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NIPS 1993: Denver, CO, USA
- Jack D. Cowan, Gerald Tesauro, Joshua Alspector:
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 - 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 Y. 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 H. 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 Y. 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 E. 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, Charles 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 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 S. Jaakkola, Michael I. Jordan, Satinder 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. Burr, Gregory J. Wolff, Tokahiro Watanabe, Edward L. Schwartz, David G. Stork, Allen 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, Thomas 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 J. 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
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