NIPS 2003: Vancouver, British Columbia, Canada

• Alexander T. Ihler, Erik B. Sudderth, William T. Freeman, Alan S. Willsky:
  Efficient Multiscale Sampling from Products of Gaussian Mixtures.
  
• Mark Girolami, Ata Kabán:
  Simplicial Mixtures of Markov Chains: Distributed Modelling of Dynamic User Profiles.
  
• David M. Blei, Thomas L. Griffiths, Michael I. Jordan, Joshua B. Tenenbaum:
  Hierarchical Topic Models and the Nested Chinese Restaurant Process.
  
• Benjamin Taskar, Carlos Guestrin, Daphne Koller:
  Max-Margin Markov Networks.
  
• Thore Graepel, Ralf Herbrich:
  Invariant Pattern Recognition by Semi-Definite Programming Machines.
  
• Matthew Schultz, Thorsten Joachims:
  Learning a Distance Metric from Relative Comparisons.
  
• Ji Zhu, Saharon Rosset, Trevor Hastie, Robert Tibshirani:
  1-norm Support Vector Machines.
  
• Koji Tsuda, Gunnar Rätsch:
  Image Reconstruction by Linear Programming.
  
• Stuart Andrews, Thomas Hofmann:
  Multiple-Instance Learning via Disjunctive Programming Boosting.
  
• Tijl De Bie, Nello Cristianini:
  Convex Methods for Transduction.
  
• Kenji Fukumizu, Francis R. Bach, Michael I. Jordan:
  Kernel Dimensionality Reduction for Supervised Learning.
  
• Bernd Fischer, Volker Roth, Joachim M. Buhmann:
  Clustering with the Connectivity Kernel.
  
• Haifeng Li, Tao Jiang, Keshu Zhang:
  Efficient and Robust Feature Extraction by Maximum Margin Criterion.
  
• Thomas Strohmann, Andrei Belitski, Gregory Z. Grudic, Dennis DeCoste:
  Sparse Greedy Minimax Probability Machine Classification.
  
• Jaco Vermaak, Simon J. Godsill, Arnaud Doucet:
  Sequential Bayesian Kernel Regression.
  
• Claudio Gentile:
  Fast Feature Selection from Microarray Expression Data via Multiplicative Large Margin Algorithms.
  
• Liva Ralaivola, Florence d'Alché-Buc:
  Dynamical Modeling with Kernels for Nonlinear Time Series Prediction.
  
• Max Welling, Felix V. Agakov, Christopher K. I. Williams:
  Extreme Components Analysis.
  
• Nathan Srebro, Tommi Jaakkola:
  Linear Dependent Dimensionality Reduction.
  
• Xiaofei He, Partha Niyogi:
  Locality Preserving Projections.
  
• Denis V. Chigirev, William Bialek:
  Optimal Manifold Representation of Data: An Information Theoretic Approach.
  
• Dengyong Zhou, Jason Weston, Arthur Gretton, Olivier Bousquet, Bernhard Schölkopf:
  Ranking on Data Manifolds.
  
• 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.
  
• Noam Shental, Assaf Zomet, Tomer Hertz, Yair Weiss:
  Pairwise Clustering and Graphical Models.
  
• Thomas P. Minka, Yuan (Alan) Qi:
  Tree-structured Approximations by Expectation Propagation.
  
• David Barber, Felix V. Agakov:
  The IM Algorithm: A Variational Approach to Information Maximization.
  
• Eiji Mizutani, James Demmel:
  Iterative Scaled Trust-Region Learning in Krylov Subspaces via Pearlmutter's Implicit Sparse Hessian-Vector Multiply.
  
• Léon Bottou, Yann LeCun:
  Large Scale Online Learning.
  
• Koby Crammer, Jaz S. Kandola, Yoram Singer:
  Online Classification on a Budget.
  
• Xavier Carreras, Lluís Màrquez:
  Online Learning via Global Feedback for Phrase Recognition.
  
• Yuanqing Li, Andrzej Cichocki, Shun-ichi Amari, Sergei L. Shishkin, Jianting Cao, Fanji Gu:
  Sparse Representation and Its Applications in Blind Source Separation.
  
• David P. Wipf, Jason A. Palmer, Bhaskar D. Rao:
  Perspectives on Sparse Bayesian Learning.
  
• Charles Kemp, Thomas L. Griffiths, Sean Stromsten, Joshua B. Tenenbaum:
  Semi-Supervised Learning with Trees.
  
• Ting Liu, Andrew W. Moore, Alexander G. Gray:
  Efficient Exact k-NN and Nonparametric Classification in High Dimensions.
  
• Christopher J. Paciorek, Mark J. Schervish:
  Nonstationary Covariance Functions for Gaussian Process Regression.
  
• Greg Hamerly, Charles Elkan:
  Learning the k in k-means.
  
• Chen Yanover, Yair Weiss:
  Finding the M Most Probable Configurations in Arbitrary Graphical Models.
  
• Jakob J. Verbeek, Sam T. Roweis, Nikos A. Vlassis:
  Non-linear CCA and PCA by Alignment of Local Models.
  
• Francis R. Bach, Michael I. Jordan:
  Learning Spectral Clustering.
  
• Corinna Cortes, Mehryar Mohri:
  AUC Optimization vs. Error Rate Minimization.
  
• Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal, Jason Weston, Bernhard Schölkopf:
  Learning with Local and Global Consistency.
  
• Neil D. Lawrence:
  Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data.
  
• Edward Snelson, Carl Edward Rasmussen, Zoubin Ghahramani:
  Warped Gaussian Processes.
  
• Allan Borodin, Ran El-Yaniv, Vincent Gogan:
  Can We Learn to Beat the Best Stock.
  
• Tom Heskes, Onno Zoeter, Wim Wiegerinck:
  Approximate Expectation Maximization.
  
• Max Welling, Yee Whye Teh:
  Linear Response for Approximate Inference.
  
• Martin J. Wainwright, Michael I. Jordan:
  Semidefinite Relaxations for Approximate Inference on Graphs with Cycles.
  
• Alina Beygelzimer, Irina Rish:
  Approximability of Probability Distributions.
  
• Quaid Morris, Brendan J. Frey:
  Denoising and Untangling Graphs Using Degree Priors.
  
• XuanLong Nguyen, Michael I. Jordan:
  On the Concentration of Expectation and Approximate Inference in Layered Networks.
  
• Radford M. Neal, Matthew J. Beal, Sam T. Roweis:
  Inferring State Sequences for Non-linear Systems with Embedded Hidden Markov Models.
  
• Pedro F. Felzenszwalb, Daniel P. Huttenlocher, Jon M. Kleinberg:
  Fast Algorithms for Large-State-Space HMMs with Applications to Web Usage Analysis.
  
• Geoffrey E. Hinton, Max Welling, Andriy Mnih:
  Wormholes Improve Contrastive Divergence.
  
• Mark A. Paskin:
  Sample Propagation.
  
• Amos J. Storkey:
  Generalised Propagation for Fast Fourier Transforms with Partial or Missing Data.
  
• Alexander J. Smola, Vishy Vishwanathan, Eleazar Eskin:
  Laplace Propagation.
  
• Gökhan H. Bakir, Jason Weston, Bernhard Schölkopf:
  Learning to Find Pre-Images.
  
• Thore Graepel, Ralf Herbrich, Andriy Kharechko, John Shawe-Taylor:
  Semi-Definite Programming by Perceptron Learning.
  
• Noam Shental, Aharon Bar-Hillel, Tomer Hertz, Daphna Weinshall:
  Computing Gaussian Mixture Models with EM Using Equivalence Constraints.
  
• Volker Roth, Tilman Lange:
  Feature Selection in Clustering Problems.
  
• David Kauchak, Sanjoy Dasgupta:
  An Iterative Improvement Procedure for Hierarchical Clustering.
  
• Zvika Marx, Ido Dagan, Eli Shamir:
  Identifying Structure across Pre-partitioned Data.
  
• Ofer Dekel, Christopher D. Manning, Yoram Singer:
  Log-Linear Models for Label Ranking.
  
• Matthew Brand:
  Minimax Embeddings.
  
• Yoshua Bengio, Yves Grandvalet:
  No Unbiased Estimator of the Variance of K-Fold Cross-Validation.
  
• Harald Steck, Tommi Jaakkola:
  Bias-Corrected Bootstrap and Model Uncertainty.
  
• Ting-Fan Wu, Chih-Jen Lin, Ruby C. Weng:
  Probability Estimates for Multi-Class Classification by Pairwise Coupling.
  
• Gang Ji, Jeff A. Bilmes:
  Necessary Intransitive Likelihood-Ratio Classifiers.
  
• Rajat Raina, Yirong Shen, Andrew Y. Ng, Andrew McCallum:
  Classification with Hybrid Generative/Discriminative Models.
  
• Victor Lavrenko, R. Manmatha, Jiwoon Jeon:
  A Model for Learning the Semantics of Pictures.
  
• Michael J. Kearns, Luis E. Ortiz:
  Algorithms for Interdependent Security Games.
  
• John C. Platt:
  Fast Embedding of Sparse Similarity Graphs.
  
• Anton Schwaighofer, Marian Grigoras, Volker Tresp, Clemens Hoffmann:
  GPPS: A Gaussian Process Positioning System for Cellular Networks.
  
• 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.
  
• Jason Weston, Christina S. Leslie, Dengyong Zhou, André Elisseeff, William Stafford Noble:
  Semi-supervised Protein Classification Using Cluster Kernels.
  
• Alice X. Zheng, Michael I. Jordan, Ben Liblit, Alexander Aiken:
  Statistical Debugging of Sampled Programs.
  
• Nicholas P. Hughes, Lionel Tarassenko, Stephen J. Roberts:
  Markov Models for Automated ECG Interval Analysis.
  
• Cynthia Archer, Todd K. Leen, António M. Baptista:
  Parameterized Novelty Detectors for Environmental Sensor Monitoring.
  
• Benjamin M. Marlin:
  Modeling User Rating Profiles For Collaborative Filtering.
  
• Michael J. Quinlan, Stephan K. Chalup, Richard H. Middleton:
  Application of SVMs for Colour Classification and Collision Detection with AIBO Robots.
  
• Jun Suzuki, Yutaka Sasaki, Eisaku Maeda:
  Kernels for Structured Natural Language Data.
  
• Daniel B. Neill, Andrew W. Moore:
  A Fast Multi-Resolution Method for Detection of Significant Spatial Disease Clusters.
  
• Benjamin Taskar, Ming Fai Wong, Pieter Abbeel, Daphne Koller:
  Link Prediction in Relational Data.
  
• Andrew Rabinovich, Sameer Agarwal, Casey Laris, Jeffrey H. Price, Serge Belongie:
  Unsupervised Color Decomposition Of Histologically Stained Tissue Samples.
  
• Su-In Lee, Serafim Batzoglou:
  ICA-based Clustering of Genes from Microarray Expression Data.
  
• Darya Chudova, Christopher E. Hart, Eric Mjolsness, Padhraic Smyth:
  Gene Expression Clustering with Functional Mixture Models.
  
• Maneesh Sahani, Srikantan S. Nagarajan:
  Reconstructing MEG Sources with Unknown Correlations.
  
• 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.
  
• Xuerui Wang, Rebecca A. Hutchinson, Tom M. Mitchell:
  Training fMRI Classifiers to Discriminate Cognitive States across Multiple Subjects.
  
• Roland Vollgraf, Michael Scholz, Ian A. Meinertzhagen, Klaus Obermayer:
  Nonlinear Filtering of Electron Micrographs by Means of Support Vector Regression.
  
• Yu Zhou, Steven G. Mason, Gary E. Birch:
  Impact of an Energy Normalization Transform on the Performance of the LF-ASD Brain Computer Interface.
  
• Guido Dornhege, Benjamin Blankertz, Gabriel Curio, Klaus-Robert Müller:
  Increase Information Transfer Rates in BCI by CSP Extension to Multi-class.
  
• Sung Chan Jun, Barak A. Pearlmutter:
  Subject-Independent Magnetoencephalographic Source Localization by a Multilayer Perceptron.
  
• Carl Edward Rasmussen, Malte Kuss:
  Gaussian Processes in Reinforcement Learning.
  
• Joelle Pineau, Geoffrey J. Gordon, Sebastian Thrun:
  Applying Metric-Trees to Belief-Point POMDPs.
  
• Maxim Likhachev, Geoffrey J. Gordon, Sebastian Thrun:
  ARA*: Anytime A* with Provable Bounds on Sub-Optimality.
  
• Georgios Theocharous, Leslie Pack Kaelbling:
  Approximate Planning in POMDPs with Macro-Actions.
  
• Natalia Hernandez-Gardiol, Leslie Pack Kaelbling:
  Envelope-based Planning in Relational MDPs.
  
• David C. Parkes, Satinder P. Singh:
  An MDP-Based Approach to Online Mechanism Design.
  
• Andrew Y. Ng, H. Jin Kim, Michael I. Jordan, Shankar Sastry:
  Autonomous Helicopter Flight via Reinforcement Learning.
  
• Yu-Han Chang, Tracey Ho, Leslie Pack Kaelbling:
  All learning is Local: Multi-agent Learning in Global Reward Games.
  
• Daniela Pucci de Farias, Nimrod Megiddo:
  How to Combine Expert (and Novice) Advice when Actions Impact the Environment?
  
• Pascal Poupart, Craig Boutilier:
  Bounded Finite State Controllers.
  
• J. Andrew Bagnell, Sham Kakade, Andrew Y. Ng, Jeff G. Schneider:
  Policy Search by Dynamic Programming.
  
• Arnab Nilim, Laurent El Ghaoui:
  Robustness in Markov Decision Problems with Uncertain Transition Matrices.
  
• Alan Fern, Sung Wook Yoon, Robert Givan:
  Approximate Policy Iteration with a Policy Language Bias.
  
• Matthew R. Rudary, Satinder P. Singh:
  A Nonlinear Predictive State Representation.
  
• Xiao Feng Wang, Tuomas Sandholm:
  Learning Near-Pareto-Optimal Conventions in Polynomial Time.
  
• Gerald Tesauro:
  Extending Q-Learning to General Adaptive Multi-Agent Systems.
  
• Curt A. Bererton, Geoffrey J. Gordon, Sebastian Thrun:
  Auction Mechanism Design for Multi-Robot Coordination.
  
• Ciamac Cyrus Moallemi, Benjamin Van Roy:
  Distributed Optimization in Adaptive Networks.
  
• Milos Hauskrecht, Branislav Kveton:
  Linear Program Approximations for Factored Continuous-State Markov Decision Processes.
  
• Arnulf B. A. Graf, Felix A. Wichmann:
  Insights from Machine Learning Applied to Human Visual Classification.
  
• Virginia R. de Sa:
  Sensory Modality Segregation.
  
• Artur S. d'Avila Garcez, Luís C. Lamb:
  Reasoning about Time and Knowledge in Neural Symbolic Learning Systems.
  
• Marc Toussaint:
  Learning a World Model and Planning with a Self-Organizing, Dynamic Neural System.
  
• Woojae Kim, Daniel J. Navarro, Mark A. Pitt, In Jae Myung:
  An MCMC-Based Method of Comparing Connectionist Models in Cognitive Science.
  
• David Philipona, J. K. O'Regan, Jean-Pierre Nadal, Olivier J. M. D. Coenen:
  Perception of the Structure of the Physical World Using Unknown Multimodal Sensors and Effectors.
  
• Thomas L. Griffiths, Joshua B. Tenenbaum:
  From Algorithmic to Subjective Randomness.
  
• Zach Solan, David Horn, Eytan Ruppin, Shimon Edelman:
  Unsupervised Context Sensitive Language Acquisition from a Large Corpus.
  
• Yuuya Sugita, Jun Tani:
  A Holistic Approach to Compositional Semantics: A Connectionist Model and Robot Experiments.
  
• Aaron C. Courville, Nathaniel D. Daw, Geoffrey J. Gordon, David S. Touretzky:
  Model Uncertainty in Classical Conditioning.
  
• Reid R. Harrison:
  A Low-Power Analog VLSI Visual Collision Detector.
  
• Paul Merolla, Kwabena Boahen:
  A Recurrent Model of Orientation Maps with Simple and Complex Cells.
  
• Rock Z. Shi, Timothy K. Horiuchi:
  A Summating, Exponentially-Decaying CMOS Synapse for Spiking Neural Systems.
  
• Hsin Chen, Patrice Fleury, Alan F. Murray:
  Minimising Contrastive Divergence in Noisy, Mixed-mode VLSI Neurons.
  
• Bob Ricks, Dan Ventura:
  Training a Quantum Neural Network.
  
• Adria Bofill-i-Petit, Alan F. Murray:
  Synchrony Detection by Analogue VLSI Neurons with Bimodal STDP Synapses.
  
• Masakazu Yagi, Hideo Yamasaki, Tadashi Shibata:
  A Mixed-Signal VLSI for Real-Time Generation of Edge-Based Image Vectors.
  
• Francesco Tenore, Ralph Etienne-Cummings, M. Anthony Lewis:
  Entrainment of Silicon Central Pattern Generators for Legged Locomotory Control.
  
• Eric K. C. Tsang, Bertram Emil Shi:
  A Neuromorphic Multi-chip Model of a Disparity Selective Complex Cell.
  
• Ingo Steinwart:
  Sparseness of Support Vector Machines---Some Asymptotically Sharp Bounds.
  
• Tong Zhang:
  An Infinity-sample Theory for Multi-category Large Margin Classification.
  
• Philip Derbeko, Ran El-Yaniv, Ron Meir:
  Error Bounds for Transductive Learning via Compression and Clustering.
  
• Claire Monteleoni, Tommi Jaakkola:
  Online Learning of Non-stationary Sequences.
  
• Cynthia Rudin, Ingrid Daubechies, Robert E. Schapire:
  On the Dynamics of Boosting.
  
• Kohei Hatano, Manfred K. Warmuth:
  Boosting versus Covering.
  
• Clayton Scott, Robert Nowak:
  Near-Minimax Optimal Classification with Dyadic Classification Trees.
  
• Jean-Yves Audibert, Olivier Bousquet:
  PAC-Bayesian Generic Chaining.
  
• Vladimir Vovk, Glenn Shafer, Ilia Nouretdinov:
  Self-calibrating Probability Forecasting.
  
• David L. Donoho, Victoria Stodden:
  When Does Non-Negative Matrix Factorization Give a Correct Decomposition into Parts?
  
• Tong Zhang:
  Learning Bounds for a Generalized Family of Bayesian Posterior Distributions.
  
• Manfred Opper, Ole Winther:
  Variational Linear Response.
  
• Susanne Still, William Bialek, Léon Bottou:
  Geometric Clustering Using the Information Bottleneck Method.
  
• Peter L. Bartlett, Michael I. Jordan, Jon D. McAuliffe:
  Large Margin Classifiers: Convex Loss, Low Noise, and Convergence Rates.
  
• David C. Hoyle, Magnus Rattray:
  Limiting Form of the Sample Covariance Eigenspectrum in PCA and Kernel PCA.
  
• Dörthe Malzahn, Manfred Opper:
  Approximate Analytical Bootstrap Averages for Support Vector Classifiers.
  
• Justin Werfel, Xiaohui Xie, H. Sebastian Seung:
  Learning Curves for Stochastic Gradient Descent in Linear Feedforward Networks.
  
• Gurinder S. Atwal, William Bialek:
  Ambiguous Model Learning Made Unambiguous with 1/f Priors.
  
• Gal Chechik, Amir Globerson, Naftali Tishby, Yair Weiss:
  Information Bottleneck for Gaussian Variables.
  
• Olivier Bousquet, Olivier Chapelle, Matthias Hein:
  Measure Based Regularization.
  
• Shai Shalev-Shwartz, Koby Crammer, Ofer Dekel, Yoram Singer:
  Online Passive-Aggressive Algorithms.
  
• Saharon Rosset, Ji Zhu, Trevor Hastie:
  Margin Maximizing Loss Functions.
  
• Yuval Aviel, David Horn, Moshe Abeles:
  The Doubly Balanced Network of Spiking Neurons: A Memory Model with High Capacity.
  
• Thomas Natschläger, Wolfgang Maass:
  Information Dynamics and Emergent Computation in Recurrent Circuits of Spiking Neurons.
  
• Peter J. Thomas, Donald J. Spencer, Sierra K. Hampton, Peter Park, Joseph P. Zurkus:
  The Diffusion-Limited Biochemical Signal-Relay Channel.
  
• 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.
  
• Nathan A. Dunn, John S. Conery, Shawn R. Lockery:
  Circuit Optimization Predicts Dynamic Network for Chemosensory Orientation in the Nematode C. elegans.
  
• Maneesh Sahani:
  A Biologically Plausible Algorithm for Reinforcement-shaped Representational Learning.
  
• Yoichi Miyawaki, Masato Okada:
  Mechanism of Neural Interference by Transcranial Magnetic Stimulation: Network or Single Neuron?
  
• Peter Dayan, Michael Häusser:
  Plasticity Kernels and Temporal Statistics.
  
• Jonathan W. Pillow, Liam Paninski, Eero P. Simoncelli:
  Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Model.
  
• Liam Paninski:
  Design of Experiments via Information Theory.
  
• Konrad P. Körding, Daniel M. Wolpert:
  Probabilistic Inference in Human Sensorimotor Processing.
  
• Kazuyuki Samejima, Kenji Doya, Yasumasa Ueda, Minoru Kimura:
  Estimating Internal Variables and Paramters of a Learning Agent by a Particle Filter.
  
• Bernd Porr, Ausra Saudargiene, Florentin Wörgötter:
  Analytical Solution of Spike-timing Dependent Plasticity Based on Synaptic Biophysics.
  
• Brian J. Fischer, Charles H. Anderson:
  A Probabilistic Model of Auditory Space Representation in the Barn Owl.
  
• Ryan C. Kelly, Tai Sing Lee:
  Decoding V1 Neuronal Activity using Particle Filtering with Volterra Kernels.
  
• 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.
  
• William M. Campbell, Joseph P. Campbell, Douglas A. Reynolds, Douglas A. Jones, Timothy R. Leek:
  Phonetic Speaker Recognition with Support Vector Machines.
  
• Pedro J. Moreno, Purdy Ho, Nuno Vasconcelos:
  A Kullback-Leibler Divergence Based Kernel for SVM Classification in Multimedia Applications.
  
• Kannan Achan, Sam T. Roweis, Brendan J. Frey:
  Probabilistic Inference of Speech Signals from Phaseless Spectrograms.
  
• James T. Kwok, Brian Mak, Simon Ho:
  Eigenvoice Speaker Adaptation via Composite Kernel PCA.
  
• Jeff Bondy, Ian C. Bruce, Suzanna Becker, Simon Haykin:
  Predicting Speech Intelligibility from a Population of Neurons.
  
• Tomohiro Nakatani, Masato Miyoshi, Keisuke Kinoshita:
  One Microphone Blind Dereverberation Based on Quasi-periodicity of Speech Signals.
  
• Nicoleta Roman, DeLiang L. Wang, Guy J. Brown:
  A Classification-based Cocktail-party Processor.
  
• Zhou Wang, Eero P. Simoncelli:
  Local Phase Coherence and the Perception of Blur.
  
• Vincent Bonin, Valerio Mante, Matteo Carandini:
  Nonlinear Processing in LGN Neurons.
  
• Scott A. Beardsley, Lucia M. Vaina:
  A Functional Architecture for Motion Pattern Processing in MSTd.
  
• Alan L. Yuille, Fang Fang, Paul R. Schrater, Daniel Kersten:
  Human and Ideal Observers for Detecting Image Curves.
  
• Nathan Sprague, Dana H. Ballard:
  Eye Movements for Reward Maximization.
  
• Matthias H. Hennig, Florentin Wörgötter:
  Eye Micro-movements Improve Stimulus Detection Beyond the Nyquist Limit in the Peripheral Retina.
  
• Reto Wyss, Paul F. M. J. Verschure:
  Bounded Invariance and the Formation of Place Fields..
  
• Salvador Ruiz-Correa, Linda G. Shapiro, Marina Meila, Gabriel Berson:
  Discriminating Deformable Shape Classes.
  
• Kevin P. Murphy, Antonio Torralba, William T. Freeman:
  Graphical Model For Recognizing Scenes and Objects.
  
• Amit Gruber, Yair Weiss:
  Factorization with Uncertainty and Missing Data: Exploiting Temporal Coherence.
  
• Michael Fink, Pietro Perona:
  Mutual Boosting for Contextual Inference.
  
• Jianxin Wu, James M. Rehg, Matthew D. Mullin:
  Learning a Rare Event Detection Cascade by Direct Feature Selection.
  
• Sanjiv Kumar, Martial Hebert:
  Discriminative Fields for Modeling Spatial Dependencies in Natural Images.
  
• Leonid Sigal, Michael Isard, Benjamin H. Sigelman, Michael J. Black:
  Attractive People: Assembling Loose-Limbed Models using Non-parametric Belief Propagation.
  
• Deva Ramanan, David A. Forsyth:
  Automatic Annotation of Everyday Movements.
  
• Lorenzo Torresani, Aaron Hertzmann, Christoph Bregler:
  Learning Non-Rigid 3D Shape from 2D Motion.
  
• 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.
  
• Song Wang, Toshiro Kubota, Jeffrey Mark Siskind:
  Salient Boundary Detection using Ratio Contour.
  
• Anuj Srivastava, Xiuwen Liu, Washington Mio, Eric Klassen:
  A Computational Geometric Approach to Shape Analysis in Images.
  
• Lyndsey C. Pickup, Stephen J. Roberts, Andrew Zisserman:
  A Sampled Texture Prior for Image Super-Resolution.
  
• Charles R. Rosenberg, Thomas P. Minka, Alok Ladsariya:
  Bayesian Color Constancy with Non-Gaussian Models.
  
• Claudio Fanti, Marzia Polito, Pietro Perona:
  An Improved Scheme for Detection and Labelling in Johansson Displays.
  

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