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 |
|---|---|---|---|
700 | Non-Intrusive Gaze Tracking Using Artificial Neural Networks Shumeet Baluja baluja@cs.cmu.edu School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 Abstract Dean Pomerleau pomerleau @cs.cmu.edu School of Computer Science Carnegie Mellon University Pittsburgh, PA ... | 1993 | 10 |
701 | Dynamic Modulation of Neurons and Networks Eve Marder Center for Complex Systems Brandeis University Waltham, MA 02254 USA Abstract Biological neurons have a variety of intrinsic properties because of the large number of voltage dependent currents that control their activity. Neuromodulatory sub... | 1993 | 100 |
702 | Training Neural Networks with Deficient Data Volker Tresp Siemens AG Central Research 81730 Munchen Germany tresp@zfe.siemens.de Subutai Ahmad Interval Research Corporation 1801-C Page Mill Rd. Palo Alto, CA 94304 ahmad@interval.com Ralph N euneier Siemens AG Central Researc... | 1993 | 101 |
703 | When Will a Genetic Algorithm Outperform Hill Climbing? Melanie Mitchell Santa Fe Institute 1660 Old Pecos Trail, Suite A Santa Fe, NM 87501 John H. HoUand Dept. of Psychology University of Michigan Ann Arbor, MI 48109 Stephanie Forrest Dept. of Computer Science University of New Mex... | 1993 | 102 |
704 | Unsupervised Parallel Feature Extraction from First Principles .. Mats Osterberg Image Processing Laboratory Dept. EE., Linkoping University S-58183 Linkoping Sweden Reiner Lenz Image Processing Laboratory Dept. EE., Linkoping University S-58183 Linkoping Sweden Abstract We describe ... | 1993 | 103 |
705 | Classification of Electroencephalogram using Artificial Neural Networks A C Tsoi*, D S C So*, A Sergejew** *Department of Electrical Engineering **Department of Psychiatry University of Queensland St Lucia, Queensland 4072 Australia Abstract In this paper, we will consider the problem of clas... | 1993 | 104 |
706 | Comparisoll Training for a Resclleduling Problem ill Neural Networks Didier Keymeulen Artificial Intelligence Laboratory Vrije Universiteit Brussel Pleinlaan 2, 1050 Brussels Belgium Abstract Martine de Gerlache Prog Laboratory Vrije Universiteit Brussel Pleinlaan 2, 1050 Brussels Be... | 1993 | 105 |
707 | Bounds on the complexity of recurrent neural network implementations of finite state machines Bill G. Horne NEC Research Institute 4 Independence Way Princeton, NJ 08540 Don R. Hush EECE Department University of New Mexico Albuquerque, NM 87131 Abstract In this paper the efficiency o... | 1993 | 106 |
708 | Classification of Multi-Spectral Pixels by the Binary Diamond Neural Network Yehuda Salu Department of Physics and CSTEA, Howard University, Washington, DC 20059 Abstract A new neural network, the Binary Diamond, is presented and its use as a classifier is demonstrated and evaluated. The network is... | 1993 | 107 |
709 | Optimal Brain Surgeon: Extensions and performance comparisons Babak Hassibi* David G. Stork Takahiro Watanabe Ricoh California Research Center 2882 Sand Hill Road Suite 115 Menlo Park, CA 94025-7022 and Gregory Wolff * Department of Electrical Engineering 105B Durand Hall Stanford Un... | 1993 | 108 |
710 | Agnostic PAC-Learning of Functions on Analog Neural Nets (Extended Abstract) Wolfgang Maass Institute for Theoretical Computer Science Technische Universitaet Graz Klosterwiesgasse 32/2 A-BOlO Graz, Austria e-mail: maass@igi.tu-graz.ac.at Abstract: There exist a number of negative results ... | 1993 | 109 |
711 | Learning Curves: Asymptotic Values and Rate of Convergence Corinna Cortes, L. D. Jackel, Sara A. Solla, Vladimir Vapnik, and John S. Denker AT&T Bell Laboratories Holmdel, NJ 07733 Abstract Training classifiers on large databases is computationally demanding. It is desirable to develop efficient pr... | 1993 | 11 |
712 | Mixtures of Controllers for Jump Linear and Non-linear Plants Timothy W. Cacciatore Department of Neurosciences University of California at San Diego La Jolla, CA 92093 Abstract Steven J. Nowlan Synaptics, Inc. 2698 Orchard Parkway San Jose, CA 95134 We describe an extension to the Mixt... | 1993 | 110 |
713 | A Hybrid Radial Basis Function Neurocomputer and Its Applications Steven S. Watkins ECE Department UCSD La Jolla. CA. 92093 Raoul Tawel JPL Bjorn Lambrigtsen JPL Paul M. Chau ECE Department UCSD La Jolla, CA. 92093 Caltech Pasadena. CA. 91109 Caltech Pasadena. CA. 9110... | 1993 | 111 |
714 | Resolving motion ambiguities K. I. Diamantaras Siemens Corporate Research 755 College Rd. East Princeton, NJ 08540 Abstract D. Geiger* Courant Institute, NYU Mercer Street New York, NY 10012 We address the problem of optical flow reconstruction and in particular the problem of resolving am... | 1993 | 112 |
715 | Developing Population Codes By Minimizing Description Length Richard S. Zemel CNL, The Salk Institute 10010 North Torrey Pines Rd. La J oUa, CA 92037 Geoffrey E. Hinton Department of Computer Science University of Toronto Toronto M5S 1A4 Canada Abstract The Minimum Description Length... | 1993 | 113 |
716 | Learning in Computer Vision and Image Understanding Hayit Greenspan Department of Electrical Engineering California Institute of Technology, 116-81 Pasadena, CA 91125 There is an increasing interest in the area of Learning in Computer Vision and Image Understanding, both from researchers in the lea... | 1993 | 114 |
717 | Neural Network Definitions of Highly Predictable Protein Secondary Structure Classes Alan Lapedes Complex Systems Group (TI3) LANL, MS B213 Los Alamos N .M. 87545 and The Santa Fe Institute, Santa Fe, New Mexico Evan Steeg Department of Computer Science University of Toronto, Toronto, Canada ... | 1993 | 115 |
718 | Coupled Dynamics of Fast Neurons and Slow Interactions A.C.C. Coolen R.W. Penney D. Sherrington Dept. of Physics - Theoretical Physics University of Oxford 1 Keble Road, Oxford OXI 3NP, U.K. Abstract A simple model of coupled dynamics of fast neurons and slow interactions, modelling self-orga... | 1993 | 116 |
719 | U sing Local Trajectory Optimizers To Speed Up Global Optimization In Dynamic Programming Christopher G. Atkeson Department of Brain and Cognitive Sciences and the Artificial Intelligence Laboratory Massachusetts Institute of Technology, NE43-771 545 Technology Square, Cambridge, MA 02139 617-25... | 1993 | 117 |
720 | Connectionist Modeling and Parallel Architectures Joachim Diederich Neurocomputing Research Centre School of Computing Science Queensland University of Technology Brisbane Q 400 1 Australia Ah Chung Tsoi Department of Electrical and Computer Engineering University of Queensland St Lucia... | 1993 | 118 |
721 | Structural and Behavioral Evolution of Recurrent Networks Gregory M. Saunders, Peter J. Angeline, and Jordan B. Pollack Laboratory for Artificial Intelligence Research Department of Computer and Information Science The Ohio State University Columbus, Ohio 43210 saunders@cis.ohio-state.edu Abstra... | 1993 | 119 |
722 | How to Describe Neuronal Activity: Spikes, Rates, or Assemblies? Wulfram Gerstner and J. Leo van Hemmen Physik-Department der TU Miinchen D-85748 Garching bei Miinchen, Germany Abstract What is the 'correct' theoretical description of neuronal activity? The analysis of the dynamics of a globally co... | 1993 | 12 |
723 | Two-Dimensional Object Localization by Coarse-to-Fine Correlation Matching Chien-Ping Lu and Eric Mjolsness Department of Computer Science Yale University New Haven, CT 06520-8285 Abstract We present a Mean Field Theory method for locating twodimensional objects that have undergone rigid transforma... | 1993 | 120 |
724 | WATTLE: A Trainable Gain Analogue VLSI Neural Network Richard Coggins and Marwan Jabri Systems Engineering and Design Automation Laboratory Department of Electrical Engineering J03, University of Sydney, 2006. Australia. Email: richardc@sedal.su.oz.au marwan@sedal.su.oz.au Abstract This pa... | 1993 | 121 |
725 | Neurobiology, Psychophysics, and Computational Models of Visual Attention Ernst Niebur Computation and Neural Systems California Institute of Technology Pasadena, CA 91125, USA Bruno A. Olshausen Department of Anatomy and Neurobiology Washington University School of Medicine St. Louis, MO ... | 1993 | 122 |
726 | Bayesian Backpropagation Over 1-0 Functions Rather Than Weights David H. Wolpert The Santa Fe Institute 1660 Old Pecos Trail Santa Fe, NM 87501 Abstract The conventional Bayesian justification of backprop is that it finds the MAP weight vector. As this paper shows, to find the MAP i-o function ... | 1993 | 123 |
727 | Fast Non-Linear Dimension Reduction Nanda Kambhatla and Todd K. Leen Department of Computer Science and Engineering Oregon Graduate Institute of Science & Technology P.O. Box 91000 Portland, OR 97291-1000 Abstract We present a fast algorithm for non-linear dimension reduction. The algorithm builds ... | 1993 | 124 |
728 | Observability of Neural Network Behavior Max Garzon Fernanda Botelho sarzonmOherme •. msci.mem.t.edu botelhoflherme •. msci.mem.t.edu Institute for Intelligent Systems Department of Mathematical Sciences Memphis State University Memphis, TN 38152 U.S.A. Abstract We prove that except pos... | 1993 | 125 |
729 | Optimal signalling in Attractor Neural Networks Isaac Meilijson Eytan Ruppin ... School of Mathematical Sciences Raymond and Beverly Sackler Faculty of Exact Sciences Tel-Aviv University, 69978 Tel-Aviv, Israel. Abstract In [Meilijson and Ruppin, 1993] we presented a methodological framework ... | 1993 | 126 |
730 | Neural Network Exploration Using Optimal Experiment Design David A. Cohn Dept. of Brain and Cognitive Sciences Massachusetts Inst. of Technology Cambridge, MA 02139 Abstract Consider the problem of learning input/output mappings through exploration, e.g. learning the kinematics or dynamics of a ... | 1993 | 127 |
731 | Directional Hearing by the Mauthner System .Audrey L. Gusik Department of Psychology University of Colorado Boulder, Co. 80309 Abstract Robert c. Eaton E. P. O. Biology University of Colorado Boulder, Co. 80309 We provide a computational description of the function of the Mauthner syste... | 1993 | 128 |
732 | Asynchronous Dynamics of Continuous Time Neural Networks Xin Wang Computer Science Department University of California at Los Angeles Los Angeles, CA 90024 Qingnan Li Department of Mathematics University of Southern California Los Angeles, CA 90089-1113 Edward K. Blum Department of Math... | 1993 | 129 |
733 | 896 Digital Boltzmann VLSI for constraint satisfaction and learning Michael Murray t Ming-Tak Leung t Kan Boonyanit t Kong Kritayakirana t James B. Burrt* Gregory J. Wolff+ Takahiro Watanabe+ Edward Schwartz+ David G. Storktt Allen M. Petersont t Department of Electrical Engineeri... | 1993 | 13 |
734 | Development of Orientation and Ocular Dominance Columns in Infant Macaques Klaus Obermayer Howard Hughes Medical Institute Salk-Institute La Jolla, CA 92037 Lynne Kiorpes Center for Neural Science New York University New York, NY 10003 Gary G. Blasdel Department of Neurobiology Harva... | 1993 | 130 |
735 | Counting function theorem for multi-layer networks Adam Kowalczyk Telecom Australia, Research Laboratories 770 Blackburn Road, Clayton, Vic. 3168, Australia (a.kowalczyk@trl.oz.au) Abstract We show that a randomly selected N-tuple x of points ofRn with probability> 0 is such that any multi-layer... | 1993 | 131 |
736 | Supervised Learning with Growing Cell Structures Bernd Fritzke Institut fiir Neuroinformatik Ruhr-U niversitat Bochum Germany Abstract We present a new incremental radial basis function network suitable for classification and regression problems. Center positions are continuously updated through... | 1993 | 132 |
737 | A Learning Analog Neural Network Chip with Continuous-Time Recurrent Dynamics Gert Cauwenberghs* California Institute of Technology Department of Electrical Engineering 128-95 Caltech, Pasadena, CA 91125 E-mail: gertalcco. cal tech. edu Abstract We present experimental results on supervised l... | 1993 | 133 |
738 | Functional Models of Selective Attention and Context Dependency Thomas H. Hildebrandt Department of Electrical Engineering and Computer Science Room 304 Packard Laboratory 19 Memorial Drive West Lehigh University Bethlehem PA 18015-3084 thildebr@aragorn.eecs.lehigh.edu Scope This workshop ... | 1993 | 134 |
739 | Learning Classification with Unlabeled Data Virginia R. de Sa desa@cs.rochester.edu Department of Computer Science University of Rochester Rochester, NY 14627 Abstract One of the advantages of supervised learning is that the final error metric is available during training. For classifiers, the algo... | 1993 | 135 |
740 | Temporal Difference Learning of Position Evaluation in the Game of Go Nicol N. Schraudolph Peter Dayan Terrence J. Sejnowski schraudo~salk.edu dayan~salk.edu terry~salk.edu Computational Neurobiology Laboratory The Salk Institute for Biological Studies San Diego, CA 92186-5800 Abstract ... | 1993 | 136 |
741 | Recovering a Feed-Forward Net From Its Output Charles Fefferman * and Scott Markel David Sarnoff Research Center CN5300 Princeton, N J 08543-5300 e-mail: cf9imath.princeton .edu smarkel@sarnoff.com ABSTRACT We study feed-forward nets with arbitrarily many layers, using the standard sigmoid, t... | 1993 | 137 |
742 | Optimal Stochastic Search and Adaptive Momentum Todd K. Leen and Genevieve B. Orr Oregon Graduate Institute of Science and Technology Department of Computer Science and Engineering P.O.Box 91000, Portland, Oregon 97291-1000 Abstract Stochastic optimization algorithms typically use learning rate ... | 1993 | 138 |
743 | The Parti-game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-spaces Andrew W. Moore School of Computer Science Carnegie-Mellon University Pittsburgh, PA 15213 Abstract Parti-game is a new algorithm for learning from delayed rewards in high dimensional real... | 1993 | 139 |
744 | Feature Densities are Required for Computing Feature Correspondences Subutai Ahmad Interval Research Corporation 1801-C Page Mill Road, Palo Alto, CA 94304 E-mail: ahmadCDinterval.com Abstract The feature correspondence problem is a classic hurdle in visual object-recognition concerned with dete... | 1993 | 14 |
745 | Complexity Issues in Neural Computation and Learning V. P. Roychowdhnry School of Electrical Engineering Purdue University West Lafayette, IN 47907 Email: vwani@ecn.purdue.edu K.-Y. Sin Dept.. of Electrical & Compo Engr. U ni versit.y of California at Irvine Irvine, CA 92717 Email: siu@... | 1993 | 140 |
746 | Stability and Observability Max Garzon Fernanda Botelho garzonmGhermea.maci.memat.edu botelhofGhermea.maci.memat.edu Institute for Intelligent Systems Department of Mathematical Sciences Memphis State University Memphis, TN 38152 U.S.A. The theme was the effect of perturbations of the defining para... | 1993 | 141 |
747 | The Role of MT Neuron Receptive Field Surrounds in Computing Object Shape from Velocity Fields G.T.Buracas & T.D.Albright Vision Center Laboratory, The Salk Institute, P.O.Box 85800, San Diego, California 92138-9216 Abstract The goal of this work was to investigate the role of primate MT neurons... | 1993 | 142 |
748 | Estimating analogical similarity by dot-products of Holographic Reduced Representations. Tony A. Plate Department of Computer Science, University of Toronto Toronto, Ontario, Canada M5S 1A4 email: tap@ai.utoronto.ca Abstract Models of analog retrieval require a computationally cheap method of es... | 1993 | 143 |
749 | Segmental Neural Net Optimization for Continuous Speech Recognition Ymg Zhao Richard Schwartz John Makhoul George Zavaliagkos BBN System and Technologies 70 Fawcett Street Cambridge MA 02138 Abstract Previously, we had developed the concept of a Segmental Neural Net (SNN) for phonetic m... | 1993 | 144 |
750 | Grammatical Inference by Attentional Control of Synchronization in an Oscillating Elman Network Bill Baird Dept Mathematics, U.C.Berkeley, Berkeley, Ca. 94720, baird@math.berkeley.edu Todd Troyer Dept of Phys., U.C.San Francisco, 513 Parnassus Ave. San Francisco, Ca. 94143, todd@p... | 1993 | 145 |
751 | Bayesian Modeling and Classification of Neural Signals 590 Michael S. Lewicki Computation and Neural Systems Program California Institute of Technology 216-76 Pasadena, CA 91125 lewickiOcns.caltech.edu Abstract Signal processing and classification algorithms often have limited applicabilit... | 1993 | 146 |
752 | Supervised learning from incomplete data via an EM approach Zoubin Ghahramani and Michael I. Jordan Department of Brain & Cognitive Sciences Massachusett.s Institute of Technology Cambridge, MA 02139 Abstract Real-world learning tasks may involve high-dimensional data sets with arbitrary pattern... | 1993 | 147 |
753 | Correlation Functions in a Large Stochastic Neural Network Iris Ginzburg School of Physics and Astronomy Raymond and Beverly Sackler Faculty of Exact Sciences Tel-Aviv University Tel-Aviv 69978, Israel Haim Sompolinsky Racah Institute of Physics and Center for Neural Computation Hebrew Univer... | 1993 | 148 |
754 | Clustering with a Domain-Specific Distance Measure Steven Gold, Eric Mjolsness and Anand Rangarajan Department of Computer Science Yale University New Haven, CT 06520-8285 Abstract With a point matching distance measure which is invariant under translation, rotation and permutation, we learn 2-D... | 1993 | 149 |
755 | Emergence of Global Structure from Local Associations Thea B. Ghiselli-Crippa Department of Infonnation Science University of Pittsburgh Pittsburgh PA 15260 Paul W. Munro Department of Infonnation Science University of Pittsburgh Pittsburgh PA 15260 ABSTRACT A variant of the encoder ... | 1993 | 15 |
756 | SPEAKER RECOGNITION USING NEURAL TREE NETWORKS Kevin R. Farrell and Richard J. Marnrnone CAIP Center, Rutgers University Core Building, Frelinghuysen Road Piscataway, New Jersey 08855 Abstract A new classifier is presented for text-independent speaker recognition. The new classifier is called th... | 1993 | 150 |
757 | Tonal Music as a Componential Code: Learning Temporal Relationships Between and Within Pitch and Timing Components Janet Wiles Catherine Stevens Department of Psychology University of Queensland QLD 4072 Australia kates@psych.psy.uq.oz.au Depts of Psychology & Computer Science University o... | 1993 | 151 |
758 | Connectionist Models for A uditory Scene Analysis Richard o. Duda Department of Electrical Engineering San Jose State University San Jose, CA 95192 Abstract Although the visual and auditory systems share the same basic tasks of informing an organism about its environment, most connectionist work... | 1993 | 152 |
759 | Learning in Compositional Hierarchies: Inducing the Structure of Objects from Data Joachim Utans Oregon Graduate Institute Department of Computer Science and Engineering P.O. Box 91000 Portland, OR 97291-1000 utans@cse.ogi.edu Abstract I propose a learning algorithm for learning hierarchical ... | 1993 | 153 |
760 | VLSI Phase Locking Architectures for Feature Linking in Multiple Target Tracking Systems Andreas G. Andreou andreou@jhunix.hcf.jhu.edu Department of Electrical and Computer Engineering The Johns Hopkins University Baltimore, MD 21218 Thomas G. Edwards tedwards@src.umd.edu Department of ... | 1993 | 154 |
761 | A Network Mechanism for the Determination of Shape-From-Texture Ko Sakai and Leif H. Finkel Department of Bioengineering and Institute of Neurological Sciences University of Pennsylvania 220 South 33rd Street, Philadelphia, PA 19104-6392 ko@ganymede.seas.upenn.edu, leif@ganymede.seas.upenn.edu A... | 1993 | 155 |
762 | Encoding Labeled Graphs by Labeling RAAM Alessandro Sperduti* Department of Computer Science Pisa University Corso Italia 40, 56125 Pisa, Italy Abstract In this paper we propose an extension to the RAAM by Pollack. This extension, the Labeling RAAM (LRAAM), can encode labeled graphs with cycles ... | 1993 | 156 |
763 | Learning Mackey-Glass from 25 examples, Plus or Minus 2 Mark Plutowski· Garrison Cottrell· Halbert White·· Institute for Neural Computation *Department of Computer Science and Engineering **Department of Economics University of California, San Diego La J oHa, CA 92093 Abstract We apply active... | 1993 | 157 |
764 | Structured Machine Learning For 'Soft' Classification with Smoothing Spline ANOVA and Stacked Tuning, Testing and Evaluation Grace Wahba Dept of Statistics University of Wisconsin Madison, WI 53706 Yuedong Wang Dept of Statistics University of Wisconsin Madison, WI 53706 Chong Gu ... | 1993 | 158 |
765 | Robot Learning: Exploration and Continuous Domains David A. Cohn MIT Dept. of Brain and Cognitive Sciences Cambridge, MA 02139 The goal of this workshop was to discuss two major issues: efficient exploration of a learner's state space, and learning in continuous domains. The common themes that emer... | 1993 | 16 |
766 | Lower Boundaries of Motoneuron Desynchronization via Renshaw Interneurons Mitchell Gil Maltenfort It Dept. of Biomedical Engineering Northwestern University Evanston, IT.. 60201 c. J. Heckman V. A. Research Service Lakeside Hospital and Dept. of Physiology Northwestern University Chi... | 1993 | 17 |
767 | Catastrophic interference in connectionist networks: Can it be predicted, can it be prevented? Robert M. French Computer Science Department Willamette University Salem, Oregon 97301 french@willamette.edu 1 OVERVIEW Catastrophic forgetting occurs when connectionist networks learn new inform... | 1993 | 18 |
768 | Signature Verification using a "Siamese" Time Delay Neural Network Jane Bromley, Isabelle Guyon, Yann LeCun, Eduard Sickinger and Roopak Shah AT&T Bell Laboratories Holmdel, N J 07733 jbromley@big.att.com Copyrighte, 1994, American Telephone and Telegraph Company used by permission. Abstract ... | 1993 | 19 |
769 | Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation Oded Maron Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge, MA 02139 Abstract Andrew W. Moore Robotics Institute School of Computer Science Carnegie Mell... | 1993 | 2 |
770 | Connectionism for Music and Audition Andreas S. Weigend Department of Computer Science and Institute of Cognitive Science University of Colorado Boulder, CO 80309-0430 Abstract This workshop explored machine learning approaches to 3 topics: (1) finding structure in music (analysis, continuation,... | 1993 | 20 |
771 | Central and Pairwise Data Clustering by Competitive Neural Networks Joachim Buhmann & Thomas Hofmann Rheinische Friedrich-Wilhelms-U niversiHit Institut fiir Informatik II, RomerstraBe 164 D-53117 Bonn, Fed. Rep. Germany Abstract Data clustering amounts to a combinatorial optimization problem... | 1993 | 21 |
772 | Processing of Visual and Auditory Space and Its Modification by Experience Josef P. Rauschecker Laboratory of Neurophysiology National Institute of Mental Health Poolesville, MD 20837 Terrence J. Sejnowski Computational Neurobiology Lab The Salk: Institute San Diego, CA 92138 Visual spatia... | 1993 | 22 |
773 | A Connectionist Model of the Owl's Sound Localization System D alliel J. Rosen· Department of Psychology Stanford University Stanford, CA 94305 David E. Rumelhart Department of Psychology Stanford University Stanford, CA 94305 Eric. I. Knudsen Department of Neurobiology Stanford Univ... | 1993 | 23 |
774 | Identifying Fault-Prone Software Modules Using Feed-Forward Networks: A Case Study N. Karunanithi Room 2E-378, Bellcore 435 South Street Morristown, NJ 07960 E-mail: karun@faline.bellcore.com Abstract Functional complexity of a software module can be measured in terms of static complexity ... | 1993 | 24 |
775 | Event-Driven Simulation of Networks of Spiking l'Ieurons Lloyd Watts Synaptics Inc. 2698 Orchard Parkway San Jose CA 95134 11oydGsynaptics.com Abstract A fast event-driven software simulator has been developed for simulating large networks of spiking neurons and synapses. The primitive network e... | 1993 | 25 |
776 | Grammatical Inference by Attentional Control of Synchronization in an Oscillating Elman Network Bill Baird Dept Mathematics, U.C.Berkeley, Berkeley, Ca. 94720, baird@math.berkeley.edu Todd Troyer Dept of Phys., U.C.San Francisco, 513 Parnassus Ave. San Francisco, Ca. 94143, todd@p... | 1993 | 26 |
777 | Bayesian Self-Organization Alan L. Yuille Division of Applied Sciences Harvard University Cambridge, MA 02138 Stelios M. Smirnakis Lyman Laboratory of Physics Harvard University Cambridge, MA 02138 Lei Xu * Dept. of Computer Science HSH ENG BLDG, Room 1006 The Chinese University of H... | 1993 | 27 |
778 | The "Softmax" Nonlinearity: Derivation Using Statistical Mechanics and Useful Properties as a Multiterminal Analog Circuit Element I. M. Elfadel J. L. Wyatt, Jr. Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge, MA 02139 Research Laboratory of Electronics ... | 1993 | 28 |
779 | Robust Parameter Estimation And Model Selection For Neural Network Regression Yong Liu Department of Physics Institute for Brain and Neural Systems Box 1843, Brown University Providence, RI 02912 yong~cns.brown.edu Abstract In this paper, it is shown that the conventional back-propagation ... | 1993 | 29 |
780 | Pulling It All Together: Methods for Combining Neural Networks Michael P. Perrone Institute for Brain and Neural Systems Brown University Providence, RI mpp@cns. brown. edu The past several years have seen a tremendous growth in the complexity of the recognition, estimation and control tasks exp... | 1993 | 3 |
781 | Computational Elements of the Adaptive Controller of the Human Arm Reza Shadmehr and Ferdinando A. Mussa-Ivaldi Dept. of Brain and Cognitive Sciences M. I. T ., Cambridge, MA 02139 Email: reza@ai.mit.edu, sandro@ai.mit.edu Abstract We consider the problem of how the CNS learns to control dynamics o... | 1993 | 30 |
782 | Transition Point Dynamic Programming Kenneth M. Buckland'" Dept. of Electrical Engineering University of British Columbia Vancouver, B.C, Canada V6T 1Z4 buckland@pmc-sierra.bc.ca Peter D. Lawrence Dept. of Electrical Engineering University of British Columbia Vancouver, B.C, Canada V6T 1Z4 ... | 1993 | 31 |
783 | Dopaminergic Neuromodulation Brings a Dynamical Plasticity to the Retina Eric Boussard Jean-Fran~ois Vibert B3E, INSERM U263 Faculte de medecine Saint-Antoine 27 rue Chaligny 75571 Paris cedex 12 Abstract The fovea of a mammal retina was simulated with its detailed biological properties to st... | 1993 | 32 |
784 | Monte Carlo Matrix Inversion and Reinforcement Learning Andrew Barto and Michael Duff Computer Science Department University of Massachusetts Amherst, MA 01003 Abstract We describe the relationship between certain reinforcement learning (RL) methods based on dynamic programming (DP) and a class ... | 1993 | 33 |
785 | Globally Trained Handwritten Word Recognizer using Spatial Representation, Convolutional Neural Networks and Hidden Markov Models Yoshua Bengio ... Dept. Informatique et Recherche Operationnelle Universite de Montreal Montreal, Qc H3C-3J7 Donnie Henderson AT&T Bell Labs Holmdel NJ 07733 ... | 1993 | 34 |
786 | Robust Reinforcement Learning Motion Planning Satinder P. Singh'" Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Cambridge, MA 02139 singh@psyche.mit.edu • In Andrew G. Barto, Roderic Grupen, and Christopher Connolly Department of Computer Science Universi... | 1993 | 35 |
787 | Optimal Stopping and Effective Machine Complexity in Learning Changfeng Wang Department of SystE'IIlS Sci. (Iud Ell/!,. Salltosh S. Venkatesh Dp»artn}(,llt (If Elf'drical EugiJlPprinJ!, U IIi v('rsi ty (If Ppnllsyl va nia Philadelphia, PA, U.S.A. 19104 UJliversity of PPIIIlsylv1I.Ili(l Philad... | 1993 | 36 |
788 | Exploiting Chaos to Control the Future Gary W. Flake* Guo-Zhen Sunt Yee-Chun Leet Hsing-Hen Chent Institute for Advance Computer Studies University of Maryland College Park, MD 20742 Abstract Recently, Ott, Grebogi and Yorke (OGY) [6] found an effective method to control chaotic systems... | 1993 | 37 |
789 | Backpropagation Convergence Via Deterministic Nonmonotone Perturbed Minimization o. L. Mangasarian & M. v. Solodov Computer Sciences Department University of Wisconsin Madison, WI 53706 Email: olvi@cs.wisc.edu, solodov@cs.wisc.edu Abstract The fundamental backpropagation (BP) algorithm for tr... | 1993 | 38 |
790 | Fool.s Gold: Extracting Finite State Machines From Recurrent Network Dynamics John F. Kolen Laboratory for Artificial Intelligence Research Department of Computer and Information Science The Ohio State University Columbus,OH 43210 kolen-j @cis.ohio-state.edu Abstract Several recurrent network... | 1993 | 39 |
791 | Cross-Validation Estimates IMSE Mark Plutowski t* Shinichi Sakata t Halbert White t* t Department of Computer Science and Engineering t Department of Economics * Institute for Neural Computation University of California, San Diego Abstract Integrated Mean Squared Error (IMSE) is a version of ... | 1993 | 4 |
792 | Two Iterative Algorithms for Computing the Singular Value Decomposition from Input / Output Samples Terence D. Sanger Jet Propulsion Laboratory MS 303-310 4800 Oak Grove Drive Pasadena, CA 91109 Abstract The Singular Value Decomposition (SVD) is an important tool for linear algebra and can... | 1993 | 40 |
793 | Efficient Computation of Complex Distance Metrics Using Hierarchical Filtering Patrice Y. Simard AT&T Bell Laboratories Holmdel, NJ 07733 Abstract By their very nature, memory based algorithms such as KNN or Parzen windows require a computationally expensive search of a large database of prot... | 1993 | 41 |
794 | Statistics of Natural Images: Scaling in the Woods Daniel L. Ruderman* and William Bialek NEe Research Institute 4 Independence Way Princeton, N.J. 08540 Abstract In order to best understand a visual system one should attempt to characterize the natural images it processes. We gather images f... | 1993 | 42 |
795 | GDS: Gradient Descent Generation of Symbolic Classification Rules Reinhard Blasig Kaiserslautern University, Germany Present address: Siemens AG, ZFE ST SN 41 81730 Miinchen, Germany Abstract Imagine you have designed a neural network that successfully learns a complex classification task. What ... | 1993 | 43 |
796 | Packet Routing in Dynamically Changing Networks: A Reinforcement Learning Approach Justin A. Boyan School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 Michael L. Littman· Cognitive Science Research Group Bellcore Morristown, NJ 07962 Abstract This paper describ... | 1993 | 44 |
797 | A Massively-Parallel SIMD Processor for Neural Network and Machine Vision Applications Michael A. Glover Current Technology, Inc. 99 Madbury Road Durham, NH 03824 W. Thomas Miller, III Department of Electrical and Computer Engineering The University of New Hampshire Durham, NH 03824 Abs... | 1993 | 45 |
798 | How to Choose an Activation Function H. N. Mhaskar Department of Mathematics California State University Los Angeles, CA 90032 hmhaska@calstatela.edu c. A. Micchelli IBM Watson Research Center P. O. Box 218 Yorktown Heights, NY 10598 cam@watson.ibm.com Abstract We study the complexit... | 1993 | 46 |
799 | Efficient Simulation of Biological Neural Networks on Massively Parallel Supercomputers with Hypercube Archi tect ure Ernst Niebur Computation and Neural Systems California Institute of Technology Pasadena, CA 91125, USA Dean Brettle Booz, Allen and Hamilton, Inc. 8283 Greensboro Drive ... | 1993 | 47 |
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