neuron-request@HPLABS.HP.COM (Neuron-Digest Moderator Peter Marvit) (12/05/88)
Neuron Digest Sunday, 4 Dec 1988 Volume 4 : Issue 30 Today's Topics: Call for Papers (ISMIS'89) Tech Report - Connectionist Speech Recognition IEEE ICNNN 1989 Call for Papers Explanatory Coherence: BBS Call for Commentators TR available Send submissions, questions, address maintenance and requests for old issues to "neuron-request@hplabs.hp.com" or "{any backbone,uunet}!hplabs!neuron-request" ------------------------------------------------------------ Subject: Call for Papers (ISMIS'89) From: wong@gvax.cs.cornell.edu (Mike Wong) Organization: Cornell Univ. CS Dept, Ithaca NY Date: 21 Nov 88 16:08:44 +0000 CALL FOR PAPERS FOURTH INTERNATIONAL SYMPOSIUM ON METHODOLOGIES FOR INTELLIGENT SYSTEMS Charlotte, North Carolina, Hilton Hotel, University Place October 12-14, 1989 SPONSORS: Energy Division of the ORNL, Martin Marietta Energy Systems, University of North Carolina - Charlotte, University of Turin (ITALY) PURPOSE OF THE SYMPOSIUM: This Symposium is intended to attract individuals who are actively engaged both in theoretical and practical aspects of intelligent systems. The goal is to provide a platform for a useful exchange between theoreticians and practitioners, and to foster the cross-fertilization of ideas in the following areas: approximate reasoning, expert systems, intelligent databases, knowledge representation, learning and adaptive systems, logic for A.I., neural networks. SYMPOSIUM CHAIRMAN: Zbigniew W. Ras (UNC-Charlotte) ORGANIZING COMMITTEE: Bill Chu (UNC-C), Mary Emrich (ORNL), Attilio Giordana (Turin, Italy), Zbigniew Michalewicz (New Zealand), Alberto Pettorossi (Rome, Italy), Pietro Torasso (Turin, Italy), S.K.Michael Wong (Cornell), Maria Zemankova (NSF & UT-Knoxville), Jan Zytkow (George Mason) PROGRAM COMMITTEE: Luigia Aiello (Italy), Andrew G. Barto (UM-Amherst), James Bezdek (Boeing), Alan W. Bierman (Duke), John Bourne (Vanderbilt), Jaime Carbonell (CMU), Peter Cheeseman (NASA), Su-shing Chen (UNC-C), Melvin Fitting (CUNY), Brian R. Gaines (Canada), Peter E. Hart (Syntelligence), Marek Karpinski (West Germany), Kurt Konolige (SRI), Catherine Lassez (IBM-T.J Watson), R. Lopez de Mantaras (Spain), Ryszard Michalski (George Mason), Jack Minker (Maryland), Jose Miro (Spain), Masao Mukaidono (Japan), Ephraim Nissan (Israel), Rohit Parikh (CUNY), Reind van de Riet (The Netherlands), Colette Rolland (France), Lorenza Saitta (Italy), Eric Sandewall (Sweden), Joachim W. Schmidt (West Germany), Richmond Thomason (Pittsburgh), David S. Warren (SUNY-Stony Brook) INVITED SPEAKERS: Jon Doyle (MIT), Ryszard Michalski (George Mason), Richard Waldinger (SRI) SUBMISSION AND INFORMATION: Send four copies of a complete paper to one of the addresses below: Dr. S.K. Michael Wong, Cornell Univ., Comp. Sci., Upson Hall, Ithaca, New York 14853-7501 or Dr. A. Giordana, Univ. of Turin, Comp. Sci., Corso Svizzera 185, 10149 Torino, Italy TIME SCHEDULE: Submission of papers..........................March 15, 1989 Notification of acceptance....................May 15, 1989 Final paper to be included in proceedings.....June 15, 1989 ------------------------------ Subject: Tech Report - Connectionist Speech Recognition From: watrous@linc.cis.upenn.edu (Raymond Watrous) Date: Wed, 23 Nov 88 15:53:30 -0500 The following technical report is available from the Department of Computer and Information Science, University of Pennsylvania: Speech Recognition Using Connectionist Networks Raymond L. Watrous MS-CIS-88-96 LINC LAB 138 Abstract The use of connectionist networks for speech recognition is assessed using a set of representative phonetic discrimination problems. The problems are chosen with respect to the physiological theory of phonetics in order to give broad coverage to the space of articulatory phonetics. Separate network solutions are sought to each phonetic discrimination problem. A connectionist network model called the Temporal Flow Model is defined which consists of simple processing units with single valued outputs interconnected by links of variable weight. The model represents temporal relationships using delay links and permits general patterns of connectivity including feedback. It is argued that the model has properties appropriate for time varying signals such as speech. Methods for selecting network architectures for different recognition problems are presented. The architectures discussed include random networks, minimally structured networks, hand crafted networks and networks automatically generated based on samples of speech data. Networks are trained by modifying their weight parameters so as to minimize the mean squared error between the actual and the desired response of the output units. The desired output unit response is specified by a target function. Training is accomplished by a second order method of iterative nonlinear optimization by gradient descent which incorporates a method for computing the complete gradient of recurrent networks. Network solutions are demonstrated for all eight phonetic discrimination problems for one male speaker. The network solutions are analyzed carefully and are shown in every case to make use of known acoustic phonetic cues. The network solutions vary in the degree to which they make use of context dependent cues to achieve phoneme recognition. The network solutions were tested on data not used for training and achieved an average accuracy of 99.5%. Methods for extending these results to a single network for recognizing the complete phoneme set from continuous speech obtained from different speakers are outlined. It is concluded that acoustic phonetic speech recognition can be accomplished using connectionist networks. +++++++++++++++++++++++++++++++++++++++++++++++++++++ This report is available from: James Lotkowski Technical Report Facility Room 269/Moore Building Computer Science Department University of Pennsylvania 200 South 33rd Street Philadelphia, PA 19104-6389 or james@central.cis.upenn.edu Please do not request copies of this report from me. Copies of the report cost approximately $19.00 which covers duplication (300 pages) and postage. I will bring a 'desk copy' to NIPS. As of December 1, I will be affiliated with the University of Toronto. My address will be: Department of Computer Science University of Toronto 10 King's College Road Toronto, Canada M5S 1A4 watrous@ai.toronto.edu ------------------------------ Subject: IEEE ICNNN 1989 Call for Papers From: pwh@ece-csc.ncsu.edu (Paul Hollis) Date: Fri, 25 Nov 88 14:21:29 -0500 NEURAL NETWORKS CALL FOR PAPERS IEEE International Conference on Neural Networks June 19-22, 1989 Washington, D.C. The 1989 IEEE International Conference on Neural Networks (ICNN-89) will be held at the Sheraton Washington Hotel in Washington, D.C., USA from June 19-22, 1989. ICNN-89 is the third annual conference in a series devoted to the technology of neurocomputing in its academic, industrial, commercial, consumer, and biomedical engineering aspects. The series is sponsored by the IEEE Technical Activities Board Neural Network Committee, created Spring 1988. ICNN-87 and 88 were huge successes, both in terms of large attendance and high quality of the technical presentations. ICNN-89 continues this tradition. It will be by far the largest and most important neural network meeting of 1989. As in the past, the full text of papers presented orally in the technical sessions will be published in the Conference Proceedings (along with some particularly outstanding papers from the Poster Sessions). The Abstract portions of all poster papers not published in full will also be published in the Proceedings. The Conference Proceedings will be distributed at the registration desk to all regular conference registrants as well as to all student registrants. This gives conference participants the full text of every paper presented in each technical session -- which greatly increases the value of the conference. ICNN is the only major neural network conference in the world to offer this feature. As is now the tradition, ICNN-89 will include a day of tutorials (June 18), the exhibit hall (the neurocomputing industry's primary annual tradeshow), plenary talks, and social events. Mark your calendar today and plan to attend IEEE ICNN-89 -- the definitive annual progress report on the neurocomputing revolution! DEADLINE FOR SUBMISSION OF PAPERS for ICNN-89 is February 1, 1989. Papers of 8 pages or less are solicited in the following areas: - -Real World Applications -Associative Memory - -Supervised Learning Theory -Image Processing - -Reinforcement Learning Theory -Self-Organization - -Robotics and Control -Neurobiological Models - -Optical Neurocomputers -Vision - -Optimization -Electronic Neurocomputers - -Neural Network Theory & Architectures Papers should be prepared in standard IEEE Conference Proceedings Format, and typed on the special forms provided in the Author's Kit. The Title, Author Name, Affiliation, and Abstract portions of the first page of the paper must be less than a half page in length. Indicate in your cover letter which of the above subject areas you wish your paper included in and whether you wish your paper to be considered for oral presentation, presentation as a poster, or both. For papers with multiple authors, indicate the name and address of the author to whom correspondence should be sent. Papers submitted for oral presentation may, at the referee's discretion, be designated for poster presentation instead, if they feel this would be more appropriate. FULL PAPERS in camera-ready form (1 original and 5 copies) should be submitted to Nomi Feldman, Conference Coordinator, at the address below. For more details, or to request your IEEE Author's Kit, call or write: Nomi Feldman, ICNN-89 Conference Coordinator 3770 Tansy Street San Diego, CA 92121 (619) 453-6222 ------------------------------ Subject: Explanatory Coherence: BBS Call for Commentators From: harnad@Princeton.EDU (Stevan Harnad) Date: Sun, 27 Nov 88 12:35:11 -0500 Below is the abstract of a forthcoming target article to appear in Behavioral and Brain Sciences (BBS), an international, interdisciplinary journal providing Open Peer Commentary on important and controversial current research in the biobehavioral and cognitive sciences. To be considered as a commentator or to suggest other appropriate commentators, please send email to: harnad@confidence.princeton.edu or write to: BBS, 20 Nassau Street, #240, Princeton NJ 08542 [tel: 609-921-7771] ____________________________________________________________________ EXPLANATORY COHERENCE Paul Thagard Cognitive Science Loboratory Princeton University Princeton NJ 08542 Keywords: Connectionist models, artificial intelligence, explanation, coherence, reasoning, decision theory, philosophy of science This paper presents a new computational theory of explanatory coherence that applies both to the acceptance and rejection of scientific hypotheses and to reasoning in everyday life. The theory consists of seven principles that establish relations of local coherence between a hypothesis and other propositions that explain it, are explained by it, or contradict it. An explanatory hypothesis is accepted if it coheres better overall than its competitors. The power of the seven principles is shown by their implementation in a connectionist program called ECHO, which has been applied to such important scientific cases as Lavoisier's argument for oxygen against the phlogiston theory and Darwin's argument for evolution against creationism, and also to cases of legal reasoning. The theory of explanatory coherence has implications for artificial intelligence, psychology, and philosophy. ------------------------------ Subject: TR available From: Bruno Olshausen <bruno@riacs.edu> Date: Fri, 02 Dec 88 15:31:14 -0800 The following technical report is available. Please send email to bruno@riacs.edu, or phone 415-694-4997, for requests: A Survey of Visual Preprocessing and Shape Representation Techniques Bruno A. Olshausen Research Institute for Advanced Computer Science NASA Ames Research Center Astract. This survey summarizes many recent theories and methods proposed for visual preprocessing and shape representation. The survey brings together research from the fields of biology, psychology, computer science, electrical engineering, and most recently, neural networks. This report was motivated by the need to preprocess images for a sparse distributed memory (SDM), but the techniques presented herein may also prove useful for applying other associative memories to visual pattern recognition. The material of this survey is divided into three sections 1) an overview of biological visual processing, 2) methods of preprocessing (extracting parts of shape, texture, motion, and depth), and 3) shape representation and recognition (form invariance, primitives and structural descriptions, and theories of attention). ------------------------------ End of Neurons Digest *********************