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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500 | A Segment-based Automatic Language Identification System Yeshwant K. Muthusamy & Ronald A. Cole Center for Spoken Language Understanding Oregon Graduate Institute of Science and Technology Beaverton OR 97006-1999 Abstract We have developed a four-language automatic language identification system fo... | 1991 | 34 |
501 | A Topographic Product for the Optimization of Self-Organizing Feature Maps Hans-Ulrich Bauer, Klaus Pawelzik, Theo Geisel Institut fUr theoretische Physik and SFB Nichtlineare Dynamik U niversitat Frankfurt Robert-Mayer-Str. 8-10 W -6000 Frankfurt 11 Fed. Rep. of Germany email: bauer@asgard.phys... | 1991 | 35 |
502 | Learning How To Teach or Selecting Minimal Surface Data Davi Geiger Siemens Corporate Research, Inc 755 College Rd. East Princeton, NJ 08540 USA Ricardo A. Marques Pereira Dipartimento di Informatica Universita di Trento Via Inama 7, Trento, TN 38100 ITALY Abstract Learning a m... | 1991 | 36 |
503 | Visual Grammars and their Neural Nets Eric Mjolsness Department of Computer Science Yale University New Haven, CT 06520-2158 Abstract I exhibit a systematic way to derive neural nets for vision problems. It involves formulating a vision problem as Bayesian inference or decision on a comprehensiv... | 1991 | 37 |
504 | Hierarchical Transformation of Space in the Visual System Alexandre Pouget Stephen A. Fisher Terrence J. Sejnowski Computational Neurobiology Laboratory The Salk Institute La Jolla, CA 92037 Abstract Neurons encoding simple visual features in area VI such as orientation, direction of motio... | 1991 | 38 |
505 | Illumination and View Position in 3D Visual Recognition Amnon Shashua M.LT. Artificial Intelligence Lab., NE43-737 and Department of Brain and Cognitive Science Cambridge, MA 02139 Abstract It is shown that both changes in viewing position and illumination conditions can be compensated for, prior t... | 1991 | 39 |
506 | Multi-State Time Delay Neural Networks for Continuous Speech Recognition Patrick Haffner CNET Lannion A TSSIRCP 22301 LANNION, FRANCE haffner@lannion.cnet.fr Abstract Alex Waibel Carnegie Mellon University Pittsburgh, PA 15213 ahw@cs.cmu.edu We present the "Multi-State Time Delay Neural... | 1991 | 4 |
507 | Constant-Time Loading of Shallow 1-Dimensional Networks Stephen Judd Siemens Corporate Research, 755 College Rd. E., Princeton, NJ 08540 judd@learning.siemens.com Abstract The complexity of learning in shallow I-Dimensional neural networks has been shown elsewhere to be linear in the size of ... | 1991 | 40 |
508 | Combined Neural Network and Rule-Based Framework for Probabilistic Pattern Recognition and Discovery Hayit K. Greenspan and Rodney Goodman Department of Electrical Engineering California Institute of Technology, 116-81 Pasadena, CA 91125 Rama Chellappa Department of Electrical Engineering Ins... | 1991 | 41 |
509 | Experimental Evaluation of Learning in a Neural Microsystem Joshua Alspector Anthony Jayakumar Stephan Lunat Bellcore Morristown, NJ 07962-1910 Abstract We report learning measurements from a system composed of a cascadable learning chip, data generators and analyzers for training pattern presentation... | 1991 | 42 |
510 | A Neural Network for Motion Detection of Drift-Balanced Stimuli Hilary Tunley* School of Cognitive and Computer Sciences Sussex University Brighton, England. Abstract This paper briefly describes an artificial neural network for preattentive visual processing. The network is capable of determiui... | 1991 | 43 |
511 | Against Edges: Function Approximation with Multiple Support Maps Trevor Darrell and Alex Pentland Vision and Modeling Group, The Media Lab Massachusetts Institute of Technology E15-388, 20 Ames Street Cambridge MA, 02139 Abstract Networks for reconstructing a sparse or noisy function often use a... | 1991 | 44 |
512 | Induction of Multiscale Temporal Structure Michael C. Moser Department of Computer Science &: Institute of Cognitive Science University of Colorado Boulder, CO 80309-0430 Abstract Learning structure in temporally-extended sequences is a difficult computational problem because only a fraction of the... | 1991 | 45 |
513 | Locomotion in a Lower Vertebrate: Studies of the Cellular Basis of Rhythmogenesis and Oscillator Coupling James T. Buchanan Department of Biology Marquette University Milwaukee, WI 53233 Abstract To test whether the known connectivies of neurons in the lamprey spinal cord are sufficient to ac... | 1991 | 46 |
514 | SINGLE NEURON MODEL: RESPONSE TO WEAK MODULATION IN THE PRESENCE OF NOISE A. R. Bu/,ara and E. W. Jaco6, Naval Ocean Syat.em.a Cenw, Materials Reaean:h Branch, San Diego, CA 92129 F.Mou Physics Dept.., Univ. of Missouri, St. Louis, MO 63121 ABSTRACT We consider a noisy bist.able single neuron model... | 1991 | 47 |
515 | Fast Learning with Predictive Forward Models Carlos Brody· Dept. of Computer Science lIMAS, UNAM Mexico D.F. 01000 Mexico. e-mail: carlos@hope. caltech. edu Abstract A method for transforming performance evaluation signals distal both in space and time into proximal signals usable by supervis... | 1991 | 48 |
516 | Nonlinear Pattern Separation in Single Hippocampal Neurons with Active Dendritic Membrane Anthony M. Zador t Brenda J. Claiborne § t Thomas H. Brown t Depts. of Psychology and Cellular §Division of Life Sciences & Molecular Physiology Yale University New Haven, CT 06511 zador@yale.edu ... | 1991 | 49 |
517 | Neural Network - Gaussian Mixture Hybrid for Speech Recognition or Density Estimation Yoshua Bengio Dept. Brain and Cognitive Sciences Massachusetts Institute of Technology Cambridge, MA 02139 Renato De Morl School of Computer Science McGill University Canada Ralf Kompe Giovanni Flammia... | 1991 | 5 |
518 | A Neural Network for Motion Detection of Drift-Balanced Stimuli Hilary Tunley* School of Cognitive and Computer Sciences Sussex University Brighton, England. Abstract This paper briefly describes an artificial neural network for preattentive visual processing. The network is capable of determiui... | 1991 | 50 |
519 | Generalization Performance in PARSEC-A Structured Connectionist Parsing Architecture Ajay N. Jain· School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213-3890 ABSTRACT This paper presents PARSEC-a system for generating connectionist parsing networks from example parses. PARSE... | 1991 | 51 |
520 | Hierarchies of adaptive experts Michael I. Jordan Robert A. Jacobs Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Cambridge, MA 02139 Abstract In this paper we present a neural network architecture that discovers a recursive decomposition of its input space. Bas... | 1991 | 52 |
521 | Optical Implementation of a Self·Organizing Feature Extractor Dana Z. Anderson*, Claus Benkert, Verena Hebler, Ju-Seog Jang, Don Montgomery, and Mark Saffinan. Joint Institute for Laboratory Astrophysics, University of Colorado and the Department of Physics, University of Colorado, Boulder Colorado 80309... | 1991 | 53 |
522 | A Neurocomputer Board Based on the ANNA Neural Network Chip Eduard Sackinger, Bernhard E. Boser, and Lawrence D. Jackel AT&T Bell Laboratories Crawfords Corner Road, Holmdel, NJ 07733 Abstract A board is described that contains the ANN A neural-network chip, and a DSP32C digital signal processor. T... | 1991 | 54 |
523 | Adaptive Synchronization of Neural and Physical Oscillators Kenji Doya University of California, San Diego La Jolla, CA 92093-0322, USA Abstract Shuji Yoshizawa University of Tokyo Bunkyo-ku, Tokyo 113, Japan Animal locomotion patterns are controlled by recurrent neural networks called cen... | 1991 | 55 |
524 | Recurrent Networks and N ARMA Modeling Jerome Connor Les E. Atlas FT-lO Interactive Systems Design Laboratory Dept. of Electrical Engineering University of Washington Seattle, Washington 98195 Abstract Douglas R. Martin B-317 Dept. of Statistics University of Washington Seattle, W... | 1991 | 56 |
525 | ANN Based Classification for Heart Defibrillators M. Jabri, S. Pickard, P. Leong, Z. Chi, B. Flower, and Y. Xie Sydney University Electrical Engineering NSW 2006 Australia Abstract Current Intra-Cardia defibrillators make use of simple classification algorithms to determine patient conditions and subsequ... | 1991 | 57 |
526 | Adaptive Development of Connectionist Decoders for Complex Error-Correcting Codes Sheri L. Gish Mario Blalull IBM Rf'search Division Almaden Research Center 650 Harry Road San Jose, C A 95120 Abstract \Ve present. an approach for df'velopment of a decoder for any complex binary error-corre... | 1991 | 58 |
527 | Self-organisation in real neurons: Anti-Hebb in 'Channel Space'? Anthony J. Bell AI-lab, Vrije U niversiteit Brussel Pleinlaan 2, B-I050 Brussels BELGIUM, (tony@arti.vub.ac.be) Abstract Ion channels are the dynamical systems of the nervous system. Their distribution within the membrane govern... | 1991 | 59 |
528 | Threshold Network Learning in the Presence of Equivalences John Shawe-Taylor Department of Computer Science Royal Holloway and Bedford New College University of London Egham, Surrey TW20 OEX, UK Abstract This paper applies the theory of Probably Approximately Correct (PAC) learning to multipl... | 1991 | 6 |
529 | Tangent Prop - A formalism for specifying selected invariances in an adaptive network Patrice Simard AT&T Bell Laboratories 101 Crawford Corner Rd Holmdel, NJ 07733 Yann Le Cun AT&T Bell Laboratories 101 Crawford Corner Rd Holmdel, NJ 07733 Abstract Bernard Victorri Universite de Cae... | 1991 | 60 |
530 | Human and Machine 'Quick Modeling' Karl Gustafson Jakob Bernasconi Asea Brown Boveri Ltd Corporate Research CH-5405 Baden, SWITZERLAND University of Colorado Department of Mathematics and Optoelectronic Computing Center Boulder, CO 80309 ABSTRACT We present here an interesting experi... | 1991 | 61 |
531 | Practical Issues in Temporal Difference Learning Gerald Tesauro IBM Thomas J. Watson Research Center P. O. Box 704 Yorktown Heights, NY 10598 tesauro@watson.ibm.com Abstract This paper examines whether temporal difference methods for training connectionist networks, such as Suttons's TO('\) algo... | 1991 | 62 |
532 | A Network of Localized Linear Discriminants Martin S. Glassman Siemens Corporate Research 755 College Road East Princeton, NJ 08540 msg@siemens.siemens.com Abstract The localized linear discriminant network (LLDN) has been designed to address classification problems containing relatively closely... | 1991 | 63 |
533 | Multi-Digit Recognition Using A Space Displacement Neural Network Ofer Matan*, Christopher J.C. Burges, Yann Le Cun and John S. Denker AT&T Bell Laboratories, Holmdel, N. J. 07733 Abstract We present a feed-forward network architecture for recognizing an unconstrained handwritten multi-digit string. T... | 1991 | 64 |
534 | JANUS: Speech-to-Speech Translation Using Connectionist and Non-Connectionist Techniques Alex Waibel· Ajay N. Jain t Arthur McNair Joe Tebelskis School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 Louise OsterhoItz Computational Linguistics Program Carnegie Mellon Unive... | 1991 | 65 |
535 | A Parallel Analog CCD/CMOS Signal Processor Charles F. Neugebauer Amnon Yariv Department of Applied Physics California Institute of Technology Pasadena, CA 91125 Abstract A CCO based signal processing IC that computes a fully parallel single quadrant vector-matrix multiplication has been designe... | 1991 | 66 |
536 | Iterative Construction of Sparse Polynomial Approximations Terence D. Sanger Massachusetts Institute of Technology Room E25-534 Cambridge, MA 02139 tds@ai.mit.edu Richard S. Sutton GTE Laboratories Incorporated 40 Sylvan Road Waltham, MA 02254 sutton@gte.com Christopher J. Math... | 1991 | 67 |
537 | A Computational Mechanism To Account For Averaged Modified Hand Trajectories Ealan A. Henis*and Tamar Flash Department of Applied Mathematics and Computer Science The Weizmann Institute of Science Rehovot 76100, Israel Abstract Using the double-step target displacement paradigm the mechanisms under... | 1991 | 68 |
538 | Learning Global Direct Inverse Kinematics David DeMers· Computer Science & Eng. UC San Diego La Jolla, CA 92093-0114 Abstract Kenneth Kreutz-Delgado t Electrical & Computer Eng. UC San Diego La Jolla, CA 92093-0407 We introduce and demonstrate a bootstrap method for construction of an inve... | 1991 | 69 |
539 | Node Splitting: A Constructive Algorithm for Feed-Forward Neural Networks 1072 Mike Wynne-Jones Research Initiative in Pattern Recognition St. Andrews Road, Great Malvern WR14 3PS, UK mikewj@hermes.mod.uk Abstract A constructive algorithm is proposed for feed-forward neural networks, which... | 1991 | 7 |
540 | 420 VISIT: A Neural Model of Covert Visual Attention Subutai AhmadSiemens Research and Development, ZFE ST SN6, Otto-Hahn Ring 6, 8000 Munich 83, Germany. ahmad~bsUD4Gztivax.siemens.eom Abstract Visual attention is the ability to dynamically restrict processing to a subset of the visual field... | 1991 | 70 |
541 | Kernel Regression and Backpropagation Training with Noise Petri Koistinen and Lasse Holmstrom Rolf Nevanlinna Institute, University of Helsinki Teollisuuskatu 23, SF-0051O Helsinki, Finland Abstract One method proposed for improving the generalization capability of a feedforward network trained with t... | 1991 | 71 |
542 | Recognition of Manipulated Objects by Motor Learning Hiroaki Gomi Mitsuo Kawato A TR Auditory and Visual Perception Research Laboratories, Inui-dani, Sanpei-dani, Seika-cho, Soraku-gun, Kyoto 619-02, Japan Abstract We present two neural network controller learning schemes based on feedbackerror-lea... | 1991 | 72 |
543 | Temporal Adaptation • In a Silicon Auditory Nerve John Lazzaro CS Division UC Berkeley 571 Evans Hall Berkeley, CA 94720 Abstract Many auditory theorists consider the temporal adaptation of the auditory nerve a key aspect of speech coding in the auditory periphery. Experiments with models ... | 1991 | 73 |
544 | Oscillatory Model of Short Term Memory David Horn School of Physics and Astronomy Raymond and Beverly Sackler Faculty of Exact Sciences Tel-Aviv University Tel Aviv 69978, Israel Marius U sher* Dept. of Applied Mathematics and Computer Science Weizmann Institute of Science Rehovot 76100... | 1991 | 74 |
545 | Estimating Average-Case Learning Curves Using Bayesian, Statistical Physics and VC Dimension Methods David Haussler University of California Santa Cruz, California Manfred Opper Institut fur Theoretische Physik Universita.t Giessen, Germany Abstract Michael Kearns· AT&T Bell Laboratorie... | 1991 | 75 |
546 | Operators and curried functions: Training and analysis of simple recurrent networks Janet Wiles Depts of Psychology and Computer Science, University of Queensland QLD 4072 Australia. janetw@CS.uq.oz.au Abstract Anthony Bloesch, Dept of Computer Science, University of Queensland, QLD 407... | 1991 | 76 |
547 | The Efficient Learning of Multiple Task Sequences Satinder P. Singh Department of Computer Science University of Massachusetts Amherst, MA 01003 Abstract I present a modular network architecture and a learning algorithm based on incremental dynamic programming that allows a single learning agent... | 1991 | 77 |
548 | Benchmarking Feed-Forward Neural Networks: Models and Measures Leonard G. C. Harney Computing Discipline Macquarie University NSW2109 AUSTRALIA Abstract Existing metrics for the learning performance of feed-forward neural networks do not provide a satisfactory basis for comparison because the... | 1991 | 78 |
549 | CCD Neural Network Processors for Pattern Recognition Alice M. Chiang Michael L. Chuang Jeffrey R. LaFranchise MIT Lincoln Laboratory 244 Wood Street Lexington, MA 02173 Abstract A CCD-based processor that we call the NNC2 is presented. The NNC2 implements a fully connected 192-input, 32-o... | 1991 | 79 |
550 | Structural Risk Minimization for Character Recognition I. Guyon, V. Vapnik, B. Boser, L. Bottou, and S. A. Solla AT&T Bell Laboratories Holmdel, NJ 07733, USA Abstract The method of Structural Risk Minimization refers to tuning the capacity of the classifier to the available amount of training data... | 1991 | 8 |
551 | A Simple Weight Decay Can Improve Generalization Anders Krogh· CONNECT, The Niels Bohr Institute Blegdamsvej 17 DK-2100 Copenhagen, Denmark krogh@cse.ucsc.edu John A. Hertz Nordita Blegdamsvej 17 DK-2100 Copenhagen, Denmark hertz@nordita.dk Abstract It has been observed in numeric... | 1991 | 80 |
552 | Neural Network Diagnosis of Avascular Necrosis from Magnetic Resonance Images Armando Manduca Dept. of Physiology and Biophysics Mayo Clinic Rochester, MN 55905 Paul Christy Dept. of Diagnostic Radiology Mayo Clinic Rochester, MN 55905 Richard Ehman Dept. of Diagnostic Radiology Mayo... | 1991 | 81 |
553 | Obstacle Avoidance through Reinforcement Learning Tony J. Prescott and John E. W. Maybew Artificial Intelligence and Vision Research Unit. University of Sheffield. S 10 2TN. England. Abstract A method is described for generating plan-like. reflexive. obstacle avoidance behaviour in a mobile robot. ... | 1991 | 82 |
554 | The VC-Dimension versus the Statistical Capacity of Multilayer Networks Chuanyi Ji "and Demetri Psaltis Department of Electrical Engineering California Institute of Technology Pasadena, CA 91125 Abstract A general relationship is developed between the VC-dimension and the statistical lower epsil... | 1991 | 83 |
555 | Improving the Performance of Radial Basis Function Networks by Learning Center Locations Dietrich Wettschereck Department of Computer Science Oregon State University Corvallis, OR 97331-3202 Thomas Dietterich Department of Computer Science Oregon State University Corvallis, OR 97331-3202 A... | 1991 | 84 |
556 | MODELS WANTED: MUST FIT DIMENSIONS OF SLEEP AND DREAMING* J. Allan Hohson, Adam N. Mamelakt and Jeffrey P. Suttont Laboratory of Neurophysiology and Department of Psychiatry Harvard Medical School 74 Fenwood Road, Boston, MA 02115 Abstract During waking and sleep, the brain and mind undergo a tight... | 1991 | 85 |
557 | Merging Constrained Optimisation with Deterministic Annealing to "Solve" Combinatorially Hard Problems Paul Stolorz· Santa Fe Institute 1660 Old Pecos Trail, Suite A Santa Fe, NM 87501 ABSTRACT Several parallel analogue algorithms, based upon mean field theory (MFT) approximations to an under... | 1991 | 86 |
558 | Neural Control for Rolling Mills: Incorporating Domain Theories to Overcome Data Deficiency Martin Roscheisen Computer Science Dept. Munich Technical University 8 Munich 40, FRG Reimar Hofmann Computer Science Dept. Edinburgh University Edinburgh, EH89A, UK Volker Tresp Corporate R&D ... | 1991 | 87 |
559 | Polynomial Uniform Convergence of Relative Frequencies to Probabilities Alberto Bertoni, Paola Carnpadelli~ Anna Morpurgo, Sandra Panizza Dipartimento di Scienze dell'Informazione Universita degli Studi di Milano via Comelico, 39 - 20135 Milano - Italy Abstract We define the concept of polynomial u... | 1991 | 88 |
560 | Forward Dynamics Modeling of Speech Motor Control Using Physiological Data Makoto Hirayama Eric Vatikiotis-Bateson Mitsuo Kawato A TR Auditory and Visual Perception Research Laboratories 2 - 2, Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-02, JAPAN Michael I. Jordan Department of Brain and Cog... | 1991 | 89 |
561 | 706 Computer Recognition of Wave Location in Graphical Data by a Neural Network Donald T. Freeman School of Medicine University of Pittsburgh Pittsburgh. PA 15261 Abstract Five experiments were performed using several neural network architectures to identify the location of a wave in the time... | 1991 | 9 |
562 | Learning to Segment Images Using Dynamic Feature Binding Michael C. Moser Dept. of Compo Science & Inst. of Cognitive Science University of Colorado Boulder, CO 80309-0430 Richard S. Zemel Dept. of Compo Science University of Toronto Toronto, Ontario Canada M5S lA4 Marlene Behrmann ... | 1991 | 90 |
563 | Simulation of Optimal Movements Using the Minimum-Muscle-Tension-Change Model. Menashe Dornay* Yoji Uno" Mitsuo Kawato * Ryoji Suzuki** ·Cognitive Processes Department, A TR Auditory and Visual Perception Research Laboratories, Sanpeidani, Inuidani, Seika-Cho, Soraku-Gun, Kyoto 619-02 Japan. ··D... | 1991 | 91 |
564 | Neural Network Analysis of Event Related Potentials and Electroencephalogram Predicts Vigilance Rita Venturini William W. Lytton Terrence J. Sejnowski Computational Neurobiology Laboratory The Salk Institute La J oBa, CA 92037 Abstract Automated monitoring of vigilance in attention intensi... | 1991 | 92 |
565 | HARMONET: A Neural Net for Harmonizing Chorales in the Style of l.S.Bach Hermann Hild Johannes Feulner Wolfram Menzel hhild@ira.uka.de johannes@ira.uka.de menzel@ira.uka.de Institut fur Logik, Komplexitat und Deduktionssysteme Am Fasanengarten 5 Universitat Karlsruhe W-7500 Karlsruhe 1,... | 1991 | 93 |
566 | A Self-Organizing Integrated Segmentation And Recognition Neural Net Jim Keeler * MCC 3500 West Balcones Center Drive Austin, TX 78729 Abstract David E. Rumelhart Psychology Department Stanford University Stanford, CA 94305 We present a neural network algorithm that simultaneously perfo... | 1991 | 94 |
567 | A Comparison of Projection Pursuit and Neural Network Regression Modeling Jellq-Nellg Hwang, Hang Li, Information Processing Laboratory Dept. of Elect. Engr., FT-lO University of Washington Seattle WA 98195 Martin Maechler, R. Douglas Martin, Jim Schimert Department of Statistics Mail Stop: G... | 1991 | 95 |
568 | 1096 Unsupervised Classifiers, Mutual Information and 'Phantom Targets' John s. Bridle Anthony J .R. Heading Defence Research Agency St. Andrew's Road, Malvern ""orcs. "\VR14 3PS, U.K. David J.e. MacKay California Institute of Technology 139-74 Pasadena CA 91125 U.S.A Abstract We der... | 1991 | 96 |
569 | Fast, Robust Adaptive Control by Learning only Forward Models Andrew W. Moore MIT Artificial Intelligence Laboratory 545 Technology Square, Cambridge, MA 02139 awmGai.JD.it.edu Abstract A large class of motor control tasks requires that on each cycle the controller is told its current state and mus... | 1991 | 97 |
570 | Induction of Finite-State Automata Using Second-Order Recurrent Networks Raymond L. Watrous Siemens Corporate Research 755 College Road East, Princeton, NJ 08540 Gary M. Kuhn Center for Communications Research, IDA Thanet Road, Princeton, NJ 08540 Abstract Second-order recurrent networks that... | 1991 | 98 |
571 | Network generalization for production: Learning and producing styled letterforms Igor Grebert 541 Cutwater Ln. David G. Stork Ricoh Calif. Research Cen. 2882 Sand Hill Rd.# 115 Menlo Park, CA 94025 Ron Keesing Dept. Physiology U. C. S. F. Steve Mims Electrical Engin. Foster City, ... | 1991 | 99 |
572 | Parameterising Feature Sensitive Cell Formation in Linsker Networks in the Auditory System Lance C. Walton University of Kent at Canterbury Canterbury Kent England David L. Bisset University of Kent at Canterbury Canterbury Kent England Abstract This paper examines and extends ... | 1992 | 1 |
573 | Rational Parametrizations of Neural Networks Uwe Helmke Department of Mathematics University of Regensburg Regensburg 8400 Germany Robert C. Williamson Department of Systems Engineering Australian National University Canberra 2601 Australia Abstract A connection is drawn between rationa... | 1992 | 10 |
574 | Mapping Between Neural and Physical Activities of the Lobster Gastric Mill Kenji Doya Mary E. T. Boyle Allen I. Selverston Department of Biology University of California, San Diego La Jolla, CA 92093-0322 Abstract A computer model of the musculoskeletal system of the lobster gastric mill w... | 1992 | 100 |
575 | A Fast Stochastic Error-Descent Algorithm for Supervised Learning and Optimization Gert Cauwenberghs California Institute of Technology Mail-Code 128-95 Pasadena, CA 91125 E-mail: gert(Qcco. cal tech. edu Abstract A parallel stochastic algorithm is investigated for error-descent learning a... | 1992 | 101 |
576 | Nets with Unreliable Hidden Nodes Learn Error-Correcting Codes Stephen Judd Siemens Corporate Research 755 College Road East Princeton NJ 08540 jUdd@learning.siemens.com Paul W. Munro Department of Infonnation Science University of Pittsburgh Pittsburgh, PA 15260 munro@lis.pitt.edu A... | 1992 | 102 |
577 | Unsmearing Vistlal Motion: Development of Long-Range Horizolltal Intrinsic Conllections Kevin E. Martin Jonathan A. Marshall Department of Computer Science, CB 3175, Sitterson Hall University of North Carolina, Chapel Hill, NC 27599-3175, U.S.A. Abstract Human VlSlon systems integrate informatio... | 1992 | 103 |
578 | Remote Sensing Image Analysis via a Texture Classification Neural Network Hayit K. Greenspan and Rodney Goodman Department of Electrical Engineering California Institute of Technology, 116-81 Pasadena, CA 91125 hayit@electra.micro.caltech.edu Abstract In this work we apply a texture classificati... | 1992 | 104 |
579 | Filter Selection Model for Generating Visual Motion Signals Steven J. Nowlan· CNL, The Salk Institute P.O. Box 85800, San Diego, CA 92186-5800 Terrence J. Sejnowski CNL, The Salk Institute P.O. Box 85800, San Diego, CA 92186-5800 Abstract Neurons in area MT of primate visual cortex enco... | 1992 | 105 |
580 | N on-Linear Dimensionality Reduction David DeMers· & Garrison CottreUt Dept. of Computer Science & Engr., 0114 Institute for Neural Computation University of California, San Diego 9500 Gilman Dr. La Jolla. CA, 92093-0114 Abstract A method for creating a non-linear encoder-decoder for multidimens... | 1992 | 106 |
581 | Feudal Reinforcement Learning Peter Dayan CNL The Salk Institute PO Box 85800 San Diego CA 92186-5800, USA dayan~helmholtz.sdsc.edu Geoffrey E Hinton Department of Computer Science University of Toronto 6 Kings College Road, Toronto, Canada M5S 1A4 hinton~ai.toronto.edu Abstract ... | 1992 | 107 |
582 | A Neural Network that Learns to Interpret Myocardial Planar Thallium Scintigrams Charles Rosenberg, Ph.D: Department of Computer Science Hebrew University Jerusalem, Israel Jacob Erel, M.D. Department of Cardiology Sapir Medical Center Meir General Hospital Kfar Saba, Israel Henri Atlan... | 1992 | 108 |
583 | Learning Cellular Automaton Dynamics with Neural Networks N H Wulff* and J A Hertz t CONNECT, the Niels Bohr Institute and Nordita Blegdamsvej 17, DK-2100 Copenhagen 0, Denmark Abstract We have trained networks of E - II units with short-range connections to simulate simple cellular automata that exhi... | 1992 | 109 |
584 | Directional-Unit Boltzmann Machines Richard S. Zemel Computer Science Dept. Christopher K. I. Williams Computer Science Dept. Michael C. Mozer Computer Science Dept. University of Toronto Toronto, ONT M5S lA4 University of Toronto Toronto, ONT M5S lA4 University of Colorado Boulder, ... | 1992 | 11 |
585 | Probability Estimation from a Database Using a Gibbs Energy Model John W. Miller Microsoft Research (9/1051) One Microsoft Way Redmond, WA 98052 Rodney M. Goodman Dept. of Electrical Engineering (116-81) California Institute of Technology Pasadena, CA 91125 Abstract We present an algori... | 1992 | 110 |
586 | Word Space Hinrich Schiitze Center for the Study of Language and Information Ventura Hall Stanford, CA 94305-4115 Abstract Representations for semantic information about words are necessary for many applications of neural networks in natural language processing. This paper describes an efficient, c... | 1992 | 111 |
587 | An Analog VLSI Chip for Radial Basis Functions J aneen Anderson .lohn C. Platt Synaptics, Inc. 2698 Orchard Parkway San Jose, CA 95134 Abstract David B. Kirk'" We have designed, fabricated, and tested an analog VLSI chip which computes radial basis functions in parallel. We have developed a s... | 1992 | 112 |
588 | Predicting Complex Behavior in Sparse Asymmetric Networks An A. Minai and William B. Levy Department of Neurosurgery Box 420. Health Sciences Center University of Virginia Charlottesville. V A 22908 Abstract Recurrent networks of threshold elements have been studied intensively as associative me... | 1992 | 113 |
589 | Destabilization and Route to Chaos in Neural Networks with Random Connectivity Bernard Doyon Unite INSERM 230 Service de Neurologie CHUPurpan F-31059 Toulouse Cedex, France Mathias Quoy Centre d'Etudes et de Recherches de Toulouse 2, avenue Edouard Belin, BP 4025 F-31055 Toulouse Ced... | 1992 | 114 |
590 | Recognition-based Segmentation of On-line Hand-printed Words M. Schenkel*, H. Weissman, I. Guyon, C. Nohl, D. Henderson AT&T Bell Laboratories, Holmdel, NJ 07733 * Swiss Federal Institute of Technology, CH-8092 Zurich Abstract This paper reports on the performance of two methods for recognition-bas... | 1992 | 115 |
591 | Some Estimates of Necessary Number of Connections and Hidden Units for Feed-Forward Networks Adam Kowalczyk Telecom Australia, Research Laboratories 770 Blackburn Road, Clayton, Vic. 3168, Australia (a.kowalczyk@trl.oz.au) Abstract The feed-forward networks with fixed hidden units (FllU-networks... | 1992 | 116 |
592 | The Power of Approximating: a Comparison of Activation Functions Bhaskar DasGupta Department of Computer Science University of Minnesota Minneapolis, MN 55455-0159 email: dasgupta~cs.umn.edu Georg Schnitger Department of Computer Science The Pennsylvania State University University Park... | 1992 | 117 |
593 | Neural Network Model Selection Using Asymptotic Jackknife Estimator and Cross-Validation Method Yong Liu Department of Physics and Institute for Brain and Neural Systems Box 1843, Brown University Providence, RI, 02912 Abstract Two theorems and a lemma are presented about the use of jackknife... | 1992 | 118 |
594 | Context-Dependent Multiple Distribution Phonetic Modeling with MLPs Michael Cohen SRI International Menlo Park. CA 94025 Horacio Franco Nelson Morgan SRl International IntI. Computer Science Inst. David Rumelhart Stanford University Stanford, CA 94305 Abstract Berkeley, CA 9470... | 1992 | 119 |
595 | STIMULUS ENCODING BY MUL TIDIMENSIONAL RECEPTIVE FIELDS IN SINGLE CELLS AND CELL POPULATIONS IN VI OF A WAKE MONKEY Edward Stern Center for Neural Computation and Department of Neurobiology Life Sciences Institute Hebrew University Jerusalem, Israel Eilon Vaadia Center for Neural Comput... | 1992 | 12 |
596 | On the Use of Evidence in Neural Networks David H. Wolpert The Santa Fe Institute 1660 Old Pecos Trail Santa Fe, NM 87501 Abstract The Bayesian "evidence" approximation has recently been employed to determine the noise and weight-penalty terms used in back-propagation. This paper shows that for ... | 1992 | 120 |
597 | Automatic Capacity Tuning of Very Large VC-dimension Classifiers I. Guyon AT&T Bell Labs, 50 Fremont st., 6th floor, San Francisco, CA 94105 isabelle@neural.att.com B. Boser· EECS Department, University of California, Berkeley, CA 94720 boser@eecs.berkeley.edu V. Vapnik AT&T Bell ... | 1992 | 121 |
598 | Interposing an ontogenic model between Genetic Algorithms and Neural Networks Richard K. Belew rikGcs.ucsd.edu Cognitive Computer Science Research Group Computer Science & Engr. Dept. (0014) University of California - San Diego La Jolla, CA 92093 Abstract The relationships between learning, d... | 1992 | 122 |
599 | Bayesian Learning via Stochastic Dynamics Radford M. Neal Department of Computer Science University of Toronto Toronto, Ontario, Canada M5S lA4 Abstract The attempt to find a single "optimal" weight vector in conventional network training can lead to overfitting and poor generalization. Bayesian me... | 1992 | 123 |
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