NIPS 1993: Denver, CO, USA

Learning Algorithms

• Geoffrey E. Hinton, Richard S. Zemel:
  Autoencoders, Minimum Description Length and Helmholtz Free Energy. 3-10
  
• Richard S. Zemel, Geoffrey E. Hinton:
  Developing Population Codes by Minimizing Description Length. 11-18
  
• Sreerupa Das, Michael Mozer:
  A Unified Gradient-Descent/Clustering Architecture for Finite State Machine Induction. 19-26
  
• Eric Saund:
  Unsupervised Learning of Mixtures of Multiple Causes in Binary Data. 27-34
  
• Asriel U. Levin, Todd K. Leen, John E. Moody:
  Fast Pruning Using Principal Components. 35-42
  
• Christoph Bregler, Stephen M. Omohundro:
  Surface Learning with Applications to Lipreading. 43-50
  
• Melanie Mitchell, John H. Holland, Stephanie Forrest:
  When will a Genetic Algorithm Outperform Hill Climbing. 51-58
  
• Oded Maron, Andrew W. Moore:
  Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation. 59-66
  
• Bill Baird, Todd Troyer, Frank H. Eeckman:
  Grammatical Inference by Attentional Control of Synchronization in an Oscillating Elman Network. 67-74
  
• Yoshua Bengio, Paolo Frasconi:
  Credit Assignment through Time: Alternatives to Backpropagation. 75-82
  
• Javier R. Movellan:
  A Local Algorithm to Learn Trajectories with Stochastic Neural Networks. 83-87
  
• Gregory M. Saunders, Peter J. Angeline, Jordan B. Pollack:
  Structural and Behavioral Evolution of Recurrent Networks. 88-95
  
• Steven Gold, Eric Mjolsness, Anand Rangarajan:
  Clustering with a Domain-Specific Distance Measure. 96-103
  
• Joachim M. Buhmann, Thomas Hofmann:
  Central and Pairwise Data Clustering by Competitive Neural Networks. 104-111
  
• Virginia R. de Sa:
  Learning Classification with Unlabeled Data. 112-119
  
• Zoubin Ghahramani, Michael I. Jordan:
  Supervised learning from incomplete data via an EM approach. 120-127
  
• Volker Tresp, Subutai Ahmad, Ralph Neuneier:
  Training Neural Networks with Deficient Data. 128-135
  
• Mats Österberg, Reiner Lenz:
  Unsupervised Parallel Feature Extraction from First Principles. 136-143
  
• Terence D. Sanger:
  Two Iterative Algorithms for Computing the Singular Value Decomposition from Input/Output Samples. 144-151
  
• Nanda Kambhatla, Todd K. Leen:
  Fast Non-Linear Dimension Reduction. 152-159
  
• Stefan Schaal, Christopher G. Atkeson:
  Assessing the Quality of Learned Local Models. 160-167
  
• Patrice Simard:
  Efficient Computation of Complex Distance Metrics Using Hierarchical Filtering. 168-175
  
• Dana Ron, Yoram Singer, Naftali Tishby:
  The Power of Amnesia. 176-183
  
• Dietrich Wettschereck, Thomas G. Dietterich:
  Locally Adaptive Nearest Neighbor Algorithms. 184-191
  
• Yong Liu:
  Robust Parameter Estimation and Model Selection for Neural Network Regression. 192-199
  
• David Wolpert:
  Bayesian Backpropagation Over I-O Functions Rather Than Weights. 200-207
  
• Hans Henrik Thodberg:
  Bayesian Backprop in Action: Pruning, Committees, Error Bars and an Application to Spectroscopy. 208-215
  
• Thomas G. Dietterich, Ajay N. Jain, Richard H. Lathrop, Tomás Lozano-Pérez:
  A Comparison of Dynamic Reposing and Tangent Distance for Drug Activity Prediction. 216-223
  
• Iris Ginzburg, David Horn:
  Combined Neural Networks for Time Series Analysis. 224-231
  
• Patrice Simard, Hans Peter Graf:
  Backpropagation without Multiplication. 232-239
  
• Richard T. J. Bostock, Alan J. Harget:
  A Comparative Study of a Modified Bumptree Neural Network with Radial Basis Function Networks and the Standard Multi Layer Perceptron. 240-246
  
• Hossein Lari-Najafi, Vladimir Cherkassky:
  Adaptive knot Placement for Nonparametric Regression. 247-254
  
• Bernd Fritzke:
  Supervised Learning with Growing Cell Structures. 255-262
  
• Babak Hassibi, David G. Stork, Gregory J. Wolff:
  Optimal Brain Surgeon: Extensions and performance comparison. 263-270
  
• Ryotaro Kamimura:
  Generation of Internal Representation by alpha. 271-278
  
• Laurens R. Leerink, Marwan A. Jabri:
  Constructive Learning Using Internal Representation Conflicts. 279-284
  
• Joachim Utans:
  Learning in Compositional Hierarchies: Inducing the Structure of Objects from Data. 285-292
  

Learning Theory, Generalization, and Complexity

• Sumio Watanabe:
  An Optimization Method of Layered Neural Networks based on the Modified Information Criterion. 293-302
  
• Changfeng Wang, Santosh S. Venkatesh, J. Stephen Judd:
  Optimal Stopping and Effective Machine Complexity in Learning. 303-310
  
• Wolfgang Maass:
  Agnostic PAC-Learning of Functions on Analog Neural Nets. 311-318
  
• Hrushikesh Narhar Mhaskar, Charles A. Micchelli:
  How to Choose an Activation Function. 319-326
  
• Corinna Cortes, Lawrence D. Jackel, Sara A. Solla, Vladimir Vapnik, John S. Denker:
  Learning Curves: Asymptotic Values and Rate of Convergence. 327-334
  
• Charles Fefferman, Scott Markel:
  Recovering a Feed-Forward Net From Its Output. 335-342
  
• Tal Grossman, Alan S. Lapedes:
  Use of Bad Training Data for Better Predictions. 343-350
  
• Babak Hassibi, Ali H. Sayed, Thomas Kailath:
  Optimality Criteria for LMS and Backpropagation. 351-358
  
• Bill G. Horne, Don R. Hush:
  Bounds on the Complexity of Recurrent Neural Network Implementations of Finite State Machines. 359-366
  
• Chuanyi Ji:
  Generalization Error and the Expected Network Complexity. 367-374
  
• Adam Kowalczyk:
  Counting Function Theorem for Multi-Layer Networks. 375-382
  
• Olvi L. Mangasarian, Mikhail V. Solodov:
  Backpropagation Convergence via Deterministic Nonmonotone Perturbed Minimization. 383-390
  
• Mark Plutowski, Shinichi Sakata, Halbert White:
  Cross-Validation Estimates ISME. 391-398
  
• Holm Schwarze, John A. Hertz:
  Discontinuous Generalization in Large Committee Machines. 399-406
  
• Jonathan L. Shapiro, Adam Prügel-Bennett:
  Non-Linear Statistical Analysis and Self-Organizing Hebbian Networks. 407-414
  
• Grace Wahba, Yuedong Wang, Chong Gu, Ronald Klein, Barbara Klein:
  Structured Machine Learning for Soft Classification with Smoothing Spline ANOVA and Stacked Tuning, Testing, and Evaluation. 415-422
  
• Sumio Watanabe:
  Solvable Models of Artificial Neural Networks. 423-430
  

Theoretical Analysis: Dynamics and Statistics

• Herbert Wiklicky:
  On the Non-Existence of a Universal Learning Algorithm for Recurrent Neural Networks. 431-436
  
• Scott Kirkpatrick, Géza Györgyi, Naftali Tishby, Lidror Troyansky:
  The Statistical Mechanics of k-Satisfaction. 439-446
  
• Anthony C. C. Coolen, R. W. Penney, D. Sherrington:
  Coupled Dynamics of Fast Neurons and Slow Interactions. 447-454
  
• Max H. Garzon, Fernanda Botelho:
  Observability of Neural Network Behavior. 455-462
  
• Wulfram Gerstner, J. Leo van Hemmen:
  How to Describe Neuronal Activity: Spikes, Rates, or Assemblies? 463-470
  
• Iris Ginzburg, Haim Sompolinsky:
  Correlation Functions in a Large Stochastic Network. 471-476
  
• Todd K. Leen, Genevieve B. Orr:
  Optimal Stochastic Search and Adaptive Momentum. 477-484
  
• Isaac Meilijson, Eytan Ruppin:
  Optimal Signalling in Attractor Neural Networks. 485-492
  
• Xin Wang, Qingnan Li, Edward K. Blum:
  Asynchronous Dynamics of Continuous Time Neural Networks. 493-500
  

Neuroscience

• John F. Kolen:
  Fool's Gold: Extracting Finite State Machines from Recurrent Network Dynamics. 501-508
  
• Eve Marder:
  Dynamic Modulation of Neurons and Networks. 511-518
  
• Öjvind Bernander, Christof Koch, Rodney J. Douglas:
  Amplifying and Linearizing Apical Synaptic Inputs to Cortical Pyramidal Cells. 519-526
  
• Christiane Linster, David Marsan, Claudine Masson, Michel Kerszberg:
  Odor Processing in the Bee: A Preliminary Study of the Role of Central Input to the Antennal Lobe. 527-534
  
• Mitchell Gil Maltenfort, Robert E. Druzinsky, C. J. Heckman, W. Zev Rymer:
  Lower Boundaries of Motoneuron Desynchronization via Renshaw Interneurons. 535-542
  
• Klaus Obermayer, Lynne Kiorpes, Gary G. Blasdel:
  Development of Orientation and Ocular Dominance Columns in Infant Macaques. 543-550
  
• Daniel L. Ruderman, William Bialek:
  Statistics of Natural Images: Scaling in the Woods. 551-558
  
• Eric Boussard, Jean-François Vibert:
  Dopaminergic Neuromodulation Brings a Dynamical Plasticity to the Retina. 559-565
  
• Kenji Doya, Allen I. Selverston, Peter F. Rowat:
  A Hodgkin-Huxley Type Neuron Model That Learns Slow Non-Spike Oscillations. 566-573
  
• Audrey L. Guzik, Robert C. Eaton:
  Directional Hearing by the Mauthner System. 574-581
  
• Timothy K. Horiuchi, Brooks Bishofberger, Christof Koch:
  An Analog VLSI Saccadic Eye Movement System. 582-589
  
• Michael S. Lewicki:
  Bayesian Modeling and Classification of Neural Signals. 590-597
  
• P. Read Montague, Peter Dayan, Terrence J. Sejnowski:
  Foraging in an Uncertain Environment Using Predictive Hebbian Learning. 598-605
  
• Daniel J. Rosen, David E. Rumelhart, Eric I. Knudsen:
  A Connectionist Model of the Owl's Sound Localization System. 606-613
  
• Terence D. Sanger:
  Optimal Unsupervised Motor Learning Predicts the Internal Representation of Barn Owl Head Movements. 614-621
  
• Micah S. Siegel:
  An Analog VLSI Model of Central Pattern Generation in the Leech. 622-628
  

Control, Navigation, and Planning

• Martin Stemmler, Marius Usher, Christof Koch, Zeev Olami:
  Synchronization, Oscillations and 1/f Noise in Networks of Spiking Neurons. 629-636
  
• Kenneth M. Buckland, Peter D. Lawrence:
  Transition Point Dynamic Programming. 639-646
  
• Gary William Flake, Guo-Zheng Sun, Yee-Chun Lee, Hsing-Hen Chen:
  Exploiting Chaos to Control the Future. 647-654
  
• Satinder P. Singh, Andrew G. Barto, Roderic A. Grupen, Christopher I. Connolly:
  Robust Reinforcement Learning in Motion Planning. 655-662
  
• Christopher G. Atkeson:
  Using Local Trajectory Optimizers to Speed Up Global Optimization in Dynamic Programming. 663-670
  
• Justin A. Boyan, Michael L. Littman:
  Packet Routing in Dynamically Changing Networks: A Reinforcement Learning Approach. 671-678
  
• David A. Cohn:
  Neural Network Exploration Using Optimal Experiment Design. 679-686
  
• Andrew G. Barto, Michael O. Duff:
  Monte Carlo Matrix Inversion and Reinforcement Learning. 687-694
  
• Vijaykumar Gullapalli, Andrew G. Barto:
  Convergence of Indirect Adaptive Asynchronous Value Iteration Algorithms. 695-702
  
• Tommi Jaakkola, Michael I. Jordan, Satinder P. Singh:
  Convergence of Stochastic Iterative Dynamic Programming Algorithms. 703-710
  
• Andrew W. Moore:
  The Parti-Game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-Spaces. 711-718
  
• Timothy W. Cacciatore, Steven J. Nowlan:
  Mixtures of Controllers for Jump Linear and Non-Linear Plants. 719-726
  

Applications

• Yasuhiro Wada, Yasuharu Koike, Eric Vatikiotis-Bateson, Mitsuo Kawato:
  A Computational Model for Cursive Handwriting Based on the Minimization Principle. 727-734
  
• Jane Bromley, Isabelle Guyon, Yann LeCun, Eduard Säckinger, Roopak Shah:
  Signature Verification Using a Siamese Time Delay Neural Network. 737-744
  
• Ralph Wolf, John C. Platt:
  Postal Address Block Location Using a Convolutional Locator Network. 745-752
  
• Shumeet Baluja, Dean Pomerleau:
  Non-Intrusive Gaze Tracking Using Artificial Neural Networks. 753-760
  
• Pierre Baldi, Søren Brunak, Yves Chauvin, Jacob Engelbrecht, Anders Krogh:
  Hidden Markov Models for Human Genes. 761-768
  
• Joachim M. Buhmann, Martin Lades, Frank H. Eeckman:
  Illumination-Invariant Face Recognition with a Contrast Sensitive Silicon Retina. 769-776
  
• Nicholas S. Flann:
  Recognition-Based Segmentation of On-Line Cursive Handwriting. 777-784
  
• Hans Peter Graf, Eric Cosatto:
  Address Block Location with a Neural Net System. 785-792
  
• Nachimuthu Karunanithi:
  Identifying Fault-Prone Software Modules Using Feed-Forward Networks: A Case Study. 793-800
  
• Didier Keymeulen, Martine de Gerlache:
  Comparison Training for a Rescheduling Problem in Neural Networks. 801-808
  
• Alan S. Lapedes, Evan W. Steeg, Robert M. Farber:
  Neural Network Definition of Highly Predictable Protein Secondary Structure Classes. 809-816
  
• Nicol N. Schraudolph, Peter Dayan, Terrence J. Sejnowski:
  Temporal Difference Learning of Position Evaluation in the Game of Go. 817-824
  
• Padhraic Smyth:
  Probabilistic Anomaly Detection in Dynamic Systems. 825-832
  

Implementations

• Yoram Singer, Naftali Tishby:
  Decoding Cursive Scripts. 833-840
  
• Michael A. Glover, W. Thomas Miller III:
  A Massively-Parallel {SIMD} Processor for Neural Network and Machine Vision Applications. 843-849
  
• Steven S. Watkins, Paul M. Chau, Raoul Tawel, Bjorn Lambrigtsen, Mark Plutowski:
  A Hybrid Radial Basis Function Neurocomputer and Its Applications. 850-857
  
• Gert Cauwenberghs:
  A Learning Analog Neural Network Chip with Continuous-Time Recurrent Dynamics. 858-865
  
• Andreas G. Andreou, Thomas G. Edwards:
  VLSI Phase Locking Architectures for Feature Linking in Multiple Target Tracking Systems. 866-873
  
• Richard Coggins, Marwan A. Jabri:
  WATTLE: A Trainable Gain Analogue VLSI Neural Network. 874-881
  
• Ibrahim M. Elfadel, John L. Wyatt Jr.:
  The Softmax Nonlinearity: Derivation Using Statistical Mechanics and Useful Properties as a Multiterminal Analog Circuit Element. 882-887
  
• Urs A. Müller, Michael Kocheisen, Anton Gunzinger:
  High Performance Neural Net Simulation on a Multiprocessor System with Intelligent Communication. 888-895
  
• Michael 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:
  Digital Boltzmann VLSI for Constraint Satisfaction and Learning. 896-903
  
• Ernst Niebur, Dean Brettle:
  Efficient Simulation of Biological Neural Networks on Massively Parallel Supercomputers with Hypercube Architecture. 904-910
  
• Arlindo L. Oliveira, Alberto L. Sangiovanni-Vincentelli:
  Learning Complex Boolean Functions: Algorithms and Applications. 911-918
  
• Tadashi Shibata, Koji Kotani, Takeo Yamashita, Hiroshi Ishii, Hideo Kosaka, Tadahiro Ohmi:
  Implementing Intelligence on Silicon Using Neuron-Like Functional MOS Transistors. 919-926
  

Visual Processing

• Lloyd Watts:
  Event-Driven Simulation of Networks of Spiking Neurons. 927-934
  
• Yoshua Bengio, Yann LeCun, Donnie Henderson:
  Globally Trained Handwritten Word Recognizer Using Spatial Representation, Convolutional Neural Networks, and Hidden Markov Models. 937-944
  
• Trevor Darrell, Alex Pentland:
  Classifying Hand Gestures with a View-Based Distributed Representation. 945-952
  
• Kô Sakai, Leif H. Finkel:
  A Network Mechanism for the Determination of Shape-from-Texture. 953-960
  
• Subutai Ahmad:
  Feature Densities Are Required for Computing Feature Correspondences. 961-968
  
• G. T. Buracas, T. D. Albright:
  The Role of MT Neuron Receptive Field Surrounds in Computing Object Shape from Velocity Fields. 969-976
  
• Kostas I. Diamantaras, Davi Geiger:
  Resolving Motion Ambiguities. 977-984
  
• Chien-Ping Lu, Eric Mjolsness:
  Two-Dimensional Object Localization by Coarse-to-Fine Correlation Matching. 985-992
  
• Paul Sajda, Leif H. Finkel:
  Dual Mechanisms for Neural Binding and Segmentation. 993-1000
  

Speech and Signal Processing

• Alan L. Yuille, Stelios M. Smirnakis, Lei Xu:
  Bayesian Self-Organization. 1001-1008
  
• José Carlos Príncipe, Hui-Huang Hsu, Jyh-Ming Kuo:
  Analysis of Short Term Memories for Neural Networks. 1011-1018
  
• Eric I. Chang, Richard Lippmann:
  Figure of Merit Training for Detection and Spotting. 1019-1026
  
• Gregory J. Wolff, K. Venkatesh Prasad, David G. Stork, Marcus E. Hennecke:
  Lipreading by Neural Networks: Visual Preprocessing, Learning, and Sensory Integration. 1027-1034
  
• Kevin R. Farrell, Richard J. Mammone:
  Speaker Recognition Using Neural Tree Networks. 1035-1042
  
• Makoto Hirayama, Eric Vatikiotis-Bateson, Mitsuo Kawato:
  Inverse Dynamics of Speech Motor Control. 1043-1050
  
• Steve Renals, Mike Hochberg, Anthony J. Robinson:
  Learning Temporal Dependencies in Connectionist Speech Recognition. 1051-1058
  

Cognitive Science

• Ying Zhao, Richard M. Schwartz, John Makhoul, George Zavaliagkos:
  Segmental Neural Net Optimization for Continuous Speech Recognition. 1059-1066
  
• Richard O. Duda:
  Connectionist Models for Auditory Scene Analysis. 1069-1076
  
• Reza Shadmehr, Ferdinando A. Mussa-Ivaldi:
  Computational Elements of the Adaptive Controller of the Human Arm. 1077-1084
  
• Catherine Stevens, Janet Wiles:
  Tonal Music as a Componential Code: Learning Temporal Relationships between and within Pitch and Timing Components. 1085-1092
  
• Reinhard Blasig:
  GDS: Gradient Descent Generation of Symbolic Classification Rules. 1093-1100
  
• Thea B. Ghiselli-Crippa, Paul W. Munro:
  Emergence of Global Structure from Local Associations. 1101-1108
  
• Tony Plate:
  Estimating Analogical Similarity by Dot-Products of Holographic Reduced Representations. 1109-1116
  
• Thomas R. Shultz, Jeffrey L. Elman:
  Analyzing Cross-Connected Networks. 1117-1124
  

Addenda to NIPS 5

• Alessandro Sperduti:
  Encoding Labeled Graphs by Labeling RAAM. 1125-1132
  
• Mark Plutowski, Garrison W. Cottrell, Halbert White:
  Learning Mackey-Glass from 25 Examples, Plus or Minus 2. 1135-1142
  
• Yehuda Salu:
  Classification of Multi-Spectral Pixels by the Binary Diamond Neural Network. 1143-1150
  

Workshops

• Ah Chung Tsoi, D. S. C. So, Alex A. Sergejew:
  Classification of Electroencephalogram Using Artificial Neural Networks. 1151-1158
  
• Vwani P. Roychowdhury, Kai-Yeung Siu:
  Complexity Issues in Neural Computation and Learning. 1161-1162
  
• Andreas S. Weigend:
  Connectionism for Music and Audition. 1163-1164
  
• Thomas G. Dietterich, Dietrich Wettschereck, Christopher G. Atkeson, Andrew W. Moore:
  Memory-Based Methods for Regression and Classification. 1165-1166
  
• Ernst Niebur, Bruno A. Olshausen:
  Neurobiology, Psychophysics, and Computational Models of Visual Attention. 1167-1168
  
• David A. Cohn:
  Robot Learning: Exploration and Continuous Domains. 1169-1170
  
• Max H. Garzon, Fernanda Botelho:
  Stability and Observability. 1171-1172
  
• Mark A. Gluck:
  What Does the Hippocampus Compute?: A Precis of the 1993 NIPS Workshop. 1173-1175
  
• Robert M. French:
  Catastrophic Interference in Connectionist Networks: Can It Be Predicted, Can It Be Prevented? 1176-1177
  
• Joachim Diederich, Ah Chung Tsoi:
  Connectionist Modeling and Parallel Architectures. 1178-1179
  
• Thomas H. Hildebrandt:
  Functional Models of Selective Attention and Context Dependency. 1180-1181
  
• Hayit Greenspan:
  Learning in Computer Vision and Image Understanding. 1182-1183
  
• Arun K. Jagota:
  Neural Network Models for Optimization Problems. 1184-1185
  
• Josef P. Rauschecker, Terrence J. Sejnowski:
  Processing of Visual and Auditory Space and Its Modification by Experience. 1186-1187
  
• Michael P. Perrone:
  Putting It All Together: Methods for Combining Neural Networks. 1188-1189
  

Last update Tue May 21 17:47:57 2013 CET by the DBLP Team — Data released under the ODC-BY 1.0 license — See also our legal information page