Table of Contents

• Autoencoders, Minimum Description Length and Helmholtz Free EnergyGeoffrey E. Hinton, Richard S. Zemel. 3-10 [doi]
• Developing Population Codes by Minimizing Description LengthRichard S. Zemel, Geoffrey E. Hinton. 11-18 [doi]
• A Unified Gradient-Descent/Clustering Architecture for Finite State Machine InductionSreerupa Das, Michael Mozer. 19-26 [doi]
• Unsupervised Learning of Mixtures of Multiple Causes in Binary DataEric Saund. 27-34 [doi]
• Fast Pruning Using Principal ComponentsAsriel U. Levin, Todd K. Leen, John E. Moody. 35-42 [doi]
• Surface Learning with Applications to LipreadingChristoph Bregler, Stephen M. Omohundro. 43-50 [doi]
• When will a Genetic Algorithm Outperform Hill ClimbingMelanie Mitchell, John H. Holland, Stephanie Forrest. 51-58 [doi]
• Hoeffding Races: Accelerating Model Selection Search for Classification and Function ApproximationOded Maron, Andrew W. Moore. 59-66 [doi]
• Grammatical Inference by Attentional Control of Synchronization in an Oscillating Elman NetworkBill Baird, Todd Troyer, Frank H. Eeckman. 67-74 [doi]
• Credit Assignment through Time: Alternatives to BackpropagationYoshua Bengio, Paolo Frasconi. 75-82 [doi]
• A Local Algorithm to Learn Trajectories with Stochastic Neural NetworksJavier R. Movellan. 83-87 [doi]
• Structural and Behavioral Evolution of Recurrent NetworksGregory M. Saunders, Peter J. Angeline, Jordan B. Pollack. 88-95 [doi]
• Clustering with a Domain-Specific Distance MeasureSteven Gold, Eric Mjolsness, Anand Rangarajan. 96-103 [doi]
• Central and Pairwise Data Clustering by Competitive Neural NetworksJoachim M. Buhmann, Thomas Hofmann. 104-111 [doi]
• Learning Classification with Unlabeled DataVirginia R. de Sa. 112-119 [doi]
• Supervised learning from incomplete data via an EM approachZoubin Ghahramani, Michael I. Jordan. 120-127 [doi]
• Training Neural Networks with Deficient DataVolker Tresp, Subutai Ahmad, Ralph Neuneier. 128-135 [doi]
• Unsupervised Parallel Feature Extraction from First PrinciplesMats Österberg, Reiner Lenz. 136-143 [doi]
• Two Iterative Algorithms for Computing the Singular Value Decomposition from Input/Output SamplesTerence D. Sanger. 144-151 [doi]
• Fast Non-Linear Dimension ReductionNanda Kambhatla, Todd K. Leen. 152-159 [doi]
• Assessing the Quality of Learned Local ModelsStefan Schaal, Christopher G. Atkeson. 160-167 [doi]
• Efficient Computation of Complex Distance Metrics Using Hierarchical FilteringPatrice Simard. 168-175 [doi]
• The Power of AmnesiaDana Ron, Yoram Singer, Naftali Tishby. 176-183 [doi]
• Locally Adaptive Nearest Neighbor AlgorithmsDietrich Wettschereck, Thomas G. Dietterich. 184-191 [doi]
• Robust Parameter Estimation and Model Selection for Neural Network RegressionYong Liu. 192-199 [doi]
• Bayesian Backpropagation Over I-O Functions Rather Than WeightsDavid Wolpert. 200-207 [doi]
• Bayesian Backprop in Action: Pruning, Committees, Error Bars and an Application to SpectroscopyHans Henrik Thodberg. 208-215 [doi]
• A Comparison of Dynamic Reposing and Tangent Distance for Drug Activity PredictionThomas G. Dietterich, Ajay N. Jain, Richard H. Lathrop, Tomás Lozano-Pérez. 216-223 [doi]
• Combined Neural Networks for Time Series AnalysisIris Ginzburg, David Horn. 224-231 [doi]
• Backpropagation without MultiplicationPatrice Simard, Hans Peter Graf. 232-239 [doi]
• A Comparative Study of a Modified Bumptree Neural Network with Radial Basis Function Networks and the Standard Multi Layer PerceptronRichard T. J. Bostock, Alan J. Harget. 240-246 [doi]
• Adaptive knot Placement for Nonparametric RegressionHossein Lari-Najafi, Vladimir Cherkassky. 247-254 [doi]
• Supervised Learning with Growing Cell StructuresBernd Fritzke. 255-262 [doi]
• Optimal Brain Surgeon: Extensions and performance comparisonBabak Hassibi, David G. Stork, Gregory J. Wolff. 263-270 [doi]
• Generation of Internal Representation by alphaRyotaro Kamimura. 271-278 [doi]
• Constructive Learning Using Internal Representation ConflictsLaurens R. Leerink, Marwan A. Jabri. 279-284 [doi]
• Learning in Compositional Hierarchies: Inducing the Structure of Objects from DataJoachim Utans. 285-292 [doi]
• An Optimization Method of Layered Neural Networks based on the Modified Information CriterionSumio Watanabe. 293-302 [doi]
• Optimal Stopping and Effective Machine Complexity in LearningChangfeng Wang, Santosh S. Venkatesh, J. Stephen Judd. 303-310 [doi]
• Agnostic PAC-Learning of Functions on Analog Neural NetsWolfgang Maass. 311-318 [doi]
• How to Choose an Activation FunctionHrushikesh Narhar Mhaskar, Charles A. Micchelli. 319-326 [doi]
• Learning Curves: Asymptotic Values and Rate of ConvergenceCorinna Cortes, Lawrence D. Jackel, Sara A. Solla, Vladimir Vapnik, John S. Denker. 327-334 [doi]
• Recovering a Feed-Forward Net From Its OutputCharles Fefferman, Scott Markel. 335-342 [doi]
• Use of Bad Training Data for Better PredictionsTal Grossman, Alan S. Lapedes. 343-350 [doi]
• Optimality Criteria for LMS and BackpropagationBabak Hassibi, Ali H. Sayed, Thomas Kailath. 351-358 [doi]
• Bounds on the Complexity of Recurrent Neural Network Implementations of Finite State MachinesBill G. Horne, Don R. Hush. 359-366 [doi]
• Generalization Error and the Expected Network ComplexityChuanyi Ji. 367-374 [doi]
• Counting Function Theorem for Multi-Layer NetworksAdam Kowalczyk. 375-382 [doi]
• Backpropagation Convergence via Deterministic Nonmonotone Perturbed MinimizationOlvi L. Mangasarian, Mikhail V. Solodov. 383-390 [doi]
• Cross-Validation Estimates ISMEMark Plutowski, Shinichi Sakata, Halbert White. 391-398 [doi]
• Discontinuous Generalization in Large Committee MachinesHolm Schwarze, John A. Hertz. 399-406 [doi]
• Non-Linear Statistical Analysis and Self-Organizing Hebbian NetworksJonathan L. Shapiro, Adam Prügel-Bennett. 407-414 [doi]
• Structured Machine Learning for Soft Classification with Smoothing Spline ANOVA and Stacked Tuning, Testing, and EvaluationGrace Wahba, Yuedong Wang, Chong Gu, Ronald Klein, Barbara Klein. 415-422 [doi]
• Solvable Models of Artificial Neural NetworksSumio Watanabe. 423-430 [doi]
• On the Non-Existence of a Universal Learning Algorithm for Recurrent Neural NetworksHerbert Wiklicky. 431-436 [doi]
• The Statistical Mechanics of k-SatisfactionScott Kirkpatrick, Géza Györgyi, Naftali Tishby, Lidror Troyansky. 439-446 [doi]
• Coupled Dynamics of Fast Neurons and Slow InteractionsAnthony C. C. Coolen, R. W. Penney, D. Sherrington. 447-454 [doi]
• Observability of Neural Network BehaviorMax H. Garzon, Fernanda Botelho. 455-462 [doi]
• How to Describe Neuronal Activity: Spikes, Rates, or Assemblies?Wulfram Gerstner, J. Leo van Hemmen. 463-470 [doi]
• Correlation Functions in a Large Stochastic NetworkIris Ginzburg, Haim Sompolinsky. 471-476 [doi]
• Optimal Stochastic Search and Adaptive MomentumTodd K. Leen, Genevieve B. Orr. 477-484 [doi]
• Optimal Signalling in Attractor Neural NetworksIsaac Meilijson, Eytan Ruppin. 485-492 [doi]
• Asynchronous Dynamics of Continuous Time Neural NetworksXin Wang, Qingnan Li, Edward K. Blum. 493-500 [doi]
• Fool s Gold: Extracting Finite State Machines from Recurrent Network DynamicsJohn F. Kolen. 501-508 [doi]
• Dynamic Modulation of Neurons and NetworksEve Marder. 511-518 [doi]
• Amplifying and Linearizing Apical Synaptic Inputs to Cortical Pyramidal CellsÖjvind Bernander, Christof Koch, Rodney J. Douglas. 519-526 [doi]
• Odor Processing in the Bee: A Preliminary Study of the Role of Central Input to the Antennal LobeChristiane Linster, David Marsan, Claudine Masson, Michel Kerszberg. 527-534 [doi]
• Lower Boundaries of Motoneuron Desynchronization via Renshaw InterneuronsMitchell Gil Maltenfort, Robert E. Druzinsky, C. J. Heckman, W. Zev Rymer. 535-542 [doi]
• Development of Orientation and Ocular Dominance Columns in Infant MacaquesKlaus Obermayer, Lynne Kiorpes, Gary G. Blasdel. 543-550 [doi]
• Statistics of Natural Images: Scaling in the WoodsDaniel L. Ruderman, William Bialek. 551-558 [doi]
• Dopaminergic Neuromodulation Brings a Dynamical Plasticity to the RetinaEric Boussard, Jean-François Vibert. 559-565 [doi]
• A Hodgkin-Huxley Type Neuron Model That Learns Slow Non-Spike OscillationsKenji Doya, Allen I. Selverston, Peter F. Rowat. 566-573 [doi]
• Directional Hearing by the Mauthner SystemAudrey L. Guzik, Robert C. Eaton. 574-581 [doi]
• An Analog VLSI Saccadic Eye Movement SystemTimothy K. Horiuchi, Brooks Bishofberger, Christof Koch. 582-589 [doi]
• Bayesian Modeling and Classification of Neural SignalsMichael S. Lewicki. 590-597 [doi]
• Foraging in an Uncertain Environment Using Predictive Hebbian LearningP. Read Montague, Peter Dayan, Terrence J. Sejnowski. 598-605 [doi]
• A Connectionist Model of the Owl s Sound Localization SystemDaniel J. Rosen, David E. Rumelhart, Eric I. Knudsen. 606-613 [doi]
• Optimal Unsupervised Motor Learning Predicts the Internal Representation of Barn Owl Head MovementsTerence D. Sanger. 614-621 [doi]
• An Analog VLSI Model of Central Pattern Generation in the LeechMicah S. Siegel. 622-628 [doi]
• Synchronization, Oscillations and 1/f Noise in Networks of Spiking NeuronsMartin Stemmler, Marius Usher, Christof Koch, Zeev Olami. 629-636 [doi]
• Transition Point Dynamic ProgrammingKenneth M. Buckland, Peter D. Lawrence. 639-646 [doi]
• Exploiting Chaos to Control the FutureGary William Flake, Guo-Zheng Sun, Yee-Chun Lee, Hsing-Hen Chen. 647-654 [doi]
• Robust Reinforcement Learning in Motion PlanningSatinder P. Singh, Andrew G. Barto, Roderic A. Grupen, Christopher I. Connolly. 655-662 [doi]
• Using Local Trajectory Optimizers to Speed Up Global Optimization in Dynamic ProgrammingChristopher G. Atkeson. 663-670 [doi]
• Packet Routing in Dynamically Changing Networks: A Reinforcement Learning ApproachJustin A. Boyan, Michael L. Littman. 671-678 [doi]
• Neural Network Exploration Using Optimal Experiment DesignDavid A. Cohn. 679-686 [doi]
• Monte Carlo Matrix Inversion and Reinforcement LearningAndrew G. Barto, Michael O. Duff. 687-694 [doi]
• Convergence of Indirect Adaptive Asynchronous Value Iteration AlgorithmsVijaykumar Gullapalli, Andrew G. Barto. 695-702 [doi]
• Convergence of Stochastic Iterative Dynamic Programming AlgorithmsTommi Jaakkola, Michael I. Jordan, Satinder P. Singh. 703-710 [doi]
• The Parti-Game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-SpacesAndrew W. Moore. 711-718 [doi]
• Mixtures of Controllers for Jump Linear and Non-Linear PlantsTimothy W. Cacciatore, Steven J. Nowlan. 719-726 [doi]
• A Computational Model for Cursive Handwriting Based on the Minimization PrincipleYasuhiro Wada, Yasuharu Koike, Eric Vatikiotis-Bateson, Mitsuo Kawato. 727-734 [doi]
• Signature Verification Using a Siamese Time Delay Neural NetworkJane Bromley, Isabelle Guyon, Yann LeCun, Eduard Säckinger, Roopak Shah. 737-744 [doi]
• Postal Address Block Location Using a Convolutional Locator NetworkRalph Wolf, John C. Platt. 745-752 [doi]
• Non-Intrusive Gaze Tracking Using Artificial Neural NetworksShumeet Baluja, Dean Pomerleau. 753-760 [doi]
• Hidden Markov Models for Human GenesPierre Baldi, Søren Brunak, Yves Chauvin, Jacob Engelbrecht, Anders Krogh. 761-768 [doi]
• Illumination-Invariant Face Recognition with a Contrast Sensitive Silicon RetinaJoachim M. Buhmann, Martin Lades, Frank H. Eeckman. 769-776 [doi]
• Recognition-Based Segmentation of On-Line Cursive HandwritingNicholas S. Flann. 777-784 [doi]
• Address Block Location with a Neural Net SystemHans Peter Graf, Eric Cosatto. 785-792 [doi]
• Identifying Fault-Prone Software Modules Using Feed-Forward Networks: A Case StudyNachimuthu Karunanithi. 793-800 [doi]
• Comparison Training for a Rescheduling Problem in Neural NetworksDidier Keymeulen, Martine de Gerlache. 801-808 [doi]
• Neural Network Definition of Highly Predictable Protein Secondary Structure ClassesAlan S. Lapedes, Evan W. Steeg, Robert M. Farber. 809-816 [doi]
• Temporal Difference Learning of Position Evaluation in the Game of GoNicol N. Schraudolph, Peter Dayan, Terrence J. Sejnowski. 817-824 [doi]
• Probabilistic Anomaly Detection in Dynamic SystemsPadhraic Smyth. 825-832 [doi]
• Decoding Cursive ScriptsYoram Singer, Naftali Tishby. 833-840 [doi]
• A Massively-Parallel {SIMD} Processor for Neural Network and Machine Vision ApplicationsMichael A. Glover, W. Thomas Miller III. 843-849 [doi]
• A Hybrid Radial Basis Function Neurocomputer and Its ApplicationsSteven S. Watkins, Paul M. Chau, Raoul Tawel, Bjorn Lambrigtsen, Mark Plutowski. 850-857 [doi]
• A Learning Analog Neural Network Chip with Continuous-Time Recurrent DynamicsGert Cauwenberghs. 858-865 [doi]
• VLSI Phase Locking Architectures for Feature Linking in Multiple Target Tracking SystemsAndreas G. Andreou, Thomas G. Edwards. 866-873 [doi]
• WATTLE: A Trainable Gain Analogue VLSI Neural NetworkRichard Coggins, Marwan A. Jabri. 874-881 [doi]
• The Softmax Nonlinearity: Derivation Using Statistical Mechanics and Useful Properties as a Multiterminal Analog Circuit ElementIbrahim M. Elfadel, John L. Wyatt Jr.. 882-887 [doi]
• High Performance Neural Net Simulation on a Multiprocessor System with Intelligent CommunicationUrs A. Müller, Michael Kocheisen, Anton Gunzinger. 888-895 [doi]
• Digital Boltzmann VLSI for Constraint Satisfaction and LearningMichael Murray, Ming-Tak Leung, Kan Boonyanit, Kong Kritayakirana, James B. Burg, Gregory J. Wolff, Tokahiro Watanabe, Edward L. Schwartz, David G. Stork, Allern M. Peterson. 896-903 [doi]
• Efficient Simulation of Biological Neural Networks on Massively Parallel Supercomputers with Hypercube ArchitectureErnst Niebur, Dean Brettle. 904-910 [doi]
• Learning Complex Boolean Functions: Algorithms and ApplicationsArlindo L. Oliveira, Alberto L. Sangiovanni-Vincentelli. 911-918 [doi]
• Implementing Intelligence on Silicon Using Neuron-Like Functional MOS TransistorsTadashi Shibata, Koji Kotani, Takeo Yamashita, Hiroshi Ishii, Hideo Kosaka, Tadahiro Ohmi. 919-926 [doi]
• Event-Driven Simulation of Networks of Spiking NeuronsLloyd Watts. 927-934 [doi]
• Globally Trained Handwritten Word Recognizer Using Spatial Representation, Convolutional Neural Networks, and Hidden Markov ModelsYoshua Bengio, Yann LeCun, Donnie Henderson. 937-944 [doi]
• Classifying Hand Gestures with a View-Based Distributed RepresentationTrevor Darrell, Alex Pentland. 945-952 [doi]
• A Network Mechanism for the Determination of Shape-from-TextureKô Sakai, Leif H. Finkel. 953-960 [doi]
• Feature Densities Are Required for Computing Feature CorrespondencesSubutai Ahmad. 961-968 [doi]
• The Role of MT Neuron Receptive Field Surrounds in Computing Object Shape from Velocity FieldsG. T. Buracas, T. D. Albright. 969-976 [doi]
• Resolving Motion AmbiguitiesKostas I. Diamantaras, Davi Geiger. 977-984 [doi]
• Two-Dimensional Object Localization by Coarse-to-Fine Correlation MatchingChien-Ping Lu, Eric Mjolsness. 985-992 [doi]
• Dual Mechanisms for Neural Binding and SegmentationPaul Sajda, Leif H. Finkel. 993-1000 [doi]
• Bayesian Self-OrganizationAlan L. Yuille, Stelios M. Smirnakis, Lei Xu. 1001-1008 [doi]
• Analysis of Short Term Memories for Neural NetworksJosé Carlos Príncipe, Hui-H. Hsu, Jyh-Ming Kuo. 1011-1018 [doi]
• Figure of Merit Training for Detection and SpottingEric I. Chang, Richard Lippmann. 1019-1026 [doi]
• Lipreading by Neural Networks: Visual Preprocessing, Learning, and Sensory IntegrationGregory J. Wolff, K. Venkatesh Prasad, David G. Stork, Marcus E. Hennecke. 1027-1034 [doi]
• Speaker Recognition Using Neural Tree NetworksKevin R. Farrell, Richard J. Mammone. 1035-1042 [doi]
• Inverse Dynamics of Speech Motor ControlMakoto Hirayama, Eric Vatikiotis-Bateson, Mitsuo Kawato. 1043-1050 [doi]
• Learning Temporal Dependencies in Connectionist Speech RecognitionSteve Renals, Mike Hochberg, Anthony J. Robinson. 1051-1058 [doi]
• Segmental Neural Net Optimization for Continuous Speech RecognitionYing Zhao, Richard M. Schwartz, John Makhoul, George Zavaliagkos. 1059-1066 [doi]
• Connectionist Models for Auditory Scene AnalysisRichard O. Duda. 1069-1076 [doi]
• Computational Elements of the Adaptive Controller of the Human ArmReza Shadmehr, Ferdinando A. Mussa-Ivaldi. 1077-1084 [doi]
• Tonal Music as a Componential Code: Learning Temporal Relationships between and within Pitch and Timing ComponentsCatherine Stevens, Janet Wiles. 1085-1092 [doi]
• GDS: Gradient Descent Generation of Symbolic Classification RulesReinhard Blasig. 1093-1100 [doi]
• Emergence of Global Structure from Local AssociationsThea B. Ghiselli-Crippa, Paul W. Munro. 1101-1108 [doi]
• Estimating Analogical Similarity by Dot-Products of Holographic Reduced RepresentationsTony Plate. 1109-1116 [doi]
• Analyzing Cross-Connected NetworksThomas R. Shultz, Jeffrey L. Elman. 1117-1124 [doi]
• Encoding Labeled Graphs by Labeling RAAMAlessandro Sperduti. 1125-1132 [doi]
• Learning Mackey-Glass from 25 Examples, Plus or Minus 2Mark Plutowski, Garrison W. Cottrell, Halbert White. 1135-1142 [doi]
• Classification of Multi-Spectral Pixels by the Binary Diamond Neural NetworkYehuda Salu. 1143-1150 [doi]
• Classification of Electroencephalogram Using Artificial Neural NetworksAh Chung Tsoi, D. S. C. So, A. Sergejew. 1151-1158 [doi]
• Complexity Issues in Neural Computation and LearningVwani P. Roychowdhury, Kai-Yeung Siu. 1161-1162 [doi]
• Connectionism for Music and AuditionAndreas S. Weigend. 1163-1164 [doi]
• Memory-Based Methods for Regression and ClassificationThomas G. Dietterich, Dietrich Wettschereck, Christopher G. Atkeson, Andrew W. Moore. 1165-1166 [doi]
• Neurobiology, Psychophysics, and Computational Models of Visual AttentionErnst Niebur, Bruno A. Olshausen. 1167-1168 [doi]
• Robot Learning: Exploration and Continuous DomainsDavid A. Cohn. 1169-1170 [doi]
• Stability and ObservabilityMax H. Garzon, Fernanda Botelho. 1171-1172 [doi]
• What Does the Hippocampus Compute?: A Precis of the 1993 NIPS WorkshopMark A. Gluck. 1173-1175 [doi]
• Catastrophic Interference in Connectionist Networks: Can It Be Predicted, Can It Be Prevented?Robert M. French. 1176-1177 [doi]
• Connectionist Modeling and Parallel ArchitecturesJoachim Diederich, Ah Chung Tsoi. 1178-1179 [doi]
• Functional Models of Selective Attention and Context DependencyThomas H. Hildebrandt. 1180-1181 [doi]
• Learning in Computer Vision and Image UnderstandingHayit Greenspan. 1182-1183 [doi]
• Neural Network Models for Optimization ProblemsArun K. Jagota. 1184-1185 [doi]
• Processing of Visual and Auditory Space and Its Modification by ExperienceJosef P. Rauschecker, Terrence J. Sejnowski. 1186-1187 [doi]
• Putting It All Together: Methods for Combining Neural NetworksMichael P. Perrone. 1188-1189 [doi]