index int64 0 20.3k | text stringlengths 0 1.3M | year stringdate 1987-01-01 00:00:00 2024-01-01 00:00:00 | No stringlengths 1 4 |
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300 | Signal Processing by Multiplexing and Demultiplexing in Neurons DavidC. Tam Division of Neuroscience Baylor College of Medicine Houston, TX 77030 dtam@next-cns.neusc.bcm.tmc.edu Abstract Signal processing capabilities of biological neurons are investigated. Temporally coded signals in neurons... | 1990 | 112 |
301 | Lg DEPTH ESTIMATION AND RIPPLE FIRE CHARACTERIZA TION USING ARTIFICIAL NEURAL NETWORKS John L. Perry and Douglas R. Baumgardt ENSCO, Inc. Signal Analysis and Systems Division 5400 Port Royal Road Springfield, Virginia 22151 (703) 321-9000, perry@dewey.css.gov Abstract This srudy has demons... | 1990 | 113 |
302 | 486 Adaptive Range Coding Bruce E. Rosen, James M. Goodwin, and Jacques J. Vidal Distributed Machine Intelligence Laboratory Computer Science Department University of California, Los Angeles Los Angeles, CA 90024 Abstract This paper examines a class of neuron based learning system... | 1990 | 114 |
303 | RecNorm: Simultaneous Normalisation and Classification applied to Speech Recognition John S. Bridle Royal Signals and Radar Est. Great Malvern UK WR143PS Abstract Stephen J. Cox British Telecom Research Labs. Ipswich UK IP57RE A particular form of neural network is described, whic... | 1990 | 115 |
304 | A four neuron circuit accounts for change sensitive inhibition in salamander retina Jeffrey L. Teeters Lawrence Livennore Lab PO Box 808, L-426 Livennore CA 94550 Frank H. Eeckman Lawrence Livennore Lab PO Box 808, L-270 Livennore CA 94550 Frank S. Werblin UC-Berkeley Room 145, LSA ... | 1990 | 116 |
305 | A Method for the Efficient Design of Boltzmann Machines for Classification Problems Ajay Gupta and Wolfgang Maass· Department of Mathematics, Statistics, and Computer Science University of Illinois at Chicago Chicago IL, 60680 Abstract We introduce a method for the efficient design of a Boltzman... | 1990 | 117 |
306 | How Receptive Field Parameters Affect Neural Learning Bartlett W. Mel CNS Program Caltech, 216-76 Pasadena, CA 91125 Stephen M. Omohundro ICSI 1947 Center St., Suite 600 Berkeley, CA 94704 Abstract We identify the three principle factors affecting the performance of learning by networks... | 1990 | 118 |
307 | Proximity Effect Corrections in Electron Beam Lithography Using a Neural Network Robert C. Frye AT &T Bell Laboratories 600 Mountain A venue Murray Hill. NJ 08854 Kevin D. Cummings* AT&T Bell Laboratories 600 Mountain Avenue Murray Hill. NJ 08854 Edward A. Rietman AT&T Bell Laboratories... | 1990 | 119 |
308 | An Analog VLSI Chip for Finding Edges from Zero-crossings Wyeth Bair Christof Koch Computation and Neural Systems Program Caltech 216-76 Pasadena, CA 91125 Abstract We have designed and tested a one-dimensional 64 pixel, analog CMOS VLSI chip which localizes intensity edges in real-time. This... | 1990 | 12 |
309 | Exploiting Syllable Structure in a Connectionist Phonology Model David S. Touretzky Deirdre W. Wheeler School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213-3890 Abstract In a previous paper (Touretzky & Wheeler, 1990a) we showed how adding a clustering operation to a connec... | 1990 | 120 |
310 | Integrated Modeling and Control Based on Reinforcement Learning and Dynamic Programming Richard S. Sutton GTE Laboratories Incorporated Waltham, MA 02254 Abstract This is a summary of results with Dyna, a class of architectures for intelligent systems based on approximating dynamic programming meth... | 1990 | 121 |
311 | A Connectionist Learning Control Architecture for Navigation Jonathan R. Bachrach Department of Computer and Information Science University of Massachusetts Amherst, MA 01003 Abstract A novel learning control architecture is used for navigation. A sophisticated test-bed is used to simulate a cylind... | 1990 | 122 |
312 | On Stochastic Complexity and Admissible Models for Neural Network Classifiers Padhraic Smyth Communications Systems Research Jet Propulsion Laboratory California Institute of Technology Pasadena, CA 91109 Abstract Given some training data how should we choose a particular network classifier from... | 1990 | 123 |
313 | Analog Computation at a Critical Point: A Novel Function for Neuronal Oscillations? Leonid Kruglyak and Willianl Bialek Depart.ment of Physics University of California at Berkeley Berkeley, California 94720 and NEC Research Institute· 4 Independence vVay Princeton, New Jersey 08540 Abstract ... | 1990 | 124 |
314 | Integrated Segmentation and Recognition of Hand-Printed Numerals James D. Keeler· MCC 3500 W. Balcones Ctr. Dr. Austin, TX 78759 David E. Rumelhart Psychology Department Stanford University Stanford, CA 94305 Wee-Kheng Leow MCC and University of Texas Austin, TX 78759 Abstract ... | 1990 | 125 |
315 | Speech Recognition Using Demi-Syllable Neural Prediction Model Ken-ichi Iso and Takao Watanabe C & C Information Technology Research Laboratories NEC Corporation 4-1-1 Miyazaki, Miyamae-ku, Kawasaki 213, JAPAN Abstract The Neural Prediction Model is the speech recognition model based on pattern ... | 1990 | 126 |
316 | A Recurrent Neural Network Model of Velocity Storage in the Vestibulo-Ocular Reflex Thomas J. Anastasio Department of Otolaryngology University of Southern California School of Medicine Los Angeles, CA 90033 Abstract A three-layered neural network model was used to explore the organization of ... | 1990 | 127 |
317 | The Devil and the Network: What Sparsity Implies to Robustness and Memory Sanjay Biswas and Santosh S. Venkatesh Department of Electrical Engineering University of Pennsylvania Philadelphia, PA 19104 Abstract Robustness is a commonly bruited property of neural networks; in particular, a folk the... | 1990 | 128 |
318 | Development and Spatial Structure of Cortical Feature Maps: A Model Study K. 0 berulayer Beckman-Institute University of Illinois Urbana, IL 61801 H. Ritter Technische Fakultiit U niversitiit Bielefeld D-4800 Bielefeld K. Schulten Beckman -Insti t u te University of Illinois Urban... | 1990 | 129 |
319 | Comparison of three classification techniques, CART, C4.5 and Multi-Layer Perceptrons A C Tsoi R A Pearson Department of Electrical EngineeringDepartment of Computer Science University of Queensland Aust Defence Force Academy St Lucia, Queensland 4072 Campbell, ACT 2600 Australia Australia... | 1990 | 13 |
320 | Connectionist Implementation of a Theory of Generalization Roger N. Shepard Department of Psychology Stanford University Stanford, CA 94305-2130 Abstract Sheila Kannappan Department of Physics Harvard University Cambridge, MA 02138 Empirically, generalization between a training and a test ... | 1990 | 130 |
321 | Discrete Affine Wavelet Transforms For Analysis And Synthesis Of Feedforward Neural Networks Y. c. Pati and P. S. Krishnaprasad Systems Research Center and Department of Electrical Engineering University of Maryland, College Park, MD 20742 Abstract In this paper we show that discrete affine wavelet tr... | 1990 | 131 |
322 | Convergence of a Neural Network Classifier John S. Baras Systems Research Center University of Maryland College Park, Maryland 20705 Anthony La Vigna Systems Research Center University of Maryland College Park, Maryland 20705 Abstract In this paper, we prove that the vectors in the LVQ lea... | 1990 | 132 |
323 | Optimal Sampling of Natural Images: A Design Principle for the Visual System? William Bialek, a,b Daniel L. Ruderman, a and A. Zee C a Department of Physics, and Department of Molecular and Cell Biology University of California at Berkeley Berkeley, California 94720 bNEC Research Institute 4 Ind... | 1990 | 133 |
324 | Time Trials on Second-Order and Variable-Learning-Rate Algorithms Richard Rohwer Centre for Speech Technology Research Edinburgh University 80, South Bridge Edinburgh EH 1 1HN, SCOTLAND Abstract The performance of seven minimization algorithms are compared on five neural network problems. The... | 1990 | 134 |
325 | A competitive modular connectionist architecture Robert A. Jacobs and Michael I. Jordan Department of Brain & Cognitive Sciences Massachusetts Institute of Technology Cambridge, MA 02139 Abstract We describe a multi-network, or modular, connectionist architecture that captures that fact that many t... | 1990 | 135 |
326 | A Model of Distributed Sensorimotor Control in the Cockroach Escape Turn R.D. Beer1,2, G.J. Kacmarcik1 , R.E. Ritzmann2 and H.J. Chie12 Departments of lComputer Engineering and Science, and 2Biology Case Western Reserve University Cleveland, OR 44106 Abstract In response to a puff of wind, the Amer... | 1990 | 136 |
327 | A VLSI Neural Network for Color Constancy Andrew Moore Geoffrey Fox· Dept. of Physics California Institute of Technology Pasadena, CA 91125 Computation and Neural Systems Program, 116-81 California Institute of Technology Pasadena, CA 91125 John Allman Rodney Goodman Dept. of Electrical... | 1990 | 137 |
328 | Stereopsis by a Neural Network Which Learns the Constraints Alireza Khotanzad and Ying-Wung Lee Image Processing and Analysis Laboratory Electrical Engineering Department Southern Methodist University Dallas, Texas 75275 Abstract This paper presents a neural network (NN) approach to the problem ... | 1990 | 138 |
329 | The Tempo 2 Algorithm: Adjusting Time-Delays By Supervised Learning Ulrich Bodenhausen and Alex Waibel School of Computer Science Carnegie Mellon University Pittsbwgh, PA 15213 Abstract In this work we describe a new method that adjusts time-delays and the widths of time-windows in artificial ne... | 1990 | 139 |
330 | ART2/BP architecture for adaptive estimation of dynamic processes Einar S~rheim * Department of Computer Science UNIK, Kjeller University of Oslo N-2007 Norway Abstract The goal has been to construct a supervised artificial neural network that learns incrementally an unknown mapping. As a res... | 1990 | 14 |
331 | Learning Theory and Experiments with Competitive Networks Griff L. Bilbro North Carolina State University Box 7914 Raleigh, NC 27695-7914 Abstract David E. Van den Bout North Carolina State University Box 7914 Raleigh, NC 27695-7914 We apply the theory of Tishby, Levin, and Sol1a (TLS) ... | 1990 | 140 |
332 | Further Studies of a Model for the Development and Regeneration of Eye-Brain Maps J.D. Cowan & A.E. Friedman Department of Mathematics, Committee on Neurobiology, and Brain Research Institute, The University of Chicago, 5734 S. Univ. Ave., Chicago, Illinois 60637 Abstract We describe a comput... | 1990 | 141 |
333 | The Recurrent Cascade-Correlation Architecture Scott E. Fahlman School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 Abstract Recurrent Cascade-Correlation CRCC) is a recurrent version of the CascadeCorrelation learning architecture of Fah I man and Lebiere [Fahlman, 1990]. RCC ... | 1990 | 142 |
334 | Sequential Adaptation of Radial Basis Function Neural Networks and its Application to Time-series Prediction v. Kadirkamanathan Engineering Department Cambridge University Cambridge CB2 IPZ, UK M. Niranjan Abstract F. Fallside We develop a sequential adaptation algorithm for radial basis f... | 1990 | 143 |
335 | Self-organization of Hebbian Synapses in Hippocampal Neurons Thomas H. Brown,t Zachary F. Mainen,t Anthony M. Zador,t and Brenda J. Claiborne· t Department of Psychology • Division of Life Sciences Yale University New Haven, cr 06511 University of Texas San Antonio, TX 78285 ABSTRACT We ar... | 1990 | 15 |
336 | Connection Topology and Dynamics in Lateral Inhibition Networks C. M. Marcus, F. R. Waugh, and R. M. Westervelt Department of Physics and Division of Applied Sciences, Harvard University Cambridge, MA 02138 ABSTRACT We show analytically how the stability of two-dimensional lateral inhibition neural... | 1990 | 16 |
337 | A Delay-Line Based Motion Detection Chip Tim Horiuchit John Lazzaro· Andrew Mooret Christof Kocht tComputation and Neural Systems Program ·Department of Computer Science California Institute of Technology MS 216-76 Pasadena, CA 91125 Abstract Inspired by a visual motion detection model ... | 1990 | 17 |
338 | Back Propagation is Sensitive to Initial Conditions John F. Kolen Jordan B. Pollack Laboratory for Artificial Intelligence Research The Ohio State University Columbus. OH 43210. USA kolen-j@cis.ohio-state.edu pollack@cis.ohio-state.edu Abstract This paper explores the effect of initial weight... | 1990 | 18 |
339 | Applications of Neural Networks in Video Signal Processing John C. Pearson, Clay D. Spence and Ronald Sverdlove David Sarnoff Research Center CN5300 Princeton, NJ 08543-5300 Abstract Although color TV is an established technology, there are a number of longstanding problems for which neural netw... | 1990 | 19 |
340 | Using Genetic Algorithms to Improve Pattern Classification Performance Eric I. Chang and Richard P. Lippmann Lincoln Laboratory, MIT Lexington, MA 02173-9108 Abstract Genetic algorithms were used to select and create features and to select reference exemplar patterns for machine vision and speech p... | 1990 | 2 |
341 | Grouping Contours by Iterated Pairing Network Amnon Shashua Shimon Ullman M.I.T. Artificial Intelligence Lab., NE43-737 and Department of Brain and Cognitive Science Cambridge, MA 02139 Abstract We describe in this paper a network that performs grouping of image contours. The input to the net are f... | 1990 | 20 |
342 | Simulation of the Neocognitron on a CCD Parallel Processing Architecture Michael L. Chuang and Alice M. Chiang M.I.T Lincoln Laboratory Lexington, MA 02173 e-mail: chuang@micro.ll.mit.edu Abstract The neocognitron is a neural network for pattern recognition and feature extraction. An analog CCD ... | 1990 | 21 |
343 | Order Reduction for Dynamical Systems Describing the Behavior of Complex Neurons Thomas B. Kepler Biology Dept. L. F. Abbott Eve Marder Physics Dept. Biology Dept. Brandeis University Waltham, MA 02254 Abstract We have devised a scheme to reduce the complexity of dynamical systems be... | 1990 | 22 |
344 | Note on Learning Rate Schedules for Stochastic Optimization Christian Darken and John Moody Yale University P.O. Box 2158 Yale Station New Haven, CT 06520 Email: moody@cs.yale.edu Abstract We present and compare learning rate schedules for stochastic gradient descent, a general algorithm whic... | 1990 | 23 |
345 | REMARKS ON INTERPOLATION AND RECOGNITION USING NEURAL NETS Eduardo D. Sontag· SYCON - Center for Systems and Control Rutgers University New Brunswick, N J 08903 Abstract We consider different types of single-hidden-Iayer feedforward nets: with or without direct input to output connections, and u... | 1990 | 24 |
346 | Rapidly Adapting Artificial Neural Networks for Autonomous Navigation Dean A. Pomerleau School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 Abstract The ALVINN (Autonomous Land Vehicle In a Neural Network) project addresses the problem of training artificial neural network... | 1990 | 25 |
347 | A Recurrent Neural Network for Word Identification from Continuous Phoneme Strings Robert B. Allen Bellcore Morristown, NJ 07962-1910 Abstract Candace A. Kamm Bellcore Morristown, NJ 07962-1910 A neural network architecture was designed for locating word boundaries and identifying words fr... | 1990 | 26 |
348 | Adjoint-Functions and Temporal Learning Algorithms in Neural Networks N. Toomarian and J. Barhen Jet Propulsion Laboratory California Institute of Technology Pasadena, CA 91109 Abstract The development of learning algorithms is generally based upon the minimization of an energy function. It is a fu... | 1990 | 27 |
349 | Language Induction by Phase Transition in Dynamical Recognizers Jordan B. Pollack Laboratory for AI Research The Ohio State University Columbus,OH 43210 pollack@cis.ohio-state.edu Abstract A higher order recurrent neural network architecture learns to recognize and generate languages after be... | 1990 | 28 |
350 | Chaitin-Kolmogorov Complexity and Generalization in Neural Networks Barak A. Pearlmutter School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 Ronald Rosenfeld School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 Abstract We present a unified f... | 1990 | 29 |
351 | Generalization Dynamics in LMS Trained Linear Networks Yves Chauvin· Psychology Department Stanford University Stanford, CA 94305 Abstract For a simple linear case, a mathematical analysis of the training and generalization (validation) performance of networks trained by gradient descent on a Le... | 1990 | 3 |
352 | VLSI Implementations of Learning and Memory Systems: A Review Mark A. Holler Intel Corporation 2250 Mission College Blvd. Santa Clara, Ca. 95052-8125 ABSTRACT A large number of VLSI implementations of neural network models have been reported. The diversity of these implementations is notewort... | 1990 | 30 |
353 | Neural Dynamics of Motion Segmentation and Grouping Ennio Mingolla Center for Adaptive Systems, and Cognitive and Neural Systems Program Boston University 111 Cummington Street Boston, MA 02215 Abstract A neural network model of motion segmentation by visual cortex is described. The model cla... | 1990 | 31 |
354 | Exploratory Feature Extraction in Speech Signals Nathan Intrator Center for Neural Science Brown U ni versity Providence, RI 02912 Abstract A novel unsupervised neural network for dimensionality reduction which seeks directions emphasizing multimodality is presented, and its connection to explorato... | 1990 | 32 |
355 | Constructing Hidden Units using Examples and Queries Eric B. Baum Kevin J. Lang NEC Research Institute 4 Independence Way Princeton, NJ 08540 ABSTRACT While the network loading problem for 2-layer threshold nets is NP-hard when learning from examples alone (as with backpropagation), (Baum, 91... | 1990 | 33 |
356 | Analog Neural Networks as Decoders Ruth Erlanson· Dept. of Electrical Engineering California Institute of Technology Pasadena, CA 91125 Yaser Abu-Mostafa Dept. of Electrical Engineering California Institute of Technology Pasadena, CA 91125 Abstract Analog neural networks with feedback can ... | 1990 | 34 |
357 | A Reinforcement Learning Variant for Control Scheduling Aloke Guha Honeywell Sensor and System Development Center 3660 Technology Drive Minneapolis, MN 55417 Abstract We present an algorithm based on reinforcement and state recurrence learning techniques to solve control scheduling problems. In ... | 1990 | 35 |
358 | Kohonen Networks and Clustering: Comparative Performance in Color Clustering Wesley Snyder Department of Radiology Bowman Gray School of Medicine Daniel Nissman, David Van den Bout, and Grift BUbro Center for Communications and Signal Processing North Carolina State University Wake Forest ... | 1990 | 36 |
359 | Reconfigurable Neural Net Chip with 32K Connections H.P. Graf, R. Janow, D. Henderson, and R. Lee AT&T Bell Laboratories, Room 4G320, Holmdel, NJ 07733 Abstract We describe a CMOS neural net chip with a reconfigurable network architecture. It contains 32,768 binary, programmable connections arranged in ... | 1990 | 37 |
360 | c-Entropy and the Complexity of Feedforward Neural Networks Robert C. Williamson Department of Systems Engineering Research School of Physical Sciences and Engineering Australian National University GPO Box 4, Canberra, 2601, Australia Abstract We develop a. new feedforward neuralnet.work repres... | 1990 | 38 |
361 | Extensions of a Theory of Networks for Approximation and Learning: Outliers and Negative Examples Federico Girosi AI Lab. M.I.T. Cambridge, MA 02139 Tomaso Poggio Al Lab. M.LT. Cambridge, MA 021:39 Abstract Bruno Caprile I.R.S.T. Povo, Italy, 38050 Learning an input-output mapping... | 1990 | 39 |
362 | Optimal Filtering in the Salamander Retina Fred Riekea,l;, W. Geoffrey Owenb and Willialll Bialeka,b,c Depart.ment.s of Physicsa and Molecular and Cell Biologyb U niversit.y of California Berkeley, California 94720 and NEC Research Inst.itute C 4 Independence \Vay Princeton, N e' ... .J ersey 08... | 1990 | 4 |
363 | Stochastic Neurodynamics J.D. Cowan Department of Mathematics, Committee on Neurobiology, and Brain Research Institute, The University of Chicago, 5734 S. Univ. Ave., Chicago, Illinois 60637 Abstract The main point of this paper is that stochastic neural networks have a mathematical structure th... | 1990 | 40 |
364 | Phonetic Classification and Recognition Using the Multi-Layer Perceptron Hong C. Leung, James R. Glass, Michael S. Phillips, and Victor W. Zue Spoken Language Systems Group Laboratory for Computer Science Massachusetts Institute of Technology Cambridge, Massachusetts 02139, U.S.A. Abstract In... | 1990 | 41 |
365 | Learning Trajectory and Force Control of an Artificial Muscle Arm by Parallel-hierarchical Neural Network Model Masazumi Katayama Mitsuo Kawato Cognitive Processes Department ATR Auditory and Visual Perception Research Laboratories Seika-cho. Soraku-gun. Kyoto 619-02. JAPAN Abstract We propos... | 1990 | 42 |
366 | Basis-Function Trees as a Generalization of Local Variable Selection Methods for Function Approximation Terence D. Sanger Dept. Electrical Engineering and Computer Science Massachusetts Institute of Technology, E25-534 Cambridge, MA 02139 Abstract Local variable selection has proven to be a powe... | 1990 | 43 |
367 | A Short-Term Memory Architecture for the Learning of Morphophonemic Rules Michael Gasser and Chan-Do Lee Computer Science Department Indiana University Bloomington, IN 47405 Abstract Despite its successes, Rumelhart and McClelland's (1986) well-known approach to the learning of morphophonemic rules... | 1990 | 44 |
368 | Associative Memory in a Network of 'biological' Neurons \Vulfram Gerstner • Department of Physics University of California Ber keley, CA 94720 Abstract The Hopfield network (Hopfield, 1982,1984) provides a simple model of an associative memory in a neuronal structure. This model, however, is bas... | 1990 | 45 |
369 | Oriented Non-Radial Basis Functions for Image Coding and Analysis A vijit Saha 1 Jim Christian D. S. Tang Microelectronics and Computer Technology Corporation 3500 West Balcones Center Drive Austin, TX 78759 Chuan-Lin Wu Department of Electrical and Computer Engineering University of Texas... | 1990 | 46 |
370 | Evaluation of Adaptive Mixtures of Competing Experts Steven J. Nowlan and Geoffrey E. Hinton Computer Science Dept. University of Toronto Toronto, ONT M5S 1A4 Abstract We compare the performance of the modular architecture, composed of competing expert networks, suggested by Jacobs, Jordan, Nowl... | 1990 | 47 |
371 | Spoken Letter Recognition Mark Fanty & Ronald Cole Dept. of Computer Science and Engineering Oregon Graduate Institute Beaverton, OR 97006 Abstract Through the use of neural network classifiers and careful feature selection, we have achieved high-accuracy speaker-independent spoken letter recogniti... | 1990 | 48 |
372 | Design and Implementation of a High Speed CMAC Neural Network Using Programmable CMOS Logic Cell Arrays W. Thomas Miller, III, Brian A. Box, and Erich C. Whitney Department of Electrical and Computer Engineering Kingsbury Hall University of New Hampshire Durham, New Hampshire 03824 James M. Glyn... | 1990 | 49 |
373 | Oscillation Onset • In Neural Delayed Feedback Andre Longtin Complex Systems Group and Center for Nonlinear Studies Theoretical Division B213, Los Alamos National Laboratory Los Alamos, NM 87545 Abstract This paper studies dynamical aspects of neural systems with delayed negative feedback modell... | 1990 | 5 |
374 | Shaping the State Space Landscape in Recurrent Networks Patrice Y. Simard >I< Computer Science Dept. University of Rochester Rochester, NY 14627 Jean Pierre Raysz LIUC U niversite de Caen 14032 Caen Cedex France Bernard Victorri ELSAP Universite de Caen 14032 Caen Cedex Fran... | 1990 | 50 |
375 | EMPATH: Face, Emotion, and Gender Recognition Using Ho10ns Garrison w. CottreU . . Computer Science and Engm~enng Dept Institute for Neural Computati~n University of California San Diego La Jolla, CA 92093 Abstract The dimens~onali~y of a set Off 160 1:~ :a:~s ~~ ·. 10 . female sub... | 1990 | 51 |
376 | INTERACTION AMONG OCULARITY, RETINOTOPY AND ON-CENTER/OFFCENTER PATHWAYS DURING DEVELOPMENT Shigeru Tanaka Fundamental Research Laboratories, NEC Corporation, 34 Miyukigaoka, Tsukuba, Ibaraki 305, Japan ABSTRACT The development of projections from the retinas to the cortex is mathematically anal... | 1990 | 52 |
377 | Phase-coupling in Two-Dimensional Networks of Interacting Oscillators Ernst Niebur, Daniel M. Kammen, Christof Koch, Daniel Ruderman! & Heinz G. Schuster2 Computation and Neural Systems Caltech 216-76 Pasadena, CA 91125 ABSTRACT Coherent oscillatory activity in large networks of biological or ar... | 1990 | 53 |
378 | An Analog VLSI Splining Network Daniel B. Schwartz and Vijay K. Samalam GTE Laboratories, Inc. 40 Sylvan Rd. Waltham, MA 02254 Abstract We have produced a VLSI circuit capable of learning to approximate arbitrary smooth of a single variable using a technique closely related to splines. The circuit ... | 1990 | 54 |
379 | Designing Linear Threshold Based Neural Network Pattern Classifiers Terrence L. Fine School of Electrical Engineering Cornell University Ithaca, NY 14853 Abstract The three problems that concern us are identifying a natural domain of pattern classification applications of feed forward neural net... | 1990 | 55 |
380 | Qualitative structure from motion Daphna Weinshall Center for Biological Information Processing MIT, E25-201, Cambridge MA 02139 Abstract Exact structure from motion is an ill-posed computation and therefore very sensitive to noise. In this work I describe how a qualitative shape representation, ba... | 1990 | 56 |
381 | Learning to See Rotation and Dilation with a Hebb Rule Martin I. Sereno and Margaret E. Sereno Cognitive Science D-015 University of California, San Diego La Jolla, CA 92093-0115 Abstract Previous work (M.I. Sereno, 1989; cf. M.E. Sereno, 1987) showed that a feedforward network with area VI-like... | 1990 | 57 |
382 | A Comparative Study of the Practical Characteristics of Neural Network and Conventional Pattern Classifiers Kenney Ng Richard P. Lippmann Lincoln Laboratory, MIT Lexington, MA 02173-9108 BBN Systems and Technologies Cambridge, MA 02138 Abstract Seven different pattern classifiers were impl... | 1990 | 58 |
383 | Real-time autonomous robot navigation using VLSI neural networks Lionel Tarassenko Michael Brownlow Gillian Marshall· Department of Engineering Science Oxford University, Oxford, OXl 3PJ, UK Jon Tombs Alan Murray Department of Electrical Engineering Edinburgh University, Edinburgh, EH9 3JL, U... | 1990 | 59 |
384 | Relaxation Networks for Large Supervised Learning Problems Joshua Alspector Robert B. Allen Anthony Jayakumar Torsten Zeppenfeld and Ronny Meir Bellcore Morristown, NJ 07962-1910 Abstract Feedback connections are required so that the teacher signal on the output neurons can modify weights during su... | 1990 | 6 |
385 | CAM Storage of Analog Patterns and Continuous Sequences with 3N2 Weights Bill Baird Dept Mathematics and Dept Molecular and Cell Biology, 129 LSA, U .C.Berkeley, Berkeley, Ca. 94720 Abstract Frank Eeckman Lawrence Livermore National Laboratory, P.O. Box 808 (L-426), Livermore, Ca. 94... | 1990 | 60 |
386 | Generalization Properties of Radial Basis Functions Sherif M. Botros Christopher G. Atkeson Brain and Cognitive Sciences Department and the Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge, MA 02139 Abstract We examine the ability of radial basis functions (R... | 1990 | 61 |
387 | Learning Time-varying Concepts Anthony Kuh Dept. of Electrical Eng. U. of Hawaii at Manoa Honolulu, HI 96822 kuh@wiliki.eng.hawaii.edu Thomas Petsche Siemens Corp. Research 755 College Road East Princeton, NJ 08540 petsche® learning. siemens.com Ronald L. Rivest Lab. for Computer Sci... | 1990 | 62 |
388 | A Theory for Neural Networks with Time Delays Bert de Vries Department of Electrical Engineering University of Horida, CSE 447 Gainesville, FL 32611 Jose C. Principe Department of Electrical Engineering University of Horida, CSE 444 Gainesville, FL 32611 Abstract We present a new neural ne... | 1990 | 63 |
389 | Natural Dolphin Echo Recog~ition Using an Integrator Gateway Network Herbert L. Roitblat Department of Psychology, University of Hawaii, Honolulu, HI 96822 Patrick W. B Moore, Paul E. Nachtigall, & Ralph H. Penner Naval Ocean Systems Center, Hawaii Laboratory, Kailua, Hawaii, 96734 Abstract ... | 1990 | 64 |
390 | Compact EEPROM-based Weight Functions A. Kramer, C. K. Sin, R. Chu, and P. K. Ko Department of Electrical Engineering and Computer Science University of California at Berkeley Berkeley, CA 94720 Abstract We are focusing on the development of a highly compact neural net weight function based on the ... | 1990 | 65 |
391 | Discovering Viewpoint-Invariant Relationships That Characterize Objects Richard S. Zemel and Geoffrey E. Hinton Department of Computer Science University of Toronto Toronto, ONT M5S lA4 Abstract Using an unsupervised learning procedure, a network is trained on an ensemble of images of the same two-... | 1990 | 66 |
392 | Reinforcenlent Learning in Markovian and Non-Markovian Environments Jiirgen Schmidhuber Institut fiir Informatik Technische Universitat Miinchen Arcistr. 21, 8000 Miinchen 2, Germany schmidhu@tumult.informatik.tu-muenchen.de Abstract This work addresses three problems with reinforcement learning... | 1990 | 67 |
393 | Second Order Properties of Error Surfaces : Learning Time and Generalization Yann Le Cun Ido Kanter Department of Physics Bar Ilan University Ramat Gan, 52100 Israel Sara A. Sona AT&T Bell Laboratories Crawfords Corner Rd. AT &T Bell Laboratories Crawfords Corner Rd. Holmdel, NJ 0773... | 1990 | 68 |
394 | Connectionist Music Composition Based on Melodic and Stylistic Constraints Michael C. Mozer Department of Computer Science and Institute of Cognitive Science University of Colorado Boulder, CO 80309-0430 Abstract Todd Soukup Department of Electrical and Computer Engineering University o... | 1990 | 69 |
395 | Neural Networks Structured for Control Application to Aircraft Landing Charles Schley, Yves Chauvin, Van Henkle, Richard Golden Thomson-CSP, Inc., Palo Alto Research Operations 630 Hansen Way, Suite 250 Palo Alto, CA 94306 Abstract We present a generic neural network architecture capable of control... | 1990 | 7 |
396 | Transforming Neural-Net Output Levels to Probability Distributions John S. Denker and Yann leCun AT&T Bell Laboratories Holmdel, NJ 07733 Abstract (1) The outputs of a typical multi-output classification network do not satisfy the axioms of probability; probabilities should be positive and sum t... | 1990 | 70 |
397 | Can neural networks do better than the Vapnik-Chervonenkis bounds? David Cohn Dept. of Compo Sci. & Eng. University of Washington Seattle, WA 98195 Abstract Gerald Tesauro IBM Watson Research Center P.O. Box 704 Yorktown Heights, NY 10598 \Ve describe a series of careful llumerical expe... | 1990 | 71 |
398 | Discovering Discrete Distributed Representations with Iterative Competitive Learning Michael C. Mozer Department of Computer Science and Institute of Cognitive Science University of Colorado Boulder, CO 80309-0430 Abstract Competitive learning is an unsupervised algorithm that classifies input p... | 1990 | 72 |
399 | A Neural Expert System with Automated Extraction of Fuzzy If-Then Rules and Its Application to Medical Diagnosis Yoichi Hayashi* Department of Computer and Information Sciences Ibaraki University Hitachi-shi,Ibaraki 316, Japan ABSTRACT This paper proposes ajuzzy neural expert system (FNES) with ... | 1990 | 73 |
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