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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1,000 | A Dynamical Model of Context Dependencies for the Vestibulo-Ocular Reflex Olivier J.M.D. Coenen* Terrence J. Sejnowskit Computational Neurobiology Laboratory Howard Hughes Medical Institute The Salk Institute for Biological Studies 10010 North Torrey Pines Road La Jolla, CA 92037, U.S.A. Depa... | 1995 | 101 |
1,001 | Constructive Algorithms for Hierarchical Mixtures of Experts S.R.Waterhouse A.J.Robinson Cambridge University Engineering Department, Trumpington St., Cambridge, CB2 1PZ, England. Tel: [+44] 1223 332754, Fax: [+44] 1223 332662, Email: srw1001.ajr@eng.cam.ac.uk Abstract We present two addition... | 1995 | 102 |
1,002 | Discovering Structure in Continuous Variables Using Bayesian Networks Reimar Hofmann and Volker Tresp* Siemens AG, Central Research Otto-Hahn-Ring 6 81730 Munchen, Germany Abstract We study Bayesian networks for continuous variables using nonlinear conditional density estimators. We demonstrate tha... | 1995 | 103 |
1,003 | Temporal coding in the sub-millisecond range: Model of barn owl auditory pathway Richard Kempter* Institut fur Theoretische Physik Physik-Department der TU Munchen D-85748 Garching bei Munchen Germany J. Leo van Hemmen Institut fur Theoretische Physik Physik-Department der TU Munchen 0-... | 1995 | 104 |
1,004 | Stable Dynamic Parameter Adaptation Stefan M. Riiger Fachbereich Informatik, Technische Universitat Berlin Sekr. FR 5-9, Franklinstr. 28/29 10587 Berlin, Germany async~cs. tu-berlin.de Abstract A stability criterion for dynamic parameter adaptation is given. In the case of the learning rate of b... | 1995 | 105 |
1,005 | Optimizing Cortical Mappings Geoffrey J. Goodhill The Salk Institute 10010 North Torrey Pines Road La Jolla, CA 92037, USA Steven Finch Human Communication Research Centre University of Edinburgh, 2 Buccleuch Place Edinburgh EH8 9LW, GREAT BRITAIN Terrence J. Sejnowski The Howard Hughes Me... | 1995 | 106 |
1,006 | Experiments with Neural Networks for Real Time Implementation of Control P. K. Campbell, M. Dale, H. L. Ferra and A. Kowalczyk Telstra Research Laboratories 770 Blackburn Road Clayton, Vic. 3168, Australia {p.campbell, m.dale, h.ferra, a.kowalczyk}@trl.oz.au Abstract This paper describes a neural n... | 1995 | 107 |
1,007 | Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex. Michael E. Hasselmo and Milos Cekic Department of Psychology and Program in Neurosciences, Harvard University, 33 Kirkland St., Cambridge, MA 02138 hasselmo@katIa.harvard.edu ... | 1995 | 108 |
1,008 | Dynamics of Attention as Near Saddle-Node Bifurcation Behavior Hiroyuki Nakahara" General Systems Studies U ni versi ty of Tokyo 3-8-1 Komaba, Meguro Tokyo 153, Japan nakahara@vermeer.c.u-tokyo.ac.jp Kenji Doya ATR Human Information Processing Research Laboratories 2-2 Hikaridai, Seika,... | 1995 | 109 |
1,009 | Learning with ensembles: How over-fitting can be useful Peter Sollich Department of Physics University of Edinburgh, U.K. P.SollichGed.ac.uk Anders Krogh'" NORDITA, Blegdamsvej 17 2100 Copenhagen, Denmark kroghGsanger.ac.uk Abstract We study the characteristics of learning with ensemble... | 1995 | 11 |
1,010 | Softassign versus Softmax: Benchmarks in Combinatorial Optimization Steven Gold Department of Computer Science Yale University New Haven, CT 06520-8285 Anand Rangarajan Dept. of Diagnostic Radiology Yale University New Haven, CT 06520-8042 Abstract A new technique, termed soft assign, i... | 1995 | 110 |
1,011 | From Isolation to Cooperation: An Alternative View of a System of Experts Stefan Schaal:!:* Christopher C. Atkeson:!: sschaal@cc.gatech.edu cga@cc.gatech.edu http://www.cc.gatech.eduifac/Stefan.Schaal http://www.cc.gatech.eduifac/Chris.Atkeson +College of Computing, Georgia Tech, 801 Atlantic Dr... | 1995 | 111 |
1,012 | Fast Learning by Bounding Likelihoods in Sigmoid Type Belief Networks Tommi Jaakkola tommi@psyche.mit.edu Lawrence K. Saul lksaul@psyche.mit.edu Michael I. Jordan jordan@psyche.mit.edu Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Cambridge, MA 02139 A... | 1995 | 112 |
1,013 | Neural Control for Nonlinear Dynamic Systems Ssu-Hsin Yu Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge, MA 02139 Email: hsin@mit.edu Anuradha M. Annaswamy Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge, MA 02139 E... | 1995 | 113 |
1,014 | Sample Complexity for Learning Recurrent Percept ron Mappings Bhaskar Dasgupta Department of Computer Science University of Waterloo Waterloo, Ontario N2L 3G 1 CANADA bdasgupt~daisy.uwaterloo.ca Eduardo D. Sontag Department of Mathematics Rutgers University New Brunswick, NJ 08903 US... | 1995 | 114 |
1,015 | Is Learning The n-th Thing Any Easier Than Learning The First? Sebastian Thrun I Computer Science Department Carnegie Mellon University Pittsburgh, PA 15213-3891 World Wide Web: http://www.cs.cmu.edul'''thrun Abstract This paper investigates learning in a lifelong context. Lifelong learning a... | 1995 | 115 |
1,016 | Parallel analog VLSI architectures for computation of heading direction and time-to-contact Giacomo Indiveri giacomo@klab.caltech.edu Jorg Kramer kramer@klab.caltech.edu Division of Biology California Institute of Technology Pasadena, CA 91125 Abstract Christof Koch koch@klab.caltech... | 1995 | 116 |
1,017 | A Dynamical Systems Approach for a Learnable Autonomous Robot J un Tani and N aohiro Fukumura Sony Computer Science Laboratory Inc. Takanawa Muse Building, 3-14-13 Higashi-gotanda, Shinagawa-ku,Tokyo, 141 JAPAN Abstract This paper discusses how a robot can learn goal-directed navigation tasks using local... | 1995 | 117 |
1,018 | Optimization Principles for the Neural Code Michael DeWeese Sloan Center, Salk Institute La Jolla, CA 92037 deweese@salk.edu Abstract Recent experiments show that the neural codes at work in a wide range of creatures share some common features. At first sight, these observations seem unrelate... | 1995 | 118 |
1,019 | A Framework for Non-rigid Matching and Correspondence Suguna Pappu, Steven Gold, and Anand Rangarajan1 Departments of Diagnostic Radiology and Computer Science and the Yale Neuroengineering and Neuroscience Center Yale University New Haven, CT 06520-8285 Abstract Matching feature point sets lies at... | 1995 | 119 |
1,020 | Analog VLSI Processor Implementing the Continuous Wavelet Transform R. Timothy Edwards and Gert Cauwenberghs Department of Electrical and Computer Engineering Johns Hopkins University 3400 North Charles Street Baltimore, MD 21218-2686 {tim,gert}@bach.ece.jhu.edu Abstract We present an integra... | 1995 | 12 |
1,021 | Hierarchical Recurrent Neural Networks for Long-Term Dependencies Salah El Hihi Dept. Informatique et Recherche Operationnelle Universite de Montreal Montreal, Qc H3C-3J7 elhihiGiro.umontreal.ca Yoshua Bengio· Dept. Informatique et Recherche Operationnelle Universite de Montreal Mont... | 1995 | 120 |
1,022 | Finite State Automata that Recurrent Cascade-Correlation Cannot Represent Stefan C. Kremer Department of Computing Science University of Alberta Edmonton, Alberta, CANADA T6H 5B5 Abstract This paper relates the computational power of Fahlman' s Recurrent Cascade Correlation (RCC) architecture to... | 1995 | 121 |
1,023 | Memory-based Stochastic Optimization Andrew W. Moore and Jeff Schneider School of Computer Science Carnegie-Mellon University Pittsburgh, PA 15213 Abstract In this paper we introduce new algorithms for optimizing noisy plants in which each experiment is very expensive. The algorithms build a glo... | 1995 | 122 |
1,024 | Handwritten Word Recognition using Contextual Hybrid Radial Basis Function NetworklHidden Markov Models Bernard Lemarie La Poste/SRTP 10, Rue de l'lle-Mabon F-44063 Nantes Cedex France lemarie@srtp.srt-poste.fr Michel Gilloux La Poste/SRTP 10, Rue de l'1le-Mabon F-44063 Nantes Cede x Fr... | 1995 | 123 |
1,025 | Universal Approximation and Learning of Trajectories Using Oscillators Pierre Baldi* Division of Biology California Institute of Technology Pasadena, CA 91125 pfbaldi@juliet.caltech.edu Kurt Hornik Technische Universitat Wien Wiedner Hauptstra8e 8-10/1071 A-1040 Wien, Austria Kurt.Horni... | 1995 | 124 |
1,026 | Correlated Neuronal Response: Time Scales and Mechanisms Wyeth Bair Howard Hughes Medical Inst. NYU Center for Neural Science 4 Washington PI., Room 809 New York, NY 10003 Ehud Zohary Dept. of Neurobiology Institute of Life Sciences The Hebrew University, Givat Ram Jerusalem, 91904 ISRA... | 1995 | 125 |
1,027 | Worst-case Loss Bounds for Single Neurons David P. Helmbold Department of Computer Science University of California, Santa Cruz Santa Cruz, CA 95064 USA Jyrki Kivinen Department of Computer Science P.O. Box 26 (Teollisuuskatu 23) FIN-00014 University of Helsinki Finland Manfred K. Wa... | 1995 | 126 |
1,028 | Stock Selection via Nonlinear Multi-Factor Models Asriel U. Levin BZW Barclays Global Investors Advanced Strategies and Research Group 45 Fremont Street San Francisco CA 94105 email: asriel.levin@bglobal.com Abstract This paper discusses the use of multilayer feed forward neural networks for ... | 1995 | 127 |
1,029 | The Geometry of Eye Rotations and Listing's Law Amir A. Handzel* Tamar Flasht Department of Applied Mathematics and Computer Science Weizmann Institute of Science Rehovot, 76100 Israel Abstract We analyse the geometry of eye rotations, and in particular saccades, using basic Lie group theory ... | 1995 | 128 |
1,030 | When is an Integrate-and-fire Neuron like a Poisson Neuron? Charles F. Stevens Salk Institute MNL/S La Jolla, CA 92037 cfs@salk.edu Anthony Zador Salk Institute MNL/S La Jolla, CA 92037 zador@salk.edu Abstract In the Poisson neuron model, the output is a rate-modulated Poisson process (... | 1995 | 129 |
1,031 | A Novel Channel Selection System in Cochlear Implants Using Artificial Neural Network Marwan A. Jabri & Raymond J. Wang Systems Engineering and Design Automation Laboratory Department of Electrical Engineering The University of Sydney NSW 2006, Australia {marwan,jwwang}Osedal.usyd.edu.au ... | 1995 | 13 |
1,032 | Examples of learning curves from a modified VC-formalism. A. Kowalczyk & J. Szymanski Telstra Research Laboratories 770 Blackbtun Road, Clayton, Vic. 3168, Australia {akowalczyk,j.szymanski }@trl.oz.au) P.L. Bartlett & R.C. Williamson Department of Systems Engineering Australian National Univ... | 1995 | 130 |
1,033 | Using Unlabeled Data for Supervised Learning Geoffrey Towell Siemens Corporate Research 755 College Road East Princeton, N J 08540 Abstract Many classification problems have the property that the only costly part of obtaining examples is the class label. This paper suggests a simple method fo... | 1995 | 131 |
1,034 | Implementation Issues in the Fourier Transform Algorithm Yishay Mansour" Sigal Sahar t Computer Science Dept. Tel-Aviv University Tel-Aviv, ISRAEL Abstract The Fourier transform of boolean functions has come to play an important role in proving many important learnability results. We aim t... | 1995 | 132 |
1,035 | A Bound on the Error of Cross Validation Using the Approximation and Estimation Rates, with Consequences for the Training-Test Split 1 INTRODUCTION Michael Kearns AT&T Research We analyze the performance of cross validation 1 in the context of model selection and complexity regularization. We work ... | 1995 | 133 |
1,036 | Classifying Facial Action Marian Stewart Bartlett, Paul A. Viola, Terrence J. Sejnowski, Beatrice A. Golomb Howard Hughes Medical Institute The Salk Institute, La Jolla, CA 92037 marni, viola, terry, beatrice @salk.edu Jan Larsen The Niels Bohr Institute 2100 Copenhagen Denmark jlarsen@fys... | 1995 | 134 |
1,037 |
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;<>= ?A@B: ;<DCFE ;HGJI K ;HLMLNIO ;HIQPD= RTSU;H< ;IPV= W XYVZ\[>]_^a`6YVbM^cedgfhc `iQj^_Y ]... | 1995 | 135 |
1,038 | Gradient and Hamiltonian Dynamics Applied to Learning in Neural Networks James W. Howse Chaouki T. Abdallah Gregory L. Heileman Department of Electrical and Computer Engineering University of New Mexico Albuquerque, NM 87131 Abstract The process of machine learning can be considered in two st... | 1995 | 136 |
1,039 | A New Learning Algorithm for Blind Signal Separation s. Amari* University of Tokyo Bunkyo-ku, Tokyo 113, JAPAN amari@sat.t. u-tokyo.ac.jp A. Cichocki Lab. for Artificial Brain Systems FRP, RIKEN Wako-Shi, Saitama, 351-01, JAPAN cia@kamo.riken.go.jp H. H. Yang Lab. for Information Rep... | 1995 | 137 |
1,040 | Adaptive Back-Propagation in On-Line Learning of Multilayer Networks Ansgar H. L. West 1,2 and David Saad2 1 Department of Physics, University of Edinburgh Edinburgh EH9 3JZ, U.K. 2Neural Computing Research Group, University of Aston Birmingham B4 7ET, U.K. Abstract An adaptive back-propagation ... | 1995 | 138 |
1,041 | Neuron-MOS Temporal Winner Search Hardware for Fully-Parallel Data Processing Tadashi SHIBATA, Tsutomu NAKAI, Tatsuo MORIMOTO Ryu KAIHARA, Takeo YAMASHITA, and Tadahiro OHMI Department of Electronic Engineering Tohoku University Aza-Aoba, Aramaki, Aobaku, Sendai 980-77 JAPAN Abstract A unique... | 1995 | 139 |
1,042 | Rapid Quality Estimation of Neural Network Input Representations Kevin J. Cherkauer Jude W. Shav lik Computer Sciences Department, University of Wisconsin-Madison 1210 W. Dayton St., Madison, WI 53706 {cherkauer,shavlik }@cs.wisc.edu Abstract The choice of an input representation for a neural ne... | 1995 | 14 |
1,043 | Human Reading and the Curse of Dimensionality Gale L. Martin MCC Austin, TX 78613 galem@mcc.com Abstract Whereas optical character recognition (OCR) systems learn to classify single characters; people learn to classify long character strings in parallel, within a single fixation. This difference is su... | 1995 | 140 |
1,044 | Unsupervised Pixel-prediction William R. Softky Math Resp.arch Branch NIDDK, NIH 9190 Wisconsin Ave #350 Bethesda, MD 20814 bill@homer.niddk.nih.gov Abstract When a sensory system constructs a model of the environment from its input, it might need to verify the model's accuracy. One method... | 1995 | 141 |
1,045 | Control of Selective Visual Attention: Modeling the "Where" Pathway Ernst Niebur· Computation and Neural Systems 139-74 California Institute of Technology Christof Koch Computation and Neural Systems 139-74 California Institute of Technology Abstract Intermediate and higher vision processes r... | 1995 | 142 |
1,046 | Quadratic-Type Lyapunov Functions for Competitive Neural Networks with Different Time-Scales Anke Meyer-Base Institute of Technical Informatics Technical University of Darmstadt Darmstadt, Germany 64283 Abstract The dynamics of complex neural networks modelling the selforganization process in co... | 1995 | 143 |
1,047 | Learning long-term dependencies is not as difficult with NARX networks Tsungnan Lin* Department of Electrical Engineering Princeton University Princeton, N J 08540 Peter Tiiio Dept. of Computer Science and Engineering Slovak Technical University Ilkovicova 3, 812 19 Bratislava, Slovakia Ab... | 1995 | 144 |
1,048 | Learning the structure of similarity Joshua B. Tenenbaum Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Cambridge, MA 02139 jbt~psyche.mit.edu Abstract The additive clustering (ADCL US) model (Shepard & Arabie, 1979) treats the similarity of two stimuli as a wei... | 1995 | 145 |
1,049 | Investment Learning with Hierarchical PSOMs Jorg Walter and Helge Ritter Department of Information Science University of Bielefeld, D-33615 Bielefeld, Germany Email: {walter.helge}@techfak.uni-bielefeld.de Abstract We propose a hierarchical scheme for rapid learning of context dependent "skills"... | 1995 | 146 |
1,050 | How Perception Guides Production Birdsong Learning Christopher L. Fry cfry@cogsci.ucsd.edu Department of Cognitive Science University of California at San Diego La Jolla, CA 92093-0515 Abstract A c.:omputational model of song learning in the song sparrow (M elospiza melodia) learns to categor... | 1995 | 147 |
1,051 | Stable Fitted Reinforcement Learning Geoffrey J. Gordon Computer Science Department Carnegie Mellon University Pittsburgh PA 15213 ggordon@cs.cmu.edu Abstract We describe the reinforcement learning problem, motivate algorithms which seek an approximation to the Q function, and present new conver... | 1995 | 148 |
1,052 | Information through a Spiking Neuron Charles F. Stevens and Anthony Zador Salk Institute MNL/S La J olIa, CA 92037 zador@salk.edu Abstract While it is generally agreed that neurons transmit information about their synaptic inputs through spike trains, the code by which this information is transm... | 1995 | 149 |
1,053 | Generating Accurate and Diverse Members of a Neural-Network Ensemble David w. Opitz Computer Science Department University of Minnesota Duluth, MN 55812 opitz@d.umn.edu Jude W. Shavlik Computer Sciences Department University of Wisconsin Madison, WI 53706 shavlik@cs.wisc.edu Abstract... | 1995 | 15 |
1,054 | Discriminant Adaptive Nearest Neighbor Classification and Regression Trevor Hastie Department of Statistics Sequoia Hall Stanford University California 94305 trevor@playfair.stanford.edu Abstract Robert Tibshirani Department of Statistics University of Toronto tibs@utstat.toronto.edu... | 1995 | 150 |
1,055 | A Predictive Switching Model of Cerebellar Movement Control Andrew G. Barto .J ay T. Buckingham Department of Computer Science University of Massachusetts Amherst, MA 01003-4610 barto@cs.umass.edu .J ames C. Houk Department of Physiology Northwestern University Medical School 303 East C... | 1995 | 151 |
1,056 | Recurrent Neural Networks for Missing or Asynchronous Data Yoshua Bengio Dept. Informatique et Recherche Operationnelle Universite de Montreal Montreal, Qc H3C-3J7 bengioy~iro.umontreal.ca Abstract Francois Gingras Dept. Informatique et Recherche Operationnelle Universite de Montreal ... | 1995 | 152 |
1,057 | A Model of Spatial Representations in Parietal Cortex Explains Hemineglect Alexandre Pouget Dept of Neurobiology UCLA Los Angeles, CA 90095-1763 alex@salk.edu Terrence J. Sejnowski Howard Hughes Medical Institute The Salk Institute Abstract La Jolla, CA 92037 terry@salk.edu We hav... | 1995 | 16 |
1,058 | Temporal Difference Learning in Continuous Time and Space Kenji Doya doya~hip.atr.co.jp ATR Human Information Processing Research Laboratories 2-2 Hikaridai, Seika.-cho, Soraku-gun, Kyoto 619-02, Japan Abstract A continuous-time, continuous-state version of the temporal difference (TD) algorithm is... | 1995 | 17 |
1,059 | Tempering Backpropagation Networks: Not All Weights are Created Equal Nicol N. Schraudolph EVOTEC BioSystems GmbH Grandweg 64 22529 Hamburg, Germany nici@evotec.de Terrence J. Sejnowski Computational Neurobiology Lab The Salk Institute for BioI. Studies San Diego, CA 92186-5800, USA ter... | 1995 | 18 |
1,060 | Improving Policies without Measuring Merits Peter Dayan! CBCL E25-201, MIT Cambridge, MA 02139 dayan~ai.mit.edu Abstract Satinder P Singh Harlequin, Inc 1 Cambridge Center Cambridge, MA 02142 singh~harlequin.com Performing policy iteration in dynamic programming should only req... | 1995 | 19 |
1,061 | Onset-based Sound Segmentation Leslie S. Smith CCCN jDepartment of Computer Science University of Stirling Stirling FK9 4LA Scotland Abstract A technique for segmenting sounds using processing based on mammalian early auditory processing is presented. The technique is based on features in sound ... | 1995 | 2 |
1,062 | Selective Attention for Handwritten Digit Recognition Ethem Alpaydm Department of Computer Engineering Bogazi<1i U ni versi ty Istanbul, TR-SOS15 Turkey alpaydin@boun.edu.tr Abstract Completely parallel object recognition is NP-complete. Achieving a recognizer with feasible complexity require... | 1995 | 20 |
1,063 | Learning Model Bias Jonathan Baxter Department of Computer Science Royal Holloway College, University of London jon~dcs.rhbnc.ac.uk Abstract In this paper the problem of learning appropriate domain-specific bias is addressed. It is shown that this can be achieved by learning many related tasks f... | 1995 | 21 |
1,064 | Estimating the Bayes Risk from Sample Data Robert R. Snapp· and Tong Xu Computer Science and Electrical Engineering Department University of Vermont Burlington, VT 05405 Abstract A new nearest-neighbor method is described for estimating the Bayes risk of a multiclass pattern claSSification problem ... | 1995 | 22 |
1,065 | A MODEL OF AUDITORY STREAMING Susan L. McCabe & Michael J. Denham Neurodynamics Research Group School of Computing University of Plymouth Plymouth PL4 8AA, u.K. ABSTRACT An essential feature of intelligent sensory processing is the ability to focus on the part of the signal of interest against a... | 1995 | 23 |
1,066 | Context-Dependent Classes in a Hybrid Recurrent Network-HMM Speech Recognition System Dan Kershaw Tony Robinson Mike Hochberg • Cambridge University Engineering Department, Trumpington Street, Cambridge CB2 1PZ, England. Tel: [+44]1223332800, Fax: [+44]1223332662. Email: djk.ajr@eng.cam.ac.uk... | 1995 | 24 |
1,067 | Exploiting Tractable Substructures in Intractable Networks Lawrence K. Saul and Michael I. Jordan {lksaul.jordan}~psyche.mit.edu Center for Biological and Computational Learning Massachusetts Institute of Technology 79 Amherst Street, ElO-243 Cambridge, MA 02139 Abstract We develop a refined ... | 1995 | 25 |
1,068 | Statistical Theory of Overtraining - Is Cross-Validation Asymptotically Effective? s. Amari, N. Murata, K.-R. Miiller* Dept. of Math. Engineering and Inf. Physics, University of Tokyo Hongo 7-3-1, Bunkyo-ku, Tokyo 113, Japan M. Finke Inst. f. Logik, University of Karlsruhe 76128 Karlsruhe, Germa... | 1995 | 26 |
1,069 | Primitive Manipulation Learning with Connectionism Yoky Matsuoka The Artificial Intelligence Laboratory NE43-819 Massachusetts Institute of Techonology Cambridge, MA 02139 Abstract Infants' manipulative exploratory behavior within the environment is a vehicle of cognitive stimulation[McCall 1... | 1995 | 27 |
1,070 | Silicon Models for A uditory Scene Analysis John Lazzaro and John Wawrzynek CS Division UC Berkeley Berkeley, CA 94720-1776 lazzaroOcs.berkeley.edu. johnvOcs.berkeley.edu Abstract We are developing special-purpose, low-power analog-to-digital converters for speech and music applications, t... | 1995 | 28 |
1,071 | Human Face Detection in Visual Scenes Henry A. Rowley Shumeet Baluja Takeo Kanade har@cs.cmu.edu baluja@cs.cmu.edu tk@cs.cmu.edu School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA Abstract We present a neural network-based face detection system. A retinally c... | 1995 | 29 |
1,072 | Beating a Defender in Robotic Soccer: Memory-Based Learning of a Continuous FUnction Peter Stone Department of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 Manuela Veloso Department of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 Abstract Learn... | 1995 | 3 |
1,073 | SPERT-II: A Vector Microprocessor System and its Application to Large Problems in Backpropagation Training John Wawrzynek, Krste Asanovic, & Brian Kingsbury University of California at Berkeley Department of Electrical Engineering and Computer Sciences Berkeley, CA 94720-1776 {johnw ,krste,bedk }@c... | 1995 | 30 |
1,074 |
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" # $ &% %('*) + , ' ) + . /*02143576!8:9 ;<3=!>?5A@B5C1EDF5G6 HJI 1EDLKM29 NPO M 6 D 5C6 QRM 6 HJM 1ED 5 6 S... | 1995 | 31 |
1,075 | Stochastic Hillclimbing as a Baseline Method for Evaluating Genetic Algorithms Ari Juels Department of Computer Science University of California at Berkeley· Martin Wattenberg Department of Mathematics University of California at Berkeleyt Abstract We investigate the effectiveness of stoch... | 1995 | 32 |
1,076 | Using the Future to "Sort Out" the Present: Rankprop and Multitask Learning for Medical Risk Evaluation Rich Caruana, Shumeet Baluja, and Tom Mitchell School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213 (caruana, baluja, mitchell)@cs.cmu.edu Abstract A patient visits the do... | 1995 | 33 |
1,077 | Exponentially many local minima for single neurons Peter Auer Mark Herbster Manfred K. Warmuth Department of Computer Science Santa Cruz, California {pauer,mark,manfred} @cs.ucsc.edu Abstract We show that for a single neuron with the logistic function as the transfer function the number of... | 1995 | 34 |
1,078 | An Information-theoretic Learning Algorithm for Neural Network Classification David J. Miller Department of Electrical Engineering The Pennsylvania State University State College, Pa: 16802 Ajit Rao, Kenneth Rose, and Allen Gersho Department of Electrical and Computer Engineering University o... | 1995 | 35 |
1,079 | Improving Elevator Performance Using Reinforcement Learning Robert H. Crites Computer Science Department University of Massachusetts Amherst, MA 01003-4610 critesGcs.umass.edu Andrew G. Barto Computer Science Department University of Massachusetts Amherst, MA 01003-4610 bartoGcs.umass.e... | 1995 | 36 |
1,080 | Optimal Asset Allocation • uSIng Adaptive Dynamic Programming Ralph Neuneier* Siemens AG, Corporate Research and Development Otto-Hahn-Ring 6, D-81730 Munchen, Germany Abstract In recent years, the interest of investors has shifted to computerized asset allocation (portfolio management) to explo... | 1995 | 37 |
1,081 | Recursive Estimation of Dynamic Modular RBF Networks Visakan Kadirkamanathan Automatic Control & Systems Eng. Dept. University of Sheffield, Sheffield Sl 4DU, UK visakan@acse.sheffield.ac. uk Abstract Maha Kadirkamanathan Dragon Systems UK Cheltenham GL52 4RW, UK maha@dragon.co.uk In th... | 1995 | 38 |
1,082 | A Neural Network Classifier for the 11000 OCR Chip John C. Platt and Timothy P. Allen Synaptics, Inc. 2698 Orchard Parkway San Jose, CA 95134 platt@synaptics.com, tpa@synaptics.com Abstract This paper describes a neural network classifier for the 11000 chip, which optically reads the E13B fon... | 1995 | 39 |
1,083 | A Neural Network Autoassociator for Induction Motor Failure Prediction Thomas Petsche, Angelo Marcantonio, Christian Darken, Stephen J. Hanson, Gary M. Kuhn and Iwan Santoso [PETSCHE, ANGELO, DARKEN, JOSE, GMK, NIS]@SCR.SIEMENS.COM Siemens Corporate Research, Inc. 755 College Road East Princeton, N... | 1995 | 4 |
1,084 | Modeling Saccadic Targeting in Visual Search Rajesh P. N. Rao Computer Science Department University of Rochester Rochester, NY 14627 rao@cs.rochester.edu Mary M. Hayhoe Center for Visual Science University of Rochester Rochester, NY 14627 mary@cvs.rochester.edu Gregory J. Zelinsky C... | 1995 | 40 |
1,085 | Plasticity of Center-Surround Opponent Receptive Fields in Real and Artificial Neural Systems of Vision S. Yasui Kyushu Institute of Technology lizuka 820, Japan M. Yamada Electrotechnical Laboratory Tsukuba 305, Japan T. Furukawa Kyushu Institute of Technology lizuka 820, Japan T. S... | 1995 | 41 |
1,086 | Active Gesture Recognition using Learned Visual Attention Trevor Darrell and Alex Pentland Perceptual Computing Group MIT Media Lab 20 Ames Street, Cambridge MA, 02138 trevor,sandy~media.mit.edu Abstract We have developed a foveated gesture recognition system that runs in an unconstrained off... | 1995 | 42 |
1,087 | On Neural Networks with Minimal Weights Vasken Bohossian J ehoshua Bruck California Institute of Technology Mail Code 136-93 Pasadena, CA 91125 E-mail: {vincent, bruck }«Iparadise. cal tech. edu Abstract Linear threshold elements are the basic building blocks of artificial neural networks.... | 1995 | 43 |
1,088 | Neural Networks with Quadratic VC Dimension Pascal Koiran* Lab. de l'Informatique du Paraltelisme Ecole Normale Superieure de Lyon - CNRS 69364 Lyon Cedex 07, France Abstract Eduardo D. Sontagt Department of Mathematics Rutgers University New Brunswick, NJ 08903, USA This paper shows th... | 1995 | 44 |
1,089 | Factorial Hidden Markov Models Zoubin Ghahramani zoubin@psyche.mit.edu Department of Computer Science University of Toronto Toronto, ON M5S 1A4 Canada Michael I. Jordan jordan@psyche.mit.edu Department of Brain & Cognitive Sciences Massachusetts Institute of Technology Cambridge, MA 021... | 1995 | 45 |
1,090 | Extracting Thee-Structured Representations of Thained Networks Mark W. Craven and Jude W. Shavlik Computer Sciences Department University of Wisconsin-Madison 1210 West Dayton St. Madison, WI 53706 craven@cs.wisc.edu, shavlik@cs.wisc.edu Abstract A significant limitation of neural networks is... | 1995 | 46 |
1,091 | Improving Committee Diagnosis with Resampling Techniques Bambang Parmanto Department of Information Science University of Pittsburgh Pittsburgh, PA 15260 parmanto@li6.pitt. edu Paul W. Munro Department of Information Science University of Pittsburgh Pittsburgh, PA 15260 munro@li6.pitt. ... | 1995 | 47 |
1,092 | The Capacity of a Bump Gary William Flake· Institute for Advance Computer Studies University of Maryland College Park, MD 20742 Abstract Recently, several researchers have reported encouraging experimental results when using Gaussian or bump-like activation functions in multilayer perceptrons. Netw... | 1995 | 48 |
1,093 | From Isolation to Cooperation: An Alternative View of a System of Experts Stefan Schaal:!:* Christopher C. Atkeson:!: sschaal@cc.gatech.edu cga@cc.gatech.edu http://www.cc.gatech.eduifac/Stefan.Schaal http://www.cc.gatech.eduifac/Chris.Atkeson +College of Computing, Georgia Tech, 801 Atlantic Dr... | 1995 | 49 |
1,094 | The Gamma MLP for Speech Phoneme Recognition Steve Lawrence~ Ah Chung Tsoi, Andrew D. Back {lawrence,act,back}Oelec.uq.edu.au Department of Electrical and Computer Engineering University of Queensland St. Lucia Qld 4072 Australia Abstract We define a Gamma multi-layer perceptron (MLP) as an MLP ... | 1995 | 5 |
1,095 | Some results on convergent unlearning algorithm Serguei A. Semenov &: Irina B. Shuvalova Institute of Physics and Technology Prechistenka St. 13/7 Moscow 119034, Russia Abstract In this paper we consider probabilities of different asymptotics of convergent unlearning algorithm for the Hopfield-t... | 1995 | 50 |
1,096 | Forward-backward retraining of recurrent neural networks Andrew Senior • Tony Robinson Cambridge University Engineering Department Trumpington Street, Cambridge, England Abstract This paper describes the training of a recurrent neural network as the letter posterior probability estimator for a h... | 1995 | 51 |
1,097 | KODAK lMAGELINK™ OCR Alphanumeric Handprint Module Alexander Shustorovich and Christopher W. Thrasher Business Imaging Systems, Eastman Kodak Company, Rochester, NY 14653-5424 ABSTRACT This paper describes the Kodak Imageliok TM OCR alphanumeric handprint module. There are two neural network algorithm... | 1995 | 52 |
1,098 | Predictive Q-Routing: A Memory-based Reinforcement Learning Approach to Adaptive Traffic Control Samuel P.M. Choi, Dit-Yan Yeung Department of Computer Science Hong Kong University of Science and Technology Clear Water Bay, Kowloon, Hong Kong {pmchoi,dyyeung}~cs.ust.hk Abstract In this paper,... | 1995 | 53 |
1,099 |
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