NIPS 2002: Vancouver, British Columbia, Canada

Suzanna Becker, Sebastian Thrun, Klaus Obermayer (Eds.): Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, NIPS 2002, December 9-14, 2002, Vancouver, British Columbia, Canada]. MIT Press 2003, ISBN 0-262-02550-7 BibTeX

@proceedings{DBLP:conf/nips/2002,
  editor    = {Suzanna Becker and
               Sebastian Thrun and
               Klaus Obermayer},
  title     = {Advances in Neural Information Processing Systems 15 [Neural
               Information Processing Systems, NIPS 2002, December 9-14, 2002,
               Vancouver, British Columbia, Canada]},
  booktitle = {NIPS},
  publisher = {MIT Press},
  year      = {2003},
  isbn      = {0-262-02550-7},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

Dan Klein, Christopher D. Manning:
Fast Exact Inference with a Factored Model for Natural Language Parsing. 3-10
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Thomas L. Griffiths, Mark Steyvers:
Prediction and Semantic Association. 11-18
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Szabolcs Káli, Peter Dayan:
Replay, Repair and Consolidation. 19-26
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Emanuel Todorov, Michael I. Jordan:
A Minimal Intervention Principle for Coordinated Movement. 27-34
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David Fass, Jacob Feldman:
Categorization Under Complexity: A Unified MDL Account of Human Learning of Regular and Irregular Categories. 35-34
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Joshua B. Tenenbaum, Thomas L. Griffiths:
Theory-Based Causal Inference. 35-42
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Willem H. Zuidema:
How the Poverty of the Stimulus Solves the Poverty of the Stimulus. 43-50
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Neville E. Sanjana, Joshua B. Tenenbaum:
Bayesian Models of Inductive Generalization. 51-58
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Daniel J. Navarro, Michael D. Lee:
Combining Dimensions and Features in Similarity-Based Representations. 59-66
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Kenneth J. Malmberg, René Zeelenberg, Richard M. Shiffrin:
Modeling Midazolam's Effect on the Hippocampus and Recognition Memory. 67-66
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David Danks, Thomas L. Griffiths, Joshua B. Tenenbaum:
Dynamical Causal Learning. 67-74
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Robert A. Jacobs, Melissa Dominguez:
Visual Development Aids the Acquisition of Motion Velocity Sensitivities. 75-82
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Nathaniel D. Daw, Aaron C. Courville, David S. Touretzky:
Timing and Partial Observability in the Dopamine System. 83-90
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Zach Solan, Eytan Ruppin, David Horn, Shimon Edelman:
Automatic Acquisition and Efficient Representation of Syntactic Structures. 91-98
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Michael DeWeese, Anthony M. Zador:
Binary Coding in Auditory Cortex. 101-108
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Maneesh Sahani, Jennifer F. Linden:
How Linear are Auditory Cortical Responses?. 109-116
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Wei Wu, Michael J. Black, Yun Gao, Elie Bienenstock, M. Serruya, A. Shaikhouni, John P. Donoghue:
Neural Decoding of Cursor Motion Using a Kalman Filter. 117-124
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Lavi Shpigelman, Yoram Singer, Rony Paz, Eilon Vaadia:
Spikernels: Embedding Spiking Neurons in Inner-Product Spaces. 125-132
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Christian K. Machens, Michael Wehr, Anthony M. Zador:
Spectro-Temporal Receptive Fields of Subthreshold Responses in Auditory Cortex. 133-140
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Jarmo Hurri, Aapo Hyvärinen:
Temporal Coherence, Natural Image Sequences, and the Visual Cortex. 141-148
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David Barber:
Learning in Spiking Neural Assemblies. 149-156
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Angela J. Yu, Peter Dayan:
Expected and Unexpected Uncertainty: ACh and NE in the Neocortex. 157-164
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Aaron J. Gruber, Sara A. Solla, James C. Houk:
Dopamine Induced Bistability Enhances Signal Processing in Spiny Neurons. 165-172
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Liam Paninski:
Convergence Properties of Some Spike-Triggered Analysis Techniques. 173-180
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Dmitri B. Chklovskii, Armen Stepanyants:
Branching Law for Axons. 181-188
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Matthias Bethge, David Rotermund, Klaus Pawelzik:
Binary Tuning is Optimal for Neural Rate Coding with High Temporal Resolution. 189-196
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Elad Schneidman, William Bialek, Michael J. Berry II:
An Information Theoretic Approach to the Functional Classification of Neurons. 197-204
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Javier R. Movellan, Thomas Wachtler, Thomas D. Albright, Terrence J. Sejnowski:
Factorial Coding of Color in Primary Visual Cortex. 205-212
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Wolfgang Maass, Thomas Natschläger, Henry Markram:
A Model for Real-Time Computation in Generic Neural Microcircuits. 213-220
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Peter Dayan, Maneesh Sahani, Gregoire Deback:
Adaptation and Unsupervised Learning. 221-228
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Alex Holub, Gilles Laurent, Pietro Perona:
A Digital Antennal Lobe for Pattern Equalization: Analysis and Design. 229-236
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Michael Eisele, Kenneth D. Miller:
Hidden Markov Model of Cortical Synaptic Plasticity: Derivation of the Learning Rule. 237-244
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Luk-Chong Yeung, Brian S. Blais, Leon N. Cooper, Harel Z. Shouval:
Selectivity and Metaplasticity in a Unified Calcium-Dependent Model. 245-252
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Alistair Bray, Dominique Martinez:
Kernel-Based Extraction of Slow Features: Complex Cells Learn Disparity and Translation Invariance from Natural Images. 253-260
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Tatyana Sharpee, Nicole C. Rust, William Bialek:
Maximally Informative Dimensions: Analyzing Neural Responses to Natural Signals. 261-268
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Arunava Banerjee, Alexandre Pouget:
Dynamical Constraints on Computing with Spike Timing in the Cortex. 269-276
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Patrik O. Hoyer, Aapo Hyvärinen:
Interpreting Neural Response Variability as Monte Carlo Sampling of the Posterior. 277-284
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Alon Fishbach, Bradford J. May:
A Neural Edge-Detection Model for Enhanced Auditory Sensitivity in Modulated Noise. 285-292
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Christian W. Eurich:
An Estimation-Theoretic Framework for the Presentation of Multiple Stimuli. 293-300
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Maneesh Sahani, Jennifer F. Linden:
Evidence Optimization Techniques for Estimating Stimulus-Response Functions. 301-308
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Duane Q. Nykamp:
Reconstructing Stimulus-Driven Neural Networks from Spike Times. 309-316
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Ron Meir, Tong Zhang:
Data-Dependent Bounds for Bayesian Mixture Methods. 319-326
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Dörthe Malzahn, Manfred Opper:
A Statistical Mechanics Approach to Approximate Analytical Bootstrap Averages. 327-334
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Noam Slonim, Yair Weiss:
Maximum Likelihood and the Information Bottleneck. 335-342
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Tom Heskes:
Stable Fixed Points of Loopy Belief Propagation Are Local Minima of the Bethe Free Energy. 343-350
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David A. McAllester, Luis E. Ortiz:
Concentration Inequalities for the Missing Mass and for Histogram Rule Error. 351-358
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Clayton Scott, Robert Nowak:
Dyadic Classification Trees via Structural Risk Minimization. 359-366
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John Shawe-Taylor, Christopher K. I. Williams:
The Stability of Kernel Principal Components Analysis and its Relation to the Process Eigenspectrum. 367-374
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John D. Lafferty, Guy Lebanon:
Information Diffusion Kernels. 375-382
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Jonathan L. Shapiro:
Scaling of Probability-Based Optimization Algorithms. 383-390
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Sumio Watanabe, Shun-ichi Amari:
The Effect of Singularities in a Learning Machine when the True Parameters Do Not Lie on such Singularities. 391-398
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Olivier Bousquet, Daniel J. L. Herrmann:
On the Complexity of Learning the Kernel Matrix. 399-406
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Tatsuto Murayama, Masato Okada:
Rate Distortion Function in the Spin Glass State: A Toy Model. 407-414
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Guy Lebanon, John D. Lafferty:
Conditional Models on the Ranking Poset. 415-422
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John Langford, John Shawe-Taylor:
PAC-Bayes & Margins. 423-430
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Eric Allender, Sanjeev Arora, Michael S. Kearns, Cristopher Moore, Alexander Russell:
A Note on the Representational Incompatibility of Function Approximation and Factored Dynamics. 431-437
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Wim Wiegerinck, Tom Heskes:
Fractional Belief Propagation. 438-445
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Jon M. Kleinberg:
An Impossibility Theorem for Clustering. 446-453
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Tong Zhang:
Effective Dimension and Generalization of Kernel Learning. 454-461
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Koby Crammer, Ran Gilad-Bachrach, Amir Navot, Naftali Tishby:
Margin Analysis of the LVQ Algorithm. 462-469
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Nicolò Cesa-Bianchi, Alex Conconi, Claudio Gentile:
Margin-Based Algorithms for Information Filtering. 470-477
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Cheng Soon Ong, Alexander J. Smola, Robert C. Williamson:
Hyperkernels. 478-485
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Carl Edward Rasmussen, Zoubin Ghahramani:
Bayesian Monte Carlo. 489-496
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Bin Wu, K. Y. Michael Wong, David Bodoff:
Mean Field Approach to a Probabilistic Model in Information Retrieval. 497-504
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Eric P. Xing, Andrew Y. Ng, Michael I. Jordan, Stuart J. Russell:
Distance Metric Learning with Application to Clustering with Side-Information. 505-512
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Gunnar Rätsch, Alexander J. Smola, Sebastian Mika:
Adapting Codes and Embeddings for Polychotomies. 513-520
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Glenn Fung, Olvi L. Mangasarian, Jude W. Shavlik:
Knowledge-Based Support Vector Machine Classifiers. 521-528
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Agathe Girard, Carl Edward Rasmussen, Joaquin Quiñonero Candela, Roderick Murray-Smith:
Gaussian Process Priors with Uncertain Inputs - Application to Multiple-Step Ahead Time Series Forecasting. 529-536
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Koby Crammer, Joseph Keshet, Yoram Singer:
Kernel Design Using Boosting. 537-544
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Sepp Hochreiter, Michael Mozer, Klaus Obermayer:
Coulomb Classifiers: Generalizing Support Vector Machines via an Analogy to Electrostatic Systems. 545-552
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Yves Grandvalet, Stéphane Canu:
Adaptive Scaling for Feature Selection in SVMs. 553-560
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Stuart Andrews, Ioannis Tsochantaridis, Thomas Hofmann:
Support Vector Machines for Multiple-Instance Learning. 561-568
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S. V. N. Vishwanathan, Alexander J. Smola:
Fast Kernels for String and Tree Matching. 569-576
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Geoffrey J. Gordon:
Generalized² Linear² Models. 577-584
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Olivier Chapelle, Jason Weston, Bernhard Schölkopf:
Cluster Kernels for Semi-Supervised Learning. 585-592
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Herbert Jaeger:
Adaptive Nonlinear System Identification with Echo State Networks. 593-600
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Corinna Cortes, Patrick Haffner, Mehryar Mohri:
Rational Kernels. 601-608
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Neil D. Lawrence, Matthias Seeger, Ralf Herbrich:
Fast Sparse Gaussian Process Methods: The Informative Vector Machine. 609-616
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Tilman Lange, Mikio L. Braun, Volker Roth, Joachim M. Buhmann:
Stability-Based Model Selection. 617-624
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Martin H. C. Law, Anil K. Jain, Mário A. T. Figueiredo:
Feature Selection in Mixture-Based Clustering. 625-632
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Craig Saunders, John Shawe-Taylor, Alexei Vinokourov:
String Kernels, Fisher Kernels and Finite State Automata. 633-640
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Saharon Rosset, Eran Segal:
Boosting Density Estimation. 641-648
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Trevor Hastie, Robert Tibshirani:
Independent Components Analysis through Product Density Estimation. 649-656
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Jaz S. Kandola, John Shawe-Taylor, Nello Cristianini:
Learning Semantic Similarity. 657-664
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Max Welling, Richard S. Zemel, Geoffrey E. Hinton:
Self Supervised Boosting. 665-672
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Alexander G. Gray, Bernd Fischer, Johann Schumann, Wray L. Buntine:
Automatic Derivation of Statistical Algorithms: The EM Family and Beyond. 673-680
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Balázs Kégl:
Intrinsic Dimension Estimation Using Packing Numbers. 681-688
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Chakra Chennubhotla, Allan D. Jepson:
Half-Lives of EigenFlows for Spectral Clustering. 689-696
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Harald Steck, Tommi Jaakkola:
On the Dirichlet Prior and Bayesian Regularization. 697-704
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Vin de Silva, Joshua B. Tenenbaum:
Global Versus Local Methods in Nonlinear Dimensionality Reduction. 705-712
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David Barber:
Dynamic Bayesian Networks with Deterministic Latent Tables. 713-720
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Naonori Ueda, Kazumi Saito:
Parametric Mixture Models for Multi-Labeled Text. 721-728
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Koji Tsuda, Motoaki Kawanabe, Klaus-Robert Müller:
Clustering with the Fisher Score. 729-736
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Peter Sykacek, Stephen J. Roberts:
Adaptive Classification by Variational Kalman Filtering. 737-744
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Baback Moghaddam, Gregory Shakhnarovich:
Boosted Dyadic Kernel Discriminants. 745-752
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Finnegan Southey, Dale Schuurmans, Ali Ghodsi:
Regularized Greedy Importance Sampling. 753-760
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Elzbieta Pekalska, David M. J. Tax, Robert P. W. Duin:
One-Class LP Classifiers for Dissimilarity Representations. 761-768
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Thomas Strohmann, Gregory Z. Grudic:
A Formulation for Minimax Probability Machine Regression. 769-776
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Christopher M. Bishop, David J. Spiegelhalter, John M. Winn:
VIBES: A Variational Inference Engine for Bayesian Networks. 777-784
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James D. Park, Adnan Darwiche:
A Differential Semantics for Jointree Algorithms. 785-784
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Sariel Har-Peled, Dan Roth, Dav Zimak:
Constraint Classification for Multiclass Classification and Ranking. 785-792
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Luis E. Ortiz, Michael J. Kearns:
Nash Propagation for Loopy Graphical Games. 793-800
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Dan Pelleg, Andrew W. Moore:
Using Tarjan's Red Rule for Fast Dependency Tree Construction. 801-808
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Martin J. Wainwright, Tommi Jaakkola, Alan S. Willsky:
Exact MAP Estimates by (Hyper)tree Agreement. 809-816
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Volker Roth, Julian Laub, Joachim M. Buhmann, Klaus-Robert Müller:
Going Metric: Denoising Pairwise Data. 817-824
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Pascal Vincent, Yoshua Bengio:
Manifold Parzen Windows. 825-832
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Geoffrey E. Hinton, Sam T. Roweis:
Stochastic Neighbor Embedding. 833-840
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Yee Whye Teh, Sam T. Roweis:
Automatic Alignment of Local Representations. 841-848
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David Cohn:
Informed Projections. 849-856
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Gal Chechik, Naftali Tishby:
Extracting Relevant Structures with Side Information. 857-864
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Kenji Fukumizu, Shotaro Akaho, Shun-ichi Amari:
Critical Lines in Symmetry of Mixture Models and its Application to Component Splitting. 865-872
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Jason Weston, Olivier Chapelle, André Elisseeff, Bernhard Schölkopf, Vladimir Vapnik:
Kernel Dependency Estimation. 873-880
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Kwokleung Chan, Te-Won Lee, Terrence J. Sejnowski:
Handling Missing Data with Variational Bayesian Learning of ICA. 881-888
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Sepp Hochreiter, Klaus Obermayer:
Feature Selection and Classification on Matrix Data: From Large Margins to Small Covering Numbers. 889-896
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Rong Jin, Zoubin Ghahramani:
Learning with Multiple Labels. 897-904
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Gert R. G. Lanckriet, Laurent El Ghaoui, Michael I. Jordan:
Robust Novelty Detection with Single-Class MPM. 905-912
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Nicholas P. Hughes, David Lowe:
Artefactual Structure from Least-Squares Multidimensional Scaling. 913-920
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Marina Sokolova, Mario Marchand, Nathalie Japkowicz, John Shawe-Taylor:
The Decision List Machine. 921-928
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Mikhail Belkin, Partha Niyogi:
Using Manifold Stucture for Partially Labeled Classification. 929-936
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Amnon Shashua, Anat Levin:
Ranking with Large Margin Principle: Two Approaches. 937-944
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Ofer Dekel, Yoram Singer:
Multiclass Learning by Probabilistic Embeddings. 945-952
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Anton Schwaighofer, Volker Tresp:
Transductive and Inductive Methods for Approximate Gaussian Process Regression. 953-960
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Matthew Brand:
Charting a Manifold. 961-968
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Albert E. Parker, Tomás Gedeon, Alexander Dimitrov:
Annealing and the Rate Distortion Problem. 969-976
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Yasemin Altun, Thomas Hofmann, Mark Johnson:
Discriminative Learning for Label Sequences via Boosting. 977-984
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Peter Meinicke, Thorsten Twellmann, Helge Ritter:
Discriminative Densities from Maximum Contrast Estimation. 985-992
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Stan Z. Li, ZhenQiu Zhang, Heung-Yeung Shum, HongJiang Zhang:
FloatBoost Learning for Classification. 993-1000
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Joaquin Quiñonero Candela, Ole Winther:
Incremental Gaussian Processes. 1001-1008
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Francis R. Bach, Michael I. Jordan:
Learning Graphical Models with Mercer Kernels. 1009-1016
Electronic Edition (link) BibTeX
David A. Ross, Richard S. Zemel:
Multiple Cause Vector Quantization. 1017-1024
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Martin Szummer, Tommi Jaakkola:
Information Regularization with Partially Labeled Data. 1025-1032
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E. Solak, Roderick Murray-Smith, William E. Leithead, Douglas J. Leith, Carl Edward Rasmussen:
Derivative Observations in Gaussian Process Models of Dynamic Systems. 1033-1040
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Fei Sha, Lawrence K. Saul, Daniel D. Lee:
Multiplicative Updates for Nonnegative Quadratic Programming in Support Vector Machines. 1041-1048
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Ali Rahimi, Trevor Darrell:
Location Estimation with a Differential Update Network. 1049-1056
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Cody C. T. Kwok, Dieter Fox, Marina Meila:
Real-Time Particle Filters. 1057-1064
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Alexandre R. S. Romariz, Kelvin Wagner:
Optoelectronic Implementation of a FitzHugh-Nagumo Neural Model. 1067-1074
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Shih-Chii Liu, Malte Boegershausen, Pascal Suter:
Circuit Model of Short-Term Synaptic Dynamics. 1075-1082
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David Hsu, Seth Bridges, Miguel Figueroa, Chris Diorio:
Adaptive Quantization and Density Estimation in Silicon. 1083-1090
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Giacomo Indiveri:
Neuromorphic Bistable VLSI Synapses with Spike-Timing-Dependent Plasticity. 1091-1098
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Ricardo Carmona-Galán, Francisco Jiménez-Garrido, Rafael Domínguez-Castro, Servando Espejo-Meana, Ángel Rodríguez-Vázquez:
Retinal Processing Emulation in a Programmable 2-Layer Analog Array Processor CMOS Chip. 1099-1106
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Peter Meinicke, Matthias Kaper, Florian Hoppe, Manfred Heumann, Helge Ritter:
Improving Transfer Rates in Brain Computer Interfacing: A Case Study. 1107-1114
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Guido Dornhege, Benjamin Blankertz, Gabriel Curio, Klaus-Robert Müller:
Combining Features for BCI. 1115-1122
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Jakob Heinzle, Alan Stocker:
Classifying Patterns of Visual Motion - a Neuromorphic Approach. 1123-1130
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Terry Elliott, Jörg Kramer:
Developing Topography and Ocular Dominance Using Two aVLSI Vision Sensors and a Neurotrophic Model of Plasticity. 1131-1138
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Brian Taba, Kwabena Boahen:
Topographic Map Formation by Silicon Growth Cones. 1139-1146
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R. Jacob Vogelstein, Francesco Tenore, Ralf Philipp, Miriam S. Adlerstein, David H. Goldberg, Gert Cauwenberghs:
Spike Timing-Dependent Plasticity in the Address Domain. 1147-1154
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Seth Bridges, Miguel Figueroa, David Hsu, Chris Diorio:
Field-Programmable Learning Arrays. 1155-1162
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Shantanu Chakrabartty, Gert Cauwenberghs:
Forward-Decoding Kernel-Based Phone Recognition. 1165-1172
Electronic Edition (link) BibTeX
Gil-Jin Jang, Te-Won Lee:
A Probabilistic Approach to Single Channel Blind Signal Separation. 1173-1180
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Lawrence K. Saul, Daniel D. Lee, Charles L. Isbell, Yann LeCun:
Real Time Voice Processing with Audiovisual Feedback: Toward Autonomous Agents with Perfect Pitch. 1181-1188
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Sachin S. Kajarekar, Hynek Hermansky:
Analysis of Information in Speech Based on MANOVA. 1189-1196
Electronic Edition (link) BibTeX
Patrick J. Wolfe, Simon J. Godsill:
Bayesian Estimation of Time-Frequency Coefficients for Audio Signal Enhancement. 1197-1204
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Hagai Attias:
Source Separation with a Sensor Array Using Graphical Models and Subband Filtering. 1205-1212
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Samy Bengio:
An Asynchronous Hidden Markov Model for Audio-Visual Speech Recognition. 1213-1220
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Guoning Hu, DeLiang L. Wang:
Monaural Speech Separation. 1221-1228
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Ehud Ben-Reuven, Yoram Singer:
Discriminative Binaural Sound Localization. 1229-1236
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Shinji Watanabe, Yasuhiro Minami, Atsushi Nakamura, Naonori Ueda:
Application of Variational Bayesian Approach to Speech Recognition. 1237-1244
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Anat Levin, Assaf Zomet, Yair Weiss:
Learning to Perceive Transparency from the Statistics of Natural Scenes. 1247-1254
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David R. Martin, Charless Fowlkes, Jitendra Malik:
Learning to Detect Natural Image Boundaries Using Brightness and Texture. 1255-1262
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Anitha Kannan, Nebojsa Jojic, Brendan J. Frey:
Fast Transformation-Invariant Factor Analysis. 1263-1270
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Marian Stewart Bartlett, Gwen Littlewort, Bjorn Braathen, Terrence J. Sejnowski, Javier R. Movellan:
A Prototype for Automatic Recognition of Spontaneous Facial Actions. 1271-1278
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Michael E. Tipping, Christopher M. Bishop:
Bayesian Image Super-Resolution. 1279-1286
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David B. Grimes, Rajesh P. N. Rao:
A Bilinear Model for Sparse Coding. 1287-1294
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Amos J. Storkey:
Dynamic Structure Super-Resolution. 1295-1302
Electronic Edition (link) BibTeX
Kinh Tieu, Erik G. Miller:
Unsupervised Color Constancy. 1303-1310
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Leonid Taycher, John W. Fisher III, Trevor Darrell:
Recovering Articulated Model Topology from Observed Rigid Motion. 1311-1318
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Matthias O. Franz, Javaan S. Chahl:
Linear Combinations of Optic Flow Vectors for Estimating Self-Motion - a Real-World Test of a Neural Model. 1319-1326
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Phil Sallee, Bruno A. Olshausen:
Learning Sparse Multiscale Image Representations. 1327-1334
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William T. Freeman, Antonio B. Torralba:
Shape Recipes: Scene Representations that Refer to the Image. 1335-1342
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Marshall F. Tappen, William T. Freeman, Edward H. Adelson:
Recovering Intrinsic Images from a Single Image. 1343-1350
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Nuno Vasconcelos:
Feature Selection by Maximum Marginal Diversity. 1351-1358
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Max Welling, Geoffrey E. Hinton, Simon Osindero:
Learning Sparse Topographic Representations with Products of Student-t Distributions. 1359-1366
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Yan Karklin, Michael S. Lewicki:
A Model for Learning Variance Components of Natural Images. 1367-1374
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Barbara Caputo, Gyuri Dorkó:
How to Combine Color and Shape Information for 3D Object Recognition: Kernels do the Trick. 1375-1382
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Stella X. Yu, Ralph Gross, Jianbo Shi:
Concurrent Object Recognition and Segmentation by Graph Partitioning. 1383-1390
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Christopher K. I. Williams, Michalis K. Titsias:
Learning About Multiple Objects in Images: Factorial Learning without Factorial Search. 1391-1398
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Hanna Pasula, Bhaskara Marthi, Brian Milch, Stuart J. Russell, Ilya Shpitser:
Identity Uncertainty and Citation Matching. 1401-1408
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Anton Schwaighofer, Volker Tresp, Peter Mayer, Alexander K. Scheel, Gerhard Müller:
The RA Scanner: Prediction of Rheumatoid Joint Inflammation Based on Laser Imaging. 1409-1416
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Christina S. Leslie, Eleazar Eskin, Jason Weston, William Stafford Noble:
Mismatch String Kernels for SVM Protein Classification. 1417-1424
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Jean-Philippe Vert, Minoru Kanehisa:
Graph-Driven Feature Extraction From Microarray Data Using Diffusion Kernels and Kernel CCA. 1425-1432
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Rubén Morales-Menéndez, Nando de Freitas, David Poole:
Real-Time Monitoring of Complex Industrial Processes with Particle Filters. 1433-1440
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Dmitry Pavlov, David M. Pennock:
A Maximum Entropy Approach to Collaborative Filtering in Dynamic, Sparse, High-Dimensional Domains. 1441-1448
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Gianluca Pollastri, Pierre Baldi, Alessandro Vullo, Paolo Frasconi:
Prediction of Protein Topologies Using Generalized IOHMMS and RNNs. 1449-1456
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Chen Yanover, Yair Weiss:
Approximate Inference and Protein-Folding. 1457-1464
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Robert B. Gramacy, Manfred K. Warmuth, Scott A. Brandt, Ismail Ari:
Adaptive Caching by Refetching. 1465-1472
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Alexei Vinokourov, John Shawe-Taylor, Nello Cristianini:
Inferring a Semantic Representation of Text via Cross-Language Correlation Analysis. 1473-1480
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William W. Cohen:
Improving a Page Classifier with Anchor Extraction and Link Analysis. 1481-1488
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Eric P. Xing, Michael I. Jordan, Richard M. Karp, Stuart J. Russell:
A Hierarchical Bayesian Markovian Model for Motifs in Biopolymer Sequences. 1489-1496
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Sergey Kirshner, Igor V. Cadez, Padhraic Smyth, Chandrika Kamath:
Learning to Classify Galaxy Shapes Using the EM Algorithm. 1497-1504
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Eric Brochu, Nando de Freitas:
"Name That Song!" A Probabilistic Approach to Querying on Music and Text. 1505-1512
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Matthew G. Snover, Michael R. Brent:
A Probabilistic Model for Learning Concatenative Morphology. 1513-1520
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Auke Jan Ijspeert, Jun Nakanishi, Stefan Schaal:
Learning Attractor Landscapes for Learning Motor Primitives. 1523-1530
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Bernd Porr, Florentin Wörgötter:
Learning a Forward Model of a Reflex. 1531-1538
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Jun Morimoto, Christopher G. Atkeson:
Minimax Differential Dynamic Programming: An Application to Robust Biped Walking. 1539-1546
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Jürgen Schmidhuber:
Bias-Optimal Incremental Problem Solving. 1547-1546
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Pascal Poupart, Craig Boutilier:
Value-Directed Compression of POMDPs. 1547-1554
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Ralf Schoknecht:
Optimality of Reinforcement Learning Algorithms with Linear Function Approximation. 1555-1562
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Maxim Likhachev, Sven Koenig:
Speeding up the Parti-Game Algorithm. 1563-1570
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Xiaofeng Wang, Tuomas Sandholm:
Reinforcement Learning to Play an Optimal Nash Equilibrium in Team Markov Games. 1571-1578
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Ralf Schoknecht, Artur Merke:
Convergent Combinations of Reinforcement Learning with Linear Function Approximation. 1579-1586
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Daniela Pucci de Farias, Benjamin Van Roy:
Approximate Linear Programming for Average-Cost Dynamic Programming. 1587-1594
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Theodore J. Perkins, Doina Precup:
A Convergent Form of Approximate Policy Iteration. 1595-1602
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Ronen I. Brafman, Moshe Tennenholtz:
Efficient Learning Equilibrium. 1603-1610
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Christopher G. Atkeson, Jun Morimoto:
Nonparametric Representation of Policies and Value Functions: A Trajectory-Based Approach. 1611-1618
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Khashayar Rohanimanesh, Sridhar Mahadevan:
Learning to Take Concurrent Actions. 1619-1626
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Michail G. Lagoudakis, Ronald Parr:
Learning in Zero-Sum Team Markov Games Using Factored Value Functions. 1627-1634
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Nicholas Roy, Geoffrey J. Gordon:
Exponential Family PCA for Belief Compression in POMDPs. 1635-1642
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