Advances in Neural Information Processing Systems 15 (NIPS 2002)
The papers below appear in Advances in Neural Information Processing Systems 15 edited by S. Becker and S. Thrun and K. Obermayer.They are proceedings from the conference, "Neural Information Processing Systems 2002."
- Fast Exact Inference with a Factored Model for Natural Language Parsing Dan Klein, Christopher D. Manning
- Prediction and Semantic Association Thomas L. Griffiths, Mark Steyvers
- Replay, Repair and Consolidation Szabolcs Káli, Peter Dayan
- A Minimal Intervention Principle for Coordinated Movement Emanuel Todorov, Michael I. Jordan
- Categorization Under Complexity: A Unified MDL Account of Human Learning of Regular and Irregular Categories David Fass, Jacob Feldman
- Theory-Based Causal Inference Joshua B. Tenenbaum, Thomas L. Griffiths
- How the Poverty of the Stimulus Solves the Poverty of the Stimulus Willem H. Zuidema
- Bayesian Models of Inductive Generalization Neville E. Sanjana, Joshua B. Tenenbaum
- Combining Dimensions and Features in Similarity-Based Representations Daniel J. Navarro, Michael D. Lee
- Modeling Midazolam's Effect on the Hippocampus and Recognition Memory Kenneth J. Malmberg, René Zeelenberg, Richard M. Shiffrin
- Dynamical Causal Learning David Danks, Thomas L. Griffiths, Joshua B. Tenenbaum
- Visual Development Aids the Acquisition of Motion Velocity Sensitivities Robert A. Jacobs, Melissa Dominguez
- Timing and Partial Observability in the Dopamine System Nathaniel D. Daw, Aaron C. Courville, David S. Touretzky
- Automatic Acquisition and Efficient Representation of Syntactic Structures Zach Solan, Eytan Ruppin, David Horn, Shimon Edelman
- Binary Coding in Auditory Cortex Michael R. Deweese, Anthony M. Zador
- How Linear are Auditory Cortical Responses? Maneesh Sahani, Jennifer F. Linden
- Neural Decoding of Cursor Motion Using a Kalman Filter W Wu, M. J. Black, Y. Gao, M. Serruya, A. Shaikhouni, J. P. Donoghue, Elie Bienenstock
- Spikernels: Embedding Spiking Neurons in Inner-Product Spaces Lavi Shpigelman, Yoram Singer, Rony Paz, Eilon Vaadia
- Spectro-Temporal Receptive Fields of Subthreshold Responses in Auditory Cortex Christian K. Machens, Michael Wehr, Anthony M. Zador
- Temporal Coherence, Natural Image Sequences, and the Visual Cortex Jarmo Hurri, Aapo Hyvärinen
- Learning in Spiking Neural Assemblies David Barber
- Expected and Unexpected Uncertainty: ACh and NE in the Neocortex Peter Dayan, Angela J. Yu
- Dopamine Induced Bistability Enhances Signal Processing in Spiny Neurons Aaron J. Gruber, Sara A. Solla, James C. Houk
- Convergence Properties of Some Spike-Triggered Analysis Techniques Liam Paninski
- Branching Law for Axons Dmitri B. Chklovskii, Armen Stepanyants
- Binary Tuning is Optimal for Neural Rate Coding with High Temporal Resolution Matthias Bethge, David Rotermund, Klaus Pawelzik
- An Information Theoretic Approach to the Functional Classification of Neurons Elad Schneidman, William Bialek, Michael Ii
- Morton-Style Factorial Coding of Color in Primary Visual Cortex Javier R. Movellan, Thomas Wachtler, Thomas D. Albright, Terrence Sejnowski
- A Model for Real-Time Computation in Generic Neural Microcircuits Wolfgang Maass, Thomas Natschläger, Henry Markram
- Adaptation and Unsupervised Learning Peter Dayan, Maneesh Sahani, Gregoire Deback
- A Digital Antennal Lobe for Pattern Equalization: Analysis and Design Alex Holub, Gilles Laurent, Pietro Perona
- Hidden Markov Model of Cortical Synaptic Plasticity: Derivation of the Learning Rule Michael Eisele, Kenneth D. Miller
- Selectivity and Metaplasticity in a Unified Calcium-Dependent Model Luk Chong Yeung, Brian S. Blais, Leon N. Cooper, Harel Z. Shouval
- Kernel-Based Extraction of Slow Features: Complex Cells Learn Disparity and Translation Invariance from Natural Images Alistair Bray, Dominique Martinez
- Maximally Informative Dimensions: Analyzing Neural Responses to Natural Signals Tatyana Sharpee, Nicole C. Rust, William Bialek
- Dynamical Constraints on Computing with Spike Timing in the Cortex Arunava Banerjee, Alexandre Pouget
- Interpreting Neural Response Variability as Monte Carlo Sampling of the Posterior Patrik O. Hoyer, Aapo Hyvärinen
- A Neural Edge-Detection Model for Enhanced Auditory Sensitivity in Modulated Noise Alon Fishbach, Bradford J. May
- An Estimation-Theoretic Framework for the Presentation of Multiple Stimuli Christian W. Eurich
- Evidence Optimization Techniques for Estimating Stimulus-Response Functions Maneesh Sahani, Jennifer F. Linden
- Reconstructing Stimulus-Driven Neural Networks from Spike Times Duane Q. Nykamp
- Data-Dependent Bounds for Bayesian Mixture Methods Ron Meir, Tong Zhang
- A Statistical Mechanics Approach to Approximate Analytical Bootstrap Averages Dörthe Malzahn, Manfred Opper
- Maximum Likelihood and the Information Bottleneck Noam Slonim, Yair Weiss
- Stable Fixed Points of Loopy Belief Propagation Are Local Minima of the Bethe Free Energy Tom Heskes
- Concentration Inequalities for the Missing Mass and for Histogram Rule Error Luis E. Ortiz, David A. McAllester
- Dyadic Classification Trees via Structural Risk Minimization Clayton Scott, Robert Nowak
- The Stability of Kernel Principal Components Analysis and its Relation to the Process Eigenspectrum Christopher Williams, John S. Shawe-taylor
- Information Diffusion Kernels Guy Lebanon, John D. Lafferty
- Scaling of Probability-Based Optimization Algorithms J. L. Shapiro
- The Effect of Singularities in a Learning Machine when the True Parameters Do Not Lie on such Singularities Sumio Watanabe, Shun-ichi Amari
- On the Complexity of Learning the Kernel Matrix Olivier Bousquet, Daniel Herrmann
- Rate Distortion Function in the Spin Glass State: A Toy Model Tatsuto Murayama, Masato Okada
- Conditional Models on the Ranking Poset Guy Lebanon, John D. Lafferty
- Fractional Belief Propagation Wim Wiegerinck, Tom Heskes
- PAC-Bayes & Margins John Langford, John Shawe-Taylor
- A Note on the Representational Incompatibility of Function Approximation and Factored Dynamics Eric Allender, Sanjeev Arora, Michael Kearns, Cristopher Moore, Alexander Russell
- An Impossibility Theorem for Clustering Jon M. Kleinberg
- Effective Dimension and Generalization of Kernel Learning Tong Zhang
- Margin Analysis of the LVQ Algorithm Koby Crammer, Ran Gilad-bachrach, Amir Navot, Naftali Tishby
- Margin-Based Algorithms for Information Filtering Nicolò Cesa-bianchi, Alex Conconi, Claudio Gentile
- Hyperkernels Cheng S. Ong, Robert C. Williamson, Alex J. Smola
- Bayesian Monte Carlo Zoubin Ghahramani, Carl E. Rasmussen
- Mean Field Approach to a Probabilistic Model in Information Retrieval Bin Wu, K. Wong, David Bodoff
- Distance Metric Learning with Application to Clustering with Side-Information Eric P. Xing, Michael I. Jordan, Stuart J. Russell, Andrew Y. Ng
- Adapting Codes and Embeddings for Polychotomies Gunnar Rätsch, Sebastian Mika, Alex J. Smola
- Knowledge-Based Support Vector Machine Classifiers Glenn M. Fung, Olvi L. Mangasarian, Jude W. Shavlik
- Gaussian Process Priors with Uncertain Inputs Application to Multiple-Step Ahead Time Series Forecasting Agathe Girard, Carl Edward Rasmussen, Joaquin Quiñonero Candela, Roderick Murray-Smith
- Kernel Design Using Boosting Koby Crammer, Joseph Keshet, Yoram Singer
- Coulomb Classifiers: Generalizing Support Vector Machines via an Analogy to Electrostatic Systems Sepp Hochreiter, Michael C. Mozer, Klaus Obermayer
- Adaptive Scaling for Feature Selection in SVMs Yves Grandvalet, Stéphane Canu
- Support Vector Machines for Multiple-Instance Learning Stuart Andrews, Ioannis Tsochantaridis, Thomas Hofmann
- Fast Kernels for String and Tree Matching Alex J. Smola, S.v.n. Vishwanathan
- Generalized² Linear² Models Geoffrey J. Gordon
- Cluster Kernels for Semi-Supervised Learning Olivier Chapelle, Jason Weston, Bernhard Schölkopf
- Adaptive Nonlinear System Identification with Echo State Networks Herbert Jaeger
- Rational Kernels Corinna Cortes, Patrick Haffner, Mehryar Mohri
- Fast Sparse Gaussian Process Methods: The Informative Vector Machine Ralf Herbrich, Neil D. Lawrence, Matthias Seeger
- Stability-Based Model Selection Tilman Lange, Mikio L. Braun, Volker Roth, Joachim M. Buhmann
- Feature Selection in Mixture-Based Clustering Martin H. Law, Anil K. Jain, Mário Figueiredo
- String Kernels, Fisher Kernels and Finite State Automata Craig Saunders, Alexei Vinokourov, John S. Shawe-taylor
- Boosting Density Estimation Saharon Rosset, Eran Segal
- Independent Components Analysis through Product Density Estimation Trevor Hastie, Rob Tibshirani
- Learning Semantic Similarity Jaz Kandola, Nello Cristianini, John S. Shawe-taylor
- Self Supervised Boosting Max Welling, Richard S. Zemel, Geoffrey E. Hinton
- Automatic Derivation of Statistical Algorithms: The EM Family and Beyond Bernd Fischer, Johann Schumann, Wray Buntine, Alexander G. Gray
- Intrinsic Dimension Estimation Using Packing Numbers Balázs Kégl
- Half-Lives of EigenFlows for Spectral Clustering Chakra Chennubhotla, Allan D. Jepson
- On the Dirichlet Prior and Bayesian Regularization Harald Steck, Tommi S. Jaakkola
- Global Versus Local Methods in Nonlinear Dimensionality Reduction Vin D. Silva, Joshua B. Tenenbaum
- Dynamic Bayesian Networks with Deterministic Latent Tables David Barber
- Parametric Mixture Models for Multi-Labeled Text Naonori Ueda, Kazumi Saito
- Clustering with the Fisher Score Koji Tsuda, Motoaki Kawanabe, Klaus-Robert Müller
- Adaptive Classification by Variational Kalman Filtering Peter Sykacek, Stephen J. Roberts
- Boosted Dyadic Kernel Discriminants Baback Moghaddam, Gregory Shakhnarovich
- Regularized Greedy Importance Sampling Finnegan Southey, Dale Schuurmans, Ali Ghodsi
- One-Class LP Classifiers for Dissimilarity Representations Elzbieta Pekalska, David M.J. Tax, Robert Duin
- A Formulation for Minimax Probability Machine Regression Thomas Strohmann, Gregory Z. Grudic
- VIBES: A Variational Inference Engine for Bayesian Networks Christopher M. Bishop, David Spiegelhalter, John Winn
- A Differential Semantics for Jointree Algorithms James D. Park, Adnan Darwiche
- Constraint Classification for Multiclass Classification and Ranking Sariel Har-Peled, Dan Roth, Dav Zimak
- Nash Propagation for Loopy Graphical Games Luis E. Ortiz, Michael Kearns
- Using Tarjan's Red Rule for Fast Dependency Tree Construction Dan Pelleg, Andrew W. Moore
- Exact MAP Estimates by (Hyper)tree Agreement Martin J. Wainwright, Tommi S. Jaakkola, Alan S. Willsky
- Going Metric: Denoising Pairwise Data Volker Roth, Julian Laub, Klaus-Robert Müller, Joachim M. Buhmann
- Manifold Parzen Windows Pascal Vincent, Yoshua Bengio
- Stochastic Neighbor Embedding Geoffrey E. Hinton, Sam T. Roweis
- Automatic Alignment of Local Representations Yee W. Teh, Sam T. Roweis
- Informed Projections David Cohn
- Extracting Relevant Structures with Side Information Gal Chechik, Naftali Tishby
- Critical Lines in Symmetry of Mixture Models and its Application to Component Splitting Kenji Fukumizu, Shotaro Akaho, Shun-ichi Amari
- Kernel Dependency Estimation Jason Weston, Olivier Chapelle, Vladimir Vapnik, André Elisseeff, Bernhard Schölkopf
- Handling Missing Data with Variational Bayesian Learning of ICA Kwokleung Chan, Te-Won Lee, Terrence J. Sejnowski
- Feature Selection and Classification on Matrix Data: From Large Margins to Small Covering Numbers Sepp Hochreiter, Klaus Obermayer
- Learning with Multiple Labels Rong Jin, Zoubin Ghahramani
- Robust Novelty Detection with Single-Class MPM Laurent E. Ghaoui, Michael I. Jordan, Gert R. Lanckriet
- Artefactual Structure from Least-Squares Multidimensional Scaling Nicholas P. Hughes, David Lowe
- The Decision List Machine Marina Sokolova, Mario Marchand, Nathalie Japkowicz, John S. Shawe-taylor
- Using Manifold Stucture for Partially Labeled Classification Mikhail Belkin, Partha Niyogi
- Ranking with Large Margin Principle: Two Approaches Amnon Shashua, Anat Levin
- Multiclass Learning by Probabilistic Embeddings Ofer Dekel, Yoram Singer
- Transductive and Inductive Methods for Approximate Gaussian Process Regression Anton Schwaighofer, Volker Tresp
- Charting a Manifold Matthew Brand
- Annealing and the Rate Distortion Problem Albert E. Parker, Tomá\v S. Gedeon, Alexander G. Dimitrov
- Discriminative Learning for Label Sequences via Boosting Yasemin Altun, Thomas Hofmann, Mark Johnson
- Discriminative Densities from Maximum Contrast Estimation Peter Meinicke, Thorsten Twellmann, Helge Ritter
- FloatBoost Learning for Classification Stan Z. Li, Zhenqiu Zhang, Heung-yeung Shum, Hongjiang Zhang
- Incremental Gaussian Processes Joaquin Quiñonero-candela, Ole Winther
- Learning Graphical Models with Mercer Kernels Francis R. Bach, Michael I. Jordan
- Multiple Cause Vector Quantization David A. Ross, Richard S. Zemel
- Information Regularization with Partially Labeled Data Martin Szummer, Tommi S. Jaakkola
- Derivative Observations in Gaussian Process Models of Dynamic Systems E. Solak, R. Murray-smith, W. E. Leithead, D. J. Leith, Carl E. Rasmussen
- Multiplicative Updates for Nonnegative Quadratic Programming in Support Vector Machines Fei Sha, Lawrence K. Saul, Daniel D. Lee
- Location Estimation with a Differential Update Network Ali Rahimi, Trevor Darrell
- Real-Time Particle Filters Cody Kwok, Dieter Fox, Marina Meila
- Optoelectronic Implementation of a FitzHugh-Nagumo Neural Model Alexandre R. Romariz, Kelvin Wagner
- Circuit Model of Short-Term Synaptic Dynamics Shih-Chii Liu, Malte Boegershausen, Pascal Suter
- Adaptive Quantization and Density Estimation in Silicon David Hsu, Seth Bridges, Miguel Figueroa, Chris Diorio
- Neuromorphic Bisable VLSI Synapses with Spike-Timing-Dependent Plasticity Giacomo Indiveri
- Retinal Processing Emulation in a Programmable 2-Layer Analog Array Processor CMOS Chip R. Carmona, F. Jiménez-garrido, R. Dominguez-castro, S. Espejo, A. Rodriguez-vázquez
- Improving Transfer Rates in Brain Computer Interfacing: A Case Study Peter Meinicke, Matthias Kaper, Florian Hoppe, Manfred Heumann, Helge Ritter
- Combining Features for BCI Guido Dornhege, Benjamin Blankertz, Gabriel Curio, Klaus-Robert Müller
- Classifying Patterns of Visual Motion - a Neuromorphic Approach Jakob Heinzle, Alan Stocker
- Developing Topography and Ocular Dominance Using Two aVLSI Vision Sensors and a Neurotrophic Model of Plasticity Terry Elliott, Jörg Kramer
- Topographic Map Formation by Silicon Growth Cones Brian Taba, Kwabena A. Boahen
- Spike Timing-Dependent Plasticity in the Address Domain R. J. Vogelstein, Francesco Tenore, Ralf Philipp, Miriam S. Adlerstein, David H. Goldberg, Gert Cauwenberghs
- Field-Programmable Learning Arrays Seth Bridges, Miguel Figueroa, Chris Diorio, David Hsu
- Forward-Decoding Kernel-Based Phone Recognition Shantanu Chakrabartty, Gert Cauwenberghs
- A Probabilistic Approach to Single Channel Blind Signal Separation Gil-jin Jang, Te-Won Lee
- Real Time Voice Processing with Audiovisual Feedback: Toward Autonomous Agents with Perfect Pitch Lawrence K. Saul, Daniel D. Lee, Charles L. Isbell, Yann L. Cun
- Analysis of Information in Speech Based on MANOVA Sachin S. Kajarekar, Hynek Hermansky
- Bayesian Estimation of Time-Frequency Coefficients for Audio Signal Enhancement Patrick J. Wolfe, Simon J. Godsill
- Source Separation with a Sensor Array using Graphical Models and Subband Filtering Hagai Attias
- An Asynchronous Hidden Markov Model for Audio-Visual Speech Recognition Samy Bengio
- Monaural Speech Separation Guoning Hu, Deliang Wang
- Discriminative Binaural Sound Localization Ehud Ben-reuven, Yoram Singer
- Application of Variational Bayesian Approach to Speech Recognition Shinji Watanabe, Yasuhiro Minami, Atsushi Nakamura, Naonori Ueda
- Learning to Perceive Transparency from the Statistics of Natural Scenes Anat Levin, Assaf Zomet, Yair Weiss
- Learning to Detect Natural Image Boundaries Using Brightness and Texture David R. Martin, Charless C. Fowlkes, Jitendra Malik
- Fast Transformation-Invariant Factor Analysis Anitha Kannan, Nebojsa Jojic, Brendan Frey
- A Prototype for Automatic Recognition of Spontaneous Facial Actions M.S. Bartlett, G.C. Littlewort, T.J. Sejnowski, J.R. Movellan
- Bayesian Image Super-Resolution Michael E. Tipping, Christopher M. Bishop
- A Bilinear Model for Sparse Coding David B. Grimes, Rajesh P. N. Rao
- Dynamic Structure Super-Resolution Amos J. Storkey
- Unsupervised Color Constancy Kinh Tieu, Erik G. Miller
- Recovering Articulated Model Topology from Observed Rigid Motion Leonid Taycher, John Iii, Trevor Darrell
- Linear Combinations of Optic Flow Vectors for Estimating Self-Motion - a Real-World Test of a Neural Model Matthias O. Franz, Javaan S. Chahl
- Learning Sparse Multiscale Image Representations Phil Sallee, Bruno A. Olshausen
- Shape Recipes: Scene Representations that Refer to the Image William T. Freeman, Antonio Torralba
- Recovering Intrinsic Images from a Single Image Marshall F. Tappen, William T. Freeman, Edward H. Adelson
- Feature Selection by Maximum Marginal Diversity Nuno Vasconcelos
- Learning Sparse Topographic Representations with Products of Student-t Distributions Max Welling, Simon Osindero, Geoffrey E. Hinton
- A Model for Learning Variance Components of Natural Images Yan Karklin, Michael S. Lewicki
- How to Combine Color and Shape Information for 3D Object Recognition: Kernels do the Trick B. Caputo, Gy. Dorkó
- Concurrent Object Recognition and Segmentation by Graph Partitioning Stella X. Yu, Ralph Gross, Jianbo Shi
- Learning About Multiple Objects in Images: Factorial Learning without Factorial Search Christopher K. I. Williams, Michalis K. Titsias
- Identity Uncertainty and Citation Matching Hanna Pasula, Bhaskara Marthi, Brian Milch, Stuart J. Russell, Ilya Shpitser
- The RA Scanner: Prediction of Rheumatoid Joint Inflammation Based on Laser Imaging Anton Schwaighofer, Volker Tresp, Peter Mayer, Alexander K. Scheel, Gerhard A. Müller
- Mismatch String Kernels for SVM Protein Classification Eleazar Eskin, Jason Weston, William S. Noble, Christina S. Leslie
- Graph-Driven Feature Extraction From Microarray Data Using Diffusion Kernels and Kernel CCA Jean-philippe Vert, Minoru Kanehisa
- Real-Time Monitoring of Complex Industrial Processes with Particle Filters Rubén Morales-Menéndez, Nando de Freitas, David Poole
- A Maximum Entropy Approach to Collaborative Filtering in Dynamic, Sparse, High-Dimensional Domains Dmitry Y. Pavlov, David M. Pennock
- Prediction of Protein Topologies Using Generalized IOHMMs and RNNs Gianluca Pollastri, Pierre Baldi, Alessandro Vullo, Paolo Frasconi
- Approximate Inference and Protein-Folding Chen Yanover, Yair Weiss
- Adaptive Caching by Refetching Robert B. Gramacy, Manfred K. Warmuth, Scott A. Brandt, Ismail Ari
- Inferring a Semantic Representation of Text via Cross-Language Correlation Analysis Alexei Vinokourov, Nello Cristianini, John Shawe-Taylor
- Improving a Page Classifier with Anchor Extraction and Link Analysis William W. Cohen
- A Hierarchical Bayesian Markovian Model for Motifs in Biopolymer Sequences Eric P. Xing, Michael I. Jordan, Richard M. Karp, Stuart J. Russell
- Learning to Classify Galaxy Shapes Using the EM Algorithm Sergey Kirshner, Igor V. Cadez, Padhraic Smyth, Chandrika Kamath
- "Name That Song!" A Probabilistic Approach to Querying on Music and Text Brochu Eric, Nando de Freitas
- A Probabilistic Model for Learning Concatenative Morphology Matthew G. Snover, Michael R. Brent
- Learning Attractor Landscapes for Learning Motor Primitives Auke J. Ijspeert, Jun Nakanishi, Stefan Schaal
- Learning a Forward Model of a Reflex Bernd Porr, Florentin Wörgötter
- Minimax Differential Dynamic Programming: An Application to Robust Biped Walking Jun Morimoto, Christopher G. Atkeson
- Bias-Optimal Incremental Problem Solving Juergen Schmidhuber
- Value-Directed Compression of POMDPs Pascal Poupart, Craig Boutilier
- Optimality of Reinforcement Learning Algorithms with Linear Function Approximation Ralf Schoknecht
- Speeding up the Parti-Game Algorithm Maxim Likhachev, Sven Koenig
- Reinforcement Learning to Play an Optimal Nash Equilibrium in Team Markov Games Xiaofeng Wang, Tuomas Sandholm
- Convergent Combinations of Reinforcement Learning with Linear Function Approximation Ralf Schoknecht, Artur Merke
- Approximate Linear Programming for Average-Cost Dynamic Programming Benjamin V. Roy, Daniela D. Farias
- A Convergent Form of Approximate Policy Iteration Theodore J. Perkins, Doina Precup
- Efficient Learning Equilibrium Ronen I. Brafman, Moshe Tennenholtz
- Nonparametric Representation of Policies and Value Functions: A Trajectory-Based Approach Christopher G. Atkeson, Jun Morimoto
- Learning to Take Concurrent Actions Khashayar Rohanimanesh, Sridhar Mahadevan
- Learning in Zero-Sum Team Markov Games Using Factored Value Functions Michail G. Lagoudakis, Ronald Parr
- Exponential Family PCA for Belief Compression in POMDPs Nicholas Roy, Geoffrey J. Gordon