Table of Contents

• Recognizing Evoked Potentials in a Virtual EnvironmentJessica D. Bayliss, Dana H. Ballard. 3-9 [doi]
• A Neurodynamical Approach to Visual AttentionGustavo Deco, Josef Zihl. 10-16 [doi]
• Effects of Spatial and Temporal Contiguity on the Acquisition of Spatial InformationThea B. Ghiselli-Crippa, Paul W. Munro. 17-23 [doi]
• Acquisition in AutoshapingSham Kakade, Peter Dayan. 24-30 [doi]
• Robust Recognition of Noisy and Superimposed Patterns via Selective AttentionSoo-Young Lee, Michael Mozer. 31-37 [doi]
• Perceptual Organization Based on Temporal DynamicsXiuwen Liu, DeLiang L. Wang. 38-44 [doi]
• Information Factorization in Connectionist Models of PerceptionJavier R. Movellan, James L. McClelland. 45-51 [doi]
• Graded Grammaticality in Prediction Fractal MachinesShan Parfitt, Peter Tiño, Georg Dorffner. 52-58 [doi]
• Rules and Similarity in Concept LearningJoshua B. Tenenbaum. 59-65 [doi]
• Evolving Learnable LanguagesBradley Tonkes, Alan D. Blair, Janet Wiles. 66-72 [doi]
• Learning Statistically Neutral Tasks without Expert GuidanceTon Weijters, Antal van den Bosch, Eric O. Postma. 73-79 [doi]
• A Generative Model for Attractor DynamicsRichard S. Zemel, Michael Mozer. 80-88 [doi]
• Recurrent Cortical Competition: Strengthen or Weaken?Péter Adorján, Lars Schwabe, Christian Piepenbrock, Klaus Obermayer. 89-95 [doi]
• Effective Learning Requires Neuronal Remodeling of Hebbian SynapsesGal Chechik, Isaac Meilijson, Eytan Ruppin. 96-102 [doi]
• Wiring Optimization in the BrainDmitri B. Chklovskii, Charles F. Stevens. 103-107 [doi]
• Optimal Sizes of Dendritic and Axonal ArborsDmitri B. Chklovskii. 108-114 [doi]
• Neural Representation of Multi-Dimensional StimuliChristian W. Eurich, Stefan D. Wilke, Helmut Schwegler. 115-121 [doi]
• Spiking Boltzmann MachinesGeoffrey E. Hinton, Andrew D. Brown. 122-128 [doi]
• Distributed Synchrony of Spiking Neurons in a Hebbian Cell AssemblyDavid Horn, Nir Levy, Isaac Meilijson, Eytan Ruppin. 129-135 [doi]
• Can VI Mechanisms Account for Figure-Ground and Medial Axis Effects?Zhaoping Li. 136-142 [doi]
• Channel Noise in Excitable Neural MembranesAmit Manwani, Peter N. Steinmetz, Christof Koch. 143-149 [doi]
• LTD Facilitates Learning in a Noisy EnvironmentPaul W. Munro, Gerardina Hernández. 150-156 [doi]
• Memory Capacity of Linear vs. Nonlinear Models of Dendritic IntegrationPanayiota Poirazi, Bartlett W. Mel. 157-163 [doi]
• Predictive Sequence Learning in Recurrent Neocortical CircuitsRajesh P. N. Rao, Terrence J. Sejnowski. 164-170 [doi]
• A Recurrent Model of the Interaction Between Prefrontal and Inferotemporal Cortex in Delay TasksAlfonso Renart, Néstor Parga, Edmund T. Rolls. 171-177 [doi]
• Information Capacity and Robustness of Stochastic Neuron ModelsElad Schneidman, Idan Segev, Naftali Tishby. 178-184 [doi]
• An MEG Study of Response Latency and Variability in the Human Visual System During a Visual-Motor Integration TaskAkaysha C. Tang, Barak A. Pearlmutter, Tim A. Hely, Michael Zibulevsky, Michael P. Weisend. 185-191 [doi]
• Population Decoding Based on an Unfaithful ModelSi Wu, Hiroyuki Nakahara, Noboru Murata, Shun-ichi Amari. 192-198 [doi]
• Spike-based Learning Rules and Stabilization of Persistent Neural ActivityXiaohui Xie, H. Sebastian Seung. 199-208 [doi]
• A Variational Baysian Framework for Graphical ModelsHagai Attias. 209-215 [doi]
• Model Selection in Clustering by Uniform Convergence BoundsJoachim M. Buhmann, Marcus Held. 216-222 [doi]
• Uniqueness of the SVM SolutionChristopher J. C. Burges, David J. Crisp. 223-229 [doi]
• Model Selection for Support Vector MachinesOlivier Chapelle, Vladimir Vapnik. 230-236 [doi]
• Dynamics of Supervised Learning with Restricted Training Sets and Noisy TeachersAnthony C. C. Coolen, C. W. H. Mace. 237-243 [doi]
• A Geometric Interpretation of v-SVM ClassifiersDavid J. Crisp, Christopher J. C. Burges. 244-250 [doi]
• Efficient Approaches to Gaussian Process ClassificationLehel Csató, Ernest Fokoué, Manfred Opper, Bernhard Schottky, Ole Winther. 251-257 [doi]
• Potential Boosters?Nigel Duffy, David P. Helmbold. 258-264 [doi]
• Bayesian Averaging is Well-TemperatedLars Kai Hansen. 265-271 [doi]
• Regular and Irregular Gallager-zype Error-Correcting CodesYoshiyuki Kabashima, Tatsuto Murayama, David Saad, Renato Vicente. 272-278 [doi]
• Mixture Density EstimationJonathan Q. Li, Andrew R. Barron. 279-285 [doi]
• Statistical Dynamics of Batch LearningSong Li, K. Y. Michael Wong. 286-292 [doi]
• Neural Computation with Winner-Take-All as the Only Nonlinear OperationWolfgang Maass. 293-299 [doi]
• Boosting with Multi-Way Branching in Decision TreesYishay Mansour, David A. McAllester. 300-306 [doi]
• Inference for the Generalization ErrorClaude Nadeau, Yoshua Bengio. 307-313 [doi]
• Resonance in a Stochastic Neuron Model with Delayed InteractionToru Ohira, Yuzuru Sato, Jack D. Cowan. 314-320 [doi]
• Understanding Stepwise Generalization of Support Vector Machines: a Toy ModelSebastian Risau-Gusman, Mirta B. Gordon. 321-327 [doi]
• Lower Bounds on the Complexity of Approximating Continuous Functions by Sigmoidal Neural NetworksMichael Schmitt. 328-334 [doi]
• Noisy Neural Networks and GeneralizationsHava T. Siegelmann, Alexander Roitershtein, Asa Ben-Hur. 335-341 [doi]
• The Entropy Regularization Information CriterionAlex J. Smola, John Shawe-Taylor, Bernhard Schölkopf, Robert C. Williamson. 342-348 [doi]
• Probabilistic Methods for Support Vector MachinesPeter Sollich. 349-355 [doi]
• Algebraic Analysis for Non-regular Learning MachinesSumio Watanabe. 356-362 [doi]
• Semiparametric Approach to Multichannel Blind Deconvolution of Nonminimum Phase SystemsLiqing Zhang, Shun-ichi Amari, Andrzej Cichocki. 363-369 [doi]
• Some Theoretical Results Concerning the Convergence of Compositions of Regularized Linear FunctionsTong Zhang. 370-378 [doi]
• Robust Full Bayesian Methods for Neural NetworksChristophe Andrieu, João F. G. de Freitas, Arnaud Doucet. 379-385 [doi]
• Independent Factor Analysis with Temporally Structured SourcesHagai Attias. 386-392 [doi]
• Gaussian Fields for Approximate Inference in Layered Sigmoid Belief NetworksDavid Barber, Peter Sollich. 393-399 [doi]
• Modeling High-Dimensional Discrete Data with Multi-Layer Neural NetworksYoshua Bengio, Samy Bengio. 400-406 [doi]
• Robust Neural Network Regression for Offline and Online LearningThomas Briegel, Volker Tresp. 407-413 [doi]
• Reconstruction of Sequential Data with Probabilistic Models and Continuity ConstraintsMiguel Á. Carreira-Perpiñán. 414-420 [doi]
• Transductive Inference for Estimating Values of FunctionsOlivier Chapelle, Vladimir Vapnik, Jason Weston. 421-427 [doi]
• The Nonnegative Boltzmann MachineOliver B. Downs, David J. C. MacKay, Daniel D. Lee. 428-434 [doi]
• Differentiating Functions of the Jacobian with Respect to the WeightsGary William Flake, Barak A. Pearlmutter. 435-441 [doi]
• Local Probability Propagation for Factor AnalysisBrendan J. Frey. 442-448 [doi]
• Variational Inference for Bayesian Mixtures of Factor AnalysersZoubin Ghahramani, Matthew J. Beal. 449-455 [doi]
• Bayesian TransductionThore Graepel, Ralf Herbrich, Klaus Obermayer. 456-462 [doi]
• Learning to Parse ImagesGeoffrey E. Hinton, Zoubin Ghahramani, Yee Whye Teh. 463-469 [doi]
• Maximum Entropy DiscriminationTommi Jaakkola, Marina Meila, Tony Jebara. 470-476 [doi]
• Topographic Transformation as a Discrete Latent VariableNebojsa Jojic, Brendan J. Frey. 477-483 [doi]
• An Improved Decomposition Algorithm for Regression Support Vector MachinesPavel Laskov. 484-490 [doi]
• Algorithms for Independent Components Analysis and Higher Order StatisticsDaniel D. Lee, Uri Rokni, Haim Sompolinsky. 491-497 [doi]
• The Relaxed Online Maximum Margin AlgorithmYi Li, Philip M. Long. 498-504 [doi]
• Bayesian Network Induction via Local NeighborhoodsDimitris Margaritis, Sebastian Thrun. 505-511 [doi]
• Boosting Algorithms as Gradient DescentLlew Mason, Jonathan Baxter, Peter L. Bartlett, Marcus R. Frean. 512-518 [doi]
• A Multi-class Linear Learning Algorithm Related to WinnowChris Mesterharm. 519-525 [doi]
• Invariant Feature Extraction and Classification in Kernel SpacesSebastian Mika, Gunnar Rätsch, Jason Weston, Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller. 526-532 [doi]
• Approximate Inference A lgorithms for Two-Layer Bayesian NetworksAndrew Y. Ng, Michael I. Jordan. 533-539 [doi]
• Optimal Kernel Shapes for Local Linear RegressionDirk Ormoneit, Trevor Hastie. 540-546 [doi]
• Large Margin DAGs for Multiclass ClassificationJohn C. Platt, Nello Cristianini, John Shawe-Taylor. 547-553 [doi]
• The Infinite Gaussian Mixture ModelCarl Edward Rasmussen. 554-560 [doi]
• v-Arc: Ensemble Learning in the Presence of OutliersGunnar Rätsch, Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller, Takashi Onoda, Sebastian Mika. 561-567 [doi]
• Nonlinear Discriminant Analysis Using Kernel FunctionsVolker Roth, Volker Steinhage. 568-574 [doi]
• An Analysis of Turbo Decoding with Gaussian DensitiesPaat Rusmevichientong, Benjamin Van Roy. 575-581 [doi]
• Support Vector Method for Novelty DetectionBernhard Schölkopf, Robert C. Williamson, Alex J. Smola, John Shawe-Taylor, John C. Platt. 582-588 [doi]
• Better Generative Models for Sequential Data Problems: Bidirectional Recurrent Mixture Density NetworksMike Schuster. 589-595 [doi]
• Greedy Importance SamplingDale Schuurmans. 596-602 [doi]
• Bayesian Model Selection for Support Vector Machines, Gaussian Processes and Other Kernel ClassifiersMatthias Seeger. 603-609 [doi]
• Leveraged Vector MachinesYoram Singer. 610-616 [doi]
• Agglomerative Information BottleneckNoam Slonim, Naftali Tishby. 617-623 [doi]
• Training Data Selection for Optimal Generalization in Trigonometric Polynomial NetworksMasashi Sugiyama, Hidemitsu Ogawa. 624-630 [doi]
• Predictive App roaches for Choosing Hyperparameters in Gaussian ProcessesS. Sundararajan, S. Sathiya Keerthi. 631-637 [doi]
• On Input Selection with Reversible Jump Markov Chain Monte Carlo SamplingPeter Sykacek. 638-644 [doi]
• Building Predictive Models from Fractal Representations of Symbolic SequencesPeter Tiño, Georg Dorffner. 645-651 [doi]
• The Relevance Vector MachineMichael E. Tipping. 652-658 [doi]
• Support Vector Method for Multivariate Density EstimationVladimir Vapnik, Sayan Mukherjee. 659-665 [doi]
• Dual Estimation and the Unscented TransformationEric A. Wan, Rudolph van der Merwe, Alex T. Nelson. 666-672 [doi]
• Correctness of Belief Propagation in Gaussian Graphical Models of Arbitrary TopologyYair Weiss, William T. Freeman. 673-679 [doi]
• A MCMC Approach to Hierarchical Mixture ModellingChristopher K. I. Williams. 680-686 [doi]
• Data Visualization and Feature Selection: New Algorithms for Nongaussian DataHoward Hua Yang, John E. Moody. 687-702 [doi]
• Manifold Stochastic Dynamics for Bayesian LearningMark Zlochin, Yoram Baram. 694-702 [doi]
• The Parallel Problems Server: an Interactive Tool for Large Scale Machine LearningCharles Lee Isbell Jr., Parry Husbands. 703-709 [doi]
• An Oculo-Motor System with Multi-Chip Neuromorphic Analog VLSI ControlOliver Landolt, Steve Gyger. 710-716 [doi]
• A Winner-Take-All Circuit with Controllable Soft Max PropertyShih-Chii Liu. 717-723 [doi]
• A Neuromorphic VLSI System for Modeling the Neural Control of Axial LocomotionGirish N. Patel, Edgar A. Brown, Stephen P. DeWeerth. 724-730 [doi]
• Bifurcation Analysis of a Silicon NeuronGirish N. Patel, Gennady S. Cymbalyuk, Ronald L. Calabrese, Stephen P. DeWeerth. 731-737 [doi]
• An Analog VLSI Model of Periodicity ExtractionAndré van Schaik. 738-746 [doi]
• An Oscillatory Correlation Frame work for Computational Auditory Scene AnalysisGuy J. Brown, DeLiang L. Wang. 747-753 [doi]
• Bayesian Modelling of fMRI lime SeriesPedro A. d. F. R. Højen-Sørensen, Lars Kai Hansen, Carl Edward Rasmussen. 754-760 [doi]
• Neural System Model of Human Sound LocalizationCraig T. Jin, Simon Carlile. 761-767 [doi]
• Spectral Cues in Human Sound LocalizationCraig T. Jin, Anna Corderoy, Simon Carlile, André van Schaik. 768-774 [doi]
• Broadband Direction-Of-Arrival Estimation Based on Second Order StatisticsJustinian P. Rosca, Joseph Ó Ruanaidh, Alexander Jourjine, Scott Rickard. 775-781 [doi]
• Constrained Hidden Markov ModelsSam T. Roweis. 782-788 [doi]
• Online Independent Component Analysis with Local Learning Rate AdaptationNicol N. Schraudolph, Xavier Giannakopoulos. 789-795 [doi]
• Speech Modelling Using Subspace and EM TechniquesGavin Smith, João F. G. de Freitas, Tony Robinson, Mahesan Niranjan. 796-802 [doi]
• Search for Information Bearing Components in SpeechHoward Hua Yang, Hynek Hermansky. 803-812 [doi]
• Audio Vision: Using Audio-Visual Synchrony to Locate SoundsJohn Hershey, Javier R. Movellan. 813-819 [doi]
• Bayesian Reconstruction of 3D Human Motion from Single-Camera VideoNicholas R. Howe, Michael E. Leventon, William T. Freeman. 820-826 [doi]
• Emergence of Topography and Complex Cell Properties from Natural Images using Extensions of ICAAapo Hyvärinen, Patrik O. Hoyer. 827-833 [doi]
• An Information-Theoretic Framework for Understanding Saccadic Eye MovementsTai Sing Lee, Stella X. Yu. 834-840 [doi]
• Learning Sparse Codes with a Mixture-of-Gaussians PriorBruno A. Olshausen, K. Jarrod Millman. 841-847 [doi]
• Hierarchical Image Probability (H1P) ModelsClay Spence, Lucas C. Parra. 848-854 [doi]
• Scale Mixtures of Gaussians and the Statistics of Natural ImagesMartin J. Wainwright, Eero P. Simoncelli. 855-861 [doi]
• A SNoW-Based Face DetectorMing-Hsuan Yang, Dan Roth, Narendra Ahuja. 862-868 [doi]
• Managing Uncertainty in Cue CombinationZhiyong Yang, Richard S. Zemel. 869-878 [doi]
• Robust Learning of Chaotic AttractorsRembrandt Bakker, Jaap C. Schouten, Marc-Olivier Coppens, Floris Takens, C. Lee Giles, Cor M. van den Bleek. 879-885 [doi]
• Image Representations for Facial Expression CodingMarian Stewart Bartlett, Gianluca Donato, Javier R. Movellan, Joseph C. Hager, Paul Ekman, Terrence J. Sejnowski. 886-892 [doi]
• Low Power Wireless Communication via Reinforcement LearningTimothy X. Brown. 893-899 [doi]
• Learning Informative Statistics: A Nonparametnic ApproachJohn W. Fisher III, Alexander T. Ihler, Paul A. Viola. 900-906 [doi]
• Kirchoff Law Markov Fields for Analog Circuit DesignRichard M. Golden. 907-913 [doi]
• Learning the Similarity of Documents: An Information-Geometric Approach to Document Retrieval and CategorizationThomas Hofmann. 914-920 [doi]
• Constructing Heterogeneous Committees Using Input Feature Grouping: Application to Economic ForecastingYuansong Liao, John E. Moody. 921-927 [doi]
• From Coexpression to Coregulation: An Approach to Inferring Transcriptional Regulation among Gene Classes from Large-Scale Expression DataEric Mjolsness, Tobias Mann, Rebecca Castaño, Barbara J. Wold. 928-934 [doi]
• Churn Reduction in the Wireless IndustryMichael Mozer, Richard H. Wolniewicz, David B. Grimes, Eric Johnson, Howard Kaushansky. 935-941 [doi]
• Unmixing Hyperspectral DataLucas C. Parra, Clay Spence, Paul Sajda, Andreas Ziehe, Klaus-Robert Müller. 942-948 [doi]
• Application of Blind Separation of Sources to Optical Recording of Brain ActivityHolger Schoner, Martin Stetter, Ingo Schießl, John E. W. Mayhew, Jennifer S. Lund, Niall McLoughlin, Klaus Obermayer. 949-955 [doi]
• Reinforcement Learning for Spoken Dialogue SystemsSatinder P. Singh, Michael J. Kearns, Diane J. Litman, Marilyn A. Walker. 956-962 [doi]
• Image Recognition in Context: Application to Microscopic UrinalysisXubo B. Song, Joseph Sill, Yaser S. Abu-Mostafa, Harvey Kasdan. 963-969 [doi]
• Generalized Model Selection for Unsupervised Learning in High DimensionsShivakumar Vaithyanathan, Byron Dom. 970-976 [doi]
• Learning from User Feedback in Image Retrieval SystemsNuno Vasconcelos, Andrew Lippman. 977-986 [doi]
• An Environment Model for Nonstationary Reinforcement LearningSamuel P. M. Choi, Dit-Yan Yeung, Nevin Lianwen Zhang. 987-993 [doi]
• State Abstraction in MAXQ Hierarchical Reinforcement LearningThomas G. Dietterich. 994-1000 [doi]
• Approximate Planning in Large POMDPs via Reusable TrajectoriesMichael J. Kearns, Yishay Mansour, Andrew Y. Ng. 1001-1007 [doi]
• Actor-Critic AlgorithmsVijay R. Konda, John N. Tsitsiklis. 1008-1014 [doi]
• Bayesian Map Learning in Dynamic EnvironmentsKevin P. Murphy. 1015-1021 [doi]
• Policy Search via Density EstimationAndrew Y. Ng, Ronald Parr, Daphne Koller. 1022-1028 [doi]
• Neural Network Based Model Predictive ControlStephen Piche, James D. Keeler, Greg Martin, Gene Boe, Doug Johnson, Mark Gerules. 1029-1035 [doi]
• Reinforcement Learning Using Approximate Belief StatesAndrés Rodríguez, Ronald Parr, Daphne Koller. 1036-1042 [doi]
• Coastal Navigation with Mobile RobotsNicholas Roy, Sebastian Thrun. 1043-1049 [doi]
• Learning Factored Representations for Partially Observable Markov Decision ProcessesBrian Sallans. 1050-1056 [doi]
• Policy Gradient Methods for Reinforcement Learning with Function ApproximationRichard S. Sutton, David A. McAllester, Satinder P. Singh, Yishay Mansour. 1057-1063 [doi]
• Monte Carlo POMDPsSebastian Thrun. 1064-1070 [doi]