NIPS 1994: Denver, CO, USA

Cognitive Science

• Humbert Suarez, Christof Koch, Rodney J. Douglas:
  Direction Selectivity In Primary Visual Cortex Using Massive Intracortical Connections. 3-10
  
• Donald W. Mathis, Michael Mozer:
  On the Computational Utility of Consciousness. 11-18
  
• Tom Brashers-Krug, Reza Shadmehr, Emanuel Todorov:
  Catastrophic Interference in Human Motor Learning. 19-26
  
• Michiro Negishi:
  Grammar Learning by a Self-Organizing Network. 27-34
  
• Eytan Ruppin, James A. Reggia:
  Patterns of damage in neural networks: The effects of lesion area, shape and number. 35-42
  
• Daniel M. Wolpert, Zoubin Ghahramani, Michael I. Jordan:
  Forward dynamic models in human motor control: Psychophysical evidence. 43-50
  

Neuroscience

• Neil Burgess:
  A solvable connectionist model of immediate recall of ordered lists. 51-58
  
• Rainer Malaka, Thomas Ragg, Martin Hammer:
  A Model for Chemosensory Reception. 61-68
  
• Nicholas T. Carnevale, Kenneth Y. Tsai, Brenda J. Claiborne, Thomas H. Brown:
  The Electrotonic Transformation: a Tool for Relating Neuronal Form to Function. 69-76
  
• Michael E. Hasselmo, Eric Schnell, Joshua Berke, Edi Barkai:
  A model of the hippocampus combibing self-organization and associative memory function. 77-84
  
• Sean D. Murphy, Edward W. Kairiss:
  Model of a Biological Neuron as a Temporal Neural Network. 85-91
  
• E. Erwin, Klaus Obermayer, Klaus Schulten:
  A Critical Comparison of Models for Orientation and Ocular Dominance Columns in the Striate Cortex. 93-100
  
• Kenji Doya, Terrence J. Sejnowski:
  A Novel Reinforcement Model of Birdsong Vocalization Learning. 101-108
  
• Joseph Sirosh, Risto Miikkulainen:
  Ocular Dominance and Patterned Lateral Connections in a Self-Organizing Model of the Primary Visual Cortex. 109-116
  
• Kalanit Grill Spector, Shimon Edelman, Rafael Malach:
  Anatomical origin and computational role of diversity in the response properties of cortical neurons. 117-124
  
• Alexandre Pouget, Cedric Deffayet, Terrence J. Sejnowski:
  Reinforcement Learning Predicts the Site of Plasticity for Auditory Remapping in the Barn Owl. 125-132
  
• Svilen Tzonev, Klaus Schulten, Joseph G. Malpeli:
  Morphogenesis of the Lateral Geniculate Nucleus: How Singularities Affect Global Structure. 133-140
  
• Todd S. Braver, Jonathan D. Cohen, David Servan-Schreiber:
  A Computational Model of Prefrontal Cortex Function. 141-148
  
• Eytan Ruppin, James A. Reggia, David Horn:
  A Neural Model of Delusions and Hallucinations in Schizophrenia. 149-156
  
• Alexandre Pouget, Terrence J. Sejnowski:
  Spatial Representations in the Parietal Cortex May Use Basis Functions. 157-164
  
• Richard S. Zemel, Terrence J. Sejnowski:
  Grouping Components of Three-Dimensional Moving Objects in Area MST of Visual Cortex. 165-172
  

Learning Theory and Dynamics

• William E. Skaggs, James J. Knierim, Hemant S. Kudrimoti, Bruce L. McNaughton:
  A Model of the Neural Basis of the Rat's Sense of Direction. 173-180
  
• Wolfgang Maass:
  On the Computational Complexity of Networks of Spiking Neurons. 183-190
  
• Babak Hassibi, Thomas Kailath:
  Optimal Training Algorithms and their Relation to Backpropagation. 191-198
  
• DeLiang L. Wang, David Terman:
  Synchrony and Desynchrony in Neural Oscillator Networks. 199-206
  
• Peter Sollich:
  Learning in large linear perceptrons and why the thermodynamic limit is relevant to the real world. 207-214
  
• Adam Kowalczyk, Herman L. Ferrá:
  Generalisation in Feedforward Networks. 215-222
  
• Todd K. Leen:
  From Data Distributions to Regularization in Invariant Learning. 223-230
  
• Anders Krogh, Jesper Vedelsby:
  Neural Network Ensembles, Cross Validation, and Active Learning. 231-238
  
• Corinna Cortes, Lawrence D. Jackel, Wan-Ping Chiang:
  Limits in Learning Machine Accuracy Imposed by Data Quality. 239-246
  
• Gustavo Deco, Wilfried Brauer:
  Higher Order Statistical Decorrelation without Information Loss. 247-254
  
• Glenn Marion, David Saad:
  Hyperparameters Evidence and Generalisation for an Unrealisable Rule. 255-262
  
• Changfeng Wang, Santosh S. Venkatesh:
  Temporal Dynamics of Generalization in Neural Networks. 263-270
  
• Toru Ohira, Jack D. Cowan:
  Stochastic Dynamics of Three-State Neural Networks. 271-278
  
• Mario Marchand, Saeed Hadjifaradji:
  Learning Stochastic Perceptrons Under k-Blocking Distributions. 279-286
  
• Peter Sollich, David Saad:
  Learning from queries for maximum information gain in imperfectly learnable problems. 287-294
  
• Ronny Meir:
  Bias, Variance and the Combination of Least Squares Estimators. 295-302
  
• N. Barkai, H. Sebastian Seung, Haim Sompolinsky:
  On-line Learning of Dichotomies. 303-310
  
• José Carlos Príncipe, Jyh-Ming Kuo:
  Dynamic Modelling of Chaotic Time Series with Neural Networks. 311-318
  
• Jianfeng Feng, Hong Pan, Vwani P. Roychowdhury:
  A Rigorous Analysis of Linsker-Type Hebbian Learning. 319-326
  
• Michael J. Turmon, Terrence Fine:
  Sample Size Requirements for Feedforward Neural Networks. 327-334
  

Reinforcement Learning

• Sayandev Mukherjee, Terrence Fine:
  Asymptotics of Gradient-based Neural Network Training Algorithms. 335-342
  
• Tommi Jaakkola, Satinder P. Singh, Michael I. Jordan:
  Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems. 345-352
  
• Mance E. Harmon, Leemon C. Baird III, A. Harry Klopf:
  Advantage Updating Applied to a Differrential Game. 353-360
  
• Satinder P. Singh, Tommi Jaakkola, Michael I. Jordan:
  Reinforcement Learning with Soft State Aggregation. 361-368
  
• Justin A. Boyan, Andrew W. Moore:
  Generalization in Reinforcement Learning: Safely Approximating the Value Function. 369-376
  
• Andrew McCallum:
  Instance-Based State Identification for Reinforcement Learning. 377-384
  
• Sebastian Thrun, Anton Schwartz:
  Finding Structure in Reinforcement Learning. 385-392
  
• Steven J. Bradtke, Michael O. Duff:
  Reinforcement Learning Methods for Continuous-Time Markov Decision Problems. 393-400
  

Algorithms and Architectures

• Robert H. Crites, Andrew G. Barto:
  An Actor/Critic Algorithm that is Equivalent to Q-Learning. 401-408
  
• Yaser S. Abu-Mostafa:
  Financial Applications of Learning from Hints. 411-418
  
• Volker Tresp, Michiaki Taniguchi:
  Combining Estimators Using Non-Constant Weighting Functions. 419-426
  
• Yoshua Bengio, Paolo Frasconi:
  An Input Output HMM Architecture. 427-434
  
• Lawrence K. Saul, Michael I. Jordan:
  Boltzmann Chains and Hidden Markov Models. 435-442
  
• Gerhard Paass, Jörg Kindermann:
  Bayesian Query Construction for Neural Network Models. 443-450
  
• Shumeet Baluja, Dean Pomerleau:
  Using a Saliency Map for Active Spatial Selective Attention: Implementation & Initial Results. 451-458
  
• Thomas Hofmann, Joachim M. Buhmann:
  Multidimensional Scaling and Data Clustering. 459-466
  
• Anthony J. Bell, Terrence J. Sejnowski:
  A Non-linear Information Maximisation Algorithm that Performs Blind Separation. 467-474
  
• Nicol N. Schraudolph, Terrence J. Sejnowski:
  Plasticity-Mediated Competitive Learning. 475-480
  
• Fu-Sheng Tsung, Garrison W. Cottrell:
  Phase-Space Learning. 481-488
  
• David A. Nix, Andreas S. Weigend:
  Learning Local Error Bars for Nonlinear Regression. 489-496
  
• Jörg Bruske, Gerald Sommer:
  Dynamic Cell Structures. 497-504
  
• Sebastian Thrun:
  Extracting Rules from Artifical Neural Networks with Distributed Representations. 505-512
  
• Bruce Graham, David J. Willshaw:
  Capacity and Information Efficiency of a Brain-like Associative Net. 513-520
  
• Michael R. Berthold, Jay Diamond:
  Boosting the Performance of RBF Networks with Dynamic Decay Adjustment. 521-528
  
• Sepp Hochreiter, Jürgen Schmidhuber:
  Simplifying Neural Nets by Discovering Flat Minima. 529-536
  
• Laurens R. Leerink, C. Lee Giles, Bill G. Horne, Marwan A. Jabri:
  Learning with Product Units. 537-544
  
• Naonori Ueda, Ryohei Nakano:
  Deterministic Annealing Variant of the EM Algorithm. 545-552
  
• Yoshua Bengio, Paolo Frasconi:
  Diffusion of Credit in Markovian Models. 553-560
  
• Joshua B. Tenenbaum, Emanuel Todorov:
  Factorial Learning by Clustering Features. 561-568
  
• Michael D. Lemmon, Peter T. Szymanski:
  Interior Point Implementations of Alternating Minimization Training. 569-576
  
• Daniel L. James, Risto Miikkulainen:
  SARDNET: A Self-Organizing Feature Map for Sequences. 577-584
  
• Léon Bottou, Yoshua Bengio:
  Convergence Properties of the K-Means Algorithms. 585-592
  
• Kah Kay Sung, Partha Niyogi:
  Active Learning for Function Approximation. 593-600
  
• Thomas R. Shultz, Yuriko Oshima-Takane, Yoshio Takane:
  Analysis of Unstandardized Contributions in Cross Connected Networks. 601-608
  
• Jay A. Alexander, Michael Mozer:
  Template-Based Algorithms for Connectionist Rule Extraction. 609-616
  
• Zoubin Ghahramani:
  Factorial Learning and the EM Algorithm. 617-624
  
• Bernd Fritzke:
  A Growing Neural Gas Network Learns Topologies. 625-632
  
• Lei Xu, Michael I. Jordan, Geoffrey E. Hinton:
  An Alternative Model for Mixtures of Experts. 633-640
  
• Christopher M. Bishop, Claire Legleye:
  Estimating Conditional Probability Densities for Periodic Variables. 641-648
  
• Kam-Chuen Jim, Bill G. Horne, C. Lee Giles:
  Effects of Noise on Convergence and Generalization in Recurrent Networks. 649-656
  
• Rich Caruana:
  Learning Many Related Tasks at the Same Time with Backpropagation. 657-664
  
• Alessandro Sperduti, David G. Stork:
  A Rapid Graph-based Method for Arbitrary Transformation-Invariant Pattern Classification. 665-672
  
• Morten With Pedersen, Lars Kai Hansen:
  Recurrent Networks: Second Order Properties and Pruning. 673-680
  
• Nanda Kambhatla, Todd K. Leen:
  Classifying with Gaussian Mixtures and Clusters. 681-688
  
• Volker Tresp, Ralph Neuneier, Subutai Ahmad:
  Efficient Methods for Dealing with Missing Data in Supervised Learning. 689-696
  
• Bill G. Horne, C. Lee Giles:
  An experimental comparison of recurrent neural networks. 697-704
  
• David A. Cohn, Zoubin Ghahramani, Michael I. Jordan:
  Active Learning with Statistical Models. 705-712
  
• Steven Gold, Anand Rangarajan, Eric Mjolsness:
  Learning with Preknowledge: Clustering with Point and Graph Matching Distance Measures. 713-720
  

Implementations

• Peter T. Kazlas, Andreas S. Weigend:
  Direct Multi-Step Time Series Prediction Using TD-lambda. 721-728
  
• Richard Coggins, Marwan A. Jabri, Barry Flower, Stephen Pickard:
  ICEG Morphology Classification using an Analogue VLSI Neural Network. 731-738
  
• Bradley A. Minch, Paul E. Hasler, Chris Diorio, Carver Mead:
  A Silicon Axon. 739-746
  
• Michael P. Perrone, Leon N. Cooper:
  The Ni1000: High Speed Parallel VLSI for Implementing Multilayer Perceptrons. 747-754
  
• Teresa Serrano-Gotarredona, Bernabé Linares-Barranco, José Luis Huertas:
  A Real Time Clustering CMOS Neural Engine. 755-762
  
• A. J. Holmes, Alan F. Murray, Stephen Churcher, J. Hajto, M. J. Rose:
  Pulsestream Synapses with Non-Volatile Analogue Amorphous-Silicon Memories. 763-769
  
• James Edward Steck, Steven R. Skinner, Alvaro A. Cruz-Cabrara, Elizabeth C. Behrman:
  A Lagrangian Formulation For Optical Backpropagation Training In Kerr-Type Optical Networks. 771-778
  
• Gert Cauwenberghs, Volnei A. Pedroni:
  A Charge-Based Parallel Analog Vector Quantizer. 779-787
  
• Timothy K. Horiuchi:
  An Auditory Localization and Coordinate Transform Chip. 787-794
  
• Fernando J. Pineda, Andreas G. Andreou:
  An Analog Neural Network Inspired by Fractal Block Coding. 795-802
  
• D. Lippe, Joshua Alspector:
  A Study of Parallel Perturbative Gradient Descent. 803-810
  
• Il Song Han, Ki-Chul Kim, Hwang-Soo Lee:
  Implementation of Neural Hardware with the Neural VLSI of URAN in Applications with Reduced Representations. 811-815
  

Speech and Signal Processing

• Paul E. Hasler, Chris Diorio, Bradley A. Minch, Carver Mead:
  Single Transistor Learning Synapses. 817-824
  
• Malcolm Slaney:
  Pattern Playback in the 90s. 827-834
  
• Steve R. Waterhouse, Anthony J. Robinson:
  Non-linear Prediction of Acoustic Vectors Using Hierarchical Mixtures of Experts. 835-842
  
• Sidney Fels, Geoffrey E. Hinton:
  Glove-TalkII: Mapping Hand Gestures to Speech Using Neural Networks. 843-850
  
• Javier R. Movellan:
  Visual Speech Recognition with Stochastic Networks. 851-858
  
• Ying Zhao, Richard M. Schwartz, Jason J. Sroka, John Makhoul:
  Hierarchical Mixtures of Experts Methodology Applied to Continuous Speech Recognition. 859-865
  
• Cesare Furlanello, Diego Giuliani, Edmondo Trentin:
  Connectionist Speaker Normalization with Generalized Resource Allocating Networks. 865-874
  
• Eric I. Chang, Richard Lippmann:
  Using Voice Transformations to Create Additional Training Talkers for Word Spotting. 875-882
  

Visual Processing

• Andrew D. Back, Ah Chung Tsoi:
  A Comparison of Discrete-Time Operator Models and for Nonlinear System Identification. 883-890
  
• Rajesh P. N. Rao, Dana H. Ballard:
  Learning Saccadic Eye Movements Using Multiscale Spatial Filters. 893-900
  
• Steven J. Nowlan, John C. Platt:
  A Convolutional Neural Network Hand Tracker. 901-908
  
• Trevor Darrell, Irfan A. Essa, Alex Pentland:
  Correlation and Interpolation Networks for Real-time Expression Analysis/Synthesis. 909-916
  
• V. Sundareswaran, Lucia M. Vaina:
  Learning direction in global motion: two classes of psychophysically-motivated models. 917-924
  
• Dawei W. Dong:
  Associative Decorrelation Dynamics: A Theory of Self-Organization and Optimization in Feedback Networks. 925-932
  
• Suzanna Becker:
  JPMAX: Learning to Recognize Moving Objects as a Model-fitting Problem. 933-940
  
• Horst Bischof, Kurt Hornik:
  PCA-Pyramids for Image Compression. 941-948
  
• Satoshi Suzuki, Hiroshi Ando:
  Unsupervised Classification of 3D Objects from 2D Views. 949-956
  
• Steven Gold, Chien-Ping Lu, Anand Rangarajan, Suguna Pappu, Eric Mjolsness:
  New Algorithms for 2D and 3D Point Matching: Pose Estimation and Correspondence. 957-964
  
• Christopher K. I. Williams, Michael Revow, Geoffrey E. Hinton:
  Using a neural net to instantiate a deformable model. 965-972
  
• Christoph Bregler, Stephen M. Omohundro:
  Nonlinear Image Interpolation using Manifold Learning. 973-980
  

Applications

• Clay Spence, John C. Pearson, Jim Bergen:
  Coarse-to-Fine Image Search Using Neural Networks. 981-988
  
• Holger Schwenk, Maurice Milgram:
  Transformation Invariant Autoassociation with Application to Handwritten Character Recognition. 992-998
  
• Trevor Hastie, Patrice Simard:
  Learning Prototype Models for Tangent Distance. 999-1006
  
• Christopher M. Bishop:
  Real-Time Control of a Tokamak Plasma Using Neural Networks. 1007-1014
  
• Geoffrey E. Hinton, Michael Revow, Peter Dayan:
  Recognizing Handwritten Digits Using Mixtures of Linear Models. 1015-1022
  
• Terence D. Sanger:
  Optimal Movement Primitives. 1023-1030
  
• Ke Liu, Robert L. Tokar, Brain D. McVey:
  An Integrated Architecture of Adaptive Neural Network Control for Dynamic Systems. 1031-1038
  
• Dean Pomerleau:
  A Connectionist Technique for Accelerated Textual Input: Letting a Network Do the Typing. 1039-1046
  
• Jürgen Schmidhuber, Stefan Heil:
  Predictive Coding with Neural Nets: Application to Text Compression. 1047-1054
  
• Richard Lippmann, Linda Kukolich, David Shahian:
  Predicting the Risk of Complications in Coronary Artery Bypass Operations using Neural Networks. 1055-1062
  
• Harry B. Burke, David B. Rosen, Philip H. Goodman:
  Comparing the prediction accuracy of artifical neural networks and other statistical models for breast cancer survival. 1063-1067
  
• Sebastian Thrun:
  Learning to Play the Game of Chess. 1069-1076
  
• Magnus Stensmo, Terrence J. Sejnowski:
  A Mixture Model System for Medical and Machine Diagnosis. 1077-1084
  
• Padhraic Smyth, Usama M. Fayyad, Michael C. Burl, Pietro Perona, Pierre Baldi:
  Inferring Ground Truth from Subjective Labelling of Venus Images. 1085-1092
  
• Stefan Manke, Michael Finke, Alex Waibel:
  The Use of Dynamic Writing Information in a Connectionist On-Line Cursive Handwriting Recognition System. 1093-1100
  
• Minoru Asogawa:
  Adaptive Elastic Input Field for Recognition Improvement. 1101-1108
  
• David Price, Stefan Knerr, Léon Personnaz, Gérard Dreyfus:
  Pairwise Neural Network Classifiers with Probabilistic Outputs. 1109-1116
  
• Reza Shadmehr, Tom Brashers-Krug, Ferdinando A. Mussa-Ivaldi:
  Interference in Learning Internal Models of Inverse Dynamics in Humans. 1117-1124
  
• Zoubin Ghahramani, Daniel M. Wolpert, Michael I. Jordan:
  Computational Structure of coordinate transformations: A generalization study. 1125-1132
  

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