIONS 1 & 2 TUESDAY EVENING: SESSION 1 COMPLEXITY, LEARNING AND GENERALIZATION 1 "Optimal Depth Neural Networks for Multiplication and Related Problems." Kai-Yeung Siu, Department of Elec. & Comp. Eng, UC Irvine Vwani Roychowdhury, School of Elec. Eng., Purdue University "Initial Complexity of Large Networks and Its Effect on Generalization." Chuanyi Ji, Department of Eled, Comp. & System Eng., Rensselaer Polytechnic Inst., Troy, NY "Using Hints to Successfully Learn Context-Free Grammars with a Neural Network Pushdown Automaton." Sreerupa Das, Dept. of Computer Science, Univ. of Colorado, Boulder, CO C. Lee Giles, NEC Richard Institute, Princeton, NJ Guo-Zheng Sun, Inst. for Advanced Computer Studies, Univ. of MD "Interposing an Ontogenic Model Between Genetic Algorithms and Neural Networks." Richard K. Belew, Cognitive Comp. Science Research Group, UC San Diego "Combining Neural and Symbolic Learning to Revise Probabilistic Rule Bases." J. Jeffrey Mahoney and Raymond J. Mooney, Dept. of Computer Science, University of Texas, Austin, TX "Learning Sequential Tasks by Incrementally Adding Higher Orders." Mark Ring, Dept. of Computer Sciences, University of Texas, Austin, TX "Kohonen Feature Maps and Growing Cell Structures -- A Performance Comparison." Bernard Fritzke, Universitat Erlangen-Nurnberg, Lehrstuhl fur Programmiersprachen, Erlangen, Germany "Latticed RBF Networks: An Alternative to Constructive Methods." Brian Bonnlander & Michael C. Mozer, Department of Computer Science & Institute of Cognitive Science, University of Colorado, Boulder, CO "A Boundary Hunting Radial Basis Function Classifier which Allocates Centers Constructively." Eric I. Chang & Richard P. Lippmann, MIT Lincoln Laboratory, Lexington, MA "How Hints affect Learning" Yaser Abu-Mostafa, Dept of Electrical Engineering & Computer Science, California Institute of Technology, Pasadena, CA CONTROL, NAVIGATION & PLANNING "Explanation-Based Neural Network Learning for Robot Control." Tom M. Mitchell & Sebastian B. Thrun, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA "Reinforcement Learning Applied to Linear Quadratic Regulation." Steven J. Bradtke, Department of Computer & Information Science, University of Massachusetts, Amherst, MA "Neural Network On-Line Learning Control of Spacecraft Smart Structure." Dr. Christopher Bowman, Ball Aerospace Systems Group, Boulder, CO "Integration of Visual and Somatosensory Information for Preshaping Hand in Grasping Movements." Yoji Uno*, Naohiro Fukumura%, Ryoji Suzuki%, and Mitsuo Kawato* *ATR Human Information Processing Research Laboratories, Kyoto, Japan %Faculty of Engineering, University of Tokyo, Tokyo, Japan "On-Line Estimation of the Optimal Value Function: HJB-Estimators." James K. Peterson, Department of Mathematical Sciences, Clemson University, Clemson, SC "Robust Control Under Extreme Uncertainty." Vijaykumar Gullapalli, CS Department, LGRC, University of Massachusetts, Amherst, MA "Trajectory Relaxation Learning for Approximation of Robot Inverse Dynamics." T. Sanger, MIT, Cambridge, MA "Learning Spatio-Temporal Planning from a Dynamic Programming Teacher: A Feed Forward Net for the Moving Obstacle Avoidance Problem." G. Fahner and R. Eckmiller, Department of Biophysics, Division of Biocybernetics, Heinrich-Heine-University of Dusseldorf, Dusseldorf, Germany "Learning Fuzzy Rule-Based Neural Networks for Control." Rodney M. Goodman and Charles M. Higgins, Department of Electrical Engineering, Cal. Tech., Pasadena, CA VISUAL PROCESSING "Computation of Stereo Disparity for Transparent and for Opaque Surfaces." Suthep Madarasmi*, Daniel Kersten%, Ting-Cheun Pong* *Computer Science Department, %Department of Psychology, *Computer Science Department, University of Minnesota, Minneapolis, MN "Some Solutions to the Missing Feature Problem in Vision." Sabutai Ahmad and Volker Tresp, Seimens Research, Munich, Germany "Improving Convergence in Hierarchial Matching Networks for Object Recognition." Joachim Utans and Gene Gindi, Yale University "An LGN Model Which Mediates Communication Between Different Spatial Frequency Channels Through Feedback From Cortex." Carlos D. Brody, Computation and Neural Systems Program, Cal. Tech., Pasadena, CA "Unsmearing Visual Motion: Development of Long-Range Horizontal Intrinsic Connections." Kevin E. Martin and Jonathan A. Marshall, Department of Computer Science, University of North Carolina, Chapel Hill, NC "LandSat Image Analysis via a Texture Classification Neural Network." Hayit K. Greenspan and Rodney M. Goodman, Department of Electrical Engineering, Cal. Tech., Pasadena, CA "Computation of Ego-Motion from Optic Flow in Visual Cortex." Markus Lappe and Josef P. Rauschecker, National Institutes of Health Animal Center, NIMH, Poolesville, MD, and Max Planck Institute for Biological Cybernetics, Tubingen, Germany "Learning to See Where and What: A Backprop Net Trained to Make Saccades and Recognize Characters." Gale L. Martin, Mosfeq Rashid, David Chapman & James Pittman, MCC, Austin, TX STOCHASTIC LEARNING AND ANALYSIS "Bayesian Learning via Stochastic Dynamics.' Radford M. Neal, Department of Computer Science, University of Toronto, Toronto, Canada "Accelerating Learning Using Query by Committee." Yoav Freund*, H. Sebastian Seung%, and Naftali Tishby+ *Computer and Info. Sciences, UC Santa Cruz %Racah Inst. of Physics and Ctr. for Neural Computation, Hebrew University, Jerusalem +Department of Computer Science and Ctr. for Neural Computation, Hebrew University, Jerusalem "Synaptic Weight Noise During MLP Learning Enhances Fault-Tolerance." Alan F. Murray and Peter J. Edwards, Dept. of Electrical Engineering, University of Edinburgh, Scotland "Self-Stabilizing Hebbian Learning: Beyond Principal Components." Nicol N. Schraudolph* and Terrence J. Sejnowski% *Computer Science & Engr. Department, UC San Diego %Computational Neurobiology Laboratory, The Salk Institute, La Jolla, CA "Probability Densities and Basin-Hopping in Stochastic Learning." Todd K. Leen and Genevieve B. Orr, Department of Computer Science and Engineering, Oregon Graduate Institute of Science and Technology, Beaverton, OR "Information Theoretic Analysis of Connection Structure from Spike Trains." S. Shiono, S. Yamada, M. Nakashima, and Kenji Matsumoto, Central Research Laboratory, Mitsubishi Electric Corp., Hyogo, Japan "Statistical Mechanics of Learning in a Large Committee Machine." H. Schwarze and J. Hertz, The Niels Bohr Institute and Nordita, Copenhagen, Denmark "Probability Estimation from a Database Using a Gibbs Energy Model." John W. Miller and Rodney M. Goodman, Department of Electrical Engr., Cal. Tech., Pasadena, CA "On the Use of Evidence in Bayesian Reasoning." David H. Wolpert, The Santa Fe Institute, Santa Fe, NM NETWORK DYNAMICS & CHAOS "Destabilization and Route to Chaos in Neural Networks with Random Connectivity." B. Doyon*, B. Cessac%+, M. Quoy%$, M. Samuelides%$ *Unite INSERM 230, Service de Neurologie, CHU Purpan, ToulouseCedex, France %Centre d'Etudes et de Recherches de Toulouse, Toulouse Cedex, France +Laboratoire de Physique Quantique, Universite Paul Sabatier, Toulouse Cedex, France $Ecole Nationale Superieure de l'Aeronautique et de l'Espace, Toulouse Cedex, France "Predicting Complex Behavior in Space Asymmetric Networks.' Ali A. Minai and William B. Levy, Department of Neurosurgery, University of Virginia, Charlottesville, VA "Single-iteration Threshold Hamming Networks." I. Meilijosn, E. Ruppin, M. Sipper, School of Mathematical Sciences, Tel Aviv University, Tel Aviv, Israel "History-Dependent Dynamics in Attractor Neural Networks: A Bayesian Approach." Isaac Meilijosn and Eytan Ruppin, School of Mathematical Sciences, Tel Aviv University, Tel Aviv, Israel "Bifurcation Analysis of a Coupled Neural Oscillator System With Application to Visual Cortex Modeling." Galina N. Borisyuk, Roman M. Borisyuk, Alexander I. Khibnki, Institute of Mathematical Problems of Biology, Russia Academy of Sciences, Pushchino, Russia "Non-Linear Dimensionality Reduction." David DeMers and Garrison Cottrell, Department of Computer Science, UC San Diego, La Jolla, CA THEORY AND ANALYSIS "On Learning m-Perceptron Networks with Binary Weights." Mostefa Golea*, Mario Marchand* and Thomas R. Hancock% *Ottawa-Carleton Institute for Physics, University of Ottawa, Ottawa, Canada %Aiken Computation Laboratory, Harvard University, Cambridge, MA "Neural Network Model Selection Using Asymptotic Jackknife Estimator and Cross-Validation Method." Yong Liu, Department of Physics and Center for Neural Science, Brown University, Providence, RI "Learning Curves, Model Selection and Complexity of Neural Networks." Noboru Murata, Shuji Yoshizawa, and Shun-ichi Amari, Department of Mathematical Engineering and Information Physics, University of Tokyo, Japan "The Power of Approximating: A Comparison of Activation Functions." Dhaskar DasGupta and Georg Schnitger, Department of Computer Science, The Pensylvania State University, Unviersity Park, PA "Rational Parameterizations of Neural Networks." Uwe Helmke* and Robert C. Williamson% *Department of Mathematics, University of Regensburg, Regensburg, Germany %Department of Systems Engineering, Australian National University, Canberra Australia "Learning Cellular Automaton Dynamics with Neural Networks." N. H. Wulff and J. A. Hertz, CONNECT, The Niels Bohr Institute and Nordita, Copenhagen, Denmark "Some Estimations of Necessary Number of Connections and Hidden Units for Feed Forward Networks." Adam Kowalczyk, Telcom Australia, Research Laboratories, Victoria, Australia WEDNESDAY EVENING: SESSION 2 COMPLEXITY, LEARNING AND GENERALIZAITON 2 "Automatic Capacity Tuning of Very Large VC-Dimension Classifiers." I. Gunyon, B. Boser*, V. Vapnik, AT& T Bell Laboratories, Holmdel, NJ *currently in EECS Department, UC Berkeley, CA "Local Computation of the Second Derivative Information in a Multi-Layer Network." Patrice Y. Simard, Yann Le Cun and Barak Pearlmutter* AT&T Bell Laboratories, Holmdel, NJ *Yale University, New Haven, CT "Improving Performance in Neural Networks Using a Boosting Algorithm." H. Drucker, R. Schapire & P. Simard, AT&T Bell Labs, Holmdel, NJ "Learning Classification With Few Labelled Examples." Joel Ratsaby and Santosh S. Venkatesh, Department of Electrical Engineering, University of Pennsylvania, Philadelphia, PA "Second Order Derivatives for Network Pruning: Optimal Brain Surgeon." Babak Hassibi and David G. Stork, Ricoh California Research Center, Menlo Park, CA, and Department of Electrical Engineering, Stanford University, Stanford, CA "Directional-Unit Boltzmann Machines." Richard S. Zemel, Christopher K. I. Williams and Michael C. Mozer* Computer Science Department, University of Toronto, Toronto, Canada *Computer Science Department, University of Colorado, Boulder, CO "Applying Classical Optimization Techniques to Neural Network Testing." Dr. Scott A. Markel and Dr. Roger L. Crane, David Sarnoff Research Center, Princeton, NJ "Time Warping Invariant Neural Networks." G. Z. Sun, H. H. Chen, Y. C. Lee and Y. D. Liu, Institute for Advanced Computer Studies / Laboratory for Plasma Research, University of Maryland, College Park, MD "Generalization Abilities of Cascade Network Architectures." E. Littmann and H. Ritter, Department of Computer Science, Bielefeld University, Bielefeld, Germany "Assessing and Improving Neural Network Predictions by the Bootstrap Algorithm." Gerhard Paa', German National Research Center for Computer Science, Augustin, Germany "Discriminability-Based Transfer between Neural Networks." L. Y. Pratt, Department of Matheamatics and Computer Science, Colorado School of mines, Golden, CO "Summed Weight Neuron Perturbation: An O(N) Improvement over Weight Perturbation." Barry Flower and Marwan Jabri, SEDAL, Department of Electrical Engineering, University of Sydney, Australia "Supervised Clustering." Virginia de Sa and Dana Ballard, Computer Science Department, University of Rochester, Rochester, NY "Extended Regularization Methods for Nonconvergent Model Selection." W. Finnoff, F. Hergert and H. G. Zimmerman, Siemans AG, Corporate Research and Development, Munich, Germany "Synchronization and Gramatical Inference in an Oscillating Elman Net." Bill Baird* and Frank Eeckman% *Department of Mathematics, UC Berkeley, CA %O-Division, Lawrence Livermore National Laboratory, Livermore, CA "Training Hidden Units in Reinforcement Learning Networks." Charles W. Anderson, Department of Computer Science, Colorado State University, Fort Collins, CO "Nets with Unreliable Hidden Nodes Learn Error-Correcting Codes." Stephen Judd and Paul Munro, Seimens Corporate Research, Princeton, NJ, and Department of Information Science, University of Pittsburgh, PA "A Fast Stochastic Error-Descent Algorithm for Supervised Learning and Optimization." Gert Cauwenberghs, Cal. Tech., Pasadena, CA SPEECH AND SIGNAL PROCESSING "Modeling Consistency in a Speaker Independent Continuous Speech Recognition System." Yochai Konig*, Nelson Morgan*, Chuck Wooters*, Victor Abrash%, Michael Cohen%, and Horacio Franco% *International Computer Science Institute, Berkeley, CA %SRI International, Menlo Park, CA "A Hybrid Linear/Nonlinear Approach to Channel Equalization Problems." Wei-Tsih Lee*, John C. Pearson*, and Manoel F. Tenorio% *David Sarnoff Research Center, Princeton, NJ %Purdue University, School of Electrical Engineering, West Lafayette, IN "Transient Detection Using Neural Networks: The Search for the Desired Signal." Abir Zahalka and Jose C. Principe, Computational NeuroEngineering Laboratory, University of Florida, Gainesville, FL "Performance Through Consistency: MS-TDNN's for Large Vocabulary Continuous Speech Recognition." Joe Tebelskis and Alex Waibel, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA "Speech Recognition Using Segmental Neural Nets with the N-Best Paradigm." G. Zavaliagkos, S. Austin, J. Makhous and R. Schwartz, BBN Systems and Technologies, Cambridge, MA "Connected Letter Recognition with a Multi-State Time Delay Neural Network." Hermann Hild and Alex Waibel, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA "Classification of Electroencephalogram Using Artificial Neural Networks." A. C. Tsoi, D. S. C. So, and A. Sergejew, Department of Electrical Engineering, University of Queensland, Queensland, Australia "Classification of Satellite Multi-Spectral Image Data by the Binary Diamond Neural Network." Yehuda Salu, The Physics Department and CSTEA, Howard University, Washington, DC "Silicon Auditory Processors as Computer Peripherals." John Lazzaro*, John Wawrzynek*, M. Mahowald%, Massimo Sivilotti+, and Dave Gillespie+ *Computer Science Division, UC Berkeley, CA %Computation and Neural Sciences, Cal. Tech, Pasadena, CA +Computer Science, Cal. Tech., Pasadena, CA "Object-Based Analog VLSI Vision Circuits." Christof Koch*, Bimal Mathur%, Shih-Chii Liu+, John G. Harris$, Jin Luo and Missimo Sivilotti$ *Computation and Neural Systems, Cal. Tech., Pasadena, CA %Rockwell International Science Center, Thousand Oaks, CA +Artificial Intelligence Laboratory, MIT, Cambridge, MA $Tanner Research, Pasadena, CA "A Parallel Gradient Descent Method for Learning in Analog VLSI Neural Networks." Joshua Alspector, Ronny Meir, Ben Yuhas, Anthony Jayakumar, Bellcore, Morristown, NJ APPLICATIONS "Dynamic Planar Warping and Planar Hidden Markov Modeling: From Speech to Optical Character Recognition." Esther Levin and Roberto Pieraccini, AT&T Bell Laboratories, Murray Hill, NJ "Forecasting Demand for Electric Power." Terrence L. Fine and Jen-Lun Yuan, School of Electrical Engineering, Cornell University, Ithaca, NY "Adaptive Algorithms for Multiple Sequence Alignments." Pierre Baldi*, Tim Hunkapiller*, Yves Chauvin%, and Marcella McClure+ *Cal. Tech, Pasadena, CA %Net-ID, Inc. +UC, Irvine "A Neural Network that Learns to Interpret Myocardial Planar Thallium Scintigrams." Charles Rosenberg*, Jacob Erel%, and Henri Atlan% *Department of Computer Science, Hebrew University, Jerusalem, Israel %Department of Biophysics and Nuclear Medicine, Hadassah Medical Center, Jeruslaem, Israel IMPLEMENTATIONS "An Analog VLSI Chip for Local Velocity Estimation Based on Reichardt's Motion Algorithm." Rahul Sarpeshkar, Wyeth Bair and Christof Koch, Department of Computation and Neural Systems, Cal. Tech., Pasadena, CA "Analog VLSI Implementation of Gradient Descent." David Kirk, Douglas Kerns, Kurt Fleischer, Alan Barr Cal. Tech., Pasadena, CA "An Object-oriented Framework and its Implementation for the Simulation of Neural Nets." Alexander Linden and Christoph Tietz, AI Research Division, German National Research Center For Computer Science, Augustin, Germany "Attractor Neural Networks with Local Inhibition." L. D'Alessandro*, E. Pasero*, and R. Zecchina% *Dipart. Elettronica, Politenico di Torino %Dipart. Fisica Teorica, Universita di Torino "Biological Neurons and Model Neurons: Construction and Study of Hybrid Networks." G. Le Masson, S. Renaud-Le Masson, E. Marder, and L. F. Abbot Department of Biology and Physics and Center for Complex Systems, Brandeis University, Waltham, MA COGNITIVE SCIENCE "A Connectionist Chunker that Induces the Structure of Context-Free Languages." Michael C. Mozer and Sreerupa Das, Department of Computer Science and Institute of Cognitive Science, University of Colorado, Boulder, CO "Network Structuring and Training Using Rule-Based Knowledge." Volker Tresp*, Jurgen Hollatz%, and Subutai Ahmad* *Siemens AG, Central Research and Development, Munich, Germany %Institut fur Informatik, Munich Germany "A Dynamic Model of Priming and Repetition Blindness.' Daphne Bavelier and Michael I. Jordan, Department of Brain and CCognitive Sciences, MIT, Cambridge, MA "A Knowledge-Based Model of Geometry Learning." Geoffrey Towell* and Richard Lehrer% *Siemens Corporate Research, Princeton, NJ %Educational Psychology, University of Wisconsin, Madison, WI "Representing Meaning With Activation Gestalts." Hinrich Schutze, CSLI, Stanford, CA "Perceiving Complex Visual Scenes: An Oscillator Neural Network Model that Integrates Location-Based Attention, Perceptual Organization, and Object-Based Selection." Rainer Goebel, Department of Psychology, University of Braunschweig, Braunschweig, Germany COMPUTATIONAL AND THEORETICAL NEUROBIOLOGY "Statistical Modeling of Cell-Assembly Activities in Prefrontal Cortex of Behaving Monkeys." Itay Gutterman and Naftali, Department of Computer Science and Center for Neural Comuptation, Hebrew University, Jerusalem, Israel "Towards Unambiguous Derivation of Receptive Fields Using a New Optimal-Encoding Criterion." Ralph Linsker, IBM, T. J. Watson Research Center, Yorktown Heights, NY "Biologically Plausible Learning Rules for the Vestibulo-Ocular Reflex (VOR)." Oliver Coenen*, Terrence J. Sejnowski*, and Stephen G. Lisberger% *Computational Neurobilogy Laboratory, The Salk Institute, La Jolla, CA %Department of Physiology, W. M. Keck Foundation Center for Integrative Neuroscience; and Neuroscience Graduata Program, UC San Francisco, CA "A Non-Hebbian LTP Learning Rule in Hippocampus Enables High-Capacity Temporal Sequence Encoding." Richard Granger, James W. Whitson, Jr., and Gary Lynch, Center for the Neurobiology of Learning and Memory, UC Irvine, CA "Using Aperiodic Reinforcement for Directed Self Organization." P. Read Montague, Steven J. Nowlan, Peter Dayan and Terrance J. Sejnowski, Computational Neurobiology Laboratory, The Salk Institute, San Diego, CA "Information Processing in Neocortical Pyramidal Cells." Bartlett W. Mel, Computation and Neural Systems Program, Cal. Tech., Pasadena, CA "How Oscillatory Neuronal Responses Reflect Bistability and Switching of the Hidden Assembly Dynamics." K. Pawelzik, H.-U. Bauer, J. Deppisch, and T. Geisel, Institute fur Theoretische Physik and SFP, Frankfurt, Germany "Topography and Ocular Dominance: A New Model that Explores Positive Between-Eye Correlations." Geoffrey Goodhill, University of Edinburgh, Centre for Cognitive Science, Edinburgh, Scotland "Statistical and Dynamical Interpretation of ISIH Data from Periodically Stimulated Sensory Neurons." Frank Moss* and Andre Longtin% *Department of Physics and Department of Biology, University of Missouri, St. Louis, MO %Department of Physics, University of Ottawa, Canada "Modelling Movement Disorders with Cascaded Jordan Networks." Alexander Britain*, Gordon D. A. Brown*, Michael Malloch* and Ian J. Mitchell% *Cognitive Neurocomputation Unit, Dept. of Psychology, University of Wales, Bangor, United Kingdom %Department of Cell and Structural Biology, Manchester, United Kingdom "Spiral Waves in Integrate-And-Fire Neural Networks." John G. Milton*, Po Hsiang Chu% and Jack D. Cowan+ *Department of Neurology, University of Chicago, Chicago, IL %Department of Computer Science, De Paul University, Chicago, IL +Department of Mathematics, University of Chicago, Chicago, IL "Parameterising Feature Sensitive Cell Formation in Linsker Networks." L. C. Walton and D. L. Bisset, Electronic Engineering Laboratories, University of Kent, United Kingdom "A Recurrent Neural Network for Generation of Ocular Saccades." Lina L. E. Massone, Departments of Physiology and Electrical Engineering and Computer Science, Northwestern University, Chicago, IL "A Formal Model of the Insect Olfactory Macroglomerulus." C. Linster*, C. Masson%, M. Kerszberg+, L. Personnaz*, and G. Dreyfus* *Ecole Superieure de Physique et de Chimie Industrielles de la Villa De Paris, Laboratoire d'Electronique, Paris, France %Laboratoire de Neurobiologie Comparees des Invertebres, INRA?CNRS, Bures Sur Yvette, France +Institut Pasteur, Paris, France "An Information-Theoretic Approach to Deciphering the Hippocampal Code." William E. Skaggs, Bruce L. McNaughton, Katalin M. Gothard, Etan J. Marksu, ARL Division of Neural Systems, Memory and Aging, University of Arizona, Tuscan, AZ ------------------------------ End of Neuron Digest [Volume 10 Issue 13] ***************************************** Received: from BUACCA by BUACCA.BU.EDU (Mailer R2.08 PTF009) with BSMTP id 5197; Wed, 04 Nov 92 11:56:51 EST Received: from noc2.dccs.upenn.edu by BUACCA.BU.EDU (IBM VM SMTP R1.2.1) with TCP; Wed, 04 Nov 92 11:56:48 EST Received: from CATTELL.PSYCH.UPENN.EDU by noc2.dccs.upenn.edu id AA11820; Wed, 4 Nov 92 11:50:57 -0500 Return-Path: Received: from LOCALHOST by cattell.psych.upenn.edu id AA16920; Wed, 4 Nov 92 10:30:34 EST Posted-Date: Wed, 04 Nov 92 10:29:59 EST From: "Neuron-Digest Moderator" To: Neuron-Distribution:; Subject: Neuron Digest V10 #14 (conferences) Reply-To: "Neuron-Request" X-Errors-To: "Neuron-Request" Organization: University of Pennsylvania Date: Wed, 04 Nov 92 10:29:59 EST Message-Id: <16868.720890999@cattell.psych.upenn.edu> Sender: marvit@cattell.psych.upenn.edu Neuron Digest Wednesday, 4 Nov 1992 Volume 10 : Issue 14 Today's Topics: NIPS*92 WORKSHOP PROGRAM Send submissions, questions, address maintenance, and requests for old issues to "neuron-request@cattell.psych.upenn.edu". The ftp archives are available from cattell.psych.upenn.edu (130.91.68.31). Back issues requested by mail will eventually be sent, but may take a while. ---------------------------------------------------------------------- Subject: NIPS*92 WORKSHOP PROGRAM From: Steve Hanson Date: Fri, 02 Oct 92 14:41:40 -0500 NIPS*92 WORKSHOP PROGRAM For Further information and queries on workshop please respond to WORKSHOP CHAIRPERSONS listed below ========================================================================= Character Recognition Workshop Organizers: C. L. Wilson and M. D. Garris, NIST Abstract: In order to discuss recent developments and research in OCR technology, six speakers have been invited to share from their organization's own perspective on the subject. Those invited, represent a diversified group of organizations actively developing OCR systems. Each speaker participated in the first OCR Systems Conference sponsored by the Bureau of the Census and hosted by NIST. Therefore, the impressions and results gained from the conference should provide significant context for discussions. Invited presentations: C. L. Wilson, NIST, "Census OCR Results - Are Neural Networks Better?" T. P. Vogl, ERIM, "Effect of Training Set Size on OCR Accuracy" C. L. Scofield, Nestor, "Multiple Network Architectures for Handprint and Cursive Recognition" A. Rao, Kodak, "Directions in OCR Research and Document Understanding at Eastman Kodak Company" C. J. C. Burges, ATT, "Overview of ATT OCR Technology" K. M. Mohiuddin, IBM, "Handwriting OCR Work at IBM Almaden Research Center" ========================================================================= Neural Chips: State of the Art and Perspectives. Organizer: Eros Pasero pasero@polito.it Abstract: We will encourage lively audience discussion of important issues in neural net hardware, such as: - - Taxonomy: neural computer, neural processor, neural coprocessor - - Digital vs. Analog: limits and benefits of the two approaches. - - Algorithms or neural constraints? - - Neural chips implemented in universities - - Industrial chips (e.g. Intel, AT&T, Synaptics) - - Future perspectives Invited presentations: TBA ========================================================================= Reading the Entrails: Understanding What's Going On Inside a Neural Net Organizer: Scott E. Fahlman, Carnegie Mellon University fahlman@cs.cmu.edu Abstract: Neural networks can be viewed as "black boxes" that learn from examples, but often it is useful to figure out what sort of internal knowledge representation (or set of "features") is being employed, or how the inputs are combined to produce particular outputs. There are many reasons why we might seek such understanding: It can tell us which inputs really are needed and which are the most critical in producing a given output. It can produce explanations that give us more confidence in the network's decisions. It can help us to understand how the network would react to new situations. It can give us insight into problems with the network's performance, stability, or learning behavior. Sometimes, it's just a matter of scientific curiosity: if a network does something impressive, we want to know how it works. In this workshop we will survey the available techniques for understanding what is happening inside a neural network, both during and after training. We plan to have a number of presenters who can describe or demonstrate various network-understanding techniques, and who can tell us what useful insights were gained using these techniques. Where appropriate, presenters will be encouraged to use slides or videotape to illustrate their favorite methods. Among the techniques we will explore are the following: Diagrams of weights, unit states, and their trajectories over time. Diagrams of the receptive fields of hidden units. How to create meaningful diagrams in high-dimensional spaces. Techniques for extracting boolean or fuzzy rule-sets from a trained network. Techniques for extracting explanations of individual network outputs or decisions. Techniques for describing the dynamic behavior of recurrent or time-domain networks. Learning pathologies and what they look like. Invited presentations: Still to be determined. The workshop organizer would like to hear from potential speakers who would like to give a short presentation of the kind described above. Techniques that have proven useful in real-world problems are especially sought, as are short videotape segments showing network ========================================================================= COMPUTATIONAL APPROACHES TO BIOLOGICAL SEQUENCE ANALYSIS-- NEURAL NET VERSUS TRADITIONAL PERPECTIVES Organizers: Paul Stolorz, Santa Fe Institute and Los Alamos National Lab Jude Shavlik, University of Wisconsin. Abstract: There has been a good deal of recent interest in the use of neural networks to tackle several important biological sequence analysis problems. These problems range from the prediction of protein secondary and tertiary structure, to the prediction of DNA protein coding regions and regulatory sites, and the identification of homologies. Several promising developments have been presented at NIPS meetings in the past few years by researchers in the connectionist field. Furthermore, a number of structural biologists and chemists have been successfully using neural network methods. The sequence analysis applications encompass a rather large amount of neural network territory, ranging from feed forward architectures to recurrent nets, Hidden Markov Models and related approaches. The aim of this workshop is to review the progress made by these disparate strands of endeavor, and to analyze their respective strengths and weaknesses. In addition, the intention is to compare the class of neural network methods with alternative approaches, both new and traditional. These alternatives include knowledge based reasoning, standard non-parametric statistical analysis, Hidden Markov models and statistical physics methods. We hope that by careful consideration and comparison of neural nets with several of the alternatives mentioned above, methods can be found which are superior to any of the individual techniques developed to date. This discussion will be a major focus of the workshop, and we both anticipate and encourage vigorous debate. Invited presentations: Jude Shavlik, U. Wisconsin: Learning Important Relations in Protein Structures Gary Stormo, U. Colorado: TBA Larry Hunter, National Library of Medicine: Bayesian Clustering of Protein Structures Soren Brunak, DTH: Network analysis of protein structure and the genetic code David Haussler, U.C. Santa Cruz: Modeling Protein Families with Hidden Markov Models Paul Stolorz and Joe Bryngelson, Santa Fe Institute and Los Alamos: Information Theory and Statistical Physics in Protein Structures ========================================================================= Statistical Regression Methods and Feedforward Nets Organizers: Lei Xu, Harvard Univ. and Adam Krzyzak, Concordia Univ. Abstract: Feedforward neural networks are often used for function approximation, density estimation and pattern classification. These tasks are also the purposes of statistical regression methods. Some methods used in the literature of neural networks and the literature of statistical regression are same, some are different, and some have close relations. Recently, the connections between the methods in the two literatures have been explored from a number of aspects. E.g., (1) connecting feedforward nets to parametric statistical regression for theoretical studies about multilayer feedforward nets; (2) relating the performances of feedforward nets to the trade-off of bias and variances in nonparameter statistics. (3) connecting Radial Basis function nets to Nonparameter Kernal Regression to get several new theoretical results on approximation ability, convergence rate and receptive field size of Radial Basis Function networks; (4) using VC dimension to study the generalization ability of multilayer feedforward nets; (5) using other statistical methods such as projection pursuit, cross-validation, EM algorithm, CART, MARS for training feedforward nets. Not only in these mentioned aspects there are still many interesting and open issues to be further explored. But also, in the literature of statistical regression there are many other methods and theoretical results on both nonparametric regression and parameteric regression (e.g., L1 kernal estimation, ..., etc). Invited presentations: Presentations will include arranged talks and submissions. Submis- sions can be sent to either of the two organizers by Email before Nov.15, 1992. Each submission can be an abstract of 200--400 words. ========================================================================= Computational Models of Visual Attention Organizer: Pete Sandon, Dartmouth College Abstract: Visual attention refers to the process by which some part of the visual field is selected over other parts for preferential processing. The details of the attentional mechanism in humans has been the subject of much recent psychophysical experimentation. Along with the abundance of new data, a number of theories of attention have been proposed, some in the form of computational models simulated on computers. The goal of this workshop is to bring together computational modelers and experimentalists to evaluate the status of current theories and to identify the most promising avenues for improving understanding of the mechanisms and behavioral roles of visual attention. Invited presentations: Pete Sandon "The time course of selection" John Tsotsos "Inhibitory beam model of visual attention" Kyle Cave "Mapping the Allocation of Spatial Attention: Knowing Where Not to Look" Mike Mozer "A principle for unsupervised decomposition and hierarchical structuring of visual objects" Eric Lumer "On the interaction between perceptual grouping, object selection, and spatial orientation of attention" Steve Yantis "Mechanisms of human visual attention: Bottom-up and top-down influences" ========================================================================= Comparison and Unification of Algorithms, Loss Functions and Complexity Measures for Learning Organizers: Isabelle Guyon, Michael Kearns and Esther Levin, AT&T Bell Labs Abstract: The purpose of the workshop is an attempt to clarify and unify the relationships between many well-studied learning algorithms, loss functions, and combinatorial and statistical measures of learning problem complexity. Many results investigating the principles underlying supervised learning from empirical observations have the following general flavor: first, a "general purpose" learning algorithm is chosen for study (for example, gradient descent or maximum a posteriori). Next, an appropriate loss function is selected, and the details of the learning model are specified (such as the mechanism generating the observations). The analysis results in a bound on the loss of the algorithm in terms of a "complexity measure" such as the Vapnik-Chervonenkis dimension or the statistical capacity. We hope that reviewing the literature with an explicit emphasis on comparisons between algorithms, loss functions and complexity measures will result in a deeper understanding of the similarities and differences of the many possible approaches to and analyses of supervised learning, and aid in extracting the common general principles underlying all of them. Significant gaps in our knowledge concerning these relationships will suggest new directions in research. Half of the available time has been reserved for discussion and informal presentations. We anticipate and encourage active audience participation. Each discussion period will begin by soliciting topics of interest from the participants for investigation. Thus, participants are strongly encouraged to think about issues they would like to see discussed and clarified prior to the workshop. All talks will be tutorial in nature. Invited presentations: Michael Kearns, Isabelle Guyon and Esther Levin: -Overview on loss functions -Overview on general purpose learning algorithms -Overview on complexity measures David Haussler: Overview on "Chinese menu" results ========================================================================= Activity-Dependent Processes in Neural Development Organizer: Adina Roskies, Salk Institute Abstract: This workshop will focus on the role of activity in setting up neural architectures. Biological systems rely upon a variety of cues, both activity-dependent and independent, in establishing their architectures. Network architectures have traditionally been pre-specified, but it is ongoing construction of architectures may endow networks with more computational power than do static architectures. Biological issues such as the role of activity in development, the mechanisms by which it operates, and the type of activity necessary will be explored, as well as computational issues such as the computational value of such processes, the relation to hebbian learning, and constructivist algorithms. Invited presentations: General Overview (Adina Roskies) The role of NMDA in cortical development (Tony Bell) Optimality, local learning rules, and the emergence of function in a sensory processing network (Ralph Linsker) Mechanisms and models of neural development through rapid volume signals (Read Montague) The role of activity in cortical development and plasticity (Brad Schlaggar) Computational advantages of constructivist algorithms (Steve Quartz) Learning, development, and evolution (Rik Belew) ========================================================================= DETERMINSTIC ANNEALING AND COMBINATORIAL OPTIMIZATION Organizer: Anand Rangarajan, Yale Univ. Abstract: Optimization problems defined on ``mixed variables'' (analog and digital) occur in a wide variety of connectionist applications. Recently, several advances have been made in deterministic annealing techniques for optimization. Deterministic annealing is a faster and more efficient alternative to simulated annealing. This workshop will focus on several of these new techniques (emerging in the last two years). Topics include improved elastic nets for the traveling salesman problem, new algorithms for graph matching, relationship between deterministic annealing algorithms and older, more conventional techniques, applications in early vision problems like surface reconstruction, internal generation of annealing schedules, etc. Invited presentations: Alan Yuille, Statistical Physics algorithms that converge Chien-Ping Lu, Competitive elastic nets for TSP Paul Stolorz, Recasting deterministic annealing as constrained optimization Davi Geiger, Surface reconstruction from uncertain data on images and stereo images. Anand Rangarajan, A new deterministic annealing algorithm for graph matching ========================================================================= The Computational Neuron Organizer: Terry Sejnowski, Salk Institute (tsejnowski@ucsd.edu) Abstract: Neurons are complex dynamical systems. Nonlinear properties arise from voltage-sensitive ionic currents and synaptic conductances; branched dendrites provide a geometric substrata for synaptic integration and learning mechanisms. What can subthreshold nonlinearities in dendrites be used to compute? How do the time courses of ionic currents affect synaptic integration and Hebbian learning mechanisms? How are ionic channels in dendrites regulated? Why are there so many different types of neurons? These are a few of the issues that will we will be discussing. In addition to short scheduled presentations designed to stimulate discussion, we invite members of the audience to present one-viewgraph talks to introduce additional topics. Invited presentations: Larry Abbott - Neurons as dynamical systems. Tony Bell - Self-organization of ionic channels in neurons. Tom McKenna - Single neuron computation. Bart Mel - Computing capacity of dendrites. ========================================================================= ROBOT LEARNING Organizers: Sebastian Thrun (CMU), Tom Mitchell (CMU), David Cohn (MIT) Abstract: Robot learning has grasped the attention of many researchers over the past few years. Previous robotics research has demonstrated the difficulty of manually encoding sufficiently accurate models of the robot and its environment to succeed at complex tasks. Recently a wide variety of learning techniques ranging from statistical calibration techniques to neural networks and reinforcement learning have been applied to problems of perception, modeling and control. Robot learning is characterized by sensor noise, control error, dynamically changing environments and the opportunity for learning by experimentation. This workshop will provide a forum for researchers active in the area of robot learning and related fields. It will include informal tutorials and presentations of recent results, given by experts in this field, as well as significant time for open discussion. Problems to be considered include: How can current learning robot techniques scale to more complex domains, characterized by massive sensor input, complex causal interactions, and long time scales? How can previously acquired knowledge accelerate subsequent learning? What representations are appropriate and how can they be learned? Invited speakers: Chris Atkeson Steve Hanson Satinder Singh Andrew W. Moore Richard Yee Andy Barto Tom Mitchell Mike Jordan Dean Pomerleau Steve Suddarth ========================================================================= Connectionist Approaches to Symbol Grounding Organizers: Georg Dorffner, Univ. Vienna; Michael Gasser, Indiana Univ. Stevan Harnad, Princeton Univ. Abstract: In recent years, there has been increasing discomfort with the disembodied nature of symbols that is a hallmark of the symbolic paradigm in cognitive science and artificial intelligence and at the same time increasing interest in the potential offered by connectionist models to ``ground'' symbols. In ignoring the mechanisms by which their symbols get ``hooked up'' to sensory and motor processes, that is, the mechanisms by which intelligent systems develop categories, symbolists have missed out on what is not only one of the more challenging areas in cognitive science but, some would argue, the very heart of what cognition is about. This workshop will focus on issues in neural network based approaches to the grounding of symbols and symbol structures. In particular, connectionist models of categorisation and of label-category association will be discussed in the light of the symbol grounding problem. Invited presentations: "Grounding Symbols in the Analog World of Objects: Can Neural Nets Make the Connection?" Stevan Harnad, Princeton University "Learning Perceptually Grounded Lexical Semantics" Terry Regier, George Lakoff, Jerry Feldman, ICSI Berkeley T.B.A. Gary Cottrell, Univ. of California, San Diego "Learning Perceptual Dimensions" Michael Gasser, Indiana University "Symbols and External Embodiments - why Grounding has to Go Two Ways" Georg Dorffner, University of Vienna "Grounding Symbols on Conceptual Knowledge" Philippe Schyns, MIT ========================================================================= Continuous Speech Recognition: Is there a connectionist advantage? Organizer: Michael Franzini (maf@cs.cmu.edu) Abstract: This workshop will address the following questions: How do neural networks compare to the alternative technologies available for speech recognition? What evidence is available to suggest that connectionism may lead to better speech recognition systems? What comparisons have been performed between connectionist and non-connectionist systems, and how ``fair'' are these comparis- ons? Which approaches to connectionist speech recognition have produced the best results, and which are likely to produce the best results in the future? Traditionally, the selection criteria for NIPS papers reflect a much greater emphasis on theoretical importance of work than on performance figures, despite the fact that recognition rate is one of the most important considerations for speech recognition researchers (and often is {\em the} most important factor in determining their financial support). For this reason, this workshop -- to be oriented more towards performance than metho- dology -- will be of interest to many NIPS participants. The issue of connectionist vs. HMM performance in speech recogni- tion is controversial in the speech recognition community. The validity of past comparisons is often disputed, as is the funda- mental value of neural networks. In this workshop, an attempt will be made to address this issue and the questions stated above by citing specific experimental results and by making arguments with a theoretical basis. Preliminary list of speakers: Ron Cole Uli Bodenhausen Hermann Hild ========================================================================= Symbolic and Subsymbolic Information Processing in Biological Neural Circuits and Systems Organizer: Vasant Honavar (honavar@iastate.edu) Abstract: Traditional information processing models in cognitive psychology which became popular with the advent of the serial computer tended to view cognition as discrete, sequential symbol processing. Neural network or connectionist models offer an alternative paradigm for modelling cognitive phenomena that relies on continuous, parallel subsymbolic processing. Biological systems appear to combine both discrete as well as continuous, sequential as well as parallel, symbolic as well as subsymbolic information processing in various forms at different levels of organization. The flow of neurotransmitter molecules and of photons into receptors is quantal; the depolarization and hyperpolarization of neuron membranes is analog; the genetic code and the decoding processes appear to be digital; global interactions mediated by neurotransmitters and slow waves appear to be both analog and digital. The purpose of this workshop is to bring together interested computer scientists, neuroscientists, psychologists, mathematicians, engineers, physicists and systems theorists to examine and discuss specific examples as well as general principles (to the extent they can be gleaned from our current state of knowledge) of information processing at various levels of organization in biological neural systems. The workshop will consist of several short presentations by participants There will be ample time for informal presentations and discussion centering around a number of key topics such as: * Computational aspects of symbolic v/s subsymbolic information processing * Coordination and control structures and processes in neural systems * Encoding and decoding structures and processes in neural systems * Generative structures and processes in neural systems * Suitability of particular paradigms for modelling specific phenomena * Software requirements for modelling biological neural systems Invited presentations: TBA Those interested in giving a presentation should write to honavar@iastate.edu ========================================================================= Computational Issues in Neural Network Training Organizers: Scott Markel and Roger Crane, Sarnoff Research Abstract: Many of the best practical neural network training results are report- ed by researchers who use variants of back-propagation and/or develop their own algorithms. Few results are obtained by using classical nu- merical optimization methods although such methods can be used effec- tively for many practical applications. Many competent researchers have concluded, based on their own experience, that classical methods have little value in solving real problems. However, use of the best commercially available implementations of such algorithms can help in understanding numerical and computational issues that arise in all training methods. Also, classical methods can be used effectively to solve practical problems. Examples of numerical issues that are ap- propriate to discuss in this workshop include: convergence rates; lo- cal minima; selection of starting points; conditioning (for higher order methods); characterization of the error surface; ... . Ample time will reserved for discussion and informal presentations. We will encourage lively audience participation. ========================================================================= Real Applications of Real Biological Circuits Organizers: Richard Granger, UC Irvine and Jim Schwaber, Du Pont Abstract: The architectures, performance rules and learning rules of most artificial neural networks are at odds with the anatomy and physiology of real biological neural circuitry. For example, mammalian telencephelon (forebrain) is characterized by extremely sparse connectivity (~1-5%), almost entirely lacks dense recurrent connections, and has extensive lateral local circuit connections; inhibition is delayed-onset and relatively long-lasting (100s of milliseconds) compared to rapid-onset brief excitation (10s of milliseconds), and they are not interchangeable. Excitatory connections learn, but there is very little evidence for plasticity in inhibitory connections. Real synaptic plasticity rules are sensitive to temporal information, are not Hebbian, and do not contain "supervision" signals in any form related to those common in ANNs. These discrepancies between natural and artificial NNs raise the question of whether such biological details are largely extraneous to the behavioral and computational utility of neural circuitry, or whether such properties may yield novel rules that confer useful computational abilities to networks that use them. In this workshop we will explicitly analyze the power and utility of a range of novel algorithms derived from detailed biology, and illustrate specific industrial applicatons of these algorithms in the fields of process control and signal processing. It is anticipated that these issues will raise controversy, and half of the workshop will be dedicated to open discussion. Preliminary list of speakers: Jim Schwaber, DuPont Bbatunde Ogunnaike, DuPont Richard Granger, University of California, Irvine John Hopfield, Cal Tech ========================================================================= Recognizing Unconstrained Handwritten Script Organizers: Krishna Nathan, IBM and James A. Pittman, MCC Abstract: Neural networks have given new life to an old research topic, the segmentation and recognition of on-line handwritten script. Isolated handprinted character recognition systems are moving from research to product development, and researchers have moved forward to integrated segmentation and recognition projects. However, the 'real world' problem is best described as one of unconstrained handwriting recognition (often on-line) since it includes both printed and cursive styles -- often within the same word. The workshop will provide a forum for participants to share ideas on preprocessing, segmentation, and recognition techniques, and the use of context to improve the performance of online handwriting recognition systems. We will also discuss issues related to what constitutes acceptable recognition performance. The collection of training and test data will also be addressed. ========================================================================= Time Series Analysis and Predic.... Organizers: John Moody, Oregon Grad. Inst., Mike Mozer, Univ. of Colorado and Andreas Weigend, Xerox PARC Abstract: Several new techniques are now being applied to the problem of predicting the future behavior of a temporal sequence and deducing properties of the system that produced the time series. We will discuss both connectionist and non-connectionist techniques. Issues include algorithms and architectures, model selection, performance measures, iterated vs long term prediction, robust prediction and estimation, the number of degrees of freedom of the system, how much noise is in the data, whether it is chaotic or not, how the error grows with prediction time, detection and classification of signals in noise, etc. Half the available time has been reserved for discussion and informal presentations. We will encourage lively audience participation. Invited presentations: Classical and Non-Neural Approaches: Advantages and Problems. (John Moody) Connectionist Approaches: Problems and Interpretations. (Mike Mozer) Beyond Prediction: What can we learn about the system? (Andreas Weigend) Physiological Time Series Modeling (Volker Tresp) Financial Forecasting (William Finnoff / Georg Zimmerman) FIR Networks (Eric Wan) Dimension Estimation (Fernando Pineda) ========================================================================= Applications of VLSI Neural Networks Organizer: Dave Andes, Naval Air Warfare Center Abstract: This workshop will provide a forum for discussion of the problems and opportunities for neural net hardware systems which solve real problems under real time and space constraints. Some of the most difficult requirements for systems of this type come, not surprisingly, from the military. Several examples of these problems and VLSI solutions will be discussed in this working group. Examples from outside the military will also be discussed. At least half the time will be devoted to open discussion of the issues raised by the experiences of those who have already applied VLSI based ANN techniques to real world problems. Preliminary list of speakers: Bill Camp, IBM Federal Systems Lynn Kern, Naval Air Warfare Center Chuck Glover, Oak Ridge National Lab Dave Andes, Naval Air Warfare Center \enddata{text822, 0} ------------------------------ End of Neuron Digest [Volume 10 Issue 14] ***************************************** Received: from BUACCA by BUACCA.BU.EDU (Mailer R2.08 PTF009) with BSMTP id 8088; Fri, 06 Nov 92 18:36:26 EST Received: from noc2.dccs.upenn.edu by BUACCA.BU.EDU (IBM VM SMTP R1.2.1) with TCP; Fri, 06 Nov 92 18:36:21 EST Received: from CATTELL.PSYCH.UPENN.EDU by noc2.dccs.upenn.edu id AA28733; Fri, 6 Nov 92 18:32:23 -0500 Return-Path: Received: from LOCALHOST by cattell.psych.upenn.edu id AA22715; Fri, 6 Nov 92 17:37:41 EST Posted-Date: Fri, 06 Nov 92 17:37:11 EST From: "Neuron-Digest Moderator" To: Neuron-Distribution:; Subject: Neuron Digest V10 #15 Reply-To: "Neuron-Request" X-Errors-To: "Neuron-Request" Organization: University of Pennsylvania Date: Fri, 06 Nov 92 17:37:11 EST Message-Id: <22697.721089431@cattell.psych.upenn.edu> Sender: marvit@cattell.psych.upenn.edu Neuron Digest Friday, 6 Nov 1992 Volume 10 : Issue 15 Today's Topics: New version of Learning Vector Quantization PD program package Info on intelligent agents? Economics and Neural Nets bibliography, addendum Effectiveness of the latest ANNSs Production scheduling systems? non-linear dynamical modelling? Modeling question Job at Booz, Allen & Hamilton Request for advice - sound localization Algorithms for masssivley parallel machines? Postdocs at Rockefellar Send submissions, questions, address maintenance, and requests for old issues to "neuron-request@cattell.psych.upenn.edu". The ftp archives are available from cattell.psych.upenn.edu (130.91.68.31). Back issues requested by mail will eventually be sent, but may take a while. ---------------------------------------------------------------------- Subject: New version of Learning Vector Quantization PD program package From: lvq@cochlea.hut.fi (LVQ_PAK) Date: Sun, 11 Oct 92 10:57:21 +0700 ************************************************************************ * * * LVQ_PAK * * * * The * * * * Learning Vector Quantization * * * * Program Package * * * * Version 2.1 (October 9, 1992) * * * * Prepared by the * * LVQ Programming Team of the * * Helsinki University of Technology * * Laboratory of Computer and Information Science * * Rakentajanaukio 2 C, SF-02150 Espoo * * FINLAND * * * * Copyright (c) 1991,1992 * * * ************************************************************************ Public-domain programs for Learning Vector Quantization (LVQ) algorithms are available via anonymous FTP on the Internet. "What is LVQ?", you may ask --- See the following reference, then: Teuvo Kohonen. The self-organizing map. Proceedings of the IEEE, 78(9):1464-1480, 1990. In short, LVQ is a group of methods applicable to statistical pattern recognition, in which the classes are described by a relatively small number of codebook vectors, properly placed within each class zone such that the decision borders are approximated by the nearest-neighbor rule. Unlike in normal k-nearest-neighbor (k-nn) classification, the original samples are not used as codebook vectors, but they tune the latter. LVQ is concerned with the optimal placement of these codebook vectors into class zones. This package contains all the programs necessary for the correct application of certain LVQ algorithms in an arbitrary statistical classification or pattern recognition task. To this package three options for the algorithms, the LVQ1, the LVQ2.1 and the LVQ3, have been selected. This code is distributed without charge on an "as is" basis. There is no warranty of any kind by the authors or by Helsinki University of Technology. In the implementation of the LVQ programs we have tried to use as simple code as possible. Therefore the programs are supposed to compile in various machines without any specific modifications made on the code. All programs have been written in ANSI C. The programs are available in two archive formats, one for the UNIX-environment, the other for MS-DOS. Both archives contain exactly the same files. These files can be accessed via FTP as follows: 1. Create an FTP connection from wherever you are to machine "cochlea.hut.fi". The internet address of this machine is 130.233.168.48, for those who need it. 2. Log in as user "anonymous" with your own e-mail address as password. 3. Change remote directory to "/pub/lvq_pak". 4. At this point FTP should be able to get a listing of files in this directory with DIR and fetch the ones you want with GET. (The exact FTP commands you use depend on your local FTP program.) Remember to use the binary transfer mode for compressed files. The lvq_pak program package includes the following files: - Documentation: README short description of the package and installation instructions lvq_doc.ps documentation in (c) PostScript format lvq_doc.ps.Z same as above but compressed lvq_doc.txt documentation in ASCII format - Source file archives (which contain the documentation, too): lvq_p2r1.exe Self-extracting MS-DOS archive file lvq_pak-2.1.tar UNIX tape archive file lvq_pak-2.1.tar.Z same as above but compressed An example of FTP access is given below unix> ftp cochlea.hut.fi (or 130.233.168.48) Name: anonymous Password: ftp> cd /pub/lvq_pak ftp> binary ftp> get lvq_pak-2.1.tar.Z ftp> quit unix> uncompress lvq_pak-2.1.tar.Z unix> tar xvfo lvq_pak-2.1.tar See file README for further installation instructions. All comments concerning this package should be addressed to lvq@cochlea.hut.fi. ************************************************************************ ------------------------------ Subject: Info on intelligent agents? From: Nick Vriend Date: Mon, 12 Oct 92 16:16:48 Nick Vriend European University Institute C.P. 2330 50100 Firenze Ferrovia Italy EARN/Bitnet: As a PhD student of economics at the European University Institute in Florence (Italy), finishing a thesis on 'Decentralized Trade', I am interested in getting contact with people who are working on the following topic: DECENTRALIZED TRADE WITH ARTIFICIALLY INTELLIGENT AGENTS. Basic characteristic of decentralized economies is that each individual agent has a very limited knowledge of his relevant environment. Each agent acts and observes his outcomes in the market (which depend on the actions of the other participants). Thus, each individual agents learns independently, using only a success measure of his own actual performance (e.g. profits, utility). At the moment I am applying Classifier Systems and Genetic Algorithms to model the learning process of each individual agent, but (given the mentioned inherent problem of misspecification in decentralized economies) Neural Networks seem very promising. However, application of Neural Networks appears more complex, as in a decentralized economy nobody would be able to tell each agent what his "target" or "correct" decision would have been. Therefore, the machines have to learn unsupervised (as in e.g. Barto, Sutton & Anderson (1983): Neuronlike Adaptive Elements That Can Solve Difficult Learning Control Problems. IEEE Transactions on Systems, Man, and Cybernetics, 13). Hence, the topic I am interested in might be restated as: REINFORCEMENT LEARNING BY INTERACTING MACHINES. ------------------------------ Subject: Economics and Neural Nets bibliography, addendum From: Duarte Trigueiros Date: Wed, 14 Oct 92 11:27:46 -0200 In addition to Paul Refenes list, I would like to mention mine and Bob's paper on the automatic forming of ratios as internal representations of the MLP. This paper shows that the problem of discovering the appropriate ratios for performing a given task in financial statement analysis can be be simplified by using some specific training schemes in an MLP. @inproceedings( xxx , author = "Trigueiros, D. and Berry, R.", title = "The Application of Neural Network Based Methods to the Extraction of knowledge From Accounting Reports", Booktitle = "Organisational Systems and Technology: Proceedings of the $24^{th}$ Hawaii International Conference on System Sciences", Year = 1991, Pages = "136-146", Publisher = "IEEE Computer Society Press, Los Alamitos, (CA) US.", Editor = "Nunamaker, E. and Sprague, R.") I also noticed that Paul didn't mention Utans and Moody's "Senlecting Neural Network Architectures via the Prediction Risk: An Application to Corporate Bond Rating Prediction" (1991), which has been published somewhere and has, or had, a version in the neuroprose archive as utans.bondrating.ps.Z .This paper is especially recommended, as the early literature on financial applications of NNs didn't care too much with things like cross-validation. The achievements, of course, were appallingly brilliant. Finally, I gathered from Paul's list of articles, that there is a book of readings entitled "Neural Network Applications in Investment and Finance". Paul is the author of an article in chapter 27. The remaining twenty six or so chapters can eventually contain interesting stuff for completing this search. When the original request for references appeared in another list I answered to it. So, I must apologise for mentioning our reference again here. I did it, as Paul list of references could give the impression, despite him, of being an attempt to be extensive. --------------------------------------------------- Duarte Trigueiros, INESC, R. Alves Redol 9, 2. 1000 Lisbon, Portugal e-mail: dmt@sara.inesc.pt FAX +351 (1) 7964710 --------------------------------------------------- ------------------------------ Subject: Effectiveness of the latest ANNSs From: Fernando Passold Organization: Universidade Federal de Santa Catarina/BRASIL Date: Thu, 15 Oct 92 14:39:35 -0300 I would like to begin a little discussion questioning the effectiveness of the latest Artificial Neural Networks Simulators (ANNSs). A majority of the ANNSs do a serial computation trying to emulate the process of the Natural (or biological) Neural Networks (NNNs). I would like to drive the attentions about the fact that the computation is doing serialy, or better, even in more improved ANNSs that profit parallel and/or concurrent processing, what is computing it's one synapse at time or in blocks (packets), different that occurs with NNNs. In a NNN, various potentials of activations of neurons (membrane voltage changes) are evoluate and processing at the same time (involving different latencies), and not the outcome of each synapse from time to time (like in conventionals ANNSs). The question that I would arise follows: Imagine that one of the neurons of an especific NNN suddenly presents a bigger latency in its response, compared with its neighbourhoods. Will it be that this 'failed' neuron, do not carry this network to a completely different result than it would be expected ? Will it be that the activation's timing (spike timing) between neurons of an especific network do not deserve too more attention than the mere emulating of the majority of ours latest ANNSs, even with parallel and/or concurrent processing ? Would not this synchronism (above mentioned) be responsible for our primitive intuitive notion of velocity and time (epistemological talking) ? Maybe this discussion would be of greater interest for the people of Construtivist IA (or Construtivist Connectionist IA). I would be glad in order to receive opinious and/or even outcomings form researchs (preferential via neuron-digest list) with this in mind (including 'neural-boards' using Analog implementations, DSPs inmplementations, neuron-chips or transputers outcomings). Fernando Passold (Master degree student) Biomedical Engineering Group Santa Catarina Federal University BRAZIL E-mail: EEL3FPS@BRUFSC.BITNET ------------------------------ Subject: Production scheduling systems? From: shim@educ.emse.fr Date: 15 Oct 92 20:03:48 +0000 Can anyone suggest some references or who have worked to apply on the production scheduling systems with neural networks. And, could someome forward me Mr. Yoon-Pin Simon Foo's email address(I think he is(or was) in Univ. South Carolina.). Thank in advance. ------------------------------ Subject: non-linear dynamical modelling? From: ABDEMOHA@th.isu.edu Organization: Idaho State University Date: 15 Oct 92 20:42:39 -0700 Dear Sir I would like to inquire about the use of neural networks in modelling non-linear dynamical systems. This may also include the ability of the neural networks to approximate systems governed by a pre-known partial differential equations?. Mohamed A. Abdel-Rahman ------------------------------ Subject: Modeling question From: ABDEMOHA@th.isu.edu Organization: Idaho State University Date: 15 Oct 92 20:46:33 -0700 Dear Sir: I was trying to use the backpropagation neural network to approximate a certain function. I found out that the resulting network could approxiamte the high value output points more than the low value points. I think thos maybe dur to that the error surface is an absolute one. Have there been any trials to construct a relative error surface (i.e. |(Fa(w) - Fd(w))/Fa(w)|). Mohamed A. AbdelRahman ------------------------------ Subject: Job at Booz, Allen & Hamilton From: brettle@picard.ads.com (Dean Brettle) Date: Mon, 19 Oct 92 18:15:51 -0500 NEURAL NETWORK DEVELOPERS Booz, Allen & Hamilton, a world leader in using technology to solve problems for government and industry, has immediate openings for experienced neural network developers in our Advanced Computational Technologies group. If chosen, you will help develop, implement and evaluate neural network architectures for image and signal processing, automatic target recognition, parallel processing, speech processing, and communications. This will involve client-funded work as well as internal research and development. To qualify, you must have experience in neural network theory, implementation, and testing; C/UNIX/X11; and parallel processing experience is a plus. Equal Opportunity Employer. U.S. citizenship may be required. Candidates selected will be subject to a background investigation and must meet eligibility requirements for access to classified information. ENTRY-LEVEL CANDIDATES should have a BS or MS degree in either computer science, applied mathematics, or some closely related discipline and experience implementing neural network paradigms. MID-LEVEL CANDIDATES should have a BS or MS degree in either computer science, applied mathematics, or some closely related discipline with >3 years experience. Candidates must possess a working knowledge of popular neural network models and experience implementing several neural network paradigms. SENIOR-LEVEL CANDIDATES should have an MS or Ph.D. degree in either computer science, mathematics, computational neuroscience, electrical engineering or some closely related discipline. Published work and/or presentations in the neural network field are highly desirable. Experience applying neural network technology to real-world problems and in developing neural network programs (including marketing, proposal writing, and technical & contractual management) is required. Booz, Allen offers a competitive salary, excellent benefits package, challenging work environment and ample opportunities to advance your career. Please send a resume to Dean Brettle either by email to brettle@picard.ads.com, fax to (703)902-3663, or surface mail to Booz, Allen & Hamilton Inc., 8283 Greensboro Drive, Room 594, McLean, VA 22102. ------------------------------ Subject: Request for advice - sound localization From: net@sdl.psych.wright.edu (Mr. Happy Net) Date: Tue, 20 Oct 92 02:17:24 -0500 Dear Sir, At Wright State University, we are working on developing an artificial neural net model of human sound localization. One of our objectives has been to show that ANN's adhere to the Duplex Theory of Localization in that they make use of high frequency intensity cues over low frequency intensity cues, and low frequency temporal cues over high frequency temporal cues. We have chosen to use the backpropagation algorithm distributed in the NeuralShell package available from Ohio State University (anonymous ftp quanta.eng.ohio-state.edu). One of our approachs has been to train ANN's with low, mid, or high band filtered signals. A problem with this is that in humans, our "net" learns to deal with broad band signals by selecting which portions of the signal to base judegments on. On the other hand, if we train an ANN with broad band signals, we would like to uncover which portions of the input spectrum are most heavily affecting the ANN's decisions. This is difficult to do because we cannot merely zero out portions of the input spectrum and test the ANN's performance, as such provides false cues indicating the signal is comming from either directly in front of or behind the head. I would greatly appreciate any suggestions on how to analyze the "weighting" the net gives to different portions of its input. Jim Janko net@sdl.psych.wright.edu ------------------------------ Subject: Algorithms for masssivley parallel machines? From: "Rogene Eichler" Date: Wed, 21 Oct 92 12:13:49 -0600 I am looking for references describing optimization algorithms for backprop type networks on either the CM-200 or CM-5. i.e. What algorithms best exploit the massive parallelism? Thanks! - Rogene eichler@ahpcrc.umn.edu ------------------------------ Subject: Postdocs at Rockefellar From: Zhaoping Li Date: Thu, 22 Oct 92 11:53:40 -0500 ROCKEFELLER UNIVERSITY anticipates the opening of one or two positions in Computational Neuroscience Laboratory. The positions are at the postdoctoral level, and are for one year, renewable to two, starting in September 1993. The focus of the research in the lab is on understanding the computational principles of the nervous system, especially the sensory pathways. It involves analytical and computational approaches with strong emphasis on connections with real neurobiology. Members of the lab include J. Atick, Z. Li, K. Obermayer, N. Redlich, and P. Penev. The lab also maintains strong interactions with other labs at Rockefeller University, including the Gilbert, Wiesel, and the biophysics labs. Interested candidates should submit a C.V. and arrange to have three letters of recommendation sent to Prof. Joseph J. Atick Head, computational neuroscience lab The Rockefeller University 1230 York Avenue New York, NY 10021 USA The Rockefeller University is an affirmative action/equal opportunity employer, and welcomes applications from women and minority candidates. ------------------------------ End of Neuron Digest [Volume 10 Issue 15] *****************************************