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ORCIDNIPS 2002: Vancouver, British Columbia, Canada
- Suzanna Becker, Sebastian Thrun, Klaus Obermayer:
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 - Dan Klein, Christopher D. Manning:
Fast Exact Inference with a Factored Model for Natural Language Parsing. 3-10 - Thomas L. Griffiths, Mark Steyvers:
Prediction and Semantic Association. 11-18 - Szabolcs Káli, Peter Dayan:
Replay, Repair and Consolidation. 19-26 - Emanuel Todorov, Michael I. Jordan:
A Minimal Intervention Principle for Coordinated Movement. 27-34 - David Fass, Jacob Feldman:
Categorization Under Complexity: A Unified MDL Account of Human Learning of Regular and Irregular Categories. 35-34 - Joshua B. Tenenbaum, Thomas L. Griffiths:
Theory-Based Causal Inference. 35-42 - Willem H. Zuidema:
How the Poverty of the Stimulus Solves the Poverty of the Stimulus. 43-50 - Neville E. Sanjana, Joshua B. Tenenbaum:
Bayesian Models of Inductive Generalization. 51-58 - Daniel J. Navarro, Michael D. Lee:
Combining Dimensions and Features in Similarity-Based Representations. 59-66 - Kenneth J. Malmberg, René Zeelenberg, Richard M. Shiffrin:
Modeling Midazolam's Effect on the Hippocampus and Recognition Memory. 67-66 - David Danks, Thomas L. Griffiths, Joshua B. Tenenbaum:
Dynamical Causal Learning. 67-74 - Robert A. Jacobs, Melissa Dominguez:
Visual Development Aids the Acquisition of Motion Velocity Sensitivities. 75-82 - Nathaniel D. Daw, Aaron C. Courville, David S. Touretzky:
Timing and Partial Observability in the Dopamine System. 83-90 - Zach Solan, Eytan Ruppin, David Horn, Shimon Edelman:
Automatic Acquisition and Efficient Representation of Syntactic Structures. 91-98 - Michael Robert DeWeese, Anthony M. Zador:
Binary Coding in Auditory Cortex. 101-108 - Maneesh Sahani, Jennifer F. Linden:
How Linear are Auditory Cortical Responses?. 109-116 - Wei Wu, Michael J. Black, Yun Gao, Elie Bienenstock, Mijail Serruya, A. Shaikhouni, John P. Donoghue:
Neural Decoding of Cursor Motion Using a Kalman Filter. 117-124 - Lavi Shpigelman, Yoram Singer, Rony Paz, Eilon Vaadia:
Spikernels: Embedding Spiking Neurons in Inner-Product Spaces. 125-132 - Christian K. Machens, Michael Wehr, Anthony M. Zador:
Spectro-Temporal Receptive Fields of Subthreshold Responses in Auditory Cortex. 133-140 - Jarmo Hurri, Aapo Hyvärinen:
Temporal Coherence, Natural Image Sequences, and the Visual Cortex. 141-148 - David Barber:
Learning in Spiking Neural Assemblies. 149-156 - Angela J. Yu, Peter Dayan:
Expected and Unexpected Uncertainty: ACh and NE in the Neocortex. 157-164 - Aaron J. Gruber, Sara A. Solla, James C. Houk:
Dopamine Induced Bistability Enhances Signal Processing in Spiny Neurons. 165-172 - Liam Paninski:
Convergence Properties of Some Spike-Triggered Analysis Techniques. 173-180 - Dmitri B. Chklovskii, Armen Stepanyants:
Branching Law for Axons. 181-188 - Matthias Bethge, David Rotermund, Klaus Pawelzik:
Binary Tuning is Optimal for Neural Rate Coding with High Temporal Resolution. 189-196 - Elad Schneidman, William Bialek, Michael J. Berry II:
An Information Theoretic Approach to the Functional Classification of Neurons. 197-204 - Javier R. Movellan, Thomas Wachtler, Thomas D. Albright, Terrence J. Sejnowski:
Factorial Coding of Color in Primary Visual Cortex. 205-212 - Wolfgang Maass, Thomas Natschläger, Henry Markram:
A Model for Real-Time Computation in Generic Neural Microcircuits. 213-220 - Peter Dayan, Maneesh Sahani, Gregoire Deback:
Adaptation and Unsupervised Learning. 221-228 - Alex Holub, Gilles Laurent, Pietro Perona:
A Digital Antennal Lobe for Pattern Equalization: Analysis and Design. 229-236 - Michael Eisele, Kenneth D. Miller:
Hidden Markov Model of Cortical Synaptic Plasticity: Derivation of the Learning Rule. 237-244 - Luk-Chong Yeung, Brian S. Blais, Leon N. Cooper, Harel Z. Shouval:
Selectivity and Metaplasticity in a Unified Calcium-Dependent Model. 245-252 - Alistair Bray, Dominique Martinez:
Kernel-Based Extraction of Slow Features: Complex Cells Learn Disparity and Translation Invariance from Natural Images. 253-260 - Tatyana O. Sharpee, Nicole C. Rust, William Bialek:
Maximally Informative Dimensions: Analyzing Neural Responses to Natural Signals. 261-268 - Arunava Banerjee, Alexandre Pouget:
Dynamical Constraints on Computing with Spike Timing in the Cortex. 269-276 - Patrik O. Hoyer, Aapo Hyvärinen:
Interpreting Neural Response Variability as Monte Carlo Sampling of the Posterior. 277-284 - Alon Fishbach, Bradford J. May:
A Neural Edge-Detection Model for Enhanced Auditory Sensitivity in Modulated Noise. 285-292 - Christian W. Eurich:
An Estimation-Theoretic Framework for the Presentation of Multiple Stimuli. 293-300 - Maneesh Sahani, Jennifer F. Linden:
Evidence Optimization Techniques for Estimating Stimulus-Response Functions. 301-308 - Duane Q. Nykamp:
Reconstructing Stimulus-Driven Neural Networks from Spike Times. 309-316 - Ron Meir, Tong Zhang:
Data-Dependent Bounds for Bayesian Mixture Methods. 319-326 - Dörthe Malzahn, Manfred Opper:
A Statistical Mechanics Approach to Approximate Analytical Bootstrap Averages. 327-334 - Noam Slonim, Yair Weiss:
Maximum Likelihood and the Information Bottleneck. 335-342 - Tom Heskes:
Stable Fixed Points of Loopy Belief Propagation Are Local Minima of the Bethe Free Energy. 343-350 - David A. McAllester, Luis E. Ortiz:
Concentration Inequalities for the Missing Mass and for Histogram Rule Error. 351-358 - Clayton D. Scott, Robert D. Nowak:
Dyadic Classification Trees via Structural Risk Minimization. 359-366 - John Shawe-Taylor, Christopher K. I. Williams:
The Stability of Kernel Principal Components Analysis and its Relation to the Process Eigenspectrum. 367-374 - John D. Lafferty, Guy Lebanon:
Information Diffusion Kernels. 375-382 - Jonathan L. Shapiro:
Scaling of Probability-Based Optimization Algorithms. 383-390 - 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 - Olivier Bousquet, Daniel J. L. Herrmann:
On the Complexity of Learning the Kernel Matrix. 399-406 - Tatsuto Murayama, Masato Okada:
Rate Distortion Function in the Spin Glass State: A Toy Model. 407-414 - Guy Lebanon, John D. Lafferty:
Conditional Models on the Ranking Poset. 415-422 - John Langford, John Shawe-Taylor:
PAC-Bayes & Margins. 423-430 - Eric Allender, Sanjeev Arora, Michael J. Kearns, Cristopher Moore, Alexander Russell:
A Note on the Representational Incompatibility of Function Approximation and Factored Dynamics. 431-437 - Wim Wiegerinck, Tom Heskes:
Fractional Belief Propagation. 438-445 - Jon M. Kleinberg:
An Impossibility Theorem for Clustering. 446-453 - Tong Zhang:
Effective Dimension and Generalization of Kernel Learning. 454-461 - Koby Crammer, Ran Gilad-Bachrach, Amir Navot, Naftali Tishby:
Margin Analysis of the LVQ Algorithm. 462-469 - Nicolò Cesa-Bianchi, Alex Conconi, Claudio Gentile:
Margin-Based Algorithms for Information Filtering. 470-477 - Cheng Soon Ong, Alexander J. Smola, Robert C. Williamson:
Hyperkernels. 478-485 - Carl Edward Rasmussen, Zoubin Ghahramani:
Bayesian Monte Carlo. 489-496 - Bin Wu, K. Y. Michael Wong, David Bodoff:
Mean Field Approach to a Probabilistic Model in Information Retrieval. 497-504 - Eric P. Xing, Andrew Y. Ng, Michael I. Jordan, Stuart Russell:
Distance Metric Learning with Application to Clustering with Side-Information. 505-512 - Gunnar Rätsch, Alexander J. Smola, Sebastian Mika:
Adapting Codes and Embeddings for Polychotomies. 513-520 - Glenn Fung, Olvi L. Mangasarian, Jude W. Shavlik:
Knowledge-Based Support Vector Machine Classifiers. 521-528 - 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 - Koby Crammer, Joseph Keshet, Yoram Singer:
Kernel Design Using Boosting. 537-544 - Sepp Hochreiter, Michael Mozer, Klaus Obermayer:
Coulomb Classifiers: Generalizing Support Vector Machines via an Analogy to Electrostatic Systems. 545-552 - Yves Grandvalet, Stéphane Canu:
Adaptive Scaling for Feature Selection in SVMs. 553-560 - Stuart Andrews, Ioannis Tsochantaridis, Thomas Hofmann:
Support Vector Machines for Multiple-Instance Learning. 561-568 - S. V. N. Vishwanathan, Alexander J. Smola:
Fast Kernels for String and Tree Matching. 569-576 - Geoffrey J. Gordon:
Generalized2 Linear2 Models. 577-584 - Olivier Chapelle, Jason Weston, Bernhard Schölkopf:
Cluster Kernels for Semi-Supervised Learning. 585-592 - Herbert Jaeger:
Adaptive Nonlinear System Identification with Echo State Networks. 593-600 - Corinna Cortes, Patrick Haffner, Mehryar Mohri:
Rational Kernels. 601-608 - Neil D. Lawrence, Matthias W. Seeger, Ralf Herbrich:
Fast Sparse Gaussian Process Methods: The Informative Vector Machine. 609-616 - Tilman Lange, Mikio L. Braun, Volker Roth, Joachim M. Buhmann:
Stability-Based Model Selection. 617-624 - Martin H. C. Law, Anil K. Jain, Mário A. T. Figueiredo:
Feature Selection in Mixture-Based Clustering. 625-632 - Craig Saunders, John Shawe-Taylor, Alexei Vinokourov:
String Kernels, Fisher Kernels and Finite State Automata. 633-640 - Saharon Rosset, Eran Segal:
Boosting Density Estimation. 641-648 - Trevor Hastie, Robert Tibshirani:
Independent Components Analysis through Product Density Estimation. 649-656 - Jaz S. Kandola, John Shawe-Taylor, Nello Cristianini:
Learning Semantic Similarity. 657-664 - Max Welling, Richard S. Zemel, Geoffrey E. Hinton:
Self Supervised Boosting. 665-672 - Alexander G. Gray, Bernd Fischer, Johann Schumann, Wray L. Buntine:
Automatic Derivation of Statistical Algorithms: The EM Family and Beyond. 673-680 - Balázs Kégl:
Intrinsic Dimension Estimation Using Packing Numbers. 681-688 - Chakra Chennubhotla, Allan D. Jepson:
Half-Lives of EigenFlows for Spectral Clustering. 689-696 - Harald Steck, Tommi S. Jaakkola:
On the Dirichlet Prior and Bayesian Regularization. 697-704 - Vin de Silva, Joshua B. Tenenbaum:
Global Versus Local Methods in Nonlinear Dimensionality Reduction. 705-712 - David Barber:
Dynamic Bayesian Networks with Deterministic Latent Tables. 713-720 - Naonori Ueda, Kazumi Saito:
Parametric Mixture Models for Multi-Labeled Text. 721-728 - Koji Tsuda, Motoaki Kawanabe, Klaus-Robert Müller:
Clustering with the Fisher Score. 729-736 - Peter Sykacek, Stephen J. Roberts:
Adaptive Classification by Variational Kalman Filtering. 737-744 - Baback Moghaddam, Gregory Shakhnarovich:
Boosted Dyadic Kernel Discriminants. 745-752 - Finnegan Southey, Dale Schuurmans, Ali Ghodsi:
Regularized Greedy Importance Sampling. 753-760 - Elzbieta Pekalska, David M. J. Tax, Robert P. W. Duin:
One-Class LP Classifiers for Dissimilarity Representations. 761-768 - Thomas Strohmann, Gregory Z. Grudic:
A Formulation for Minimax Probability Machine Regression. 769-776 - Christopher M. Bishop, David J. Spiegelhalter, John M. Winn:
VIBES: A Variational Inference Engine for Bayesian Networks. 777-784 - James D. Park, Adnan Darwiche:
A Differential Semantics for Jointree Algorithms. 785-784 - Sariel Har-Peled
, Dan Roth, Dav Zimak:
Constraint Classification for Multiclass Classification and Ranking. 785-792 - Luis E. Ortiz, Michael J. Kearns:
Nash Propagation for Loopy Graphical Games. 793-800 - Dan Pelleg, Andrew W. Moore:
Using Tarjan's Red Rule for Fast Dependency Tree Construction. 801-808 - Martin J. Wainwright, Tommi S. Jaakkola, Alan S. Willsky:
Exact MAP Estimates by (Hyper)tree Agreement. 809-816 - Volker Roth, Julian Laub, Joachim M. Buhmann, Klaus-Robert Müller:
Going Metric: Denoising Pairwise Data. 817-824 - Pascal Vincent, Yoshua Bengio:
Manifold Parzen Windows. 825-832 - Geoffrey E. Hinton, Sam T. Roweis:
Stochastic Neighbor Embedding. 833-840 - Yee Whye Teh, Sam T. Roweis:
Automatic Alignment of Local Representations. 841-848 - David Cohn:
Informed Projections. 849-856 - Gal Chechik, Naftali Tishby:
Extracting Relevant Structures with Side Information. 857-864 - Kenji Fukumizu, Shotaro Akaho, Shun-ichi Amari:
Critical Lines in Symmetry of Mixture Models and its Application to Component Splitting. 865-872 - Jason Weston, Olivier Chapelle, André Elisseeff, Bernhard Schölkopf, Vladimir Vapnik:
Kernel Dependency Estimation. 873-880 - Kwokleung Chan, Te-Won Lee, Terrence J. Sejnowski:
Handling Missing Data with Variational Bayesian Learning of ICA. 881-888 - Sepp Hochreiter, Klaus Obermayer:
Feature Selection and Classification on Matrix Data: From Large Margins to Small Covering Numbers. 889-896 - Rong Jin, Zoubin Ghahramani:
Learning with Multiple Labels. 897-904 - Gert R. G. Lanckriet, Laurent El Ghaoui, Michael I. Jordan:
Robust Novelty Detection with Single-Class MPM. 905-912 - Nicholas P. Hughes, David Lowe:
Artefactual Structure from Least-Squares Multidimensional Scaling. 913-920 - Marina Sokolova, Mario Marchand, Nathalie Japkowicz, John Shawe-Taylor:
The Decision List Machine. 921-928 - Mikhail Belkin, Partha Niyogi:
Using Manifold Stucture for Partially Labeled Classification. 929-936 - Amnon Shashua, Anat Levin:
Ranking with Large Margin Principle: Two Approaches. 937-944 - Ofer Dekel, Yoram Singer:
Multiclass Learning by Probabilistic Embeddings. 945-952 - Anton Schwaighofer, Volker Tresp:
Transductive and Inductive Methods for Approximate Gaussian Process Regression. 953-960 - Matthew Brand:
Charting a Manifold. 961-968 - Albert E. Parker, Tomás Gedeon, Alexander Dimitrov:
Annealing and the Rate Distortion Problem. 969-976