Table of Contents

• Fast Exact Inference with a Factored Model for Natural Language ParsingDan Klein, Christopher D. Manning. 3-10 [doi]
• Prediction and Semantic AssociationThomas L. Griffiths, Mark Steyvers. 11-18 [doi]
• Replay, Repair and ConsolidationSzabolcs Káli, Peter Dayan. 19-26 [doi]
• A Minimal Intervention Principle for Coordinated MovementEmanuel Todorov, Michael I. Jordan. 27-34 [doi]
• Categorization Under Complexity: A Unified MDL Account of Human Learning of Regular and Irregular CategoriesDavid Fass, Jacob Feldman. 35-34 [doi]
• Theory-Based Causal InferenceJoshua B. Tenenbaum, Thomas L. Griffiths. 35-42 [doi]
• How the Poverty of the Stimulus Solves the Poverty of the StimulusWillem H. Zuidema. 43-50 [doi]
• Bayesian Models of Inductive GeneralizationNeville E. Sanjana, Joshua B. Tenenbaum. 51-58 [doi]
• Combining Dimensions and Features in Similarity-Based RepresentationsDaniel J. Navarro, Michael D. Lee. 59-66 [doi]
• Dynamical Causal LearningDavid Danks, Thomas L. Griffiths, Joshua B. Tenenbaum. 67-74 [doi]
• Modeling Midazolam s Effect on the Hippocampus and Recognition MemoryKenneth J. Malmberg, René Zeelenberg, Richard M. Shiffrin. 67-66 [doi]
• Visual Development Aids the Acquisition of Motion Velocity SensitivitiesRobert A. Jacobs, Melissa Dominguez. 75-82 [doi]
• Timing and Partial Observability in the Dopamine SystemNathaniel D. Daw, Aaron C. Courville, David S. Touretzky. 83-90 [doi]
• Automatic Acquisition and Efficient Representation of Syntactic StructuresZach Solan, Eytan Ruppin, David Horn, Shimon Edelman. 91-98 [doi]
• Binary Coding in Auditory CortexMichael DeWeese, Anthony M. Zador. 101-108 [doi]
• How Linear are Auditory Cortical Responses?Maneesh Sahani, Jennifer F. Linden. 109-116 [doi]
• Neural Decoding of Cursor Motion Using a Kalman FilterWei Wu, Michael J. Black, Yun Gao, Elie Bienenstock, M. Serruya, A. Shaikhouni, John P. Donoghue. 117-124 [doi]
• Spikernels: Embedding Spiking Neurons in Inner-Product SpacesLavi Shpigelman, Yoram Singer, Rony Paz, Eilon Vaadia. 125-132 [doi]
• Spectro-Temporal Receptive Fields of Subthreshold Responses in Auditory CortexChristian K. Machens, Michael Wehr, Anthony M. Zador. 133-140 [doi]
• Temporal Coherence, Natural Image Sequences, and the Visual CortexJarmo Hurri, Aapo Hyvärinen. 141-148 [doi]
• Learning in Spiking Neural AssembliesDavid Barber. 149-156 [doi]
• Expected and Unexpected Uncertainty: ACh and NE in the NeocortexAngela J. Yu, Peter Dayan. 157-164 [doi]
• Dopamine Induced Bistability Enhances Signal Processing in Spiny NeuronsAaron J. Gruber, Sara A. Solla, James C. Houk. 165-172 [doi]
• Convergence Properties of Some Spike-Triggered Analysis TechniquesLiam Paninski. 173-180 [doi]
• Branching Law for AxonsDmitri B. Chklovskii, Armen Stepanyants. 181-188 [doi]
• Binary Tuning is Optimal for Neural Rate Coding with High Temporal ResolutionMatthias Bethge, David Rotermund, Klaus Pawelzik. 189-196 [doi]
• An Information Theoretic Approach to the Functional Classification of NeuronsElad Schneidman, William Bialek, Michael J. Berry II. 197-204 [doi]
• Factorial Coding of Color in Primary Visual CortexJavier R. Movellan, Thomas Wachtler, Thomas D. Albright, Terrence J. Sejnowski. 205-212 [doi]
• A Model for Real-Time Computation in Generic Neural MicrocircuitsWolfgang Maass, Thomas Natschläger, Henry Markram. 213-220 [doi]
• Adaptation and Unsupervised LearningPeter Dayan, Maneesh Sahani, Gregoire Deback. 221-228 [doi]
• A Digital Antennal Lobe for Pattern Equalization: Analysis and DesignAlex Holub, Gilles Laurent, Pietro Perona. 229-236 [doi]
• Hidden Markov Model of Cortical Synaptic Plasticity: Derivation of the Learning RuleMichael Eisele, Kenneth D. Miller. 237-244 [doi]
• Selectivity and Metaplasticity in a Unified Calcium-Dependent ModelLuk-Chong Yeung, Brian S. Blais, Leon N. Cooper, Harel Z. Shouval. 245-252 [doi]
• Kernel-Based Extraction of Slow Features: Complex Cells Learn Disparity and Translation Invariance from Natural ImagesAlistair Bray, Dominique Martinez. 253-260 [doi]
• Maximally Informative Dimensions: Analyzing Neural Responses to Natural SignalsTatyana Sharpee, Nicole C. Rust, William Bialek. 261-268 [doi]
• Dynamical Constraints on Computing with Spike Timing in the CortexArunava Banerjee, Alexandre Pouget. 269-276 [doi]
• Interpreting Neural Response Variability as Monte Carlo Sampling of the PosteriorPatrik O. Hoyer, Aapo Hyvärinen. 277-284 [doi]
• A Neural Edge-Detection Model for Enhanced Auditory Sensitivity in Modulated NoiseAlon Fishbach, Bradford J. May. 285-292 [doi]
• An Estimation-Theoretic Framework for the Presentation of Multiple StimuliChristian W. Eurich. 293-300 [doi]
• Evidence Optimization Techniques for Estimating Stimulus-Response FunctionsManeesh Sahani, Jennifer F. Linden. 301-308 [doi]
• Reconstructing Stimulus-Driven Neural Networks from Spike TimesDuane Q. Nykamp. 309-316 [doi]
• Data-Dependent Bounds for Bayesian Mixture MethodsRon Meir, Tong Zhang. 319-326 [doi]
• A Statistical Mechanics Approach to Approximate Analytical Bootstrap AveragesDörthe Malzahn, Manfred Opper. 327-334 [doi]
• Maximum Likelihood and the Information BottleneckNoam Slonim, Yair Weiss. 335-342 [doi]
• Stable Fixed Points of Loopy Belief Propagation Are Local Minima of the Bethe Free EnergyTom Heskes. 343-350 [doi]
• Concentration Inequalities for the Missing Mass and for Histogram Rule ErrorDavid A. McAllester, Luis E. Ortiz. 351-358 [doi]
• Dyadic Classification Trees via Structural Risk MinimizationClayton Scott, Robert Nowak. 359-366 [doi]
• The Stability of Kernel Principal Components Analysis and its Relation to the Process EigenspectrumJohn Shawe-Taylor, Christopher K. I. Williams. 367-374 [doi]
• Information Diffusion KernelsJohn D. Lafferty, Guy Lebanon. 375-382 [doi]
• Scaling of Probability-Based Optimization AlgorithmsJonathan L. Shapiro. 383-390 [doi]
• The Effect of Singularities in a Learning Machine when the True Parameters Do Not Lie on such SingularitiesSumio Watanabe, Shun-ichi Amari. 391-398 [doi]
• On the Complexity of Learning the Kernel MatrixOlivier Bousquet, Daniel J. L. Herrmann. 399-406 [doi]
• Rate Distortion Function in the Spin Glass State: A Toy ModelTatsuto Murayama, Masato Okada. 407-414 [doi]
• Conditional Models on the Ranking PosetGuy Lebanon, John D. Lafferty. 415-422 [doi]
• PAC-Bayes & MarginsJohn Langford, John Shawe-Taylor. 423-430 [doi]
• A Note on the Representational Incompatibility of Function Approximation and Factored DynamicsEric Allender, Sanjeev Arora, Michael S. Kearns, Cristopher Moore, Alexander Russell. 431-437 [doi]
• Fractional Belief PropagationWim Wiegerinck, Tom Heskes. 438-445 [doi]
• An Impossibility Theorem for ClusteringJon M. Kleinberg. 446-453 [doi]
• Effective Dimension and Generalization of Kernel LearningTong Zhang. 454-461 [doi]
• Margin Analysis of the LVQ AlgorithmKoby Crammer, Ran Gilad-Bachrach, Amir Navot, Naftali Tishby. 462-469 [doi]
• Margin-Based Algorithms for Information FilteringNicolò Cesa-Bianchi, Alex Conconi, Claudio Gentile. 470-477 [doi]
• HyperkernelsCheng Soon Ong, Alexander J. Smola, Robert C. Williamson. 478-485 [doi]
• Bayesian Monte CarloCarl Edward Rasmussen, Zoubin Ghahramani. 489-496 [doi]
• Mean Field Approach to a Probabilistic Model in Information RetrievalBin Wu, K. Y. Michael Wong, David Bodoff. 497-504 [doi]
• Distance Metric Learning with Application to Clustering with Side-InformationEric P. Xing, Andrew Y. Ng, Michael I. Jordan, Stuart J. Russell. 505-512 [doi]
• Adapting Codes and Embeddings for PolychotomiesGunnar Rätsch, Alexander J. Smola, Sebastian Mika. 513-520 [doi]
• Knowledge-Based Support Vector Machine ClassifiersGlenn Fung, Olvi L. Mangasarian, Jude W. Shavlik. 521-528 [doi]
• Gaussian Process Priors with Uncertain Inputs - Application to Multiple-Step Ahead Time Series ForecastingAgathe Girard, Carl Edward Rasmussen, Joaquin Quiñonero Candela, Roderick Murray-Smith. 529-536 [doi]
• Kernel Design Using BoostingKoby Crammer, Joseph Keshet, Yoram Singer. 537-544 [doi]
• Coulomb Classifiers: Generalizing Support Vector Machines via an Analogy to Electrostatic SystemsSepp Hochreiter, Michael Mozer, Klaus Obermayer. 545-552 [doi]
• Adaptive Scaling for Feature Selection in SVMsYves Grandvalet, Stéphane Canu. 553-560 [doi]
• Support Vector Machines for Multiple-Instance LearningStuart Andrews, Ioannis Tsochantaridis, Thomas Hofmann. 561-568 [doi]
• Fast Kernels for String and Tree MatchingS. V. N. Vishwanathan, Alexander J. Smola. 569-576 [doi]
• Generalized:::2::: Linear:::2::: ModelsGeoffrey J. Gordon. 577-584 [doi]
• Cluster Kernels for Semi-Supervised LearningOlivier Chapelle, Jason Weston, Bernhard Schölkopf. 585-592 [doi]
• Adaptive Nonlinear System Identification with Echo State NetworksHerbert Jaeger. 593-600 [doi]
• Rational KernelsCorinna Cortes, Patrick Haffner, Mehryar Mohri. 601-608 [doi]
• Fast Sparse Gaussian Process Methods: The Informative Vector MachineNeil D. Lawrence, Matthias Seeger, Ralf Herbrich. 609-616 [doi]
• Stability-Based Model SelectionTilman Lange, Mikio L. Braun, Volker Roth, Joachim M. Buhmann. 617-624 [doi]
• Feature Selection in Mixture-Based ClusteringMartin H. C. Law, Anil K. Jain, Mário A. T. Figueiredo. 625-632 [doi]
• String Kernels, Fisher Kernels and Finite State AutomataCraig Saunders, John Shawe-Taylor, Alexei Vinokourov. 633-640 [doi]
• Boosting Density EstimationSaharon Rosset, Eran Segal. 641-648 [doi]
• Independent Components Analysis through Product Density EstimationTrevor Hastie, Robert Tibshirani. 649-656 [doi]
• Learning Semantic SimilarityJaz S. Kandola, John Shawe-Taylor, Nello Cristianini. 657-664 [doi]
• Self Supervised BoostingMax Welling, Richard S. Zemel, Geoffrey E. Hinton. 665-672 [doi]
• Automatic Derivation of Statistical Algorithms: The EM Family and BeyondAlexander G. Gray, Bernd Fischer, Johann Schumann, Wray Buntine. 673-680 [doi]
• Intrinsic Dimension Estimation Using Packing NumbersBalázs Kégl. 681-688 [doi]
• Half-Lives of EigenFlows for Spectral ClusteringChakra Chennubhotla, Allan D. Jepson. 689-696 [doi]
• On the Dirichlet Prior and Bayesian RegularizationHarald Steck, Tommi Jaakkola. 697-704 [doi]
• Global Versus Local Methods in Nonlinear Dimensionality ReductionVin de Silva, Joshua B. Tenenbaum. 705-712 [doi]
• Dynamic Bayesian Networks with Deterministic Latent TablesDavid Barber. 713-720 [doi]
• Parametric Mixture Models for Multi-Labeled TextNaonori Ueda, Kazumi Saito. 721-728 [doi]
• Clustering with the Fisher ScoreKoji Tsuda, Motoaki Kawanabe, Klaus-Robert Müller. 729-736 [doi]
• Adaptive Classification by Variational Kalman FilteringPeter Sykacek, Stephen J. Roberts. 737-744 [doi]
• Boosted Dyadic Kernel DiscriminantsBaback Moghaddam, Gregory Shakhnarovich. 745-752 [doi]
• Regularized Greedy Importance SamplingFinnegan Southey, Dale Schuurmans, Ali Ghodsi. 753-760 [doi]
• One-Class LP Classifiers for Dissimilarity RepresentationsElzbieta Pekalska, David M. J. Tax, Robert P. W. Duin. 761-768 [doi]
• A Formulation for Minimax Probability Machine RegressionThomas Strohmann, Gregory Z. Grudic. 769-776 [doi]
• VIBES: A Variational Inference Engine for Bayesian NetworksChristopher M. Bishop, David J. Spiegelhalter, John M. Winn. 777-784 [doi]
• A Differential Semantics for Jointree AlgorithmsJames D. Park, Adnan Darwiche. 785-784 [doi]
• Constraint Classification for Multiclass Classification and RankingSariel Har-Peled, Dan Roth, Dav Zimak. 785-792 [doi]
• Nash Propagation for Loopy Graphical GamesLuis E. Ortiz, Michael J. Kearns. 793-800 [doi]
• Using Tarjan s Red Rule for Fast Dependency Tree ConstructionDan Pelleg, Andrew W. Moore. 801-808 [doi]
• Exact MAP Estimates by (Hyper)tree AgreementMartin J. Wainwright, Tommi Jaakkola, Alan S. Willsky. 809-816 [doi]
• Going Metric: Denoising Pairwise DataVolker Roth, Julian Laub, Joachim M. Buhmann, Klaus-Robert Müller. 817-824 [doi]
• Manifold Parzen WindowsPascal Vincent, Yoshua Bengio. 825-832 [doi]
• Stochastic Neighbor EmbeddingGeoffrey E. Hinton, Sam T. Roweis. 833-840 [doi]
• Automatic Alignment of Local RepresentationsYee Whye Teh, Sam T. Roweis. 841-848 [doi]
• Informed ProjectionsDavid Cohn. 849-856 [doi]
• Extracting Relevant Structures with Side InformationGal Chechik, Naftali Tishby. 857-864 [doi]
• Critical Lines in Symmetry of Mixture Models and its Application to Component SplittingKenji Fukumizu, Shotaro Akaho, Shun-ichi Amari. 865-872 [doi]
• Kernel Dependency EstimationJason Weston, Olivier Chapelle, André Elisseeff, Bernhard Schölkopf, Vladimir Vapnik. 873-880 [doi]
• Handling Missing Data with Variational Bayesian Learning of ICAKwokleung Chan, Te-Won Lee, Terrence J. Sejnowski. 881-888 [doi]
• Feature Selection and Classification on Matrix Data: From Large Margins to Small Covering NumbersSepp Hochreiter, Klaus Obermayer. 889-896 [doi]
• Learning with Multiple LabelsRong Jin, Zoubin Ghahramani. 897-904 [doi]
• Robust Novelty Detection with Single-Class MPMGert R. G. Lanckriet, Laurent El Ghaoui, Michael I. Jordan. 905-912 [doi]
• Artefactual Structure from Least-Squares Multidimensional ScalingNicholas P. Hughes, David Lowe. 913-920 [doi]
• The Decision List MachineMarina Sokolova, Mario Marchand, Nathalie Japkowicz, John Shawe-Taylor. 921-928 [doi]
• Using Manifold Stucture for Partially Labeled ClassificationMikhail Belkin, Partha Niyogi. 929-936 [doi]
• Ranking with Large Margin Principle: Two ApproachesAmnon Shashua, Anat Levin. 937-944 [doi]
• Multiclass Learning by Probabilistic EmbeddingsOfer Dekel, Yoram Singer. 945-952 [doi]
• Transductive and Inductive Methods for Approximate Gaussian Process RegressionAnton Schwaighofer, Volker Tresp. 953-960 [doi]
• Charting a ManifoldMatthew Brand. 961-968 [doi]
• Annealing and the Rate Distortion ProblemAlbert E. Parker, Tomás Gedeon, Alexander Dimitrov. 969-976 [doi]
• Discriminative Learning for Label Sequences via BoostingYasemin Altun, Thomas Hofmann, Mark Johnson. 977-984 [doi]
• Discriminative Densities from Maximum Contrast EstimationPeter Meinicke, Thorsten Twellmann, Helge Ritter. 985-992 [doi]
• FloatBoost Learning for ClassificationStan Z. Li, ZhenQiu Zhang, Heung-Yeung Shum, HongJiang Zhang. 993-1000 [doi]
• Incremental Gaussian ProcessesJoaquin Quiñonero Candela, Ole Winther. 1001-1008 [doi]
• Learning Graphical Models with Mercer KernelsFrancis R. Bach, Michael I. Jordan. 1009-1016 [doi]
• Multiple Cause Vector QuantizationDavid A. Ross, Richard S. Zemel. 1017-1024 [doi]
• Information Regularization with Partially Labeled DataMartin Szummer, Tommi Jaakkola. 1025-1032 [doi]
• Derivative Observations in Gaussian Process Models of Dynamic SystemsE. Solak, Roderick Murray-Smith, William E. Leithead, Douglas J. Leith, Carl Edward Rasmussen. 1033-1040 [doi]
• Multiplicative Updates for Nonnegative Quadratic Programming in Support Vector MachinesFei Sha, Lawrence K. Saul, Daniel D. Lee. 1041-1048 [doi]
• Location Estimation with a Differential Update NetworkAli Rahimi, Trevor Darrell. 1049-1056 [doi]
• Real-Time Particle FiltersCody C. T. Kwok, Dieter Fox, Marina Meila. 1057-1064 [doi]
• Optoelectronic Implementation of a FitzHugh-Nagumo Neural ModelAlexandre R. S. Romariz, Kelvin Wagner. 1067-1074 [doi]
• Circuit Model of Short-Term Synaptic DynamicsShih-Chii Liu, Malte Boegershausen, Pascal Suter. 1075-1082 [doi]
• Adaptive Quantization and Density Estimation in SiliconDavid Hsu, Seth Bridges, Miguel Figueroa, Chris Diorio. 1083-1090 [doi]
• Neuromorphic Bistable VLSI Synapses with Spike-Timing-Dependent PlasticityGiacomo Indiveri. 1091-1098 [doi]
• Retinal Processing Emulation in a Programmable 2-Layer Analog Array Processor CMOS ChipRicardo Carmona-Galán, Francisco Jiménez-Garrido, Rafael Domínguez-Castro, Servando Espejo-Meana, Ángel Rodríguez-Vázquez. 1099-1106 [doi]
• Improving Transfer Rates in Brain Computer Interfacing: A Case StudyPeter Meinicke, Matthias Kaper, Florian Hoppe, Manfred Heumann, Helge Ritter. 1107-1114 [doi]
• Combining Features for BCIGuido Dornhege, Benjamin Blankertz, Gabriel Curio, Klaus-Robert Müller. 1115-1122 [doi]
• Classifying Patterns of Visual Motion - a Neuromorphic ApproachJakob Heinzle, Alan Stocker. 1123-1130 [doi]
• Developing Topography and Ocular Dominance Using Two aVLSI Vision Sensors and a Neurotrophic Model of PlasticityTerry Elliott, Jörg Kramer. 1131-1138 [doi]
• Topographic Map Formation by Silicon Growth ConesBrian Taba, Kwabena Boahen. 1139-1146 [doi]
• Spike Timing-Dependent Plasticity in the Address DomainR. Jacob Vogelstein, Francesco Tenore, Ralf Philipp, Miriam S. Adlerstein, David H. Goldberg, Gert Cauwenberghs. 1147-1154 [doi]
• Field-Programmable Learning ArraysSeth Bridges, Miguel Figueroa, David Hsu, Chris Diorio. 1155-1162 [doi]
• Forward-Decoding Kernel-Based Phone RecognitionShantanu Chakrabartty, Gert Cauwenberghs. 1165-1172 [doi]
• A Probabilistic Approach to Single Channel Blind Signal SeparationGil-Jin Jang, Te-Won Lee. 1173-1180 [doi]
• Real Time Voice Processing with Audiovisual Feedback: Toward Autonomous Agents with Perfect PitchLawrence K. Saul, Daniel D. Lee, Charles L. Isbell, Yann LeCun. 1181-1188 [doi]
• Analysis of Information in Speech Based on MANOVASachin S. Kajarekar, Hynek Hermansky. 1189-1196 [doi]
• Bayesian Estimation of Time-Frequency Coefficients for Audio Signal EnhancementPatrick J. Wolfe, Simon J. Godsill. 1197-1204 [doi]
• Source Separation with a Sensor Array Using Graphical Models and Subband FilteringHagai Attias. 1205-1212 [doi]
• An Asynchronous Hidden Markov Model for Audio-Visual Speech RecognitionSamy Bengio. 1213-1220 [doi]
• Monaural Speech SeparationGuoning Hu, DeLiang L. Wang. 1221-1228 [doi]
• Discriminative Binaural Sound LocalizationEhud Ben-Reuven, Yoram Singer. 1229-1236 [doi]
• Application of Variational Bayesian Approach to Speech RecognitionShinji Watanabe, Yasuhiro Minami, Atsushi Nakamura, Naonori Ueda. 1237-1244 [doi]
• Learning to Perceive Transparency from the Statistics of Natural ScenesAnat Levin, Assaf Zomet, Yair Weiss. 1247-1254 [doi]
• Learning to Detect Natural Image Boundaries Using Brightness and TextureDavid R. Martin, Charless Fowlkes, Jitendra Malik. 1255-1262 [doi]
• Fast Transformation-Invariant Factor AnalysisAnitha Kannan, Nebojsa Jojic, Brendan J. Frey. 1263-1270 [doi]
• A Prototype for Automatic Recognition of Spontaneous Facial ActionsMarian Stewart Bartlett, Gwen Littlewort, Bjorn Braathen, Terrence J. Sejnowski, Javier R. Movellan. 1271-1278 [doi]
• Bayesian Image Super-ResolutionMichael E. Tipping, Christopher M. Bishop. 1279-1286 [doi]
• A Bilinear Model for Sparse CodingDavid B. Grimes, Rajesh P. N. Rao. 1287-1294 [doi]
• Dynamic Structure Super-ResolutionAmos J. Storkey. 1295-1302 [doi]
• Unsupervised Color ConstancyKinh Tieu, Erik G. Miller. 1303-1310 [doi]
• Recovering Articulated Model Topology from Observed Rigid MotionLeonid Taycher, John W. Fisher III, Trevor Darrell. 1311-1318 [doi]
• Linear Combinations of Optic Flow Vectors for Estimating Self-Motion - a Real-World Test of a Neural ModelMatthias O. Franz, Javaan S. Chahl. 1319-1326 [doi]
• Learning Sparse Multiscale Image RepresentationsPhil Sallee, Bruno A. Olshausen. 1327-1334 [doi]
• Shape Recipes: Scene Representations that Refer to the ImageWilliam T. Freeman, Antonio B. Torralba. 1335-1342 [doi]
• Recovering Intrinsic Images from a Single ImageMarshall F. Tappen, William T. Freeman, Edward H. Adelson. 1343-1350 [doi]
• Feature Selection by Maximum Marginal DiversityNuno Vasconcelos. 1351-1358 [doi]
• Learning Sparse Topographic Representations with Products of Student-t DistributionsMax Welling, Geoffrey E. Hinton, Simon Osindero. 1359-1366 [doi]
• A Model for Learning Variance Components of Natural ImagesYan Karklin, Michael S. Lewicki. 1367-1374 [doi]
• How to Combine Color and Shape Information for 3D Object Recognition: Kernels do the TrickBarbara Caputo, Gyuri Dorkó. 1375-1382 [doi]
• Concurrent Object Recognition and Segmentation by Graph PartitioningStella X. Yu, Ralph Gross, Jianbo Shi. 1383-1390 [doi]
• Learning About Multiple Objects in Images: Factorial Learning without Factorial SearchChristopher K. I. Williams, Michalis K. Titsias. 1391-1398 [doi]
• Identity Uncertainty and Citation MatchingHanna Pasula, Bhaskara Marthi, Brian Milch, Stuart J. Russell, Ilya Shpitser. 1401-1408 [doi]
• The RA Scanner: Prediction of Rheumatoid Joint Inflammation Based on Laser ImagingAnton Schwaighofer, Volker Tresp, Peter Mayer, Alexander K. Scheel, Gerhard Müller. 1409-1416 [doi]
• Mismatch String Kernels for SVM Protein ClassificationChristina S. Leslie, Eleazar Eskin, Jason Weston, William Stafford Noble. 1417-1424 [doi]
• Graph-Driven Feature Extraction From Microarray Data Using Diffusion Kernels and Kernel CCAJean-Philippe Vert, Minoru Kanehisa. 1425-1432 [doi]
• Real-Time Monitoring of Complex Industrial Processes with Particle FiltersRubén Morales-Menéndez, Nando de Freitas, David Poole. 1433-1440 [doi]
• A Maximum Entropy Approach to Collaborative Filtering in Dynamic, Sparse, High-Dimensional DomainsDmitry Pavlov, David M. Pennock. 1441-1448 [doi]
• Prediction of Protein Topologies Using Generalized IOHMMS and RNNsGianluca Pollastri, Pierre Baldi, Alessandro Vullo, Paolo Frasconi. 1449-1456 [doi]
• Approximate Inference and Protein-FoldingChen Yanover, Yair Weiss. 1457-1464 [doi]
• Adaptive Caching by RefetchingRobert B. Gramacy, Manfred K. Warmuth, Scott A. Brandt, Ismail Ari. 1465-1472 [doi]
• Inferring a Semantic Representation of Text via Cross-Language Correlation AnalysisAlexei Vinokourov, John Shawe-Taylor, Nello Cristianini. 1473-1480 [doi]
• Improving a Page Classifier with Anchor Extraction and Link AnalysisWilliam W. Cohen. 1481-1488 [doi]
• A Hierarchical Bayesian Markovian Model for Motifs in Biopolymer SequencesEric P. Xing, Michael I. Jordan, Richard M. Karp, Stuart J. Russell. 1489-1496 [doi]
• Learning to Classify Galaxy Shapes Using the EM AlgorithmSergey Kirshner, Igor V. Cadez, Padhraic Smyth, Chandrika Kamath. 1497-1504 [doi]
• Name That Song! A Probabilistic Approach to Querying on Music and TextEric Brochu, Nando de Freitas. 1505-1512 [doi]
• A Probabilistic Model for Learning Concatenative MorphologyMatthew G. Snover, Michael R. Brent. 1513-1520 [doi]
• Learning Attractor Landscapes for Learning Motor PrimitivesAuke Jan Ijspeert, Jun Nakanishi, Stefan Schaal. 1523-1530 [doi]
• Learning a Forward Model of a ReflexBernd Porr, Florentin Wörgötter. 1531-1538 [doi]
• Minimax Differential Dynamic Programming: An Application to Robust Biped WalkingJun Morimoto, Christopher G. Atkeson. 1539-1546 [doi]
• Bias-Optimal Incremental Problem SolvingJürgen Schmidhuber. 1547-1546 [doi]
• Value-Directed Compression of POMDPsPascal Poupart, Craig Boutilier. 1547-1554 [doi]
• Optimality of Reinforcement Learning Algorithms with Linear Function ApproximationRalf Schoknecht. 1555-1562 [doi]
• Speeding up the Parti-Game AlgorithmMaxim Likhachev, Sven Koenig. 1563-1570 [doi]
• Reinforcement Learning to Play an Optimal Nash Equilibrium in Team Markov GamesXiaofeng Wang, Tuomas Sandholm. 1571-1578 [doi]
• Convergent Combinations of Reinforcement Learning with Linear Function ApproximationRalf Schoknecht, Artur Merke. 1579-1586 [doi]
• Approximate Linear Programming for Average-Cost Dynamic ProgrammingDaniela Pucci de Farias, Benjamin Van Roy. 1587-1594 [doi]
• A Convergent Form of Approximate Policy IterationTheodore J. Perkins, Doina Precup. 1595-1602 [doi]
• Efficient Learning EquilibriumRonen I. Brafman, Moshe Tennenholtz. 1603-1610 [doi]
• Nonparametric Representation of Policies and Value Functions: A Trajectory-Based ApproachChristopher G. Atkeson, Jun Morimoto. 1611-1618 [doi]
• Learning to Take Concurrent ActionsKhashayar Rohanimanesh, Sridhar Mahadevan. 1619-1626 [doi]
• Learning in Zero-Sum Team Markov Games Using Factored Value FunctionsMichail G. Lagoudakis, Ronald Parr. 1627-1634 [doi]
• Exponential Family PCA for Belief Compression in POMDPsNicholas Roy, Geoffrey J. Gordon. 1635-1642 [doi]