NIPS 1993: Denver, CO, USA
Jack D. Cowan, Gerald Tesauro, Joshua Alspector (Eds.): Advances in Neural Information Processing Systems 6, [7th NIPS Conference, Denver, Colorado, USA, 1993]. Morgan Kaufmann 1994 ISBN 1-55860-322-0
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
- 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
- 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
- Patrice Simard: Efficient Computation of Complex Distance Metrics Using Hierarchical Filtering. 168-175
- 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
- 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
- Corinna Cortes, Lawrence D. Jackel, Sara A. Solla, Vladimir Vapnik, John S. Denker: Learning Curves: Asymptotic Values and Rate of Convergence. 327-334
- 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
- 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
- Wulfram Gerstner, J. Leo van Hemmen: How to Describe Neuronal Activity: Spikes, Rates, or Assemblies? 463-470
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Chien-Ping Lu, Eric Mjolsness: Two-Dimensional Object Localization by Coarse-to-Fine Correlation Matching. 985-992
Speech and Signal Processing
- José Carlos Príncipe, Hui-Huang Hsu, Jyh-Ming Kuo: Analysis of Short Term Memories for Neural Networks. 1011-1018
- Gregory J. Wolff, K. Venkatesh Prasad, David G. Stork, Marcus E. Hennecke: Lipreading by Neural Networks: Visual Preprocessing, Learning, and Sensory Integration. 1027-1034
- 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
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
- 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
- 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
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last updated on 2013-06-19T203413 by the dblp team