NIPS 1995: Denver, CO, USA

Cognitive Science

• Joshua B. Tenenbaum:
  Learning the Structure of Similarity. 3-9
  
• Alexandre Pouget, Terrence J. Sejnowski:
  A Model of Spatial Representations in Parietal Cortex Explains Hemineglect. 10-16
  
• Gale Martin:
  Human Reading and the Curse of Dimensionality. 17-23
  
• Mark Craven, Jude W. Shavlik:
  Extracting Tree-Structured Representations of Trained Networks. 24-30
  
• René Gourley:
  Harmony Networks Do Not Work. 31-37
  
• Hiroyuki Nakahara, Kenji Doya:
  Dynamics of Attention as Near Saddle-Node Bifurcation Behavior. 38-44
  
• Kevin J. Cherkauer, Jude W. Shavlik:
  Rapid Quality Estimation of Neural Network Input Representations. 45-51
  
• Susan L. McCabe, Michael J. Denham:
  A Model of Auditory Streaming. 52-58
  

Neuroscience

• A. David Redish, David S. Touretzky:
  Modeling Interactions of the Rat's Place and Head Direction Systems. 61-67
  
• Wyeth Bair, Ehud Zohary, Christof Koch:
  Correlated Neuronal Response: Time Scales and Mechanisms. 68-74
  
• Charles F. Stevens, Anthony M. Zador:
  Information through a Spiking Neuron. 75-81
  
• Rasmus S. Petersen, John G. Taylor:
  Reorganisation of Somatosensory Cortex after Tactile Training. 82-88
  
• Olivier J. M. D. Coenen, Terrence J. Sejnowski:
  A Dynamical Moedl of Context Dependencies for the Vestibulo-Ocular Reflex. 89-95
  
• Samuel R. H. Joseph, David J. Willshaw:
  The Role of Activity in Synaptic Competition at the Neuromuscular Junction. 96-102
  
• Charles F. Stevens, Anthony M. Zador:
  When is an Integrate-and-fire Neuron like a Poisson Neuron? 103-109
  
• Christopher L. Fry:
  How Perception Guides Production in Birdsong Learning. 110-116
  
• Amir A. Handzel, Tamar Flash:
  The Geometry of Eye Rotations and Listing's Law. 117-123
  
• Richard Kempter, Wulfram Gerstner, J. Leo van Hemmen, Hermann Wagner:
  Temporal coding in the sub-millisecond range: Model of barn owl auditory pathway. 124-130
  
• Michael E. Hasselmo, Milos Cekic:
  Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex. 131-137
  
• Andrew G. Barto, James C. Houk:
  A Predictive Switching Model of Cerebellar Movement Control. 138-144
  
• Scott Makeig, Anthony J. Bell, Tzyy-Ping Jung, Terrence J. Sejnowski:
  Independent Component Analysis of Electroencephalographic Data. 145-151
  
• Hugh T. Blair:
  Simualtion of a Thalamocortical Circuit for Computing Directional Heading in the Rat. 152-158
  
• S. Yasui, T. Furukawa, M. Yamada, T. Saito:
  Plasticity of Center-Surround Opponent Receptive Fields in Real and Artificial Neural Systems of Vision. 159-165
  

Theory

• Jonathan Baxter:
  Learning Model Bias. 169-175
  
• Shun-ichi Amari, Noboru Murata, Klaus-Robert Müller, Michael Finke, Howard Hua Yang:
  Statistical Theory of Overtraining - Is Cross-Validation Asymptotically Effective? 176-182
  
• Michael J. Kearns:
  A Bound on the Error of Cross Validation Using the Approximation and Estimation Rates, with Consequences for the Training-Test Split. 183-189
  
• Peter Sollich, Anders Krogh:
  Learning with ensembles: How overfitting can be useful. 190-196
  
• Pascal Koiran, Eduardo D. Sontag:
  Neural Networks with Quadratic VC Dimension. 197-203
  
• Bhaskar DasGupta, Eduardo D. Sontag:
  Sample Complexity for Learning Recurrent Perceptron Mappings. 204-210
  
• Wolfgang Maass:
  On the Computational Power of Noisy Spiking Neurons. 211-217
  
• Siegfried Bös:
  A Realizable Learning Task which Exhibits Overfitting. 218-224
  
• Stefan M. Rüger:
  Stable Dynamic Parameter Adaption. 225-231
  
• Robert R. Snapp, Tong Xu:
  Estimating the Bayes Risk from Sample Data. 232-238
  
• Visakan Kadirkamanathan, Maha Kadirkamanathan:
  Recursive Estimation of Dynamic Modular RBF Networks. 239-245
  
• Vasken Bohossian, Jehoshua Bruck:
  On Neural Networks with Minimal Weights. 246-252
  
• Anthony C. C. Coolen, S. N. Laughton, D. Sherrington:
  Modern Analytic Techniques to Solve the Dynamics of Recuurent Neural Networks. 253-259
  
• Yishay Mansour, Sigal Sahar:
  Implementation Issues in the Fourier Transform Algorithm. 260-266
  
• John Shawe-Taylor, Jieyu Zhao:
  Generalisation of A Class of Continuous Neural Networks. 267-273
  
• James W. Howse, Chaouki T. Abdallah, Gregory L. Heileman:
  Gradient and Hamiltonian Dynamics Applied to Learning in Neural Networks. 274-280
  
• Michael DeWeese:
  Optimization Principles for the Neural Code. 281-287
  
• Mario Marchand, Saeed Hadjifaradji:
  Strong Unimodality and Exact Learning of Constant Depth µ-Perceptron Networks. 288-294
  
• Kenji Fukumizu:
  Active Learning in Multilayer Perceptrons. 295-301
  
• David Saad, Sara A. Solla:
  Dynamics of On-Line Gradient Descent Learning for Multilayer Neural Networks. 302-308
  
• David P. Helmbold, Jyrki Kivinen, Manfred K. Warmuth:
  Worst-case Loss Bounds for Single Neurons. 309-315
  
• Peter Auer, Mark Herbster, Manfred K. Warmuth:
  Exponentially many local minima for single neurons. 316-322
  
• Ansgar H. L. West, David Saad:
  Adaptive Back-Propagation in On-Line Learning of Multilayer Networks. 323-329
  
• Geoffrey J. Goodhill, Steven Finch, Terrence J. Sejnowski:
  Optimizing Cortical Mappings. 330-336
  
• Anke Meyer-Bäse:
  Quadratic-Type Lyapunov Functions for Competitive Neural Networks with Different Time-Scales. 337-343
  
• Adam Kowalczyk, Jacek Szymanski, Peter L. Bartlett, Robert C. Williamson:
  Examples of learning curves from a modified VC-formalism. 344-350
  
• Steve R. Waterhouse, David MacKay, Anthony J. Robinson:
  Bayesian Methods for Mixtures of Experts. 351-357
  
• Serguei A. Semenov, Irina B. Shuvalova:
  Some results on convergent unlearning algorithm. 358-364
  
• Robert H. Dodier:
  Geometry of Early Stopping in Linear Networks. 365-371
  
• Xin Wang, Arun K. Jagota, Fernanda Botelho, Max H. Garzon:
  Absence of Cycles in Symmetric Neural Networks. 372-378
  

Algorithms and Architectures

• Yoram Singer:
  Adaptive Mixture of Probabilistic Transducers. 381-387
  
• Yochai Konig, Hervé Bourlard, Nelson Morgan:
  REMAP: Recursive Estimation and Maximization of A Posteriori Probabilities - Application to Transition-Based Connectionist Speech Recognition. 388-394
  
• Yoshua Bengio, Francois Gingras:
  Recurrent Neural Networks for Missing or Asynchronous Data. 395-401
  
• Stephen M. Omohundro:
  Family Discovery. 402-408
  
• Trevor Hastie, Robert Tibshirani:
  Discriminant Adaptive Nearest Neighbor Classification and Regression. 409-415
  
• Marcelo Blatt, Shai Wiseman, Eytan Domany:
  Clustering data through an analogy to the Potts model. 416-422
  
• Atsushi Sato, Keiji Yamada:
  Generalized Learning Vector Quantization. 423-429
  
• Ari Juels, Martin Wattenberg:
  Stochastic Hillclimbing as a Baseline Mathod for Evaluating Genetic Algorithms. 430-436
  
• Lucas C. Parra:
  Symplectic Nonlinear Component Analysis. 437-443
  
• Lei Xu:
  A Unified Learning Scheme: Bayesian-Kullback Ying-Yang Machines. 444-450
  
• Pierre Baldi, Kurt Hornik:
  Universal Approximnation and Learning of Trajectories Using Oscillators. 451-457
  
• Lizhong Wu, John E. Moody:
  A Smoothing Regularizer for Recurrent Neural Networks. 458-464
  
• Christopher M. Bishop, Markus Svensén, Christopher K. I. Williams:
  EM Optimization of Latent-Variables Density Models. 465-471
  
• Zoubin Ghahramani, Michael I. Jordan:
  Factorial Hidden Markov Models. 472-478
  
• Harris Drucker, Corinna Cortes:
  Boosting Decision Trees. 479-485
  
• Lawrence K. Saul, Michael I. Jordan:
  Exploiting Tractable Substructures in Intractable Networks. 486-492
  
• Salah El Hihi, Yoshua Bengio:
  Hierarchical Recurrent Neural Networks for Long-Term Dependencies. 493-499
  
• Reimar Hofmann, Volker Tresp:
  Discovering Structure in Continuous Variables Using Bayesian Networks. 500-506
  
• Geoffrey E. Hinton, Michael Revow:
  Using Pairs of Data-Points to Define Splits for Decision Trees. 507-513
  
• Christopher K. I. Williams, Carl Edward Rasmussen:
  Gaussian Processes for Regression. 514-520
  
• Morten With Pedersen, Lars Kai Hansen, Jan Larsen:
  Pruning with generalization based weight saliencies: gamma-OBD, gamma-OBS. 521-527
  
• Tommi Jaakkola, Lawrence K. Saul, Michael I. Jordan:
  Fast Learning by Bounding Likelihoods in Sigmoid Type Belief Networks. 528-534
  
• David W. Opitz, Jude W. Shavlik:
  Generating Accurate and Diverse Members of a Neural-Network Ensemble. 535-541
  
• Dirk Ormoneit, Volker Tresp:
  Improved Gaussian Mixture Density Estimates Using Bayesian Penalty Terms and Network Averaging. 542-548
  
• Thomas P. Rebotier, Jeffrey L. Elman:
  Explorations with the Dynamic Wave Model. 549-555
  
• Gary William Flake:
  The Capacity of a Bump. 556-562
  
• Nicol N. Schraudolph, Terrence J. Sejnowski:
  Tempering Backpropagation Networks: Not All Weights are Created Equal. 563-569
  
• Jörg A. Walter, Helge Ritter:
  Investment Learning with Hierarchical PSOMs. 570-576
  
• Tsungnan Lin, Bill G. Horne, Peter Tiño, C. Lee Giles:
  Learning long-term dependencies is not as difficult with NARX networks. 577-583
  
• Steve R. Waterhouse, Anthony J. Robinson:
  Constructive Algorithms for Hierarchical Mixtures of Experts. 584-590
  
• David J. Miller, Ajit V. Rao, Kenneth Rose, Allen Gersho:
  An Information-theoretic Learning Algorithm for Neural Network Classification. 591-597
  
• Carl Edward Rasmussen:
  A Practical Monte Carlo Implementation of Bayesian Learning. 598-604
  
• Stefan Schaal, Christopher G. Atkeson:
  From Isolation to Cooperation: An Alternative View of a System of Experts. 605-611
  
• Stefan C. Kremer:
  Finite State Automata that Recurrent Cascade-Correlation Cannot Represent. 612-618
  
• John Wawrzynek, Krste Asanovic, Brian Kingsbury, James Beck, David Johnson, Nelson Morgan:
  SPERT-II: A Vector Microprocessor System and its Application to Large Problems in Backpropagation Training. 619-625
  
• Steven Gold, Anand Rangarajan:
  Softassign versus Softmax: Benchmarks in Combinatorial Optimization. 626-632
  
• Dimitris I. Tsioutsias, Eric Mjolsness:
  A Mulitscale Attentional Framework for Relaxation Neural Networks. 633-639
  
• Sebastian Thrun:
  Is Learning The n-th Thing Any Easier Than Learning The First? 640-646
  
• Geoffrey G. Towell:
  Using Unlabeled Data for Supervised Learning. 647-653
  
• Jeffrey C. Jackson, Mark Craven:
  Learning Sparse Perceptrons. 654-660
  
• Brendan J. Frey, Geoffrey E. Hinton, Peter Dayan:
  Does the Wake-sleep Algorithm Produce Good Density Estimators? 661-667
  

Implementations

• André van Schaik, Eric Fragnière, Eric A. Vittoz:
  Improved Silicon Cochlea using Compatible Lateral Bipolar Transistors. 671-677
  
• Shih-Chii Liu, Kwabena Boahen:
  Adaptive Retina with Center-Surround Receptive Field. 678-684
  
• Tadashi Shibata, Tsutomu Nakai, Tatsuo Morimoto, Ryu Kaihara, Takeo Yamashita, Tadahiro Ohmi:
  Neuron-MOS Temporal Winner Search Hardware for Fully-Parallel Data Processing. 685-691
  
• R. Timothy Edwards, Gert Cauwenberghs:
  Analog VLSI Processor Implementing the Continuous Wavelet Transform. 692-698
  
• John Lazzaro, John Wawrzynek:
  Silicon Models for Auditory Scene Analysis. 699-705
  
• Ralph Etienne-Cummings, Jan Van der Spiegel, Paul Mueller:
  VLSI Model of Primate Visual Smooth Pursuit. 706-712
  
• Steven Rehfuss, Dan W. Hammerstrom:
  Model Matching and SFMD Computation. 713-719
  
• Giacomo Indiveri, Jörg Kramer, Christof Koch:
  Parallel analog VLSI architectures for computation of heading direction and time-to-contact. 720-726
  

Speech and Signal Processing

• Leslie S. Smith:
  Onset-based Sound Segmentation. 729-735
  
• Yoonsuck Choe, Joseph Sirosh, Risto Miikkulainen:
  Laterally Interconnected Self-Organizing Maps in Hand-Written Digit Recognition. 736-742
  
• Andrew W. Senior, Anthony J. Robinson:
  Forward-backward retraining of recurrent neural networks. 743-749
  
• Dan J. Kershaw, Anthony J. Robinson, Mike Hochberg:
  Context-Dependent Classes in a Hybrid Recurrent Network-HMM Speech Recognition System. 750-756
  
• Shun-ichi Amari, Andrzej Cichocki, Howard Hua Yang:
  A New Learning Algorithm for Blind Signal Separation. 757-763
  
• Bernard Lemarié, Michel Gilloux, Manuel Leroux:
  Handwritten Word Recognition using Contextual Hybrid Radial Basis Function Network/Hidden Markov Models. 764-770
  
• Ethem Alpaydin:
  Selective Attention for Handwritten Digit Recognition. 771-777
  
• Alexander Shustorovich, Christopher W. Thrasher:
  Kodak Imagelink^TM OCR Alphanumeric Handprint Module. 778-784
  
• Steve Lawrence, Ah Chung Tsoi, Andrew D. Back:
  The Gamma MLP for Speech Phoneme Recognition. 785-791
  

Vision

• Suguna Pappu, Steven Gold, Anand Rangarajan:
  A Framework for Non-rigid Matching and Correspondence. 795-801
  
• Ernst Niebur, Christof Koch:
  Control of Selective Visual Attention: Modeling the Where Pathway. 802-808
  
• William R. Softky:
  Unsupervised Pixel-prediction. 809-815
  
• Jonathan A. Marshall, Richard K. Alley, Robert S. Hubbard:
  Learning to Predict Visibility and Invisibility from Occlusion Events. 816-822
  
• Marian Stewart Bartlett, Paul A. Viola, Terrence J. Sejnowski, Beatrice A. Golomb, Jan Larsen, Joseph C. Hager, Paul Ekman:
  Classifying Facial Action. 823-829
  
• Rajesh P. N. Rao, Gregory J. Zelinsky, Mary M. Hayhoe, Dana H. Ballard:
  Modeling Saccadic Targeting in Visual Search. 830-836
  
• Alexander Grunewald:
  A model of transparent motion and non-transparent motion aftereffects. 837-843
  
• Luiz Pessoa, William D. Ross:
  A Neural Network Model of 3-D Lightness Perception. 844-850
  
• Paul A. Viola, Nicol N. Schraudolph, Terrence J. Sejnowski:
  Empirical Entropy Manipulation for Real-World Problems. 851-857
  
• Trevor Darrell, Alex Pentland:
  Active Gesture Recognition using Learned Visual Attention. 858-864
  
• Bartlett W. Mel:
  SEEMORE: A View-Based Approach to 3-D Object Recognition Using Multiple Visual Cues. 865-871
  

Applications

• Henry A. Rowley, Shumeet Baluja, Takeo Kanade:
  Human Face Detection in Visual Scenes. 875-881
  
• Bambang Parmanto, Paul W. Munro, Howard R. Doyle:
  Improving Committee Diagnosis with Resampling Techniques. 882-888
  
• Yoky Matsuoka:
  Primitive Manipulation Learning with Connectionism. 889-895
  
• Peter Stone, Manuela M. Veloso:
  Beating a Defender in Robotic Soccer: Memory-Based Learning of a Continuous Function. 896-902
  
• Enno Littmann, Andrea Drees, Helge Ritter:
  Visual gesture-based robot guidance with a modular neural system. 903-909
  
• Marwan A. Jabri, Raymond J. Wang:
  A Novel Channel Selection System in Cochlear Implants Using Artificial Neural Network. 910-916
  
• Anders Krogh, Soren Kamaric Riis:
  Prediction of Beta Sheets in Proteins. 917-923
  
• Thomas Petsche, Angelo Marcantonio, Christian Darken, Stephen Jose Hanson, Gary M. Kuhn, N. Iwan Santoso:
  A Neural Network Autoassociator for Induction Motor Failure Prediction. 924-930
  
• Scott Makeig, Tzyy-Ping Jung, Terrence J. Sejnowski:
  Using Feedforward Neural Networks to Monitor Alertness from Changes in EEG Correlation and Coherence. 931-937
  
• John C. Platt, Timothy P. Allen:
  A Neural Network Classifier for the I100 OCR Chip. 938-944
  
• Samuel P. M. Choi, Dit-Yan Yeung:
  Predictive Q-Routing: A Memory-based Reinforcement Learning Approach to Adaptive Traffic Control. 945-951
  
• Ralph Neuneier:
  Optimal Asset Allocation using Adaptive Dynamic Programming. 952-958
  
• Rich Caruana, Shumeet Baluja, Tom M. Mitchell:
  Using the Future to Sort Out the Present: Rankprop and Multitask Learning for Medical Risk Evaluation. 959-965
  
• Asriel E. Levin:
  Stock Selection via Nonlinear Multi-Factor Models. 966-972
  
• Peter K. Campbell, Michael Dale, Herman L. Ferrá, Adam Kowalczyk:
  Experiments with Neural Networks for Real Time Implementation of Control. 973-979
  
• Alistair Ferguson, Theo Sabisch, Paul Kaye, Laurence C. Dixon, Hamid Bolouri:
  High-Speed Airborne Particle Monitoring Using Artificial Neural Networks. 980-986
  

Control

• Jun Tani, Naohiro Fukumura:
  A Dynamical Systems Approach for a Learnable Autonomous Robot. 989-995
  
• Jefferson A. Coelho Jr., R. Sitaraman, Roderic A. Grupen:
  Parallel Optimization of Motion Controllers via Policy Iteration. 996-1002
  
• Marina Meila, Michael I. Jordan:
  Learning Fine Motion by Markov Mixtures of Experts. 1003-1009
  
• Ssu-Hsin Yu, Anuradha M. Annaswamy:
  Neural Control for Nonlinear Dynamic Systems. 1010-1016
  
• Robert H. Crites, Andrew G. Barto:
  Improving Elevator Performance Using Reinforcement Learning. 1017-1023
  
• Wei Zhang, Thomas G. Dietterich:
  High-Performance Job-Shop Scheduling With A Time-Delay TD-lambda Network. 1024-1030
  
• Geoffrey B. Jackson, Alan F. Murray:
  Competence Acquisition in an Autonomous Mobile Robot using Hardware Neural Techniques. 1031-1037
  
• Richard S. Sutton:
  Generalization in Reinforcement Learning: Successful Examples Using Sparse Coarse Coding. 1038-1044
  
• Benjamin Van Roy, John N. Tsitsiklis:
  Stable LInear Approximations to Dynamic Programming for Stochastic Control Problems with Local Transitions. 1045-1051
  
• Geoffrey J. Gordon:
  Stable Fitted Reinforcement Learning. 1052-1058
  
• Peter Dayan, Satinder P. Singh:
  Improving Policies without Measuring Merits. 1059-1065
  
• Andrew W. Moore, Jeff G. Schneider:
  Memory-based Stochastic Optimization. 1066-1072
  
• Kenji Doya:
  Temporal Difference Learning in Continuous Time and Space. 1073-1079
  
• Philip N. Sabes, Michael I. Jordan:
  Reinforcement Learning by Probability Matching. 1080-1086
  

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