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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400 | Learning by Combining Memorization and Gradient Descent John C. Platt Synaptics, Inc. 2860 Zanker Road, Suite 206 San Jose, CA 95134 ABSTRACT We have created a radial basis function network that allocates a new computational unit whenever an unusual pattern is presented to the network. The ne... | 1990 | 74 |
401 | A Neural Network Approach for Three-Dimensional Object Recognition Volker 'bap Siemens AG, Central Reeearch and Development Otto-HaIm-Ring 6, 0.8000 Munchen 83 GermaD)' Ab.tract The model-bued neural vision Iystem presented here determines the p~ aition and identity of three-dimensional objects.... | 1990 | 75 |
402 | Distributed Recursive Structure Processing Geraldine Legendre Department of Linguistics Yoshiro Miyata Optoelectronic Computing Systems Center University of Colorado Boulder, CO 80309-0430· Paul Smolensky Department of Computer Science Abstract Harmonic grammar (Legendre, et al., ... | 1990 | 76 |
403 | Dynamics of Learning in Recurrent Feature-Discovery Networks Todd K. Leen Department of Computer Science and Engineering Oregon Graduate Institute of Science & Technology Beaverton, OR 97006-1999 Abstract The self-organization of recurrent feature-discovery networks is studied from the perspecti... | 1990 | 77 |
404 | Navigating through Temporal Difference Peter Dayan Centre for Cognitive Science &. Department of Physics University of Edinburgh 2 Buccleuch Place, Edinburgh EH8 9LW dayantcns.ed.ac.uk Abstract Barto, Sutton and Watkins [2] introduced a grid task as a didactic example of temporal difference plannin... | 1990 | 78 |
405 | On The Circuit Complexity of Neural Networks v. P. Roychowdhury Information Systems Laboratory Stanford University Stanford, CA, 94305 A. Orlitsky AT &T Bell Laboratories 600 Mountain A venue Murray Hill, NJ, 07974 Abstract K. Y. Sill Information Systems Laboratory Stanford Universit... | 1990 | 79 |
406 | Closed-Form Inversion of Backpropagation Networks: Theory and Optimization Issues Michael L. Rossen HNC, Inc. 5.501 Oberlin Drive San Diego, CA 92121 rossen@amos.ucsd.edu Abstract We describe a closed-form technique for mapping the output of a trained backpropagation network int.o input activ... | 1990 | 8 |
407 | An Attractor Neural Network Model of Recall and Recognition Eytan Ruppin Department of Computer Science School of Mathematical Sciences Sackler Faculty of Exact Sciences Tel Aviv University 69978, Tel Aviv, Israel Abstract Yechezkel Yeshurun Department of Computer Science School of Math... | 1990 | 80 |
408 | Training Knowledge-Based Neural Networks to Recognize Genes in DNA Sequences Michiel O. Noordewier Computer Science Geoffrey G. Towell Computer Sciences University of Wisconsin Madison, WI 53706 Jude W. Shavlik Computer Sciences University of Wisconsin Madison, WI 53706 Rutgers Unive... | 1990 | 81 |
409 | ALCOVE: A Connectionist Model of Human Category Learning John K. Kruschke Department of Psychology and Cognitive Science Program Indiana University, Bloomington IN 47405-4201 USA e-mail: kruschke@ucs.indiana.edu Abstract ALCOVE is a connectionist model of human category learning that fits a broa... | 1990 | 82 |
410 | Multi-Layer Perceptrons with B-SpIine Receptive Field Functions Stephen H. Lane, Marshall G. Flax, David A. Handelman and JackJ. Gelfand Human Information Processing Group Department of Psychology Princeton University Princeton, New Jersey 08544 ABSTRACT Multi-layer perceptrons are often slow to... | 1990 | 83 |
411 | Bumptrees for Efficient Function, Constraint, and Classification Learning Stephen M. Omohundro International Computer Science Institute 1947 Center Street. Suite 600 Berkeley. California 94704 Abstract A new class of data structures called "bumptrees" is described. These structures are useful fo... | 1990 | 84 |
412 | Planning with an Adaptive World Model Sebastian B. Thrun German National Research Center for Computer Science (GMD) Knut Moller University of Bonn Department of Computer Science D-5300 Bonn, FRG Alexander Linden German National Research Center for Computer Science (GMD) D-5205 ... | 1990 | 85 |
413 | Simple Spin Models for the Development of Ocular Dominance Columns and Iso-Orientation Patches 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 Simple... | 1990 | 86 |
414 | A Multiscale Adaptive Network Model of Motion Computation in Primates H. Taichi Wang Science Center, A18 Rockwell International 1049 Camino Dos Rios Thousand Oaks, CA 91360 Dimal Mathur Christor Koch Computation & Neural Systems Caltech,216-76 Pasadena, CA 91125 Science Center, A 7 A... | 1990 | 87 |
415 | Spherical Units as Dynamic Consequential Regions: Implications for Attention, Competition and Categorization Stephen Jose Hanson* Learning and Knowledge Acquisition Group Mark A. Gluck Center for Molecular & Behavioral Neuroscience Rutgers University Newark, NJ 07102 Siemens Corporate Rese... | 1990 | 88 |
416 | Speech Recognition using Connectionist Approaches Khalid Choukri SPRINT Coordinator CAP GEMINI INNOVATION 118 rue de Tocqueville, 75017 Paris. France e-mail: choukri@capsogeti.fr Abstract This paper is a summary of SPRINT project aims and results. The project focus on the use of neuro-computing ... | 1990 | 89 |
417 | Dynamics of Generalization in Linear Perceptrons Anders Krogh Niels Bohr Institute Blegdamsvej 17 DK-2100 Copenhagen, Denmark John A. Hertz NORDITA Blegdamsvej 17 DK-2100 Copenhagen, Denmark Abstract We study the evolution of the generalization ability of a simple linear perceptron with N ... | 1990 | 9 |
418 | FEEDBACK SYNAPSE TO CONE AND LIGHT ADAPTATION Josef Skrzypek Machine Perception Laboratory UCLA - Los Angeles, California 90024 INTERNET: SKRZYPEK@CS.UCLA.EDU Abstract Light adaptation (LA) allows cone vIslOn to remain functional between twilight and the brightest time of day even though, at anyone... | 1990 | 90 |
419 | Direct memory access using two cues: Finding the intersection of sets in a connectionist model Janet Wiles, Michael S. Humphreys, John D. Bain and Simon Dennis Departments of Psychology and Computer Science University of Queensland QLD 4072 Australia email: janet@psych.psy.uq.oz.au Abstract For lac... | 1990 | 91 |
420 | Flight Control in the Dragonfly: A Neurobiological Simulation William E. Faller and Marvin W. Luttges Aerospace Engineering Sciences, University of Colorndo, Boulder, Colorado 80309-0429. ABSTRACT Neural network simulations of the dragonfly flight neurocontrol system have been developed to understa... | 1990 | 92 |
421 | A Framework for the Cooperation of Learning Algorithms Leon Bottou Patrick Gallinari Laboratoire de Recherche en Informatique Universite de Paris XI 91405 Orsay Cedex France Abstract We introduce a framework for training architectures composed of several modules. This framework, which uses... | 1990 | 93 |
422 | Continuous Speech Recognition by Linked Predictive Neural Networks Joe Tebelskis, Alex Waibel, Bojan Petek, and Otto Schmidbauer School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 Abstract We present a large vocabulary, continuous speech recognition system based on Linked... | 1990 | 94 |
423 | A B-P ANN Commodity Trader Joseph E. Collard Martingale Research Corporation 100 Allentown Pkwy., Suite 211 Allen, Texas 75002 Abstract An Artificial Neural Network (ANN) is trained to recognize a buy/sell (long/short) pattern for a particular commodity future ... | 1990 | 95 |
424 | Statistical Mechanics of Temporal Association in Neural Networks with Delayed Interactions Andreas V.M. Herz Division of Chemistry Caltech 139-74 Pasadena, CA 91125 Zhaoping Li School of Natural Sciences Institute for Advanced Study Princeton, N J 08540 J. Leo van Hemmen Physik-Departme... | 1990 | 96 |
425 | Cholinergic Modulation May Enhance Cortical Associative Memory Function Michael E. Hasselmo· Computation and Neural Systems Brooke P. Anderson t Computation and Neural Systems Caltech 139-74 Pasadena, CA 91125 James M. Bower Computation and Neural Systems Caltech 216-76 Pasaden... | 1990 | 97 |
426 | Neural Network Application to Diagnostics and Control of Vehicle Control Systems Kenneth A. Marko Research Staff Ford Motor Company Dearborn, Michigan 48121 ABSTRACT Diagnosis of faults in complex, real-time control systems is a complicated task that has resisted solution by traditional methods.... | 1990 | 98 |
427 | EVOLUTION AND LEARNING IN NEURAL NETWORKS: THE NUMBER AND DISTRIBUTION OF LEARNING TRIALS AFFECT THE RATE OF EVOLUTION Ron Keesing and David G. Stork* Ricoh California Research Center and *Dept. of Electrical Engineering Stanford University Stanford, CA 94305 stork@psych.stanford.edu ... | 1990 | 99 |
428 | English Alphabet Recognition with Telephone Speech Mark Fanty, Ronald A. Cole and Krist Roginski Center for Spoken Language Understanding Oregon Graduate Institute of Science and Technology 19600 N.W. Von Neumann Dr., Beaverton, OR 97006 Abstract A recognition system is reported which recognizes na... | 1991 | 1 |
429 | Improved Hidden Markov Model Speech Recognition Using Radial Basis Function Networks Elliot Singer and Richard P. Lippmann Lincoln Laboratory, MIT Lexington, MA 02173-9108, USA Abstract A high performance speaker-independent isolated-word hybrid speech recognizer was developed which combines Hidden... | 1991 | 10 |
430 | Green's Function Method for Fast On-line Learning Algorithm of Recurrent Neural Networks Guo-Zheng Sun, Hsing-Hen Chen and Yee-Chun Lee Institute for Advanced Computer Studies and Laboratory for Plasma Research, University of Maryland College Park, MD 20742 Abstract The two well known learnin... | 1991 | 100 |
431 | Constrained Optimization Applied to the Parameter Setting Problem for Analog Circuits David Kirk, Kurt Fleischer, Lloyd Watts~ Alan Barr Computer Graphics 350-74 California Institute of Technology Pasadena, CA 91125 Abstract We use constrained optimization to select operating parameters for two ... | 1991 | 101 |
432 | Fault Diagnosis of Antenna Pointing Systems using Hybrid Neural Network and Signal Processing Models Padhraic Smyth, J eft" Mellstrom Jet Propulsion Laboratory 238-420 California Institute of Technology Pasadena, CA 91109 Abstract We describe in this paper a novel application of neural networks ... | 1991 | 102 |
433 | NETWORK MODEL OF STATE-DEPENDENT SEQUENCING Jeffrey P. Sutton: Adam N. Mamelakt and J. Allan Hobson Laboratory of Neurophysiology and Department of Psychiatry Harvard Medical School 74 Fenwood Road, Boston, MA 02115 Abstract A network model with temporal sequencing and state-dependent modulatory fe... | 1991 | 103 |
434 | Splines, Rational Functions and Neural Networks Robert C. Willialnson Department of Systems Engineering Australian National University Canberra, 2601 Australia Peter L. Bartlett Department of Electrical Engineering University of Queensland Queensland, 4072 Australia Abstract Connecti... | 1991 | 104 |
435 | Learning to Make Coherent Predictions in Domains with Discontinuities Suzanna Becker and Geoffrey E. Hinton Department of Computer Science, University of Toronto Toronto, Ontario, Canada M5S 1A4 Abstract We have previously described an unsupervised learning procedure that discovers spatially cohere... | 1991 | 105 |
436 | Bayesian Model Comparison and Backprop Nets David J.C. MacKay· Computation and Neural Systems California Institute of Technology 139-14 Pasadena CA 91125 mackayGras.phy.cam.ac.uk Abstract The Bayesian model comparison framework is reviewed, and the Bayesian Occam's razor is explained. This frame... | 1991 | 106 |
437 | Networks for the Separation of Sources that are Superimposed and Delayed John C. Platt Federico Faggin Synaptics, Inc. 2860 Zanker Road, Suite 206 San Jose, CA 95134 ABSTRACT We have created new networks to unmix signals which have been mixed either with time delays or via filtering. We first... | 1991 | 107 |
438 | Burst Synchronization Without Frequency-Locking in a Completely Solvable Network Model Heinz Schuster Institut fur theoretische Physik Universitat Kiel OlshausenstraBe 40 2300 Kiel 1, Germany Christof Koch Computation and Neural System Program California Institute of Technology Pasadena... | 1991 | 108 |
439 | Incrementally Learning Time-varying Half-planes Anthony Kuh * Dept. of Electrical Engineering University of Hawaii at Manoa Honolulu, ill 96822 Thomas Petsche t Siemens Corporate Research 755 College Road East Princeton, NJ 08540 Ronald L. Rivest+ Laboratory for Computer Science MIT ... | 1991 | 109 |
440 | Connectionist Optimisation of Tied Mixture Hidden Markov Models Steve Renals Nelson Morgan ICSI Berkeley CA 94704 USA Herve Bourlard L&H Speech products leper B-9800 Belgium Abstract Horacio Franco Michael Cohen SRI International Menlo Park CA 94025 USA Issues relating... | 1991 | 11 |
441 | Oscillatory Neural Fields for Globally Optimal Path Planning Michael Lemmon Dept. of Electrical Engineering University of Notre Dame Notre Dame, Indiana 46556 Abstract A neural network solution is proposed for solving path planning problems faced by mobile robots. The proposed network is a two-d... | 1991 | 110 |
442 | Retinogeniculate Development: The Role of Competition and Correlated Retinal Activity Ron Keesing* Dept. of Physiology U.C. San Francisco San Francisco, CA 94143 keesing@phy.ucsf.edu David G. Stork *Ricoh California Research Center 2882 Sand Hill Rd., Suite 115 Menlo Park, CA 94025 s... | 1991 | 111 |
443 | A Neural Net Model for Adaptive Control of Saccadic Accuracy by Primate Cerebellum and Brainstem Paul Deana, John E. W. Mayhew and Pat Langdon Department of Psychology a and Artificial Intelligence Vision Research Unit, University of Sheffield, Sheffield S10 2TN, England. Abstract Accurate sacca... | 1991 | 112 |
444 | Segmentation Circuits Using Constrained Optimization John G. Harris'" MIT AI Lab 545 Technology Sq., Rm 767 Cambridge, MA 02139 Abstract A novel segmentation algorithm has been developed utilizing an absolutevalue smoothness penalty instead of the more common quadratic regularizer. This functional ... | 1991 | 113 |
445 | Rule Induction through Integrated Symbolic and Subsymbolic Processing Clayton McMillan, Michael C. Mozer, Paul Smolensky Department of Computer Science and Institute of Cognitive Science University of Colorado Boulder, CO 80309-0430 Abstract We describe a neural network, called RufeNet, that lea... | 1991 | 114 |
446 | A comparison between a neural network model for the formation of brain maps and experimental data K. Obermayer Beckman-Institute University of Illinois Urbana, IL 61801 K. Schulten Beckman-Institute University of Illinois Urbana, IL 61801 Abstract G.G. Blasdel Harvard Medical School ... | 1991 | 115 |
447 | A Connectionist Learning Approach to Analyzing Linguistic Stress Prahlad Gupta Department of Psychology Carnegie Mellon University Pittsburgh, PA 15213 Abstract David S. Touretzky School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 We use connectionist modeling to... | 1991 | 116 |
448 | Stationarity of Synaptic Coupling Strength Between Neurons with Nonstationary Discharge Properties Mark R. Sydorenko and Eric D. Young Dept. of Biomedical Engineering & Center for Hearing Sciences The Johns Hopkins School of Medicine 720 Rutland Avenue Baltimore. Maryland 21205 Abstract Based on... | 1991 | 117 |
449 | Time-Warping Network: A Hybrid Framework for Speech Recognition Esther Levin Roberto Pieraccini AT&T Bell Laboratories Speech Research Department Murray Hill, NJ 00974 USA ABSTRACT Enrico Bocchieri Recently. much interest has been generated regarding speech recognition systems based on Hid... | 1991 | 118 |
450 | The Effective Number of Parameters: An Analysis of Generalization and Regularization in Nonlinear Learning Systems John E. Moody Department of Computer Science, Yale University P.O. Box 2158 Yale Station, New Haven, CT 06520-2158 Internet: moody@cs.yale.edu, Phone: (203)432-1200 Abstract We pres... | 1991 | 119 |
451 | Principled Architecture Selection for Neural Networks: Application to Corporate Bond Rating Prediction John Moody Department of Computer Science Yale University P. O. Box 2158 Yale Station New Haven, CT 06520 Joachim U tans Department of Electrical Engineering Yale University P. O. Box ... | 1991 | 12 |
452 | Dual Inhibitory Mechanisms for Definition of Receptive Field Characteristics in Cat Striate Cortex A. B. Bonds Dept. of Electrical Engineering Vanderbilt University N ashville, TN 37235 Abstract In single cells of the cat striate cortex, lateral inhibition across orientation and/or spatial frequ... | 1991 | 120 |
453 | Statistical Reliability of a Blowfly Movement-Sensitive Neuron Rob de Ruyter van Steveninck .. Biophysics Group, Rijksuniversiteit Groningen, Groningen, The Netherlands Abstract William Bialek NEe Research Institute 4 Independence Way, Princeton, N J 08540 We develop a model-independent... | 1991 | 121 |
454 | Networks with Learned Unit Response Functions John Moody and Norman Yarvin Yale Computer Science, 51 Prospect St. P.O. Box 2158 Yale Station, New Haven, CT 06520-2158 Abstract Feedforward networks composed of units which compute a sigmoidal function of a weighted sum of their inputs have been much invest... | 1991 | 122 |
455 | 512 Adaptive Elastic Models for Hand-Printed Character Recognition Geoffrey E. Hinton, Christopher K. I. Williams and Michael D. Revow Department of Computer Science, University of Toronto Toronto, Ontario, Canada M5S lA4 Abstract Hand-printed digits can be modeled as splines that are governed by a... | 1991 | 123 |
456 | Modeling Applications with the Focused Gamma Net Jose C. Principe, Bert de Vries, Jyh-Ming Kuo and Pedro Guedes de Oliveira· Department of Electrical Engineering University of Florida, CSE 447 Gainesville, FL 32611 principe@synapse.ee.ufl.edu Abstract *Departamento EletronicalINESC Universidade ... | 1991 | 124 |
457 | Towards Faster Stochastic Gradient Search Christian Darken and John Moody Yale Computer Science, P.O. Box 2158, New Haven, CT 06520 Email: darken@cs.yale.edu Abstract Stochastic gradient descent is a general algorithm which includes LMS, on-line backpropagation, and adaptive k-means clustering as spec... | 1991 | 125 |
458 | Gradient Descent: Second-Order Momentum and Saturating Error Barak Pearlmutter Department of Psychology P.O. Box llA Yale Station New Haven, CT 06520-7447 pearlmutter-barak@yale.edu Abstract Batch gradient descent, ~w(t) = -7JdE/dw(t), conver~es to a minimum of quadratic form with a time cons... | 1991 | 126 |
459 | Active Exploration in Dynamic Environments Sebastian B. Thrun School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 E-mail: thrun@cs.cmu.edu Knut Moller University of Bonn Dept. of Computer Science ROmerstr. 164 D-5300 Bonn, Germany Abstract \Vhenever an agent le... | 1991 | 127 |
460 | Data Analysis using G/SPLINES David Rogers· Research Institute for Advanced Computer Science MS T041-5, NASA/Ames Research Center Moffett Field, CA 94035 INTERNET: drogerS@riacs.edu Abstract G/SPLINES is an algorithm for building functional models of data. It uses genetic search to discover comb... | 1991 | 128 |
461 | A Contrast Sensitive Silicon Retina with Reciprocal Synapses Kwabena A. Boahen Computation and Neural Systems California Institute of Technology Pasadena, CA 91125 Andreas G. Andreou Electrical and Computer Engineering Johns Hopkins University Baltimore, MD 21218 Abstract The goal of pe... | 1991 | 129 |
462 | Extracting and Learning an Unknown Grammar with Recurrent Neural Networks C.L.Gnes·, C.B. Miller NEC Research Institute 4 Independence Way Princeton. NJ. 08540 giles@research.nj.nec.COOl D. Chen, G.Z. Sun, B.H. Chen, V.C. Lee *Institute for Advanced Computer Studies Dept of Physics and Astron... | 1991 | 13 |
463 | Markov Random Fields Can Bridge Levels of Abstraction Paul R. Cooper Institute for the Learning Sciences Northwestern University Evanston, IL cooper@ils.nwu.edu Peter N. Prokopowicz Institute for the Learning Sciences Northwestern U ni versity Evanston, IL prokopowicz@ils.nwu.edu Abs... | 1991 | 130 |
464 | Information Measure Based Skeletonisation Sowmya Ramachandran Department of Computer Science University of Texas at Austin Austin, TX 78712-1188 Lorien Y. Pratt * Department of Computer Science Rutgers University New Brunswick, NJ 08903 Abstract Automatic determination of proper neural net... | 1991 | 131 |
465 | Interpretation of Artificial Neural Networks: Mapping Knowledge-Based Neural Networks into Rules Geoffrey Towell Jude W. Shavlik Computer Sciences Department U ni versity of Wisconsin Madison, WI 53706 Abstract We propose and empirically evaluate a method for the extraction of expertcomprehensib... | 1991 | 132 |
466 | Competitive Anti-Hebbian Learning of Invariants Nicol N. Schraudolph Computer Science & Engr. Dept. University of California, San Diego La Jolla, CA 92093-0114 nici@cs.ucsd.edu Terrence J. Sejnowski Computational Neurobiology Laboratory The Salk Institute for Biological Studies La Jolla, CA 9... | 1991 | 133 |
467 | 3D Object Recognition Using Unsupervised Feature Extraction Nathan Intrator Center for Neural Science, Brown University Providence, RI 02912, USA Heinrich H. Biilthoff Dept. of Cognitive Science, Brown University, and Center for Biological Information Processing, MIT, Cambridge, MA 0213... | 1991 | 134 |
468 | Information Processing to Create Eye Movements David A. Robinson Departments of Ophthalmology and Biomedical Engineering The Johns Hopkins University School of Medicine Baltimore, MD 21205 ABSTRACT Because eye muscles never cocontract and do not deal with external loads, one can write an equa... | 1991 | 135 |
469 | Propagation Filters in PDS Networks for Sequencing and Ambiguity Resolution Ronald A. Sumida Michael G. Dyer Artificial Intelligence Laboratory Computer Science Department University of California Los Angeles, CA, 90024 sumida@cs.ucla.edu Abstract We present a Parallel Distributed Semantic... | 1991 | 136 |
470 | Neural Network Routing for Random Multistage Interconnection Networks Mark W. Goudreau Princeton University and NEe Research Institute, Inc. 4 Independence Way Princeton, NJ 08540 c. Lee Giles NEC Research Institute, Inc. 4 Independence Way Princeton, NJ 08540 Abstract A routing s... | 1991 | 137 |
471 | Direction Selective Silicon Retina that uses N uIl Inhibition Ronald G. Benson and Tobi Delbriick Computation and Neural Systems Program, 139-74 California Institute of Technology Pasadena CA 91125 email: benson@cns.caltech.edu and tdelbruck@caltech.edu Abstract Biological retinas extract spatia... | 1991 | 138 |
472 | Software for ANN training on a Ring Array Processor Phil Kohn, Jeff Bilmes, Nelson Morgan, James Beck International Computer Science Institute, 1947 Center St., Berkeley CA 94704, USA Abstract Experimental research on Artificial Neural Network (ANN) algorithms requires either writing variations on the... | 1991 | 139 |
473 | Network activity determines spatio-temporal integration in single cells Ojvind Bernander, Christof Koch * Computation and Neural Systems Program, California Institut.e of Technology, Pasadena, Ca 91125, USA. Rodney J. Douglas Anatomical Neuropharmacology Unit, Dept. Pharmacology, Oxford, UK. ... | 1991 | 14 |
474 | The Clusteron: Toward a Simple Abstraction for a Complex Neuron Bartlett W. Mel Computation and Neural Systems Division of Biology Caltech, 216-76 Pasadena, CA 91125 mel@cns.caltech.edu Abstract Are single neocortical neurons as powerful as multi-layered networks? A recent compartmental mo... | 1991 | 140 |
475 | Application of Neural Network Methodology to the Modelling of the Yield Strength in a Steel Rolling Plate Mill Ah Chung Tsoi Department of Electrical Engineering University of Queensland, St Lucia, Queensland 4072, Australia. Abstract In this paper, a tree based neural network viz. MARS (Frie... | 1991 | 141 |
476 | Reverse TDNN: An Architecture for Trajectory Generation Patrice Simard AT &T Bell Laboratories 101 Crawford Corner Rd Holmdel, NJ 07733 Abstract Yann Le Cun AT&T Bell Laboratories 101 Crawford Corner Rd Holmdel, NJ 07733 The backpropagation algorithm can be used for both recognition and... | 1991 | 142 |
477 | Principles of Risk Minimization for Learning Theory V. Vapnik AT &T Bell Laboratories Holmdel, NJ 07733, USA Abstract Learning is posed as a problem of function estimation, for which two principles of solution are considered: empirical risk minimization and structural risk minimization. These two p... | 1991 | 143 |
478 | Networks with Learned Unit Response Functions John Moody and Norman Yarvin Yale Computer Science, 51 Prospect St. P.O. Box 2158 Yale Station, New Haven, CT 06520-2158 Abstract Feedforward networks composed of units which compute a sigmoidal function of a weighted sum of their inputs have been much invest... | 1991 | 144 |
479 | 480 Image Segmentation with Networks of Variable Scales Hans P. Grar Craig R. Nohl AT&T Bell Laboratories Crawfords Comer Road Holmdel, NJ 07733, USA ABSTRACT Jan Ben We developed a neural net architecture for segmenting complex images, i.e., to localize two-dimensional geometrical shapes ... | 1991 | 15 |
480 | Decoding of Neuronal Signals in Visual Pattern Recognition Emad N Eskandar Laboratory of Neuropsychology National Institute of Mental Health Bethesda MD 20892 USA Barry J Richmond Laboratory of Neuropsychology National Institute of Mental Health Bethesda MD 20892 USA John A Hertz NORDIT... | 1991 | 16 |
481 |
! " $# % '&)(*+-, . + / 0!21 + 43 3 5 4687 9;:=<?>A@CB-DEGFHJILK-MNFO PRQSO... | 1991 | 17 |
482 | Learning in Feedforward Networks with Nonsmooth Functions Nicholas J. Redding· Information Technology Division Defence Science and Tech. Org. P.O. Box 1600 Salisbury Adelaide SA 5108 Australia T.Downs Intelligent Machines Laboratory Dept of Electrical Engineering University of Queensland ... | 1991 | 18 |
483 | Constructing Proofs in Symmetric Networks Gadi Pinkas Computer Science Department Washington University Campus Box 1045 St. Louis, MO 63130 Abstract This paper considers the problem of expressing predicate calculus in connectionist networks that are based on energy minimization. Given a firstorder-... | 1991 | 19 |
484 | 504 Recognizing Overlapping Hand-Printed Characters by Centered-Object Integrated Segmentation and Recognition Gale L. Martin- & Mosfeq Rashid MCC Austin, Thxas 78759 USA Abstract This paper describes an approach, called centered object integrated segmentation and recognition (COISR). for integrati... | 1991 | 2 |
485 | A Weighted Probabilistic Neural Network David Montana Bolt Beranek and Newman Inc. 10 Moulton Street Cambridge, MA 02138 Abstract The Probabilistic Neural Network (PNN) algorithm represents the likelihood function of a given class as the sum of identical, isotropic Gaussians. In practice, PNN is of... | 1991 | 20 |
486 | Shooting Craps in Search of an Optimal Strategy for Training Connectionist Pattern Classifiers J. B. Hampshire IT and B. V. K. Vijaya Kumar Department of Electrical & Computer Engineering Carnegie Mellon University Pittsbwgh. PA 15213-3890 hamps@speechl.cs.cmu.edu and kumar@gauss.ece.cmu.e... | 1991 | 21 |
487 | Multimodular Architecture for Remote Sensing Operations. Sylvie Thiria(1,2) Fouad Badran(1,2) Carlos Mejia(l) Michel Crepon(3) (1) Laboratoire de Recherche en Informatique Universite de Paris Sud, B 490 - 91405 ORSAY Cedex France (2) CEDRIC, Conservatoire National des Arts et Metiers 292 rue ... | 1991 | 22 |
488 | Learning in the Vestibular System: Simulations of Vestibular Compensation Using Recurrent Back-Propagation Thomas J. Anastasio University of Dlinois Beckman Institute 405 N. Mathews Ave. Urbana, II... 61801 Abstract Vestibular compensation is the process whereby normal functioning is regai... | 1991 | 23 |
489 | Repeat Until Bored: A Pattern Selection Strategy Paul W. Munro Depamnent of Information Science University of Pittsburgh Pittsburgh, PA 15260 ABSTRACT An alternative to the typical technique of selecting training examples independently from a fixed distribution is fonnulated and analyzed, in whi... | 1991 | 24 |
490 | Refining PIn Controllers using Neural Networks Gary M. Scott Department of Chemical Engineering 1415 Johnson Drive University of Wisconsin Madison, WI 53706 Jude W. Shavlik Department of Computer Sciences 1210 W. Dayton Street University of Wisconsin Madison, WI 53706 W. Harmon Ray D... | 1991 | 25 |
491 | A Cortico-Cerebellar Model that Learns to Generate Distributed Motor Commands to Control a Kinematic Arm N.E. Berthier S.P. Singh A.G. Barto Department of Computer Science University of Massachusetts Amherst, MA 01002 .T.C. Honk Department of Physiology Northwestern University Medical S... | 1991 | 26 |
492 | Linear Operator for Object Recognition Ronen Bssri Shimon Ullman· M.I.T. Artificial Intelligence Laboratory and Department of Brain and Cognitive Science 545 Technology Square Cambridge, MA 02139 Abstract Visual object recognition involves the identification of images of 3-D objects seen from ar... | 1991 | 27 |
493 | LEARNING UNAMBIGUOUS REDUCED SEQUENCE DESCRIPTIONS Jiirgen Schmidhuber Dept. of Computer Science University of Colorado Campus Box 430 Boulder, CO 80309, USA yirgan@cs.colorado.edu Abstract Do you want your neural net algorithm to learn sequences? Do not limit yourself to conventional gradien... | 1991 | 28 |
494 | Dynamically-Adaptive Winner-Take-All Networks Treat E. Laale Artif1cia1IntcUigeoce Laboratory Computer Science Department Univmity of California. Los Angeles. CA 90024 Abstract Winner-Take-All (WTA) networks. in which inhibitory interconnections are used to determine the most highly-activated of a poo... | 1991 | 29 |
495 | Adaptive Soft Weight Tying using Gaussian Mixtures Steven J. Nowlan Computational Neuroscience Laboratory The Salk Institute, P.O . Box 5800 San Diego, CA 92186-5800 Abstract Geoffrey E. Hinton Department of Computer Science . U ni versi ty of Toran to Toronto, Canada M5S lA4 One way of... | 1991 | 3 |
496 | Some Approximation Properties of Projection Pursuit Learning Networks Ying Zhao Christopher G. Atkeson The Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge, MA 02139 Abstract This paper will address an important question in machine learning: What kind of netw... | 1991 | 30 |
497 | Unsupervised learning of distributions on binary vectors using two layer networks Yoav Freund· Computer and Information Sciences University of California Santa Cruz Santa Cruz, CA 95064 Abstract David Haussler Computer and Information Sciences University of California Santa Cruz Santa C... | 1991 | 31 |
498 | Best-First Model Merging for Dynamic Learning and Recognition Stephen M. Omohundro International Computer Science Institute 1947 CenteJ' Street, Suite 600 Berkeley, California 94704 Abstract "Best-first model merging" is a general technique for dynamically choosing the structure of a neural or r... | 1991 | 32 |
499 | Neural Computing with Small Weights Kai-Yeung Siu Dept. of Electrical & Computer Engineering University of California, Irvine Irvine, CA 92717 Abstract J ehoshua Bruck IBM Research Division Almaden Research Center San Jose, CA 95120-6099 An important issue in neural computation is the dyna... | 1991 | 33 |
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