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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600 | Biologically Plausible Local Learning Rules for the Adaptation of the Vestibulo-Ocular Reflex Olivier Coenen* Terrence J. Sejnowski Computational Neurobiology Laboratory Howard Hughes Medical Institute The Salk Institute P.O.Box 85800 San Diego, CA 92186-5800 Stephen G. Lisberger Departmen... | 1992 | 124 |
601 | Combining Neural and Symbolic Learning to Revise Probabilistic Rule Bases J. Jeffrey Mahoney and Raymond J. Mooney Dept. of Computer Sciences University of Texas Austin, TX 78712 mahoney@cs.utexas.edu, mooney@cs.utexas.edu Abstract This paper describes RAPTURE a system for revising probabilis... | 1992 | 125 |
602 | Transient Signal Detection with Neural Networks: The Search for the Desired Signal Jose C. Principe and Abir Zahalka Computational NeuroEngineering Laboratory Department of Electrical Engineering University of Florida, CSE 447 Gainesville, FL 32611 principe@synapse.ee.ufl.edu Abstract Matched... | 1992 | 126 |
603 | Information Theoretic Analysis of Connection Structure from Spike Trains Satoru Shiono· Cen tral Research Laboratory Mi tsu bishi Electric Corporation Amagasaki, Hyogo 661, Japan Michio Nakashima Cen tral Research Laboratory Mi tsu bishi Electric Corporation Amagasaki, Hyogo 661, Japan Sat... | 1992 | 127 |
604 | On the Use of Projection Pursuit Constraints for Training Neural Networks Nathan Illtl'ator'" Comput.er Science Department Tel-Aviv Universit.y Ramat.-A viv, 69978 ISRAEL and Inst.itute for Brain and Neural Systems, Brown University nin~math,tau.ac.il Abstract \Ve present a novel classi... | 1992 | 13 |
605 | Assessing and Improving Neural Network Predictions by the Bootstrap Algorithm Gerhard Paass German National Research Center for Computer Science (GMD) D-5205 Sankt Augustin, Germany e-mail: paass<Dgmd.de Abstract The bootstrap algorithm is a computational intensive procedure to derive nonparamet... | 1992 | 14 |
606 | Self-Organizing Rules for Robust Principal Component Analysis Lei Xu l ,2"'and Alan Yuillel 1. Division of Applied Sciences, Harvard University, Cambridge, MA 02138 2. Dept. of Mathematics, Peking University, Beijing, P.R.China Abstract In the presence of outliers, the existing self-organizing rules f... | 1992 | 15 |
607 | Intersecting regions: The key to combinatorial structure in hidden unit space Janet Wiles Depts of Psychology and Computer Science, University of Queensland QLD 4072 Australia. janetw@cs.uq.oz.au Mark Ollila, Vision Lab, CITRI Dept of Computer Science, University of Melbourne, Vic 30... | 1992 | 16 |
608 | A Parallel Gradient Descent Method for Learning in Analog VLSI Neural Networks J. Alspector R. Meir'" B. Yuhas A. Jayakumar D. Lippet Bellcore Morristown, NJ 07962-1910 Abstract Typical methods for gradient descent in neural network learning involve calculation of derivatives based on a detai... | 1992 | 17 |
609 | A Model of Feedback to the Lateral Geniculate Nucleus Carlos D. Brody Computation and Neural Systems Program California Institute of Technology Pasadena, CA 91125 Abstract Simplified models of the lateral geniculate nucles (LGN) and striate cortex illustrate the possibility that feedback to the LG ... | 1992 | 18 |
610 | History-dependent Attractor Neural Networks Isaac Meilijson Eytan Ruppin School of Mathematical Sciences Raymond and Beverly Sackler Faculty of Exact Sciences Tel-A viv University, 69978 Tel-Aviv, Israel. Abstract We present a methodological framework enabling a detailed description of the perfo... | 1992 | 19 |
611 | Learning Spatio-Temporal Planning from a Dynamic Programming Teacher: Feed-Forward N eurocontrol for Moving Obstacle A voidance Gerald Fahner * Department of Neuroinformatics University of Bonn Romerstr. 164 W -5300 Bonn 1, Germany Rolf Eckmiller Department of Neuroinformatics Universit... | 1992 | 2 |
612 | Single-iteration Threshold Hamming Networks Isaac Meilijson Eytan Ruppin Moshe Sipper School of Mathematical Sciences Raymond and Beverly Sackler Faculty of Exact Sciences Tel Aviv University, 69978 Tel Aviv, Israel Abstract We analyze in detail the performance of a Hamming network classifyin... | 1992 | 20 |
613 | Reinforcement Learning Applied to Linear Quadratic Regulation Steven J. Bradtke Computer Science Department University of Massachusetts Amherst, MA 01003 bradtke@cs.umass.edu Abstract Recent research on reinforcement learning has focused on algorithms based on the principles of Dynamic Programmi... | 1992 | 21 |
614 | The Computation of Stereo Disparity for Transparent and for Opaque Surfaces Suthep Madarasmi Computer Science Department University of Minnesota Minneapolis, MN 55455 Daniel Kersten Department of Psychology University of Minnesota Ting-Chuen Pong Computer Science Department University o... | 1992 | 22 |
615 | Forecasting Demand for Electric Power Jen-Lun Yuan and Terrence L. Fine School of Electrical Engineering Cornell University Ithaca, NY 14853 Abstract We are developing a forecaster for daily extremes of demand for electric power encountered in the service area of a large midwestern utility and usin... | 1992 | 23 |
616 | Visual Motion Computation in Analog VLSI using Pulses Rahul Sarpeshkar, Wyeth Bair and Christof Koch Computation and Neural Systems Program California Institute of Technology Pasadena, CA 91125. Abstract The real time computation of motion from real images using a single chip with integrated sen... | 1992 | 24 |
617 | Learning Control Under Extreme Uncertainty Vijaykumar Gullapalli Computer Science Department University of Massachusetts Amherst, MA 01003 Abstract A peg-in-hole insertion task is used as an example to illustrate the utility of direct associative reinforcement learning methods for learning co... | 1992 | 25 |
618 | Efficient Pattern Recognition Using a New Transformation Distance Patrice Simard Yann Le Cun John Denker AT&T Bell Laboratories, 101 Crawford Corner Road, Holmdel, NJ 07724 Abstract Memory-based classification algorithms such as radial basis functions or K-nearest neighbors typically rely on simple... | 1992 | 26 |
619 | Planar Hidden Markov Modeling: from Speech to Optical Character Recognition Esther Levin and Roberto Pieraccini A IT Bell Laboratories 600 Mountain Ave. Murray Hill, NJ 07974 Abstract We propose in this paper a statistical model (planar hidden Markov model PHMM) describing statistical properties of... | 1992 | 27 |
620 | Global Regularization of Inverse Kinematics for Redundant Manipulators David DeMers Dept. of Computer Science & Engr. Institute for Neural Computation University of California, San Diego La Jolla. CA 92093-0114 Kenneth Kreutz-Delgado Dept. of Electrical & Computer Engr. Institute for Neural C... | 1992 | 28 |
621 | Object-Based Analog VLSI Vision Circuits Christof Koch Computation and Neural Systems California Institute of Technology Pasadena, CA John G. Harris Bimal Mathur, Shih-Chii Liu Rockwell International Science Center Thousand Oaks, CA MIT Artificial Intelligence Laboratory Cambridge, MA ... | 1992 | 29 |
622 | Hidden Markov Models in Molecular Biology: New Algorithms and Applications Pierre Baldi • Jet Propulsion Laboratory California Institute of Technology Pasadena, CA 91109 Tim H lmkapiller Division of Biology California Institute of Technology Yves Chauvin t Net-ID, Inc. 8, Cathy Place... | 1992 | 3 |
623 | Perceiving Complex Visual Scenes: An Oscillator Neural Network Model that Integrates Selective Attention, Perceptual Organisation, and Invariant Recognition Rainer Goebel Department of Psychology University of Braunschweig Spielmannstr. 19 W-3300 Braunschweig, Germany Abstract Which proces... | 1992 | 30 |
624 | Second order derivatives for network pruning: Optimal Brain Surgeon Babak Hassibi* and David G. Stork Ricoh California Research Center 2882 Sand Hill Road, Suite 115 Menlo Park, CA 94025-7022 stork@crc.ricoh.com and * Department of Electrical Engineering Stanford University Stanford, CA 94... | 1992 | 31 |
625 | Automatic Learning Rate Maximization by On-Line Estimation of the Hessian's Eigenvectors Yann LeCun,l Patrice Y. Simard,l and Barak Pearlmutter2 1 AT&T Bell Laboratories 101 Crawfords Corner Rd, Holmdel, NJ 07733 2CS&E Dept. Oregon Grad. Inst., 19600 NW vonNeumann Dr, Beaverton, OR 97006 Abstract W... | 1992 | 32 |
626 | Computation of Heading Direction From Optic Flow in Visual Cortex Markus Lappe· JosefP. Rauschecker Laboratory of Neurophysiology, NIMH, Poolesville, MD, U.S.A. and Max-Planck-Institut fur Biologische Kybernetik, Tiibingen, Germany Abstract We have designed a neural network which detects the direct... | 1992 | 33 |
627 | Statistical Modeling of Cell-Assemblies Activities in Associative Cortex of Behaving Monkeys Itay Gat and Naftali Tishby Institute of Computer Science and Center for Neural Computation Hebrew University, Jerusalem 91904, Israel * Abstract So far there has been no general method for relating extr... | 1992 | 34 |
628 | Harmonic Grammars for Formal Languages Paul Smolensky Department of Computer Science & Institute of Cognitive Science U ni versity of Colorado Boulder, Colorado 80309-0430 Abstract Basic connectionist principles imply that grammars should take the form of systems of parallel soft constraints ... | 1992 | 35 |
629 | Silicon Auditory Processors as Computer Peripherals .T ollll Lmr,7.Hl'o . .T ollll Wawl'7.Ylwk CS Division UC B(~rk('ley Evans lIall Bcrl,plpy. Ct\ !H720 lazzaro~cs.berkeley.edu, johnw~cs.berkeley.edu M. Mahowald'" ~ Massimo Sivilottit, Dave Gillcspict Califol'lIia lnst,itult' of Technolog... | 1992 | 36 |
630 | Information, prediction, and query by committee Yoav Freund Computer and Information Sciences University of California, Santa Cruz yoavQcse.ucsc.edu Eli Shamir Institute of Computer Science Hebrew University, Jerusalem sharnirQcs.huji.ac.il H. Sebastian Seung AT &T Bell Laboratories ... | 1992 | 37 |
631 | Improving Convergence in Hierarchical Matching Networks for Object Recognition Joachim Utans* Gene Gindit Department of Electrical Engineering Yale University P. O. Box 2157 Yale Station New Haven, CT 06520 Abstract We are interested in the use of analog neural networks for recognizing vis... | 1992 | 38 |
632 | A dynamical model of priming and repetition blindness Daphne Bavelier Laboratory of Neuropsychology The Salk Institute La J oHa, CA 92037 Michael I. Jordan Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Cambridge MA 02139 Abstract We describe a model of... | 1992 | 39 |
633 | Statistical and Dynamical Interpretation of ISIH Data from Periodically Stimulated Sensory Neurons John K. Douglass and Frank Moss Department of Biology and Department of Physics University of Missouri at St. Louis St. Louis, MO 63121 Andre Longtin Department of Physics University of Ottawa O... | 1992 | 4 |
634 | Unsupervised Discrimination of Clustered Data via Optimization of Binary Information Gain 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 Insti... | 1992 | 40 |
635 | On-Line Estimation of the Optimal Value Function: HJB-Estimators James K. Peterson Department of Mathematical Sciences Martin Hall Box 341907 Clemson University Clemson, SC 29634-1907 email: petersonOmath. clemson. edu Abstract In this paper, we discuss on-line estimation strategies that mode... | 1992 | 41 |
636 | Using Aperiodic Reinforcement for Directed Self-Organization During Development PR Montague P Dayan SJ Nowlan A Pouget TJ Sejnowski CNL, The Salk Institute 10010 North Torrey Pines Rd. La Jolla, CA 92037, USA read~helmholtz.sdsc.edu Abstract We present a local learning rule in which Hebbian lear... | 1992 | 42 |
637 | A Connectionist Symbol Manipulator That Discovers the Structure of Context-Free Languages Michael C. Mozer and Sreerupa Das Department of Computer Science & Institute of Cognitive Science University of Colorado Boulder, CO 80309-0430 Abstract We present a neural net architecture that can disc... | 1992 | 43 |
638 | Analogy--Watershed or Waterloo? Structural alignment and the development of connectionist models of analogy Dedre Gentner Department of Psychology Northwestern University 2029 Sheridan Rd. Evanston, IL 60208 Arthur B. Markman Department of Psychology Northwestern University 2029 Sherida... | 1992 | 44 |
639 | A Recurrent Neural Network for Generation of Ocular Saccades Lina L.E. Massone Department of Physiology Department of Electrical Engineering and Computer Scienc~ Northwestern University 303 E. Chicago Avenue, Chicago, 1160611 Abstract This paper presents a neural network able to control saccadic... | 1992 | 45 |
640 | Network Structuring And Training Using Rule-based Knowledge Volker Tresp Siemens AG Central Research Otto-Hahn-Ring 6 8000 Munchen 83, Germany Jiirgen Hollatz* Institut fur Informatik TV Munchen ArcisstraBe 21 8000 Munchen 2, Germany Subutai Ahmad Siemens AG Central Research ... | 1992 | 46 |
641 | Hybrid Circuits of Interacting Computer Model and Biological Neurons Sylvie Renaud-LeMassonDepartment of Physics Brandeis University Waltham. MA 02254 Eve Marder Department of Biology Brandeis University Waltham. MA 02254 Abstract Gwendal LeMasson' Department of Biology Brandeis Univ... | 1992 | 47 |
642 | A Knowledge-Based Model of Geometry Learning Geoffrey Towell Siemens Corporate Research 755 College Road East Princeton, NJ 08540 towe ll@ learning. siemens. com Abstract Richard Lehrer Educational Psychology University of Wisconsin 1025 West Johnson St. Madison, WI 53706 lehrer@vms.... | 1992 | 48 |
643 | Integration of Visual and Somatosensory Information for Preshaping Hand in Grasping Movements Yoji Uno ATR Human Information Processing Research Laboratories 2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-02, Japan Naohiro Fukumura* Faculty of Engineering University of Tokyo 7-3-1 Hong... | 1992 | 49 |
644 | Spiral Waves in Integrate-and-Fire Neural Networks John G. Milton Department of Neurology The University of Chicago Chicago, IL 60637 Po Hsiang Chu Department of Computer Science DePaul University Chicago, IL 60614 Jack D. Cowan Department of Mathematics The University of Chicago ... | 1992 | 5 |
645 | Kohonen Feature Maps and Growing Cell Structures a Performance Comparison Bernd Fritzke International Computer Science Institute 1947 Center Street, Suite 600 Berkeley, CA 94704-1105, USA Abstract A performance comparison of two self-organizing networks, the Kohonen Feature Map and the recently pro... | 1992 | 50 |
646 | A Neural Model of Descending Gain Control in the Electrosensory System Mark E. Nelson Beckman Institute University of Illinois 405 N. Mathews Urbana, IL 61801 Abstract In the electrosensory system of weakly electric fish, descending pathways to a first-order sensory nucleus have been shown to... | 1992 | 51 |
647 | Memory-based Reinforcement Learning: Efficient Computation with Prioritized Sweeping Andrew W. Moore awm@ai.mit.edu NE43-759 MIT AI Lab. 545 Technology Square Cambridge MA 02139 Christopher G. At:iteson Abstract cga@ai.mit.edu NE43-771 MIT AI Lab. 545 Technology Square Cambridge MA 0... | 1992 | 52 |
648 | Learning Fuzzy Rule-Based Neural Networks for Control Charles M. Higgins and Rodney M. Goodman Department of Electrical Engineering, 116-81 California Institute of Technology Pasadena, CA 91125 Abstract A three-step method for function approximation with a fuzzy system is proposed. First, the membe... | 1992 | 53 |
649 | Neural Network On-Line Learning Control of Spacecraft Smart Structures Dr. Christopher Bowman Ball Aerospace Systems Group P.O. Box 1062 Boulder. CO 80306 Abstract The overall goal is to reduce spacecraft weight. volume, and cost by online adaptive non-linear control of flexible structural componen... | 1992 | 54 |
650 | Hidden Markov Model Induction by Bayesian Model Merging Andreas Stolcke*'** *Computer Science Division University of California Berkeley, CA 94720 stolcke@icsi.berkeley.edu Stephen Omohundro" **International Computer Science Institute 1947 Center Street, Suite 600 Berkeley, CA 94704 om@... | 1992 | 55 |
651 | Statistical Mechanics of Learning Large Committee Machine Holm Schwarze CONNECT, The Niels Bohr Institute Blegdamsvej 17, DK-2100 Copenhagen 0, Denmark John Hertz· Nordita Blegdamsvej 17, DK-2100 Copenhagen 0, Denmark Abstract • In a We use statistical mechanics to study generalization in ... | 1992 | 56 |
652 | An Information-Theoretic Approach to Deciphering the Hippocampal Code William E. Skaggs Bruce L. McNaughton Katalin M. Gothard Etan J. Markus Center for Neural Systems, Memory, and Aging 344 Life Sciences North University of Arizona Tucson AZ 85724 bill@nsma.arizona.edu Abstract Info... | 1992 | 57 |
653 | Using hippocampal 'place cells' for navigation, exploiting phase coding Neil Burgess, John O'Keefe and Michael Recce Department of Anatomy, University College London, London WC1E 6BT, England. (e-mail: n.burgess<Ducl.ac . uk) Abstract A model of the hippocampus as a central element in rat navigatio... | 1992 | 58 |
654 | Weight Space Probability Densities in Stochastic Learning: I. Dynamics and Equilibria Todd K. Leen and John E. Moody Department of Computer Science and Engineering Oregon Graduate Institute of Science & Technology 19600 N.W. von Neumann Dr. Beaverton, OR 97006-1999 Abstract The ensemble dynam... | 1992 | 59 |
655 | Adaptive Stimulus Representations: A Computational Theory of Hippocampal-Region Function Mark A. Gluck Catherine E. Myers Center for Molecular and Behavioral Neuroscience Rutgers University. Newark. NJ 07102 g IlIck@pOl·/OI·.I'lI(gers.edll mycrs@p(/\-Iol'.rl/(gers.edll Abstract We present ... | 1992 | 6 |
656 | Discriminability-Based Transfer between Neural Networks L. Y. Pratt Department of Mathematical and Computer Sciences Colorado School of Mines Golden, CO 80401 lpratt@mines.colorado.edu Abstract Previously, we have introduced the idea of neural network transfer, where learning on a target prob... | 1992 | 60 |
657 | Learning Sequential Tasks by Incrementally Adding Higher Orders Mark Ring Department of Computer Sciences, Taylor 2.124 University of Texas at Austin Austin, Texas 78712 (ring@cs. utexas.edu) Abstract An incremental, higher-order, non-recurrent network combines two properties found to be usef... | 1992 | 61 |
658 | Topography and Ocular Dominance with Positive Correlations Geoffrey J. Goodhill University of Edinburgh Centre for Cognitive Science 2 Buccleuch Place Edinburgh EH8 9LW SCOTLAND Abstract A new computational model that addresses the formation of both topography and ocular dominance is presented. This i... | 1992 | 62 |
659 | A Hybrid Neural Net System for State-of-the-Art Continuous Speech Recognition G. Zavaliagkos Northeastern University Boston MA 02115 R. Schwartz BBN Systems and Technologies Cambridge, MA 02138 Y. Zhao BBN Systems and Technologies Cambridge, MA 02138 J. Makhoul BBN Systems and Techno... | 1992 | 63 |
660 | A Formal Model of the Insect Olfactory Macroglomerulus: Simulations and Analytical Results. Christiane Linster David Marsan ESPCI, Laboratoire d'Electronique 10, Rue Vauquelin Claudine Masson Laboratoire de Neurobiologie Comparee des Invertebrees INRA/CNRS (URA 1190) 75005 Paris, France... | 1992 | 64 |
661 | Using Prior Knowledge in a NNPDA to Learn Context-Free Languages Guo-Zheng SUD Sreerupa Das Dept. of Compo Sc. & Inst. of Cognitive Sc. University of Colorado Boulder, CO 80309 c. Lee Giles· NEC Research Inst. 4 Independence Way Princeton, NJ 08540 "'lnst. for Adv. Compo Studies U... | 1992 | 65 |
662 | A Method for Learning from Hints Yaser s. Abu-Mostafa Departments of Electrical Engineering, Computer Science, and Computation and Neural Systems California Institute of Technology Pasadena, CA 91125 e-mail: yaser@caltech.edu Abstract We address the problem of learning an unknown function by ... | 1992 | 66 |
663 | Holographic Recurrent Networks Tony A. Plate Department of Computer Science University of Toronto Toronto, M5S lA4 Canada Abstract Holographic Recurrent Networks (HRNs) are recurrent networks which incorporate associative memory techniques for storing sequential structure. HRNs can be easily and qu... | 1992 | 67 |
664 | Generalization Abilities of Cascade Network Architectures E. Littmann* Department of Information Science Bielefeld University D-4800 Bielefeld, FRG littmann@techfak.uni-bielefeld.de H. Ritter Department of Information Science Bielefeld University D-4800 Bielefeld, FRG helge@techfak.uni-... | 1992 | 68 |
665 | Explanation-Based Neural Network Learning for Robot Control Tom M. Mitchell School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 E-mail: mitchell@cs.cmu.edu Sebastian B. Thrun University of Bonn Institut fUr Infonnatik III ROmerstr. 164, D-5300 Bonn, Germany thrnn@... | 1992 | 69 |
666 | Computing with Almost Optimal Size Neural Networks Kai-Yeung Siu Dept. of Electrical & Compo Engineering University of California, Irvine Irvine, CA 92717 V wani Roychowdhury School of Electrical Engineering Purdue University West Lafayette, IN 47907 Thomas Kailath Information Systems L... | 1992 | 7 |
667 | Some Solutions to the Missing Feature Problem in Vision Subutai Ahmad Siemens AG, Central Research and Development ZFE ST SN61, Otto-Hahn Ring 6 8000 Miinchen 83, Gennany. ahmad@icsi.berkeley.edu Volker Tresp Siemens AG, Central Research and Development ZFE ST SN41, Otto-Hahn Ring 6 ... | 1992 | 70 |
668 | A Note on Learning Vector Quantization Virginia R. de Sa Department of Computer Science University of Rochester Rochester, NY 14627 Dana H. Ballard Department of Computer Science University of Rochester Rochester, NY 14627 Abstract Vector Quantization is useful for data compression. Compet... | 1992 | 71 |
669 | Diffusion Approximations for the Constant Learning Rate Backpropagation Algorithm and Resistence to Local Minima William Finnoff Siemens AG, Corporate Research and Development Otto-Hahn-Ring 6 8000 Munich 83, Fed. Rep. Germany Abstract In this paper we discuss the asymptotic properties of the... | 1992 | 72 |
670 | Analog Cochlear Model for Multiresolution Speech Analysis Weimin Liu~ Andreas G. Andreou and Moise H. Goldstein, Jr. Department of Electrical and Computer Engineering The Johns Hopkins University, Baltimore, Maryland 21218 USA Abstract This paper discusses the parameterization of speech by an analog c... | 1992 | 73 |
671 |
"! # $ "% & ' ( ! )*,+.-0/214365 *8749 ) :<;=?> )<@BADCFEHG IKJLJNM0OMQPSRTVUXWYJ[Z]\_^a`,bNcNd?cNe J W f ZhgKikjml bonqp rts cLuYvxw P R w goy{z.|_}... | 1992 | 74 |
672 | A Boundary Hunting Radial Basis Function Classifier Which Allocates Centers Constructively Eric I. Chang and Richard P. Lippmann MIT Lincoln Laboratory Lexington, MA02173-0073, USA Abstract A new boundary hunting radial basis function (BH-RBF) classifier which allocates RBF centers constructivel... | 1992 | 75 |
673 | Learning to See Where and What: Training a Net to Make Saccades and Recognize Handwritten Characters Gale Martin, Mosfeq Rashid, David Chapman, and James Pittman MCC, 3500 Balcones Center Drive, Austin, Texas 78759 ABSTRACT This paper describes an approach to integrated segmentation and recognit... | 1992 | 76 |
674 | How Oscillatory Neuronal Responses Reflect Bistability and Switching of the Hidden Assembly Dynamics K. Pawelzik, H.-V. Bauert, J. Deppisch, and T. Geisel Institut fur Theoretische Physik and SFB 185 Nichtlineare Dynamik Universitat Frankfurt, Robert-Mayer-Str. 8-10, D-6000 Frankfurt/M. 11, FRG ttempo... | 1992 | 77 |
675 | Summed Weight Neuron Perturbation: An O(N) Improvement over Weight Perturbation. Barry Flower and Marwan Jabri SEDAL Department of Electrical Engineering University of Sydney NSW 2006 Australia Abstract The algorithm presented performs gradient descent on the weight space of an Artificial Neu... | 1992 | 78 |
676 | Extended Regularization Methods for N onconvergent Model Selection W. Finnoff, F. Hergert and H.G. Zimmermann Siemens AG, Corporate Research and Development Otto-Hahn-Ring 6 8000 Munich 83, Fed. Rep. Germany Abstract Many techniques for model selection in the field of neural networks correspond ... | 1992 | 79 |
677 | Q-Learning with Hidden-Unit Restarting Charles W. Anderson Department of Computer Science Colorado State University Fort Collins, CO 80523 Abstract Platt's resource-allocation network (RAN) (Platt, 1991a, 1991b) is modified for a reinforcement-learning paradigm and to "restart" existing hidden u... | 1992 | 8 |
678 | 236 Synchronization and Grammatical Inference in an Oscillating Elman Net Bill Baird Dept Mathematics, U .C.Berkeley, Berkeley, Ca. 94720, baird@math.berkeley.edu Todd Troyer Dept Mathematics, U .C.Berkeley, Berkeley, Ca. 94720 Abstract Frank Eeckman Lawrence Livermore Natio... | 1992 | 80 |
679 | Attractor Neural Networks with Local Inhibition: from Statistical Physics to a Digital Programmable Integrated Circuit E. Pasero Dipartimento di Elettronica Politecnico di Torino 1-10129 Torino, Italy R. Zecchina Dipartimento di Fisica Teorica e INFN U niversita. di Torino 1-10125 Torino, ... | 1992 | 81 |
680 | Metamorphosis Networks: An Alternative to Constructive Methods Brian v. Bonnlander Michael C. Mozer Department of Computer Science & Institute of Cognitive Science University of Colorado Boulder, CO 80309-0430 Abstract Given a set oft raining examples, determining the appropriate number of fr... | 1992 | 82 |
681 | A Hybrid Linear/Nonlinear Approach to Channel Equalization Problems Wei-Tsih Lee John Pearson David Sarnoff Research Center CN5300 Princeton, NJ 08543 Abstract Channel equalization problem is an important problem in high-speed communications. The sequences of symbols transmitted are distorted... | 1992 | 83 |
682 | Physiologically Based Speech Synthesis ~akoto Hirayanaa t ATR Human Information Processing Research Laboratories 2-2, Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-02 Japan Eric Vatikiotis-Bateson tATR Auditory and Visual Perception Research Laboratories Kiyoshi Hondat Yasuharu Koiket ~itsuo Kawat... | 1992 | 84 |
683 | Weight Space Probability Densities in Stochastic Learning: II. Transients and Basin Hopping Times Genevieve B. Orr and Todd K. Leen Department of Computer Science and Engineering Oregon Graduate Institute of Science & Technology 19600 N.W. von Neumann Drive Beaverton, OR 97006-1999 Abstract I... | 1992 | 85 |
684 | Performance Through Consistency: MS-TDNN's for Large Vocabulary Continuous Speech Recognition Joe Tebelskis and Alex Waibel School of Computf'f Science Carnegie MeHon University Pittsburgh, PA 15213 Abstract Connectionist Rpeech recognition systems are often handicapped by an inconsistency be... | 1992 | 86 |
685 | Generic Analog Neural Computation The EPSILON Chip Stepben Cburcber Dept. of Elee. Engineering University of Edinburgh King's Buildings Edinburgh. EH9 3JL Donald J. Baxter Dept of Elec. Engineering University of Edinburgh King's Buildings Edinburgh. EH9 3JL Alister Hamilton Deptof... | 1992 | 87 |
686 | Analog VLSI Implementation of Multi-dimensional Gradient Descent David B. Kirk, Douglas Kerns, Kurt Fleischer, Alan H. Barr California Institute of Technology Beckman Institute 350-74 Pasadena, CA 91125 E-mail: dkIDegg.gg . cal tech. edu Abstract We describe an analog VLSI implementation of a mu... | 1992 | 88 |
687 | Improving Performance in Neural Networks Using a Boosting Algorithm Harris Drucker AT&T Bell Laboratories Holmdel, NJ 07733 Robert Schapire AT&T Bell Laboratories Murray Hill, NJ 07974 Abstract Patrice Simard AT &T Bell Laboratories Holmdel, NJ 07733 A boosting algorithm converts a l... | 1992 | 89 |
688 | Synaptic Weight Noise During MLP Learning Enhances Fault-Tolerance, Generalisation and Learning Trajectory Alan F. Murray Dept. of Electrical Engineering Edinburgh University Scotland Peter J. Edwards Dept. of Electrical Engjneering Edinburgh University Scotland Abstract We analyse t... | 1992 | 9 |
689 | An Object-Oriented Framework for the Simulation of Neural Nets A. Linden Th. Sudbrak Ch. Tietz F. Weber German National Research Center for Computer Science D-5205 Sankt Augustin 1, Germany Abstract The field of software simulators for neural networks has been expanding very rapidly in the la... | 1992 | 90 |
690 | A Practice Strategy for Robot Learning Control Terence D. Sanger Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology, room E25-534 Cambridge, MA 02139 tds@ai.mit.edu Abstract "Trajectory Extension Learning" is a new technique for Learning Control in... | 1992 | 91 |
691 | Modeling Consistency in a Speaker Independent Continuous Speech Recognition System Yochai Konig, Nelson Morgan, Chuck Wooters International Computer Science Institute 1947 Center Street, Suite 600 Berkeley, CA 94704, USA. Victor Abrash, Michael Cohen, Horacio Franco SRI International 333 Ravensw... | 1992 | 92 |
692 | Learning to categorize objects using temporal coherence Suzanna Becker· The Rotman Research Institute Baycrest Center 3560 Bathurst St. Toronto, Ontario, M6A 2E1 Abstract The invariance of an objects' identity as it transformed over time provides a powerful cue for perceptual learning. We pre... | 1992 | 93 |
693 | Learning Curves, Model Selection and Complexity of Neural Networks Noboru Murata Department of IVIathematical Engineering and Information Physics University of Tokyo, Tokyo 113, JAPAN E-mail: mura~sat.t.u-tokyo.ac.jp Shuji Yoshizawa Dept. Mech. Info. University of Tokyo ShUll-ichi Amari De... | 1992 | 94 |
694 | Optimal Depth Neural Networks for Multiplication and Related Problems Kai-Yeung Siu Dept. of Electrical & Compo Engineering University of California, Irvine Irvine, CA 92717 Abstract Vwani Roychowdhury School of Electrical Engineering Purdue University West Lafayette, IN 47907 An artifi... | 1992 | 95 |
695 | Time Warping Invariant 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 We proposed a model of Time Warping Invariant Neural Networks (TWINN) to han... | 1992 | 96 |
696 | Deriving Receptive Fields Using An Optimal Encoding Criterion Ralph Linsker IBM T. J. Watson Research Center P. O. Box 218, Yorktown Heights, NY 10598 Abstract An information-theoretic optimization principle ('infomax') has previously been used for unsupervised learning of statistical regularities ... | 1992 | 97 |
697 | Connected Letter Recognition with a Multi-State Time Delay Neural Network Hermann Hild and Alex Waibel School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213-3891, USA Abstract The Multi-State Time Delay Neural Network (MS-TDNN) integrates a nonlinear time alignment procedure (D... | 1992 | 98 |
698 | Input Reconstruction Reliability Estimation Dean A. Pomerleau School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 Abstract This paper describes a technique called Input Reconstruction Reliability Estimation (IRRE) for determining the response reliability of a restricted class... | 1992 | 99 |
699 | Backpropagation without Multiplication Patrice Y. Simard AT &T Bell Laboratories Holmdel, NJ 07733 Abstract Hans Peter Graf AT&T Bell Laboratories Holmdel, NJ 07733 The back propagation algorithm has been modified to work without any multiplications and to tolerate comput.ations with a low re... | 1993 | 1 |
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