Seed42Lab/ACL-papers · Datasets at Hugging Face

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
200	178 Lang and Hinton Dimensionality Reduction and Prior Knowledge in E-set Recognition Kevin J. Lang1 Geoffrey E. Hinton Computer Science Dept. Computer Science Dept. Carnegie Mellon University Pittsburgh, PA 15213 University of Toronto Toronto, Ontario M5S lA4 Canada USA ABSTRA...	1989	22
201	274 WeinshalI, Edelman and BiiIthofT A self-organizing multiple-view representation of 3D objects Daphna Weinshall Center for Biological Information Processing MIT E25-201 Cambridge, MA 02139 Shimon Edelman Center for Biological Information Processing MIT E25-201 Cambridge, MA 021...	1989	23
202	68 Baird Associative Memory in a Simple Model of Oscillating Cortex Bill Baird Dept Molecular and Cell Biology, U .C.Berkeley, Berkeley, Ca. 94720 ABSTRACT A generic model of oscillating cortex, which assumes "minimal" coupling justified by known anatomy, is shown to function as an associativ...	1989	24
203	498 Barben, Toomarian and Gulati Adjoint Operator Algorithms for Faster Learning in Dynamical Neural Networks Jacob Barhen Nikzad Toomarian Center for Space Microelectronics Technology Jet Propulsion Laboratory California Institute of Technology Pasadena, CA 91109 ABSTRACT Sandeep Gulat...	1989	25
204	84 Wilson and Bower Computer Simulation of Oscillatory Behavior in Cerebral Cortical Networks Matthew A. Wilson and James M. Bower! Computation and Neural Systems Program Division of Biology, 216-76 California Institute of Technology Pasadena, CA 9 1125 ABSTRACT It has been known for many ...	1989	26
205	660 Geiger and Girosi Coupled Markov Random Fields and Mean Field Theory Davi Geigerl Artificial Intelligence Laboratory, MIT 545 Tech. Sq. # 792 Cambridge, MA 02139 and ABSTRACT Federico Girosi Artificial Intelligence Laboratory, MIT 545 Tech. Sq. # 788 Cambridge, MA 02139 ...	1989	27
206	Pulse-Firing Neural Chips for Hundreds of Neurons 785 PULSE-FIRING NEURAL CIDPS FOR HUNDREDS OF NEURONS Michael Brownlow Lionel Tarassenko Dept. Eng. Science Univ. of Oxford Oxford OX1 3PJ Alan F. Murray Dept. Electrical Eng. Univ. of Edinburgh Mayfield Road Edinburgh EH9 3JL A...	1989	28
207	Rule Representations in a Connectionist Chunker 431 Rule Representations in a Connectionist Chunker David S. Touretzky Gillette Elvgren m School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 ABSTRACT We present two connectionist architectures for chunking of symbolic ...	1989	29
208	Training Stochastic Model Recognition Algorithms 211 Training Stochastic Model Recognition Algorithms as Networks can lead to Maximum Mutual Information Estimation of Parameters John s. Bridle Royal Signals and Radar Establishment Great Malvern Worcs. UK WR143PS ABSTRACT One of the a...	1989	3
209	248 MalkofT A Neural Network for Real-Time Signal Processing Donald B. Malkoff General Electric / Advanced Technology Laboratories Moorestown Corporate Center Building 145-2, Route 38 Moorestown, NJ 08057 ABSTRACT This paper describes a neural network algorithm that (1) performs temporal p...	1989	30
210	218 Bengio, De Mori and Cardin Speaker Independent Speech Recognition with Neural Networks and Speech Knowledge Y oshua Bengio Dept Computer Science Renato De Mori Dept Computer Science McGill University McGill University Montreal, Canada H3A2A 7 ABSTRACT Regis Cardin Dept Compute...	1989	31
211	160 Tang Analytic Solutions to the Formation of Feature-Analysing Cells of a Three-Layer Feedforward Visual Information Processing Neural Net D.S. Tang Microelectronics and Computer Technology Corporation 3500 West Balcones Center Drive Austin, TX 78759-6509 email: tang@mcc.com ABSTRACT...	1989	32
212	742 DeWeerth and Mead An Analog VLSI Model of Adaptation in the Vestibulo-Ocular Reflex Stephen P. DeWeerth and Carver A. Mead California Institute of Technology Pasadena, CA 91125 ABSTRACT The vestibulo-ocular reflex (VOR) is the primary mechanism that controls the compensatory eye movements...	1989	33
213	758 Satyanarayana, Tsividis and Graf A Reconfigurable Analog VLSI Neural Network Chip Srinagesh Satyanarayana and Yannis Tsividis Department of Electrical Engineering and Center for Telecommunications Research Columbia University, New York, NY 10027, USA ABSTRACT Hans Peter Graf AT&T ...	1989	34
214	396 Le Cun, Boser, Denker, Henderson, Howard, Hubbard and Jackel Handwritten Digit Recognition with a Back-Propagation Network Y. Le Cun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel AT&T Bell Laboratories, Holmdel, N. J. 07733 ABSTRACT We present an applicati...	1989	35
215	Digital-Analog Hybrid Synapse Chips for Electronic Neural Networks 769 Digital-Analog Hybrid Synapse Chips for Electronic Neural Networks A Moopenn, T. Duong, and AP. Thakoor Center for Space Microelectronics Technology Jet Propulsion Laboratory/California Institute of Technology Pasadena, CA 91109...	1989	36
216	60 Nelson and Bower Computational Efficiency: A Common Organizing Principle for Parallel Computer Maps and Brain Maps? Mark E. Nelson James M. Bower Computation and Neural Systems Program Division of Biology, 216-76 California Institute of Technology Pasadena, CA 91125 ABSTRACT It is we...	1989	37
217	A Cost Function for Internal Representations 733 A Cost Function for Internal Representations Anders Krogh The Niels Bohr Institute Blegdamsvej 17 2100 Copenhagen Denmark G. I. Thorbergsson Nordita Blegdamsvej 17 2100 Copenhagen Denmark ABSTRACT John A. Hertz Nordita Bleg...	1989	38
218	194 Huang and Lippmann HMM Speech Recognition with Neural Net Discrimination* William Y. Huang and Richard P. Lippmann Lincoln Laboratory, MIT Room B-349 Lexington, MA 02173-9108 ABSTRACT Two approaches were explored which integrate neural net classifiers with Hidden Markov Model (HMM) spe...	1989	39
219	Can Simple Cells Learn Curves? A Hebbian Model in a Structured Environment 125 Can Simple Cells Learn Curves? A Hebbian Model in a Structured Environment William R. Softky Divisions of Biology and Physics 103-33 Caltech Pasadena, CA 91125 bill@aurel.caltech.edu Daniel M. Kammen Division...	1989	4
220	226 Mann The Effects of Circuit Integration on a Feature Map Vector Quantizer Jim lVIann MIT Lincoln Laboratory 244 Wood St. Lexington, ~IA 02173 email: mann@vlsi.ll.mit.edu ABSTRACT The effects of parameter modifications imposed by hardware constraints on a self-organizing feature map alg...	1989	40
221	630 Morgan and Bourfard Generalization and Parameter Estimation in Feedforward Nets: Some Experiments ~. Morgant International Computer Science Institute Berkeley, CA 94704, USA H. Bourlardt* *Philips Research Laboratory Brussels B-1170 Brussels, Belgium ABSTRACT We have done an empi...	1989	41
222	VLSI Implementation of a High-Capacity Neural Network 793 VLSI Implementation of a High-Capacity Neural Network Associative Memory Tzi-Dar Chiueh 1 and Rodney M. Goodman Department of Electrical Engineering (116-81) California Institute of Technology Pasadena, CA 91125, USA ABSTRACT In this p...	1989	42
223	524 Fablman and Lebiere The Cascade-Correlation Learning Architecture Scott E. Fahlman and Christian Lebiere School of Computer Science Carnegie-Mellon University Pittsburgh, PA 15213 ABSTRACT Cascade-Correlation is a new architecture and supervised learning algorithm for artificial neural netwo...	1989	43
224	474 Mel and Koch Sigma-Pi Learning: On Radial Basis Functions and Cortical Associative Learning Bartlett W. Mel Christof Koch Computation and Neural Systems Program Caltech, 216-76 Pasadena, CA 91125 ABSTRACT The goal in this work has been to identify the neuronal elements of the cor...	1989	44
225	598 Le Cun, Denker and Solla Optimal Brain Damage Yann Le Cun, John S. Denker and Sara A. Sol1a AT&T Bell Laboratories, Holmdel, N. J. 07733 ABSTRACT We have used information-theoretic ideas to derive a class of practical and nearly optimal schemes for adapting the size of a neural network. By remo...	1989	45
226	258 Seibert and Waxman Learning Aspect Graph Representations from View Sequences Michael Seibert and Allen M. Waxnlan Lincoln Laborat.ory, l\IIassachusetts Institute of Technology Lexington, MA 02173-9108 ABSTRACT In our effort to develop a modular neural system for invariant learning and recogn...	1989	46
227	100 Servan-Schreiber, Printz and Cohen The Effect of Catecholamines on Performance: From Unit to System Behavior David Servan-Schreiber, Harry Printz and Jonathan D. Cohen School of Computer Science and Department of Psychology Carnegie Mellon University Pittsburgh. PA 15213 ABSTRACT At the l...	1989	47
228	Meiosis Networks 1 Stephen Jose Hanson Learning and Knowledge Acquisition Group Siemens Research Center Princeton, NJ 08540 ABSTRACT Meiosis Networks 533 A central problem in connectionist modelling is the control of network and architectural resources during learning. In the present ap...	1989	48
229	650 Lincoln and Skrzypek Synergy Of Clustering Multiple Back Propagation Networks William P. Lincoln* and Josef Skrzypekt UCLA Machine Perception Laboratory Computer Science Department Los Angeles, CA 90024 ABSTRACT The properties of a cluster of multiple back-propagation (BP) networks are ex...	1989	49
230	92 Cowan and Friedman Development and Regeneration of Eye-Brain Maps: A Computational Model J.D. Cowan and A.E. Friedman Department of Mathematics. Committee on Neurobiology. and Brain Research Institute. The University of Chicago. 5734 S. Univ. Ave .• Chicago. Illinois 60637 ABSTRACT We o...	1989	5
231	364 Jain and Waibel Incremental Parsing by Modular Recurrent Connectionist Networks Ajay N. Jain Alex H. Waibel School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 ABSTRACT We present a novel, modular, recurrent connectionist network architecture which learns to robu...	1989	50
232	36 Bialek, Rieke, van Steveninck and Warland Reading a Neural Code William Bialek, Fred Rieke, R. R. de Ruyter van Steveninck 1 and David Warland Department of Physics, and Department of Molecular and Cell Biology University of California at Berkeley Berkeley, California 94720 ABSTRACT Tra...	1989	51
233	590 Atiya and Abu-Mostafa A Method for the Associative Storage of Analog Vectors Amir Atiya () and Yaser Abu-Mostafa () () Department of Electrical Engineering (**) Departments of Electrical Engineering and Computer Science California Institute Technology Pasadena, Ca 91125 ABSTRACT A ...	1989	52
234	316 Atkeson Using Local Models to Control Movement 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 cga@ai.mit.edu ABSTRACT This paper ex...	1989	53
235	324 Jordan and Jacobs Learning to Control an Unstable System with Forward Modeling Michael I. Jordan Brain and Cognitive Sciences MIT Cambridge, MA 02139 Robert A. Jacobs Computer and Information Sciences University of Massachusetts Amherst, MA 01003 ABSTRACT The forward modeling ...	1989	54
236	490 Bell Learning in higher-order' artificial dendritic trees' Tony Bell Artificial Intelligence Laboratory Vrije Universiteit Brussel Pleinlaan 2, B-1050 Brussels, BELGIUM (tony@arti.vub.ac.be) ABSTRACT If neurons sum up their inputs in a non-linear way, as some simulations suggest, how is t...	1989	55
237	642 Chauvin Dynamic Behavior of Constrained Back-Propagation Networks Yves Chauvin! Thomson-CSF, Inc. 630 Hansen Way, Suite 250 Palo Alto, CA. 94304 ABSTRACT The learning dynamics of the back-propagation algorithm are investigated when complexity constraints are added to the standard Le...	1989	56
238	Designing Application-Specific Neural Networks 447 Designing Application-Specific Neural Networks Using the Genetic Algorithm Steven A. Harp, Tariq Samad, Aloke Guha Honeywell SSDC 1000 Boone Avenue North Golden Valley, MN 55427 ABSTRACT We present a general and systematic method for neura...	1989	57
239	372 Touretzky and Wheeler A Computational Basis for Phonology David S. Touretzky School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 ABSTRACT Deirdre W. Wheeler Department of Linguistics University of Pittsburgh Pittsburgh, PA 15260 The phonological structure o...	1989	58
240	52 Grajski and Merzenich Neural Network Simulation of Somatosensory Representational Plasticity Kamil A. Grajski Ford Aerospace San Jose, CA 95161-9041 kamil@wd11.fac.ford.com Michael M. Merzenich Coleman Laboratories UC San Francisco San Francisco, CA 94143 ABSTRACT The brain ...	1989	59
241	Predicting Weather Using a Genetic Memory 455 Predicting Weather Using a Genetic Memory: a Combination of Kanerva's Sparse Distributed Memory with Holland's Genetic Algorithms David Rogers Research Institute for Advanced Computer Science MS 230-5, NASA Ames Research Center Moffett Field, CA 94035 ...	1989	6
242	A Neural Network for Feature Extraction 719 A Neural Network for Feature Extraction Nathan Intrator Div. of Applied Mathematics, and Center for Neural Science Brown University Providence, RI 02912 ABSTRACT The paper suggests a statistical framework for the parameter estimation problem associa...	1989	60
243	Generalized Hopfield Networks and Nonlinear Optimization 355 Generalized Hopfield Networks and Gintaras v. Reklaitis Dept. of Chemical Eng. Purdue University W. Lafayette, IN. 47907 Nonlinear Optimization Athanasios G. Tsirukis1 Dept. of Chemical Eng. Purdue University W. Lafayette, ...	1989	61
244	Connectionist Architectures for Multi-Speaker Phoneme Recognition 203 Connectionist Architectures/or Multi-Speaker Phoneme Recognition John B. Hampshire n and Alex Waibel School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213-3890 ABSTRACT We present a number of Time-Delay...	1989	62
245	388 Smith and Miller Bayesian Inference of Regular Grammar and Markov Source Models Kurt R. Smith and Michael I. Miller Biomedical Computer Laboratory and Electronic Signals and Systems Research Laboratory Washington University, SL Louis. MO 63130 ABSTRACT In this paper we develop a Bayes ...	1989	63
246	818 Smotroff Dataflow Architectures: Flexible Platforms for Neural Network Simulation Ira G. Smotroff MITRE-Bedford Neural Network Group The MITRE Corporation Bedford, MA 01730 ABSTRACT Dataflow architectures are general computation engines optimized for the execution of fme-grain paral...	1989	64
247	676 Baum The Perceptron Algorithm Is Fast tor Non-Malicious Distributions Erice B. Baum NEC Research Institute 4 Independence Way Princeton, NJ 08540 Abstract: Within the context of Valiant's protocol for learning, the Perceptron algorithm is shown to learn an arbitrary half-space in time O(r...	1989	65
248	Discovering High Order Features with Mean Field Modules 509 Discovering high order features with mean field modules Conrad C. Galland and Geoffrey E. Hinton Physics Dept. and Computer Science Dept. University of Toronto Toronto, Canada M5S lA4 ABSTRACT A new form of the deterministic Boltz...	1989	66
249	686 Barto, Sutton and Watkins Sequential Decision Problems and Neural Networks A. G. Barto Dept. of Computer and Information Science Univ. of Massachusetts Amherst, MA 01003 R. S. Sutton GTE Laboratories Inc. Waltham, MA 02254 ABSTRACT c. J. C. H. Watkins 25B Framfield Highb...	1989	67
250	606 Ahmad, Thsauro and He Asymptotic Convergence of Backpropagation: Subutai Ahmad ICSI 1947 Center St. Berkeley, CA 94704 Numerical Experiments Gerald Tesauro mM Watson Labs. P. O. Box 704 Yorktown Heights, NY 10598 ABSTRACT Yu He Dept. of Physics Ohio State Univ. Col...	1989	68
251	566 Atlas, Cohn and Ladner Training Connectionist Networks with Queries and Selective Sampling Les Atlas Dept. of E.E. David Cohn Dept. of C.S. & E. Richard Ladner Dept. of C.S. & E. M.A. El-Sharkawi, R.J. Marks II, M.E. Aggoune, and D.C. Park Dept. of E.E. University of Washington, ...	1989	69
252	28 Lockery t Fang and Sejnowski Neu.·al Network Analysis of Distributed Representations of Dynamical Sensory-Motor rrransformations in the Leech Shawn R. LockerYt Van Fangt and Terrence J. Sejnowski Computational Neurobiology Laboratory Salk Institute for Biological Studies Box 85800, San Diego,...	1989	7
253	Unsupervised Learning in Neurodynamics 583 Unsupervised Learning in Neurodynamics Using the Phase Velocity Field Approach Michail Zak Nikzad Toornarian Center for Space Microelectronics Technology Jet Propulsion Laboratory California Institute of Technology Pasadena, CA 91109 ABSTRACT A...	1989	70
254	186 Bourlard and Morgan A Continuous Speech Recognition System Embedding MLP into HMM Herve Bourlard Philips Research Laboratory Av. van Becelaere 2. Box 8 B-1170 Brussels. Belgium ABSTRACT Nelson Morgan IntI. Compo Sc. Institute 1947 Center Street. Suite 600 Berkeley. CA 94704. USA ...	1989	71
255	694 MacKay and Miller Analysis of Linsker's Simulations of Hebbian rules David J. C. MacKay Computation and Neural Systems Caltech 164-30 CNS Pasadena, CA 91125 mackayOaurel.cns.caltech.edu Kenneth D. Miller Department of Physiology University of California San Francisco, CA 94143 - ...	1989	72
256	702 Obradovic and Pclrberry Analog Neural Networks of Limited Precision I: Computing with Multilinear Threshold Functions (Preliminary Version) Zoran Obradovic and Ian Parberry Department of Computer Science. Penn State University. University Park. Pa. 16802. ABSTRACT Experimental evidence...	1989	73
257	A Systematic Study or the Input/Output Properties 149 A Systematic Study of the Input/Output Properties of a 2 Compartment Model Neuron With Active Membranes Paul Rhodes University of California, San Diego ABSTRACT The input/output properties of a 2 compartment model neuron are systematically ...	1989	74
258	638 Zipser Subgrouping Reduces Complexity and Speeds Up Learning in Recurrent Networks 1 INTRODUCTION David Zipser Department of Cognitive Science University of California, San Diego La Jolla, CA 92093 Recurrent nets are more powerful than feedforward nets because they allow simulation of ...	1989	75
259	44 Beer and Chiel Neural Implementation of Motivated Behavior: Feeding in an Artificial Insect Randall D. Beerl,2 and Hillel J. Chiel2 Departments of 1 Computer Engineering and Science, and 2Biology and the Center for Automation and Intelligent Systems Research Case Western Reserve University Cl...	1989	76
260	A Neural Network to Detect Homologies in Proteins 423 A Neural Network to Detect Homologies in Proteins Y oshua Bengio School of Computer Science McGill University Montreal, Canada H3A 2A7 Samy Bengio Departement dlnformatique Universite de Montreal .ABSTRACT Yannick Pouliot Depar...	1989	77
261	380 Giles, Sun, Chen, Lee and Chen HIGHER ORDER RECURRENT NETWORKS & GRAMMATICAL INFERENCE C. L. Giles·, G. Z. Sun, H. H. Chen, Y. C. Lee, D. Chen Department of Physics and Astronomy and Institute for Advanced Computer Studies University of Maryland. College Park. MD 20742 * NEC Research Inst...	1989	78
262	168 Lee and Lippmann Practical Characteristics of Neural Network and Conventional Pattern Classifiers on Artificial and Speech Problems* Yuchun Lee Digital Equipment Corp. 40 Old Bolton Road, OGOl-2Ull Stow, MA 01775-1215 ABSTRACT Richard P. Lippmann Lincoln Laboratory, MIT Room B...	1989	79
263	A Computer Modeling Approach to Understanding 117 A computer modeling approach to understanding the inferior olive and its relationship to the cerebellar cortex in rats Maurice Lee and James M. Bower Computation and Neural Systems Program California Institute of Technology Pasadena, CA 91125 ...	1989	8
264	282 Kanerva Contour-Map Encoding of Shape for Early Vision Pentti Kanerva Research Institute for Advanced Computer Science Mail Stop 230-5, NASA Ames Research Center Moffett Field, California 94035 ABSTRACT Contour maps provide a general method for recognizing two-dimensional shapes. Al...	1989	80
265	574 Nowlan Maximum Likelihood Competitive Learning Steven J. Nowlan1 Department of Computer Science University of Toronto Toronto, Canada M5S lA4 ABSTRACT One popular class of unsupervised algorithms are competitive algorithms. In the traditional view of competition, only one competitor, t...	1989	81
266	Note on Development or Modularity in Simple Cortical Models 133 Note on Development of Modularity in Simple Cortical Models Alex Chernjavskyl Neuroscience Graduate Program Section of Molecular Neurobiology Howard Hughes Medical Institute Yale University ABSTRACT John Moody2 Yale Compute...	1989	82
267	298 Okamoto, Kawato, Ioui aod Miyake Model Based Image Compression and Adaptive Data Representation by Interacting Filter Banks Toshiaki Okamoto, Mitsuo Kawato, Toshio Ioui ATR Auditory and Visual Perception Research Laboratories Sanpeidani, Inuidani. Seika-cho. Soraku-gun Kyoto 619-02. Japan...	1989	83
268	On the Distribution of the Number of Local Minima 727 On the Distribution of the Number of Local Minima of a Random Function on a Graph Pierre Baldi JPL, Caltech Pasadena, CA 91109 1 INTRODUCTION Yosef Rinott UCSD La Jolla, CA 92093 Charles Stein Stanford University Stanford, C...	1989	84
269	240 Lee Using A Translation-Invariant Neural Network To Diagnose Heart Arrhythmia Susan Ciarrocca Lee The lohns Hopkins University Applied Physics Laboratory Laurel. Maryland 20707 ABSTRACT Distinctive electrocardiogram (EeG) patterns are created when the heart is beating normally and when...	1989	85
270	Non-Boltzmann Dynamics in Networks of Spiking Neurons 109 Non-Boltzmann Dynamics in Networks of Spiking Neurons Michael C. Crair and William Bialek Department of Physics, and Department of Molecular and Cell Biology University of California at Berkeley Berkeley, CA 94720 ABSTRACT We study ...	1989	86
271	558 Rohwer The 'Moving Targets' Training Algorithm Richard Rohwer Centre for Speech Technology Research Edinburgh University 80, South Bridge Edinburgh EH1 1HN SCOTLAND ABSTRACT A simple method for training the dynamical behavior of a neural network is derived. It is applicable to any trainin...	1989	87
272	622 Atlas, Cole, Connor, EI-Sharkawi, Marks, Muthusamy and Barnard Performance Comparisons Between Backpropagation Networks and Classification Trees on Three Real-World Applications Les Atlas Dept. of EE. Fr -10 University of Washington Seattle. Washington 98195 Ronald Cole Dept. of CS&E ...	1989	88
273	An Efficient Implementation of the Back-propagation Algorithm 801 A n Efficient Implementation of the Back-propagation Algorithm on the Connection Machine CM-2 Xiru Zhang! Michael Mckenna Jill P. Mesirov David L. Waltz Thinking Machines Corporation 245 First Street, Cambridge, MA 02142-1214 ...	1989	89
274	550 Ackley and Littman Generalization and scaling in reinforcement learning David H. Ackley Michael L. Littman Cognitive Science Research Group Bellcore Morristown, NJ 07960 ABSTRACT In associative reinforcement learning, an environment generates input vectors, a learning system generat...	1989	9
275	482 Saba and Keeler Algorithms/or Better Representation and Faster Learning in Radial Basis Function Networks A vijit Saba 1 James D. Keeler Microelectronics and Computer Technology corporation 3500 West Balcones Center Drive Austin, Tx 78759 ABSTRACT In this paper we present upper boun...	1989	90
276	542 Kassebaum, Thnorio and Schaefers The Cocktail Party Problem: Speech/Data Signal Separation Comparison between Backpropagation and SONN John Kassebaum jak@ec.ecn.purdue.edu Manoel Fernando Tenorio tenorio@ee.ecn.purdue.edu Christoph Schaefers Parallel Distributed Structures Laboratory ...	1989	91
277	750 Koch, Bair, Harris, Horiuchi, Hsu and Luo Real- Time Computer Vision and Robotics Using Analog VLSI Circuits Christof Koch Wyeth Bair John G. Harris Timothy Horiuchi Andrew Hsu Jin Luo Computation and Neural Systems Program Caltech 216-76 Pasadena, CA 91125 ABSTRACT The lon...	1989	92
278	516 Grossman The CHIR Algorithm for Feed Forward Networks with Binary Weights Tal Grossman Department of Electronics Weizmann Institute of Science Rehovot 76100 Israel ABSTRACT A new learning algorithm, Learning by Choice of Internal Represetations (CHIR), was recently introduced. Whereas man...	1989	93
279	A Large-Scale Neural Network 415 A LARGE-SCALE NEURAL NETWORK WHICH RECOGNIZES HANDWRITTEN KANJI CHARACTERS Yoshihiro Mori Kazuki Joe A TR Auditory and Visual Perception Research Laboratories Sanpeidani Inuidani Seika-cho Soraku-gun Kyoto 619-02 Japan ABSTRACT We propose a new way to const...	1989	94
280	18 Harris-Warrick MECHANISMS FOR NEUROMODULATION OF BIOLOGICAL NEURAL NETWORKS Ronald M. Harris-Warrick Section of Neurobiology and Behavior Cornell University Ithaca, NY 14853 ABSTRACT The pyloric Central Pattern Generator of the crustacean stomatogastric ganglion is a well-defined biolog...	1989	95
281	Recognizing Hand-Printed Letters and Digits 405 Recognizing Hand-Printed Letters and Digits Gale L. Martin James A. Pittman MCC, Austin, Texas 78759 ABSTRACT We are developing a hand-printed character recognition system using a multilayered neural net trained through backpropagation. We report on r...	1989	96
282	266 Zemel, Mozer and Hinton TRAFFIC: Recognizing Objects Using Hierarchical Reference Frame Transformations Richard S. Zemel Computer Science Dept. University of Toronto Toronto, ONT M5S lA4 Michael C. Mozer Computer Science Dept. University of Colorado Boulder, CO 80309-0430 ABSTRAC...	1989	97
283	810 Nunez and Fortes Performance of Connectionist Learning Algorithms on 2-D SIMD Processor Arrays Fernando J. Nunez* and Jose A.B. Fortes School of Electrical Engineering Purdue University West Lafayette, IN 47907 ABSTRACT The mapping of the back-propagation and mean field theory learning...	1989	98
284	290 Viola Neurally Inspired Plasticity in Oculomotor Processes Paul A. Viola Artificial Intelligence Laboratory M"assachusetts Institute of Technology Cambridge, MA 02139 ABSTRACT We have constructed a two axis camera positioning system which is roughly analogous to a single human eye. Thi...	1989	99
285	Modeling Time Varying Systems Using Hidden Control Neural Architecture Esther Levin AT&T Bell Laboratories Speech Research Department Murray Hill, NJ 07974 USA ABSTRACT Multi-layered neural networks have recently been proposed for nonlinear prediction and system modeling. Although proven successful...	1990	1
286	Translating Locative Prepositions Paul W. Munro and Mary Tabasko Department of Information Science University of Pittsburgh Pittsburgh, PA 15260 ABSTRACT A network was trained by back propagation to map locative expressions of the form "noun-preposition-noun" to a semantic representation, as in ...	1990	10
287	A Lagrangian Approach to Fixed Points Eric Mjolsness Department of Computer Science Yale University Willard L. Miranker IBM Watson Research Center Yorktown Heights, NY 10598 P.O. Box 2158 Yale Station New Haven, CT 16520-2158 Abstract We present a new way to derive dissipative, optimizing ...	1990	100
288	Neural Network Implementation of Admission Control Rodolfo A. Milito, Isabelle Guyon, and Sara A. SoDa AT&T Bell Laboratories, Crawfords Corner Rd., Holmdel, NJ 07733 Abstract A feedforward layered network implements a mapping required to control an unknown stochastic nonlinear dynamical system. Training...	1990	101
289	Computing with Arrays of Bell-Shaped and Sigmoid Functions Pierre Baldi· Jet Propulsion Laboratory California Institute of Technology Pasadena, CA 91109 Abstract We consider feed-forward neural networks with one non-linear hidden layer and linear output units. The transfer function in the hidden...	1990	102
290	VLSI Implementation of TInMANN Matt Melton Tan Phan Doug Reeves Dave Van den Bout Electrical and Computer Engineering Dept. North Carolina State University Raleigh, NC 27695-7911 Abstract A massively parallel, all-digital, stochastic architecture TlnMAN N is described which performs competitive ...	1990	103
291	SEXNET: A NEURAL NETWORK IDENTIFIES SEX FROM HUMAN FACES B.A. Golomb, D.T. Lawrence, and T.J. Sejnowski The Salk Institute 10010 N. Torrey Pines Rd. La Jolla, CA 92037 Abstract Sex identification in animals has biological importance. Humans are good at making this determination visually, but mac...	1990	104
292	From Speech Recognition to Spoken Language Understanding: The Development of the MIT SUMMIT and VOYAGER Systems Victor Zue, James Glass, David Goodine, Lynette Hirschman, Hong Leung, Michael Phillips, Joseph Polifroni, and Stephanie Seneff' Room NE43-601 Spoken Language Systems Group Laboratory for...	1990	105
293	Generalization by Weight-Elimination with Application to Forecasting Andreas S. Weigend Physics Department Stanford University Stanford, CA 94305 David E. Rumelhart Psychology Department Stanford University Stanford, CA 94305 Bernardo A. Huberman Dynamics of Computation XeroxPARC ...	1990	106
294	A Novel Approach to Prediction of the 3-Dimensional Structures of Protein Backbones by Neural Networks Henrik Fredholrnl ,5 and Henrik Bol1l' 2 , Jakob Bohr3 , S0ren Brunak4 , Rodney M.J. Cotterill\ Benny Lautrup5 and Steffen B. Petersenl 1 MR-Senteret, SINTEF, N-7034 Trondheim, Norway. 2Univers...	1990	107
295	Back Propagation Implementation on the Adaptive Solutions CNAPS Neurocomputer Chip Hal McCartor Adaptive Solutions Inc. 1400 N.W. Compton Drive Suite 340 Beaverton, OR 97006 Abstract The Adaptive Solutions CN APS architecture chip is a general purpose neurocomputer chip. It has 64 processors,...	1990	108
296	Asymptotic slowing down of the nearest-neighbor classifier Robert R. Snapp CS lEE Department University of Vermont Burlington, VT 05405 Demetri Psaltis Electrical Engineering Caltech 116-81 Pasadena, CA 91125 Santosh S. Venkatesh Electrical Engineering University of Pennsylvania P...	1990	109
297	ADAPTIVE SPLINE NETWORKS Jerome H. Friedman Department of Statistics and Stanford Linear Accelerator Center Stanford University Stanford, CA 94305 Abstract A network based on splines is described. It automatically adapts the number of units, unit parameters, and the architecture of the network for ...	1990	11
298	Connectionist Approaches to the Use of Markov Models for Speech Recognition Herve Bourlard t,~ t L & H Speechproducts Koning Albert 1 laan, 64 1780 Wemmel, BELGIUM Nelson Morgan ~ I.e Chuck Wooters ~ ~ IntI. Compo Sc. Institute 1947, Center St., Suite 600 Berkeley, CA 94704, USA ABSTRACT ...	1990	110
299	A Second-Order Translation, Rotation and Scale Invariant Neural Network Shelly D.D. Goggin Kristina M. Johnson Karl E. Gustafson· Optoelectronic Computing Systems Center and Department of Electrical and Computer Engineering University of Colorado at Boulder Boulder, CO 80309 shellg@boulder.co...	1990	111

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178 Lang and Hinton Dimensionality Reduction and Prior Knowledge in E-set Recognition Kevin J. Lang1 Geoffrey E. Hinton Computer Science Dept. Computer Science Dept. Carnegie Mellon University Pittsburgh, PA 15213 University of Toronto Toronto, Ontario M5S lA4 Canada USA ABSTRA...

1989

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274 WeinshalI, Edelman and BiiIthofT A self-organizing multiple-view representation of 3D objects Daphna Weinshall Center for Biological Information Processing MIT E25-201 Cambridge, MA 02139 Shimon Edelman Center for Biological Information Processing MIT E25-201 Cambridge, MA 021...

1989

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68 Baird Associative Memory in a Simple Model of Oscillating Cortex Bill Baird Dept Molecular and Cell Biology, U .C.Berkeley, Berkeley, Ca. 94720 ABSTRACT A generic model of oscillating cortex, which assumes "minimal" coupling justified by known anatomy, is shown to function as an associativ...

1989

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203

498 Barben, Toomarian and Gulati Adjoint Operator Algorithms for Faster Learning in Dynamical Neural Networks Jacob Barhen Nikzad Toomarian Center for Space Microelectronics Technology Jet Propulsion Laboratory California Institute of Technology Pasadena, CA 91109 ABSTRACT Sandeep Gulat...

1989

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84 Wilson and Bower Computer Simulation of Oscillatory Behavior in Cerebral Cortical Networks Matthew A. Wilson and James M. Bower! Computation and Neural Systems Program Division of Biology, 216-76 California Institute of Technology Pasadena, CA 9 1125 ABSTRACT It has been known for many ...

1989

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660 Geiger and Girosi Coupled Markov Random Fields and Mean Field Theory Davi Geigerl Artificial Intelligence Laboratory, MIT 545 Tech. Sq. # 792 Cambridge, MA 02139 and ABSTRACT Federico Girosi Artificial Intelligence Laboratory, MIT 545 Tech. Sq. # 788 Cambridge, MA 02139 ...

1989

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Pulse-Firing Neural Chips for Hundreds of Neurons 785 PULSE-FIRING NEURAL CIDPS FOR HUNDREDS OF NEURONS Michael Brownlow Lionel Tarassenko Dept. Eng. Science Univ. of Oxford Oxford OX1 3PJ Alan F. Murray Dept. Electrical Eng. Univ. of Edinburgh Mayfield Road Edinburgh EH9 3JL A...

1989

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Rule Representations in a Connectionist Chunker 431 Rule Representations in a Connectionist Chunker David S. Touretzky Gillette Elvgren m School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 ABSTRACT We present two connectionist architectures for chunking of symbolic ...

1989

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Training Stochastic Model Recognition Algorithms 211 Training Stochastic Model Recognition Algorithms as Networks can lead to Maximum Mutual Information Estimation of Parameters John s. Bridle Royal Signals and Radar Establishment Great Malvern Worcs. UK WR143PS ABSTRACT One of the a...

1989

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248 MalkofT A Neural Network for Real-Time Signal Processing Donald B. Malkoff General Electric / Advanced Technology Laboratories Moorestown Corporate Center Building 145-2, Route 38 Moorestown, NJ 08057 ABSTRACT This paper describes a neural network algorithm that (1) performs temporal p...

1989

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218 Bengio, De Mori and Cardin Speaker Independent Speech Recognition with Neural Networks and Speech Knowledge Y oshua Bengio Dept Computer Science Renato De Mori Dept Computer Science McGill University McGill University Montreal, Canada H3A2A 7 ABSTRACT Regis Cardin Dept Compute...

1989

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160 Tang Analytic Solutions to the Formation of Feature-Analysing Cells of a Three-Layer Feedforward Visual Information Processing Neural Net D.S. Tang Microelectronics and Computer Technology Corporation 3500 West Balcones Center Drive Austin, TX 78759-6509 email: tang@mcc.com ABSTRACT...

1989

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742 DeWeerth and Mead An Analog VLSI Model of Adaptation in the Vestibulo-Ocular Reflex Stephen P. DeWeerth and Carver A. Mead California Institute of Technology Pasadena, CA 91125 ABSTRACT The vestibulo-ocular reflex (VOR) is the primary mechanism that controls the compensatory eye movements...

1989

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758 Satyanarayana, Tsividis and Graf A Reconfigurable Analog VLSI Neural Network Chip Srinagesh Satyanarayana and Yannis Tsividis Department of Electrical Engineering and Center for Telecommunications Research Columbia University, New York, NY 10027, USA ABSTRACT Hans Peter Graf AT&T ...

1989

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396 Le Cun, Boser, Denker, Henderson, Howard, Hubbard and Jackel Handwritten Digit Recognition with a Back-Propagation Network Y. Le Cun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel AT&T Bell Laboratories, Holmdel, N. J. 07733 ABSTRACT We present an applicati...

1989

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Digital-Analog Hybrid Synapse Chips for Electronic Neural Networks 769 Digital-Analog Hybrid Synapse Chips for Electronic Neural Networks A Moopenn, T. Duong, and AP. Thakoor Center for Space Microelectronics Technology Jet Propulsion Laboratory/California Institute of Technology Pasadena, CA 91109...

1989

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60 Nelson and Bower Computational Efficiency: A Common Organizing Principle for Parallel Computer Maps and Brain Maps? Mark E. Nelson James M. Bower Computation and Neural Systems Program Division of Biology, 216-76 California Institute of Technology Pasadena, CA 91125 ABSTRACT It is we...

1989

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A Cost Function for Internal Representations 733 A Cost Function for Internal Representations Anders Krogh The Niels Bohr Institute Blegdamsvej 17 2100 Copenhagen Denmark G. I. Thorbergsson Nordita Blegdamsvej 17 2100 Copenhagen Denmark ABSTRACT John A. Hertz Nordita Bleg...

1989

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194 Huang and Lippmann HMM Speech Recognition with Neural Net Discrimination* William Y. Huang and Richard P. Lippmann Lincoln Laboratory, MIT Room B-349 Lexington, MA 02173-9108 ABSTRACT Two approaches were explored which integrate neural net classifiers with Hidden Markov Model (HMM) spe...

1989

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Can Simple Cells Learn Curves? A Hebbian Model in a Structured Environment 125 Can Simple Cells Learn Curves? A Hebbian Model in a Structured Environment William R. Softky Divisions of Biology and Physics 103-33 Caltech Pasadena, CA 91125 bill@aurel.caltech.edu Daniel M. Kammen Division...

1989

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226 Mann The Effects of Circuit Integration on a Feature Map Vector Quantizer Jim lVIann MIT Lincoln Laboratory 244 Wood St. Lexington, ~IA 02173 email: mann@vlsi.ll.mit.edu ABSTRACT The effects of parameter modifications imposed by hardware constraints on a self-organizing feature map alg...

1989

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630 Morgan and Bourfard Generalization and Parameter Estimation in Feedforward Nets: Some Experiments ~. Morgant International Computer Science Institute Berkeley, CA 94704, USA H. Bourlardt* *Philips Research Laboratory Brussels B-1170 Brussels, Belgium ABSTRACT We have done an empi...

1989

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VLSI Implementation of a High-Capacity Neural Network 793 VLSI Implementation of a High-Capacity Neural Network Associative Memory Tzi-Dar Chiueh 1 and Rodney M. Goodman Department of Electrical Engineering (116-81) California Institute of Technology Pasadena, CA 91125, USA ABSTRACT In this p...

1989

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524 Fablman and Lebiere The Cascade-Correlation Learning Architecture Scott E. Fahlman and Christian Lebiere School of Computer Science Carnegie-Mellon University Pittsburgh, PA 15213 ABSTRACT Cascade-Correlation is a new architecture and supervised learning algorithm for artificial neural netwo...

1989

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474 Mel and Koch Sigma-Pi Learning: On Radial Basis Functions and Cortical Associative Learning Bartlett W. Mel Christof Koch Computation and Neural Systems Program Caltech, 216-76 Pasadena, CA 91125 ABSTRACT The goal in this work has been to identify the neuronal elements of the cor...

1989

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598 Le Cun, Denker and Solla Optimal Brain Damage Yann Le Cun, John S. Denker and Sara A. Sol1a AT&T Bell Laboratories, Holmdel, N. J. 07733 ABSTRACT We have used information-theoretic ideas to derive a class of practical and nearly optimal schemes for adapting the size of a neural network. By remo...

1989

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258 Seibert and Waxman Learning Aspect Graph Representations from View Sequences Michael Seibert and Allen M. Waxnlan Lincoln Laborat.ory, l\IIassachusetts Institute of Technology Lexington, MA 02173-9108 ABSTRACT In our effort to develop a modular neural system for invariant learning and recogn...

1989

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100 Servan-Schreiber, Printz and Cohen The Effect of Catecholamines on Performance: From Unit to System Behavior David Servan-Schreiber, Harry Printz and Jonathan D. Cohen School of Computer Science and Department of Psychology Carnegie Mellon University Pittsburgh. PA 15213 ABSTRACT At the l...

1989

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Meiosis Networks 1 Stephen Jose Hanson Learning and Knowledge Acquisition Group Siemens Research Center Princeton, NJ 08540 ABSTRACT Meiosis Networks 533 A central problem in connectionist modelling is the control of network and architectural resources during learning. In the present ap...

1989

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650 Lincoln and Skrzypek Synergy Of Clustering Multiple Back Propagation Networks William P. Lincoln* and Josef Skrzypekt UCLA Machine Perception Laboratory Computer Science Department Los Angeles, CA 90024 ABSTRACT The properties of a cluster of multiple back-propagation (BP) networks are ex...

1989

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92 Cowan and Friedman Development and Regeneration of Eye-Brain Maps: A Computational Model J.D. Cowan and A.E. Friedman Department of Mathematics. Committee on Neurobiology. and Brain Research Institute. The University of Chicago. 5734 S. Univ. Ave .• Chicago. Illinois 60637 ABSTRACT We o...

1989

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364 Jain and Waibel Incremental Parsing by Modular Recurrent Connectionist Networks Ajay N. Jain Alex H. Waibel School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 ABSTRACT We present a novel, modular, recurrent connectionist network architecture which learns to robu...

1989

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36 Bialek, Rieke, van Steveninck and Warland Reading a Neural Code William Bialek, Fred Rieke, R. R. de Ruyter van Steveninck 1 and David Warland Department of Physics, and Department of Molecular and Cell Biology University of California at Berkeley Berkeley, California 94720 ABSTRACT Tra...

1989

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590 Atiya and Abu-Mostafa A Method for the Associative Storage of Analog Vectors Amir Atiya (*) and Yaser Abu-Mostafa (**) (*) Department of Electrical Engineering (**) Departments of Electrical Engineering and Computer Science California Institute Technology Pasadena, Ca 91125 ABSTRACT A ...

1989

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316 Atkeson Using Local Models to Control Movement 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 cga@ai.mit.edu ABSTRACT This paper ex...

1989

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324 Jordan and Jacobs Learning to Control an Unstable System with Forward Modeling Michael I. Jordan Brain and Cognitive Sciences MIT Cambridge, MA 02139 Robert A. Jacobs Computer and Information Sciences University of Massachusetts Amherst, MA 01003 ABSTRACT The forward modeling ...

1989

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490 Bell Learning in higher-order' artificial dendritic trees' Tony Bell Artificial Intelligence Laboratory Vrije Universiteit Brussel Pleinlaan 2, B-1050 Brussels, BELGIUM (tony@arti.vub.ac.be) ABSTRACT If neurons sum up their inputs in a non-linear way, as some simulations suggest, how is t...

1989

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642 Chauvin Dynamic Behavior of Constrained Back-Propagation Networks Yves Chauvin! Thomson-CSF, Inc. 630 Hansen Way, Suite 250 Palo Alto, CA. 94304 ABSTRACT The learning dynamics of the back-propagation algorithm are investigated when complexity constraints are added to the standard Le...

1989

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Designing Application-Specific Neural Networks 447 Designing Application-Specific Neural Networks Using the Genetic Algorithm Steven A. Harp, Tariq Samad, Aloke Guha Honeywell SSDC 1000 Boone Avenue North Golden Valley, MN 55427 ABSTRACT We present a general and systematic method for neura...

1989

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372 Touretzky and Wheeler A Computational Basis for Phonology David S. Touretzky School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 ABSTRACT Deirdre W. Wheeler Department of Linguistics University of Pittsburgh Pittsburgh, PA 15260 The phonological structure o...

1989

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52 Grajski and Merzenich Neural Network Simulation of Somatosensory Representational Plasticity Kamil A. Grajski Ford Aerospace San Jose, CA 95161-9041 kamil@wd11.fac.ford.com Michael M. Merzenich Coleman Laboratories UC San Francisco San Francisco, CA 94143 ABSTRACT The brain ...

1989

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Predicting Weather Using a Genetic Memory 455 Predicting Weather Using a Genetic Memory: a Combination of Kanerva's Sparse Distributed Memory with Holland's Genetic Algorithms David Rogers Research Institute for Advanced Computer Science MS 230-5, NASA Ames Research Center Moffett Field, CA 94035 ...

1989

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A Neural Network for Feature Extraction 719 A Neural Network for Feature Extraction Nathan Intrator Div. of Applied Mathematics, and Center for Neural Science Brown University Providence, RI 02912 ABSTRACT The paper suggests a statistical framework for the parameter estimation problem associa...

1989

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Generalized Hopfield Networks and Nonlinear Optimization 355 Generalized Hopfield Networks and Gintaras v. Reklaitis Dept. of Chemical Eng. Purdue University W. Lafayette, IN. 47907 Nonlinear Optimization Athanasios G. Tsirukis1 Dept. of Chemical Eng. Purdue University W. Lafayette, ...

1989

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Connectionist Architectures for Multi-Speaker Phoneme Recognition 203 Connectionist Architectures/or Multi-Speaker Phoneme Recognition John B. Hampshire n and Alex Waibel School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213-3890 ABSTRACT We present a number of Time-Delay...

1989

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388 Smith and Miller Bayesian Inference of Regular Grammar and Markov Source Models Kurt R. Smith and Michael I. Miller Biomedical Computer Laboratory and Electronic Signals and Systems Research Laboratory Washington University, SL Louis. MO 63130 ABSTRACT In this paper we develop a Bayes ...

1989

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818 Smotroff Dataflow Architectures: Flexible Platforms for Neural Network Simulation Ira G. Smotroff MITRE-Bedford Neural Network Group The MITRE Corporation Bedford, MA 01730 ABSTRACT Dataflow architectures are general computation engines optimized for the execution of fme-grain paral...

1989

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676 Baum The Perceptron Algorithm Is Fast tor Non-Malicious Distributions Erice B. Baum NEC Research Institute 4 Independence Way Princeton, NJ 08540 Abstract: Within the context of Valiant's protocol for learning, the Perceptron algorithm is shown to learn an arbitrary half-space in time O(r...

1989

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Discovering High Order Features with Mean Field Modules 509 Discovering high order features with mean field modules Conrad C. Galland and Geoffrey E. Hinton Physics Dept. and Computer Science Dept. University of Toronto Toronto, Canada M5S lA4 ABSTRACT A new form of the deterministic Boltz...

1989

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686 Barto, Sutton and Watkins Sequential Decision Problems and Neural Networks A. G. Barto Dept. of Computer and Information Science Univ. of Massachusetts Amherst, MA 01003 R. S. Sutton GTE Laboratories Inc. Waltham, MA 02254 ABSTRACT c. J. C. H. Watkins 25B Framfield Highb...

1989

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606 Ahmad, Thsauro and He Asymptotic Convergence of Backpropagation: Subutai Ahmad ICSI 1947 Center St. Berkeley, CA 94704 Numerical Experiments Gerald Tesauro mM Watson Labs. P. O. Box 704 Yorktown Heights, NY 10598 ABSTRACT Yu He Dept. of Physics Ohio State Univ. Col...

1989

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566 Atlas, Cohn and Ladner Training Connectionist Networks with Queries and Selective Sampling Les Atlas Dept. of E.E. David Cohn Dept. of C.S. & E. Richard Ladner Dept. of C.S. & E. M.A. El-Sharkawi, R.J. Marks II, M.E. Aggoune, and D.C. Park Dept. of E.E. University of Washington, ...

1989

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28 Lockery t Fang and Sejnowski Neu.·al Network Analysis of Distributed Representations of Dynamical Sensory-Motor rrransformations in the Leech Shawn R. LockerYt Van Fangt and Terrence J. Sejnowski Computational Neurobiology Laboratory Salk Institute for Biological Studies Box 85800, San Diego,...

1989

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Unsupervised Learning in Neurodynamics 583 Unsupervised Learning in Neurodynamics Using the Phase Velocity Field Approach Michail Zak Nikzad Toornarian Center for Space Microelectronics Technology Jet Propulsion Laboratory California Institute of Technology Pasadena, CA 91109 ABSTRACT A...

1989

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186 Bourlard and Morgan A Continuous Speech Recognition System Embedding MLP into HMM Herve Bourlard Philips Research Laboratory Av. van Becelaere 2. Box 8 B-1170 Brussels. Belgium ABSTRACT Nelson Morgan IntI. Compo Sc. Institute 1947 Center Street. Suite 600 Berkeley. CA 94704. USA ...

1989

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694 MacKay and Miller Analysis of Linsker's Simulations of Hebbian rules David J. C. MacKay Computation and Neural Systems Caltech 164-30 CNS Pasadena, CA 91125 mackayOaurel.cns.caltech.edu Kenneth D. Miller Department of Physiology University of California San Francisco, CA 94143 - ...

1989

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702 Obradovic and Pclrberry Analog Neural Networks of Limited Precision I: Computing with Multilinear Threshold Functions (Preliminary Version) Zoran Obradovic and Ian Parberry Department of Computer Science. Penn State University. University Park. Pa. 16802. ABSTRACT Experimental evidence...

1989

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A Systematic Study or the Input/Output Properties 149 A Systematic Study of the Input/Output Properties of a 2 Compartment Model Neuron With Active Membranes Paul Rhodes University of California, San Diego ABSTRACT The input/output properties of a 2 compartment model neuron are systematically ...

1989

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638 Zipser Subgrouping Reduces Complexity and Speeds Up Learning in Recurrent Networks 1 INTRODUCTION David Zipser Department of Cognitive Science University of California, San Diego La Jolla, CA 92093 Recurrent nets are more powerful than feedforward nets because they allow simulation of ...

1989

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44 Beer and Chiel Neural Implementation of Motivated Behavior: Feeding in an Artificial Insect Randall D. Beerl,2 and Hillel J. Chiel2 Departments of 1 Computer Engineering and Science, and 2Biology and the Center for Automation and Intelligent Systems Research Case Western Reserve University Cl...

1989

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A Neural Network to Detect Homologies in Proteins 423 A Neural Network to Detect Homologies in Proteins Y oshua Bengio School of Computer Science McGill University Montreal, Canada H3A 2A7 Samy Bengio Departement dlnformatique Universite de Montreal .ABSTRACT Yannick Pouliot Depar...

1989

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380 Giles, Sun, Chen, Lee and Chen HIGHER ORDER RECURRENT NETWORKS & GRAMMATICAL INFERENCE C. L. Giles·, G. Z. Sun, H. H. Chen, Y. C. Lee, D. Chen Department of Physics and Astronomy and Institute for Advanced Computer Studies University of Maryland. College Park. MD 20742 * NEC Research Inst...

1989

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168 Lee and Lippmann Practical Characteristics of Neural Network and Conventional Pattern Classifiers on Artificial and Speech Problems* Yuchun Lee Digital Equipment Corp. 40 Old Bolton Road, OGOl-2Ull Stow, MA 01775-1215 ABSTRACT Richard P. Lippmann Lincoln Laboratory, MIT Room B...

1989

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A Computer Modeling Approach to Understanding 117 A computer modeling approach to understanding the inferior olive and its relationship to the cerebellar cortex in rats Maurice Lee and James M. Bower Computation and Neural Systems Program California Institute of Technology Pasadena, CA 91125 ...

1989

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282 Kanerva Contour-Map Encoding of Shape for Early Vision Pentti Kanerva Research Institute for Advanced Computer Science Mail Stop 230-5, NASA Ames Research Center Moffett Field, California 94035 ABSTRACT Contour maps provide a general method for recognizing two-dimensional shapes. Al...

1989

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574 Nowlan Maximum Likelihood Competitive Learning Steven J. Nowlan1 Department of Computer Science University of Toronto Toronto, Canada M5S lA4 ABSTRACT One popular class of unsupervised algorithms are competitive algorithms. In the traditional view of competition, only one competitor, t...

1989

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Note on Development or Modularity in Simple Cortical Models 133 Note on Development of Modularity in Simple Cortical Models Alex Chernjavskyl Neuroscience Graduate Program Section of Molecular Neurobiology Howard Hughes Medical Institute Yale University ABSTRACT John Moody2 Yale Compute...

1989

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298 Okamoto, Kawato, Ioui aod Miyake Model Based Image Compression and Adaptive Data Representation by Interacting Filter Banks Toshiaki Okamoto, Mitsuo Kawato, Toshio Ioui ATR Auditory and Visual Perception Research Laboratories Sanpeidani, Inuidani. Seika-cho. Soraku-gun Kyoto 619-02. Japan...

1989

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On the Distribution of the Number of Local Minima 727 On the Distribution of the Number of Local Minima of a Random Function on a Graph Pierre Baldi JPL, Caltech Pasadena, CA 91109 1 INTRODUCTION Yosef Rinott UCSD La Jolla, CA 92093 Charles Stein Stanford University Stanford, C...

1989

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240 Lee Using A Translation-Invariant Neural Network To Diagnose Heart Arrhythmia Susan Ciarrocca Lee The lohns Hopkins University Applied Physics Laboratory Laurel. Maryland 20707 ABSTRACT Distinctive electrocardiogram (EeG) patterns are created when the heart is beating normally and when...

1989

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Non-Boltzmann Dynamics in Networks of Spiking Neurons 109 Non-Boltzmann Dynamics in Networks of Spiking Neurons Michael C. Crair and William Bialek Department of Physics, and Department of Molecular and Cell Biology University of California at Berkeley Berkeley, CA 94720 ABSTRACT We study ...

1989

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558 Rohwer The 'Moving Targets' Training Algorithm Richard Rohwer Centre for Speech Technology Research Edinburgh University 80, South Bridge Edinburgh EH1 1HN SCOTLAND ABSTRACT A simple method for training the dynamical behavior of a neural network is derived. It is applicable to any trainin...

1989

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622 Atlas, Cole, Connor, EI-Sharkawi, Marks, Muthusamy and Barnard Performance Comparisons Between Backpropagation Networks and Classification Trees on Three Real-World Applications Les Atlas Dept. of EE. Fr -10 University of Washington Seattle. Washington 98195 Ronald Cole Dept. of CS&E ...

1989

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An Efficient Implementation of the Back-propagation Algorithm 801 A n Efficient Implementation of the Back-propagation Algorithm on the Connection Machine CM-2 Xiru Zhang! Michael Mckenna Jill P. Mesirov David L. Waltz Thinking Machines Corporation 245 First Street, Cambridge, MA 02142-1214 ...

1989

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550 Ackley and Littman Generalization and scaling in reinforcement learning David H. Ackley Michael L. Littman Cognitive Science Research Group Bellcore Morristown, NJ 07960 ABSTRACT In associative reinforcement learning, an environment generates input vectors, a learning system generat...

1989

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482 Saba and Keeler Algorithms/or Better Representation and Faster Learning in Radial Basis Function Networks A vijit Saba 1 James D. Keeler Microelectronics and Computer Technology corporation 3500 West Balcones Center Drive Austin, Tx 78759 ABSTRACT In this paper we present upper boun...

1989

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542 Kassebaum, Thnorio and Schaefers The Cocktail Party Problem: Speech/Data Signal Separation Comparison between Backpropagation and SONN John Kassebaum jak@ec.ecn.purdue.edu Manoel Fernando Tenorio tenorio@ee.ecn.purdue.edu Christoph Schaefers Parallel Distributed Structures Laboratory ...

1989

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750 Koch, Bair, Harris, Horiuchi, Hsu and Luo Real- Time Computer Vision and Robotics Using Analog VLSI Circuits Christof Koch Wyeth Bair John G. Harris Timothy Horiuchi Andrew Hsu Jin Luo Computation and Neural Systems Program Caltech 216-76 Pasadena, CA 91125 ABSTRACT The lon...

1989

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516 Grossman The CHIR Algorithm for Feed Forward Networks with Binary Weights Tal Grossman Department of Electronics Weizmann Institute of Science Rehovot 76100 Israel ABSTRACT A new learning algorithm, Learning by Choice of Internal Represetations (CHIR), was recently introduced. Whereas man...

1989

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A Large-Scale Neural Network 415 A LARGE-SCALE NEURAL NETWORK WHICH RECOGNIZES HANDWRITTEN KANJI CHARACTERS Yoshihiro Mori Kazuki Joe A TR Auditory and Visual Perception Research Laboratories Sanpeidani Inuidani Seika-cho Soraku-gun Kyoto 619-02 Japan ABSTRACT We propose a new way to const...

1989

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18 Harris-Warrick MECHANISMS FOR NEUROMODULATION OF BIOLOGICAL NEURAL NETWORKS Ronald M. Harris-Warrick Section of Neurobiology and Behavior Cornell University Ithaca, NY 14853 ABSTRACT The pyloric Central Pattern Generator of the crustacean stomatogastric ganglion is a well-defined biolog...

1989

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Recognizing Hand-Printed Letters and Digits 405 Recognizing Hand-Printed Letters and Digits Gale L. Martin James A. Pittman MCC, Austin, Texas 78759 ABSTRACT We are developing a hand-printed character recognition system using a multilayered neural net trained through backpropagation. We report on r...

1989

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266 Zemel, Mozer and Hinton TRAFFIC: Recognizing Objects Using Hierarchical Reference Frame Transformations Richard S. Zemel Computer Science Dept. University of Toronto Toronto, ONT M5S lA4 Michael C. Mozer Computer Science Dept. University of Colorado Boulder, CO 80309-0430 ABSTRAC...

1989

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810 Nunez and Fortes Performance of Connectionist Learning Algorithms on 2-D SIMD Processor Arrays Fernando J. Nunez* and Jose A.B. Fortes School of Electrical Engineering Purdue University West Lafayette, IN 47907 ABSTRACT The mapping of the back-propagation and mean field theory learning...

1989

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290 Viola Neurally Inspired Plasticity in Oculomotor Processes Paul A. Viola Artificial Intelligence Laboratory M"assachusetts Institute of Technology Cambridge, MA 02139 ABSTRACT We have constructed a two axis camera positioning system which is roughly analogous to a single human eye. Thi...

1989

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Modeling Time Varying Systems Using Hidden Control Neural Architecture Esther Levin AT&T Bell Laboratories Speech Research Department Murray Hill, NJ 07974 USA ABSTRACT Multi-layered neural networks have recently been proposed for nonlinear prediction and system modeling. Although proven successful...

1990

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286

Translating Locative Prepositions Paul W. Munro and Mary Tabasko Department of Information Science University of Pittsburgh Pittsburgh, PA 15260 ABSTRACT A network was trained by back propagation to map locative expressions of the form "noun-preposition-noun" to a semantic representation, as in ...

1990

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287

A Lagrangian Approach to Fixed Points Eric Mjolsness Department of Computer Science Yale University Willard L. Miranker IBM Watson Research Center Yorktown Heights, NY 10598 P.O. Box 2158 Yale Station New Haven, CT 16520-2158 Abstract We present a new way to derive dissipative, optimizing ...

1990

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Neural Network Implementation of Admission Control Rodolfo A. Milito, Isabelle Guyon, and Sara A. SoDa AT&T Bell Laboratories, Crawfords Corner Rd., Holmdel, NJ 07733 Abstract A feedforward layered network implements a mapping required to control an unknown stochastic nonlinear dynamical system. Training...

1990

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Computing with Arrays of Bell-Shaped and Sigmoid Functions Pierre Baldi· Jet Propulsion Laboratory California Institute of Technology Pasadena, CA 91109 Abstract We consider feed-forward neural networks with one non-linear hidden layer and linear output units. The transfer function in the hidden...

1990

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VLSI Implementation of TInMANN Matt Melton Tan Phan Doug Reeves Dave Van den Bout Electrical and Computer Engineering Dept. North Carolina State University Raleigh, NC 27695-7911 Abstract A massively parallel, all-digital, stochastic architecture TlnMAN N is described which performs competitive ...

1990

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SEXNET: A NEURAL NETWORK IDENTIFIES SEX FROM HUMAN FACES B.A. Golomb, D.T. Lawrence, and T.J. Sejnowski The Salk Institute 10010 N. Torrey Pines Rd. La Jolla, CA 92037 Abstract Sex identification in animals has biological importance. Humans are good at making this determination visually, but mac...

1990

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From Speech Recognition to Spoken Language Understanding: The Development of the MIT SUMMIT and VOYAGER Systems Victor Zue, James Glass, David Goodine, Lynette Hirschman, Hong Leung, Michael Phillips, Joseph Polifroni, and Stephanie Seneff' Room NE43-601 Spoken Language Systems Group Laboratory for...

1990

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Generalization by Weight-Elimination with Application to Forecasting Andreas S. Weigend Physics Department Stanford University Stanford, CA 94305 David E. Rumelhart Psychology Department Stanford University Stanford, CA 94305 Bernardo A. Huberman Dynamics of Computation XeroxPARC ...

1990

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A Novel Approach to Prediction of the 3-Dimensional Structures of Protein Backbones by Neural Networks Henrik Fredholrnl ,5 and Henrik Bol1l' 2 , Jakob Bohr3 , S0ren Brunak4 , Rodney M.J. Cotterill\ Benny Lautrup5 and Steffen B. Petersenl 1 MR-Senteret, SINTEF, N-7034 Trondheim, Norway. 2Univers...

1990

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Back Propagation Implementation on the Adaptive Solutions CNAPS Neurocomputer Chip Hal McCartor Adaptive Solutions Inc. 1400 N.W. Compton Drive Suite 340 Beaverton, OR 97006 Abstract The Adaptive Solutions CN APS architecture chip is a general purpose neurocomputer chip. It has 64 processors,...

1990

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Asymptotic slowing down of the nearest-neighbor classifier Robert R. Snapp CS lEE Department University of Vermont Burlington, VT 05405 Demetri Psaltis Electrical Engineering Caltech 116-81 Pasadena, CA 91125 Santosh S. Venkatesh Electrical Engineering University of Pennsylvania P...

1990

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ADAPTIVE SPLINE NETWORKS Jerome H. Friedman Department of Statistics and Stanford Linear Accelerator Center Stanford University Stanford, CA 94305 Abstract A network based on splines is described. It automatically adapts the number of units, unit parameters, and the architecture of the network for ...

1990

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Connectionist Approaches to the Use of Markov Models for Speech Recognition Herve Bourlard t,~ t L & H Speechproducts Koning Albert 1 laan, 64 1780 Wemmel, BELGIUM Nelson Morgan ~ I.e Chuck Wooters ~ ~ IntI. Compo Sc. Institute 1947, Center St., Suite 600 Berkeley, CA 94704, USA ABSTRACT ...

1990

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A Second-Order Translation, Rotation and Scale Invariant Neural Network Shelly D.D. Goggin Kristina M. Johnson Karl E. Gustafson· Optoelectronic Computing Systems Center and Department of Electrical and Computer Engineering University of Colorado at Boulder Boulder, CO 80309 shellg@boulder.co...

1990

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