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ORCIDNIPS 2003: Vancouver, British Columbia, Canada
- Sebastian Thrun, Lawrence K. Saul, Bernhard Schölkopf:
Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, NIPS 2003, December 8-13, 2003, Vancouver and Whistler, British Columbia, Canada]. MIT Press 2004, ISBN 0-262-20152-6 - Alexander T. Ihler, Erik B. Sudderth, William T. Freeman, Alan S. Willsky:
Efficient Multiscale Sampling from Products of Gaussian Mixtures. 1-8 - Mark A. Girolami, Ata Kabán:
Simplicial Mixtures of Markov Chains: Distributed Modelling of Dynamic User Profiles. 9-16 - David M. Blei, Thomas L. Griffiths, Michael I. Jordan, Joshua B. Tenenbaum:
Hierarchical Topic Models and the Nested Chinese Restaurant Process. 17-24 - Benjamin Taskar, Carlos Guestrin, Daphne Koller:
Max-Margin Markov Networks. 25-32 - Thore Graepel, Ralf Herbrich:
Invariant Pattern Recognition by Semi-Definite Programming Machines. 33-40 - Matthew Schultz, Thorsten Joachims:
Learning a Distance Metric from Relative Comparisons. 41-48 - Ji Zhu, Saharon Rosset, Trevor Hastie, Robert Tibshirani:
1-norm Support Vector Machines. 49-56 - Koji Tsuda, Gunnar Rätsch:
Image Reconstruction by Linear Programming. 57-64 - Stuart Andrews, Thomas Hofmann:
Multiple-Instance Learning via Disjunctive Programming Boosting. 65-72 - Tijl De Bie, Nello Cristianini:
Convex Methods for Transduction. 73-80 - Kenji Fukumizu, Francis R. Bach, Michael I. Jordan:
Kernel Dimensionality Reduction for Supervised Learning. 81-88 - Bernd Fischer, Volker Roth, Joachim M. Buhmann:
Clustering with the Connectivity Kernel. 89-96 - Haifeng Li, Tao Jiang, Keshu Zhang:
Efficient and Robust Feature Extraction by Maximum Margin Criterion. 97-104 - Thomas Strohmann, Andrei Belitski, Gregory Z. Grudic, Dennis DeCoste:
Sparse Greedy Minimax Probability Machine Classification. 105-112 - Jaco Vermaak, Simon J. Godsill, Arnaud Doucet:
Sequential Bayesian Kernel Regression. 113-120 - Claudio Gentile:
Fast Feature Selection from Microarray Expression Data via Multiplicative Large Margin Algorithms. 121-128 - Liva Ralaivola, Florence d'Alché-Buc:
Dynamical Modeling with Kernels for Nonlinear Time Series Prediction. 129-136 - Max Welling, Felix V. Agakov, Christopher K. I. Williams:
Extreme Components Analysis. 137-144 - Nathan Srebro, Tommi S. Jaakkola:
Linear Dependent Dimensionality Reduction. 145-152 - Xiaofei He, Partha Niyogi:
Locality Preserving Projections. 153-160 - Denis V. Chigirev, William Bialek:
Optimal Manifold Representation of Data: An Information Theoretic Approach. 161-168 - Dengyong Zhou, Jason Weston, Arthur Gretton, Olivier Bousquet, Bernhard Schölkopf:
Ranking on Data Manifolds. 169-176 - Yoshua Bengio, Jean-François Paiement, Pascal Vincent, Olivier Delalleau, Nicolas Le Roux, Marie Ouimet:
Out-of-Sample Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering. 177-184 - Noam Shental, Assaf Zomet, Tomer Hertz, Yair Weiss:
Pairwise Clustering and Graphical Models. 185-192 - Thomas P. Minka, Yuan (Alan) Qi:
Tree-structured Approximations by Expectation Propagation. 193-200 - David Barber, Felix V. Agakov:
Information Maximization in Noisy Channels : A Variational Approach. 201-208 - Eiji Mizutani, James Demmel:
Iterative Scaled Trust-Region Learning in Krylov Subspaces via Pearlmutter's Implicit Sparse Hessian-Vector Multiply. 209-216 - Léon Bottou, Yann LeCun:
Large Scale Online Learning. 217-224 - Koby Crammer, Jaz S. Kandola, Yoram Singer:
Online Classification on a Budget. 225-232 - Xavier Carreras, Lluís Màrquez:
Online Learning via Global Feedback for Phrase Recognition. 233-240 - Yuanqing Li, Andrzej Cichocki, Shun-ichi Amari, Sergei L. Shishkin, Jianting Cao, Fanji Gu:
Sparse Representation and Its Applications in Blind Source Separation. 241-248 - David P. Wipf, Jason A. Palmer, Bhaskar D. Rao:
Perspectives on Sparse Bayesian Learning. 249-256 - Charles Kemp, Thomas L. Griffiths, Sean Stromsten, Joshua B. Tenenbaum:
Semi-Supervised Learning with Trees. 257-264 - Ting Liu, Andrew W. Moore, Alexander G. Gray:
New Algorithms for Efficient High Dimensional Non-parametric Classification. 265-272 - Christopher J. Paciorek, Mark J. Schervish:
Nonstationary Covariance Functions for Gaussian Process Regression. 273-280 - Greg Hamerly, Charles Elkan:
Learning the k in k-means. 281-288 - Chen Yanover, Yair Weiss:
Finding the M Most Probable Configurations in Arbitrary Graphical Models. 289-296 - Jakob J. Verbeek, Sam T. Roweis, Nikos Vlassis:
Non-linear CCA and PCA by Alignment of Local Models. 297-304 - Francis R. Bach, Michael I. Jordan:
Learning Spectral Clustering. 305-312 - Corinna Cortes, Mehryar Mohri:
AUC Optimization vs. Error Rate Minimization. 313-320 - Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal, Jason Weston, Bernhard Schölkopf:
Learning with Local and Global Consistency. 321-328 - Neil D. Lawrence:
Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data. 329-336 - Edward Lloyd Snelson, Carl Edward Rasmussen, Zoubin Ghahramani:
Warped Gaussian Processes. 337-344 - Allan Borodin, Ran El-Yaniv, Vincent Gogan:
Can We Learn to Beat the Best Stock. 345-352 - Tom Heskes, Onno Zoeter, Wim Wiegerinck:
Approximate Expectation Maximization. 353-360 - Max Welling, Yee Whye Teh:
Linear Response for Approximate Inference. 361-368 - Martin J. Wainwright, Michael I. Jordan:
Semidefinite Relaxations for Approximate Inference on Graphs with Cycles. 369-376 - Alina Beygelzimer, Irina Rish:
Approximability of Probability Distributions. 377-384 - Quaid Morris, Brendan J. Frey:
Denoising and Untangling Graphs Using Degree Priors. 385-392 - XuanLong Nguyen, Michael I. Jordan:
On the Concentration of Expectation and Approximate Inference in Layered Networks. 393-400 - Radford M. Neal, Matthew J. Beal, Sam T. Roweis:
Inferring State Sequences for Non-linear Systems with Embedded Hidden Markov Models. 401-408 - Pedro F. Felzenszwalb, Daniel P. Huttenlocher, Jon M. Kleinberg:
Fast Algorithms for Large-State-Space HMMs with Applications to Web Usage Analysis. 409-416 - Geoffrey E. Hinton, Max Welling, Andriy Mnih:
Wormholes Improve Contrastive Divergence. 417-424 - Mark A. Paskin:
Sample Propagation. 425-432 - Amos J. Storkey:
Generalised Propagation for Fast Fourier Transforms with Partial or Missing Data. 433-440 - Alexander J. Smola, Vishy Vishwanathan, Eleazar Eskin:
Laplace Propagation. 441-448 - Gökhan H. Bakir, Jason Weston, Bernhard Schölkopf:
Learning to Find Pre-Images. 449-456 - Thore Graepel, Ralf Herbrich, Andriy Kharechko, John Shawe-Taylor:
Semi-Definite Programming by Perceptron Learning. 457-464 - Noam Shental, Aharon Bar-Hillel, Tomer Hertz, Daphna Weinshall:
Computing Gaussian Mixture Models with EM Using Equivalence Constraints. 465-472 - Volker Roth, Tilman Lange:
Feature Selection in Clustering Problems. 473-480 - David Kauchak, Sanjoy Dasgupta:
An Iterative Improvement Procedure for Hierarchical Clustering. 481-488 - Zvika Marx, Ido Dagan, Eli Shamir:
Identifying Structure across Pre-partitioned Data. 489-496 - Ofer Dekel, Christopher D. Manning, Yoram Singer:
Log-Linear Models for Label Ranking. 497-504 - Matthew Brand:
Minimax Embeddings. 505-512 - Yoshua Bengio, Yves Grandvalet:
No Unbiased Estimator of the Variance of K-Fold Cross-Validation. 513-520 - Harald Steck, Tommi S. Jaakkola:
Bias-Corrected Bootstrap and Model Uncertainty. 521-528 - Ting-Fan Wu, Chih-Jen Lin, Ruby C. Weng:
Probability Estimates for Multi-Class Classification by Pairwise Coupling. 529-536 - Gang Ji, Jeff A. Bilmes:
Necessary Intransitive Likelihood-Ratio Classifiers. 537-544 - Rajat Raina, Yirong Shen, Andrew Y. Ng, Andrew McCallum:
Classification with Hybrid Generative/Discriminative Models. 545-552 - Victor Lavrenko, R. Manmatha, Jiwoon Jeon:
A Model for Learning the Semantics of Pictures. 553-560 - Michael J. Kearns, Luis E. Ortiz:
Algorithms for Interdependent Security Games. 561-568 - John C. Platt:
Fast Embedding of Sparse Similarity Graphs. 571-578 - Anton Schwaighofer, Marian Grigoras, Volker Tresp, Clemens Hoffmann:
GPPS: A Gaussian Process Positioning System for Cellular Networks. 579-586 - David I. Ferguson, Aaron Morris, Dirk Hähnel, Christopher R. Baker, Zachary Omohundro, Carlos F. Reverte, Scott Thayer, Charles Whittaker, William Whittaker, Wolfram Burgard, Sebastian Thrun:
An Autonomous Robotic System for Mapping Abandoned Mines. 587-594 - Jason Weston, Christina S. Leslie, Dengyong Zhou, André Elisseeff, William Stafford Noble:
Semi-supervised Protein Classification Using Cluster Kernels. 595-602 - Alice X. Zheng, Michael I. Jordan, Ben Liblit, Alex Aiken:
Statistical Debugging of Sampled Programs. 603-610 - Nicholas P. Hughes, Lionel Tarassenko, Stephen J. Roberts:
Markov Models for Automated ECG Interval Analysis. 611-618 - Cynthia Archer, Todd K. Leen, António M. Baptista:
Parameterized Novelty Detectors for Environmental Sensor Monitoring. 619-626 - Benjamin M. Marlin:
Modeling User Rating Profiles For Collaborative Filtering. 627-634 - Michael J. Quinlan, Stephan K. Chalup, Richard H. Middleton:
Application of SVMs for Colour Classification and Collision Detection with AIBO Robots. 635-642 - Jun Suzuki, Yutaka Sasaki, Eisaku Maeda:
Kernels for Structured Natural Language Data. 643-650 - Daniel B. Neill, Andrew W. Moore:
A Fast Multi-Resolution Method for Detection of Significant Spatial Disease Clusters. 651-658 - Benjamin Taskar, Ming Fai Wong, Pieter Abbeel, Daphne Koller:
Link Prediction in Relational Data. 659-666 - Andrew Rabinovich, Sameer Agarwal, Casey Laris, Jeffrey H. Price, Serge J. Belongie:
Unsupervised Color Decomposition Of Histologically Stained Tissue Samples. 667-674 - Su-In Lee, Serafim Batzoglou:
ICA-based Clustering of Genes from Microarray Expression Data. 675-682 - Darya Chudova, Christopher E. Hart, Eric Mjolsness, Padhraic Smyth:
Gene Expression Clustering with Functional Mixture Models. 683-690 - Maneesh Sahani, Srikantan S. Nagarajan:
Reconstructing MEG Sources with Unknown Correlations. 693-700 - Saori C. Tanaka, Kenji Doya, Go Okada, Kazutaka Ueda, Yasumasa Okamoto, Shigeto Yamawaki:
Different Cortico-Basal Ganglia Loops Specialize in Reward Prediction at Different Time Scales. 701-708 - Xuerui Wang, Rebecca A. Hutchinson, Tom M. Mitchell:
Training fMRI Classifiers to Discriminate Cognitive States across Multiple Subjects. 709-716 - Roland Vollgraf, Michael Scholz, Ian A. Meinertzhagen, Klaus Obermayer:
Nonlinear Filtering of Electron Micrographs by Means of Support Vector Regression. 717-724 - Yu Zhou, Steven G. Mason, Gary E. Birch:
Impact of an Energy Normalization Transform on the Performance of the LF-ASD Brain Computer Interface. 725-732 - Guido Dornhege, Benjamin Blankertz, Gabriel Curio, Klaus-Robert Müller:
Increase Information Transfer Rates in BCI by CSP Extension to Multi-class. 733-740 - Sung Chan Jun, Barak A. Pearlmutter:
Subject-Independent Magnetoencephalographic Source Localization by a Multilayer Perceptron. 741-748 - Carl Edward Rasmussen, Malte Kuss:
Gaussian Processes in Reinforcement Learning. 751-758 - Joelle Pineau, Geoffrey J. Gordon, Sebastian Thrun:
Applying Metric-Trees to Belief-Point POMDPs. 759-766 - Maxim Likhachev, Geoffrey J. Gordon, Sebastian Thrun:
ARA*: Anytime A* with Provable Bounds on Sub-Optimality. 767-774 - Georgios Theocharous, Leslie Pack Kaelbling:
Approximate Planning in POMDPs with Macro-Actions. 775-782 - Natalia Hernandez-Gardiol, Leslie Pack Kaelbling:
Envelope-based Planning in Relational MDPs. 783-790 - David C. Parkes, Satinder Singh:
An MDP-Based Approach to Online Mechanism Design. 791-798 - Andrew Y. Ng, H. Jin Kim, Michael I. Jordan, Shankar Sastry:
Autonomous Helicopter Flight via Reinforcement Learning. 799-806 - Yu-Han Chang, Tracey Ho, Leslie Pack Kaelbling:
All learning is Local: Multi-agent Learning in Global Reward Games. 807-814 - Daniela Pucci de Farias, Nimrod Megiddo:
How to Combine Expert (and Novice) Advice when Actions Impact the Environment? 815-822 - Pascal Poupart, Craig Boutilier:
Bounded Finite State Controllers. 823-830 - J. Andrew Bagnell, Sham M. Kakade, Andrew Y. Ng, Jeff G. Schneider:
Policy Search by Dynamic Programming. 831-838 - Arnab Nilim, Laurent El Ghaoui:
Robustness in Markov Decision Problems with Uncertain Transition Matrices. 839-846 - Alan Fern, Sung Wook Yoon, Robert Givan:
Approximate Policy Iteration with a Policy Language Bias. 847-854 - Matthew R. Rudary, Satinder Singh:
A Nonlinear Predictive State Representation. 855-862 - Xiao Feng Wang, Tuomas Sandholm:
Learning Near-Pareto-Optimal Conventions in Polynomial Time. 863-870 - Gerald Tesauro:
Extending Q-Learning to General Adaptive Multi-Agent Systems. 871-878 - Curt A. Bererton, Geoffrey J. Gordon, Sebastian Thrun:
Auction Mechanism Design for Multi-Robot Coordination. 879-886 - Ciamac Cyrus Moallemi, Benjamin Van Roy:
Distributed Optimization in Adaptive Networks. 887-894 - Milos Hauskrecht, Branislav Kveton:
Linear Program Approximations for Factored Continuous-State Markov Decision Processes. 895-902 - Arnulf B. A. Graf, Felix A. Wichmann:
Insights from Machine Learning Applied to Human Visual Classification. 905-912 - Virginia R. de Sa:
Sensory Modality Segregation. 913-920 - Artur S. d'Avila Garcez, Luís C. Lamb:
Reasoning about Time and Knowledge in Neural Symbolic Learning Systems. 921-928 - Marc Toussaint:
Learning a World Model and Planning with a Self-Organizing, Dynamic Neural System. 926-936 - Woojae Kim, Daniel J. Navarro, Mark A. Pitt, In Jae Myung:
An MCMC-Based Method of Comparing Connectionist Models in Cognitive Science. 937-944 - David Philipona, J. Kevin O'Regan, Jean-Pierre Nadal, Olivier J. M. D. Coenen:
Perception of the Structure of the Physical World Using Unknown Multimodal Sensors and Effectors. 945-952 - Thomas L. Griffiths, Joshua B. Tenenbaum:
From Algorithmic to Subjective Randomness. 953-960 - Zach Solan, David Horn, Eytan Ruppin, Shimon Edelman:
Unsupervised Context Sensitive Language Acquisition from a Large Corpus. 961-968 - Yuuya Sugita, Jun Tani:
A Holistic Approach to Compositional Semantics: A Connectionist Model and Robot Experiments. 969-976 - Aaron C. Courville, Nathaniel D. Daw, Geoffrey J. Gordon, David S. Touretzky:
Model Uncertainty in Classical Conditioning. 977-984 - Reid R. Harrison:
A Low-Power Analog VLSI Visual Collision Detector. 987-994 - Paul Merolla, Kwabena Boahen:
A Recurrent Model of Orientation Maps with Simple and Complex Cells. 995-1002 - Rock Z. Shi, Timothy K. Horiuchi:
A Summating, Exponentially-Decaying CMOS Synapse for Spiking Neural Systems. 1003-1010 - Hsin Chen, Patrice Fleury, Alan F. Murray:
Minimising Contrastive Divergence in Noisy, Mixed-mode VLSI Neurons. 1011-1018 - Bob Ricks, Dan Ventura:
Training a Quantum Neural Network. 1019-1026 - Adria Bofill-i-Petit, Alan F. Murray:
Synchrony Detection by Analogue VLSI Neurons with Bimodal STDP Synapses. 1027-1034 - Masakazu Yagi, Hideo Yamasaki, Tadashi Shibata:
A Mixed-Signal VLSI for Real-Time Generation of Edge-Based Image Vectors. 1035-1042 - Francesco Tenore, Ralph Etienne-Cummings, M. Anthony Lewis:
Entrainment of Silicon Central Pattern Generators for Legged Locomotory Control. 1043-1050 - Eric K. C. Tsang, Bertram Emil Shi:
A Neuromorphic Multi-chip Model of a Disparity Selective Complex Cell. 1051-1058 - Ingo Steinwart:
Sparseness of Support Vector Machines---Some Asymptotically Sharp Bounds. 1069-1076 - Tong Zhang:
An Infinity-sample Theory for Multi-category Large Margin Classification. 1077-1084 - Philip Derbeko, Ran El-Yaniv, Ron Meir:
Error Bounds for Transductive Learning via Compression and Clustering. 1085-1092 - Claire Monteleoni, Tommi S. Jaakkola:
Online Learning of Non-stationary Sequences. 1093-1100 - Cynthia Rudin, Ingrid Daubechies, Robert E. Schapire:
On the Dynamics of Boosting. 1101-1108 - Kohei Hatano, Manfred K. Warmuth:
Boosting versus Covering. 1109-1116 - Clayton D. Scott, Robert D. Nowak:
Near-Minimax Optimal Classification with Dyadic Classification Trees. 1117-1124 - Jean-Yves Audibert, Olivier Bousquet:
PAC-Bayesian Generic Chaining. 1125-1132 - Vladimir Vovk, Glenn Shafer, Ilia Nouretdinov:
Self-calibrating Probability Forecasting. 1133-1140 - David L. Donoho, Victoria Stodden:
When Does Non-Negative Matrix Factorization Give a Correct Decomposition into Parts? 1141-1148 - Tong Zhang:
Learning Bounds for a Generalized Family of Bayesian Posterior Distributions. 1149-1156 - Manfred Opper, Ole Winther:
Variational Linear Response. 1157-1164 - Susanne Still, William Bialek, Léon Bottou:
Geometric Clustering Using the Information Bottleneck Method. 1165-1172 - Peter L. Bartlett, Michael I. Jordan, Jon D. McAuliffe:
Large Margin Classifiers: Convex Loss, Low Noise, and Convergence Rates. 1173-1180 - David C. Hoyle, Magnus Rattray:
Limiting Form of the Sample Covariance Eigenspectrum in PCA and Kernel PCA. 1181-1188 - Dörthe Malzahn, Manfred Opper:
Approximate Analytical Bootstrap Averages for Support Vector Classifiers. 1189-1196 - Justin Werfel, Xiaohui Xie, H. Sebastian Seung:
Learning Curves for Stochastic Gradient Descent in Linear Feedforward Networks. 1197-1204 - Gurinder S. Atwal, William Bialek:
Ambiguous Model Learning Made Unambiguous with 1/f Priors. 1205-1212 - Gal Chechik, Amir Globerson, Naftali Tishby, Yair Weiss:
Information Bottleneck for Gaussian Variables. 1213-1220 - Olivier Bousquet, Olivier Chapelle, Matthias Hein:
Measure Based Regularization. 1221-1228 - Shai Shalev-Shwartz, Koby Crammer, Ofer Dekel, Yoram Singer:
Online Passive-Aggressive Algorithms. 1229-1236 - Saharon Rosset, Ji Zhu, Trevor Hastie:
Margin Maximizing Loss Functions. 1237-1244 - Yuval Aviel, David Horn, Moshe Abeles:
The Doubly Balanced Network of Spiking Neurons: A Memory Model with High Capacity. 1247-1254 - Thomas Natschläger, Wolfgang Maass:
Information Dynamics and Emergent Computation in Recurrent Circuits of Spiking Neurons. 1255-1262 - Peter J. Thomas, Donald J. Spencer, Sierra K. Hampton, Peter Park, Joseph P. Zurkus:
The Diffusion-Limited Biochemical Signal-Relay Channel. 1263-1270 - Aaron J. Gruber, Peter Dayan, Boris S. Gutkin, Sara A. Solla:
Dopamine Modulation in a Basal Ganglio-cortical Network Implements Saliency-based Gating of Working Memory. 1271-1278 - Nathan A. Dunn, John S. Conery, Shawn R. Lockery:
Circuit Optimization Predicts Dynamic Network for Chemosensory Orientation in the Nematode C. elegans. 1279-1286 - Maneesh Sahani:
A Biologically Plausible Algorithm for Reinforcement-shaped Representational Learning. 1287-1294 - Yoichi Miyawaki, Masato Okada:
Mechanism of Neural Interference by Transcranial Magnetic Stimulation: Network or Single Neuron? 1295-1302 - Peter Dayan, Michael Häusser:
Plasticity Kernels and Temporal Statistics. 1303-1310 - Jonathan W. Pillow, Liam Paninski, Eero P. Simoncelli:
Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Model. 1311-1318 - Liam Paninski:
Design of Experiments via Information Theory. 1319-1326 - Konrad P. Körding, Daniel M. Wolpert:
Probabilistic Inference in Human Sensorimotor Processing. 1327-1334 - Kazuyuki Samejima, Kenji Doya, Yasumasa Ueda, Minoru Kimura:
Estimating Internal Variables and Paramters of a Learning Agent by a Particle Filter. 1335-1342 - Bernd Porr, Ausra Saudargiene, Florentin Wörgötter:
Analytical Solution of Spike-timing Dependent Plasticity Based on Synaptic Biophysics. 1343-1350 - Brian J. Fischer, Charles H. Anderson:
A Probabilistic Model of Auditory Space Representation in the Barn Owl. 1351-1358 - Ryan C. Kelly, Tai Sing Lee:
Decoding V1 Neuronal Activity using Particle Filtering with Volterra Kernels. 1359-1366 - Jan Eichhorn, Andreas S. Tolias, Alexander Zien, Malte Kuss, Carl Edward Rasmussen, Jason Weston, Nikos K. Logothetis, Bernhard Schölkopf:
Prediction on Spike Data Using Kernel Algorithms. 1367-1374 - William M. Campbell, Joseph P. Campbell, Douglas A. Reynolds, Douglas A. Jones, Timothy R. Leek:
Phonetic Speaker Recognition with Support Vector Machines. 1377-1384 - Pedro J. Moreno, Purdy Ho, Nuno Vasconcelos:
A Kullback-Leibler Divergence Based Kernel for SVM Classification in Multimedia Applications. 1385-1392 - Kannan Achan, Sam T. Roweis, Brendan J. Frey:
Probabilistic Inference of Speech Signals from Phaseless Spectrograms. 1393-1400 - James T. Kwok, Brian Mak, Simon Ka-Lung Ho:
Eigenvoice Speaker Adaptation via Composite Kernel PCA. 1401-1408 - Jeff Bondy, Ian C. Bruce, Suzanna Becker, Simon Haykin:
Predicting Speech Intelligibility from a Population of Neurons. 1409-1416 - Tomohiro Nakatani, Masato Miyoshi, Keisuke Kinoshita:
One Microphone Blind Dereverberation Based on Quasi-periodicity of Speech Signals. 1417-1424 - Nicoleta Roman, DeLiang L. Wang, Guy J. Brown:
A Classification-based Cocktail-party Processor. 1425-1432 - Zhou Wang, Eero P. Simoncelli:
Local Phase Coherence and the Perception of Blur. 1435-1442 - Vincent Bonin, Valerio Mante, Matteo Carandini:
Nonlinear Processing in LGN Neurons. 1443-1450 - Scott A. Beardsley, Lucia Maria Vaina:
A Functional Architecture for Motion Pattern Processing in MSTd. 1451-1458 - Alan L. Yuille, Fang Fang, Paul R. Schrater, Daniel J. Kersten:
Human and Ideal Observers for Detecting Image Curves. 1459-1466 - Nathan Sprague, Dana H. Ballard:
Eye Movements for Reward Maximization. 1467-1474 - Matthias H. Hennig, Florentin Wörgötter:
Eye Micro-movements Improve Stimulus Detection Beyond the Nyquist Limit in the Peripheral Retina. 1475-1482 - Reto Wyss, Paul F. M. J. Verschure:
Bounded Invariance and the Formation of Place Fields.. 1483-1490 - Salvador Ruiz-Correa, Linda G. Shapiro, Marina Meila, Gabriel Berson:
Discriminating Deformable Shape Classes. 1491-1498 - Kevin P. Murphy, Antonio Torralba, William T. Freeman:
Using the Forest to See the Trees: A Graphical Model Relating Features, Objects, and Scenes. 1499-1506 - Amit Gruber, Yair Weiss:
Factorization with Uncertainty and Missing Data: Exploiting Temporal Coherence. 1507-1514 - Michael Fink, Pietro Perona:
Mutual Boosting for Contextual Inference. 1515-1522 - Jianxin Wu, James M. Rehg, Matthew D. Mullin:
Learning a Rare Event Detection Cascade by Direct Feature Selection. 1523-1530 - Sanjiv Kumar, Martial Hebert:
Discriminative Fields for Modeling Spatial Dependencies in Natural Images. 1531-1538 - Leonid Sigal, Michael Isard, Benjamin H. Sigelman, Michael J. Black:
Attractive People: Assembling Loose-Limbed Models using Non-parametric Belief Propagation. 1539-1546 - Deva Ramanan, David A. Forsyth:
Automatic Annotation of Everyday Movements. 1547-1554 - Lorenzo Torresani, Aaron Hertzmann, Christoph Bregler:
Learning Non-Rigid 3D Shape from 2D Motion. 1555-1562 - Gwen Littlewort, Marian Stewart Bartlett, Ian R. Fasel, Joel Chenu, Takayuki Kanda, Hiroshi Ishiguro, Javier R. Movellan:
Towards Social Robots: Automatic Evaluation of Human-robot Interaction by Face Detection and Expression Classification. 1563-1570 - Song Wang, Toshiro Kubota, Jeffrey Mark Siskind:
Salient Boundary Detection using Ratio Contour. 1571-1578 - Anuj Srivastava, Xiuwen Liu, Washington Mio, Eric Klassen:
Geometric Analysis of Constrained Curves. 1579-1586 - Lyndsey C. Pickup, Stephen J. Roberts, Andrew Zisserman:
A Sampled Texture Prior for Image Super-Resolution. 1587-1594 - Charles R. Rosenberg, Thomas P. Minka, Alok Ladsariya:
Bayesian Color Constancy with Non-Gaussian Models. 1595-1602 - Claudio Fanti, Marzia Polito, Pietro Perona:
An Improved Scheme for Detection and Labelling in Johansson Displays. 1603-1610
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