Online Preproceedings		Neural Information Processing Systems: Natural and Synthetic
		Quick links Download all papers (45MB, includes conference brochure) Download conference brochure (abstracts and program only, no papers) Special Papers AA00 Winner of the Award "Most original submission": Data Set Selection, by Doudou LaLoudouana and Mambobo Bonouliqui Tarare. Please note: This award-winning paper will not be included in the regular proceedings, but it will be presented at the workshop banquet. Algorithms And Architectures AA01 Bayesian Monte Carlo, by Carl Edward Rasmussen and Zoubin Ghahramani AA02 Mean Field Approach to a Probabilistic Model in Information Retrieval, by Bin Wu, K. Y. Michael Wong, and David Bodoff AA03 Distance Metric Learning, with application to Clustering with side-information, by Eric P. Xing, Andrew Y. Ng, Michael I. Jordan, and Stuart Russell AA04 Adapting Codes und Embeddings for Polychotomies, by Gunter Rätsch, Alexander Smola, and Sebastian Mika AA05 Knowledge-Based Support Vector Machine Classifiers, by Glenn M. Fung, Olvi L. Mangasarian, and Jude W. Shavlik, Finalist for the Ben Wegbreit Best Student Paper Award AA06 Multiple-step ahead prediction for non linear dynamic systems -- A Gaussian Process treatment with propagation of the uncertainty, by Agathe Girard, Carl Edward Rasmussen, and Roderick Murray-Smith AA07 Kernel Design using Boosting, by Koby Crammer, Joseph Keshet, and Yoram Singer AA08 Coulomb Classifiers: Generalizing Support Vector Machines via an Analogy to Electrostatic Systems, by Sepp Hochreiter, Michael C. Mozer, and Klaus Obermayer AA09 Adaptive Scaling for Feature Selection in SVMs, by Yves Grandvalet and Stephane Canu AA10 Support Vector Machines for Multiple- Instance Learning, by Stuart Andrews, Ioannis Tsochantaridis, and Thomas Hofmann AA11 Fast Kernels for String and Tree Matching, by S.V.N. Vishwanathan and Alexander J. Smola AA12 Generalized^2 Linear^2 Models, by Geoffrey J. Gordon AA13 Cluster Kernels for Semi-Supervised Learning, by Olivier Chapelle, Jason Weston, and Bernhard Schoelkopf AA14 Adaptive nonlinear system identification with echo state networks, by Herbert Jaeger AA15 Rational Kernels, by Corinna Cortes, Patrick Haffner, and Mehryar Mohri AA16 Fast Sparse Gaussian Process Methods: The Informative Vector Machine, by Matthias Seeger, Neil Lawrence, and Ralf Herbrich AA17 Stability-Based Model Selection, by Tilman Lange, Mikio Braun, Volker Roth, and Joachim Buhmann AA18 Feature Selection in Mixture-Based Clustering, by Martin H. Law, Anil K. Jain, and Mario A. T. Figueiredo AA19 String Kernels, Fisher Kernels and Finite State Automata, by Craig Saunders, John Shawe-Taylor, and Alexei Vinokourov AA20 Boosting Density Estimation, by Saharon Rosset, and Eran Segal AA21 Independent Components Analysis through Product Density Estimation, by Trevor Hastie and Robert Tibshirani AA22 Learning Semantic Similarity, by Jaz Kandola, John Shawe-Taylor, and Nello Cristianini AA23 Self Supervised Boosting, by Max Welling, Richard Zemel, and Geoffrey Hinton AA24 Automatic Derivation of Statistical Algorithms: The EM Family and Beyond, by Alexander G. Gray, Bernd Fischer, Johann Schumann, and Wray Buntine AA25 Intrinsic Dimension Estimation Using Packing Numbers, by Balazs Kegl AA26 Half-Lives of EigenFlows for Spectral Clustering, by Chakra Chennubhotla and Allan Jepson AA27 On the Dirichlet Prior and Bayesian Regularization, by Harald Steck and Tommi Jaakkola AA28 Global versus local approaches to nonlinear dimensionality reduction., by Vin de Silva and Joshua B. Tenenbaum AA29 Dynamic Bayesian Networks with Deterministic Latent Tables, by David Barber AA30 Parametric mixture models for multi-labeled text, by Naonori Ueda and Kazumi Saito AA31 Clustering with the Fisher Score, by Koji Tsuda, Motoaki Kawanabe, and Klaus-Robert Mueller AA32 Adaptive classification by variational Kalman filtering, by Peter Sykacek and Stephen Roberts AA33 Boosted Dyadic Kernel Discriminants, by Baback Moghaddam and Gregory Shakhnarovich AA34 Regularized greedy importance sampling, by Finnegan Southey, Dale Schuurmans, and Ali Ghodsi Boushehri AA35 One-class LP classifier for dissimilarity representations, by Elzbieta Pekalska, David M.J. Tax, and Robert P.W. Duin AA36 A Formulation for Minimax Probability Machine Regression, by Thomas Strohmann and Gregory Z. Grudic AA37 VIBES: A Variational Inference Engine for Bayesian Networks, by Christopher M. Bishop, David Spiegelhalter, and John Winn AA38 A Differential Semantics for Jointree Algorithms, by James Park and Adnan Darwiche AA39 Constraint Classification for Multiclass Classification and Ranking, by Sariel Har-Peled, Dan Roth, and Dav Zimak AA40 Nash Propagation for Loopy Graphical Games, by Luis E. Ortiz and Michael Kearns AA41 Using Tarjan's Red Rule for Fast Dependency Tree Construction, by Dan Pelleg and Andrew Moore AA42 Exact MAP estimates by (hyper)tree agreement, by Martin Wainwright, Tommi Jaakkola, and Alan Willsky AA43 Going metric: Denoising pairwise data, by Volker Roth, Julian Laub, Joachim M. Buhmann, and Klaus-Robert Muller AA44 Manifold Parzen Windows, by Pascal Vincent and Yoshua Bengio AA45 Stochastic Neighbor Embedding, by Geoff Hinton and Sam Roweis AA46 Automatic Alignment of Local Representations, by Yee Whye Teh and Sam Roweis AA47 Informed Projections, by David Cohn AA48 Extracting relevant structures with side information, by Gal Chechik and Naftali Tishby AA49 Critical Lines in Symmetry of Mixture Models and its Application to Component Splitting, by Kenji Fukumizu, Shotaro Akaho, and Shun-ichi Amari AA50 Kernel Dependency Estimation, by Jason Weston, Olivier Chapelle, Andre Elisseeff, Bernhard Schoelkopf, and Vladimir Vapnik AA51 Handling Missing Data with Variational Bayesian Learning of ICA, by Kwokleung Chan, Te-Won Lee, and Terrence Sejnowski AA52 Feature Selection and Classification on Matrix Data: From Large Margins To Small Covering Numbers, by Sepp Hochreiter and Klaus Obermayer AA53 Learning with Multiple Labels, by Rong Jin and Zoubin Ghahramani AA54 Robust Novelty Detection with Single-Class MPM, by Gert R.G. Lanckriet, Laurent El Ghaoui, and Michael I. Jordan AA55 Artefactual Structure from Least-squares Multidimensional Scaling, by Nicholas P. Hughes and David Lowe AA56 The Decision List Machine, by Marina Sokolova, Mario Marchand, Nathalie Japkowicz, and John Shawe-Taylor AA57 Using Manifold Structure for Partially Labelled Classification, by Mikhail Belkin and Partha Niyogi AA58 Taxonomy of Large Margin Principle Algorithms for Ordinal Regression Problems, by Amnon Shashua and Anat Levin AA59 Multiclass Learning by Probabilistic Embeddings, by Ofer Dekel and Yoram Singer AA60 Transductive and Inductive Methods for Approximate Gaussian Process Regression, by Anton Schwaighofer and Volker Tresp AA61 Charting a manifold, by Matthew Brand AA62 Annealing and the rate distortion problem, by Albert E. Parker, Tomas Gedeon, Alexander G. Dimitrov, and Bryan Roosien AA63 Discriminative Learning for Label Sequences via Boosting, by Yasemin Altun, Thomas Hofmann, and Mark Johnson AA64 Discriminative Densities from Maximum Contrast Estimation, by P. Meinicke, T. Twellmann, and H. Ritter AA65 FloatBoost Learning for Classification, by Stan Z. Li, ZhenQiu Zhang, Heung-Yeung Shum, and HongJiang Zhang AA66 Incremental Gaussian Processes, by Joaquin Quinonero-Candela and Ole Winther AA67 Learning Graphical Models with Mercer Kernels, by Francis R. Bach and Michael I. Jordan AA68 Multiple Cause Vector Quantization, by David A. Ross and Richard S. Zemel AA69 Information Regularization with Partially Labeled Data, by Martin Szummer and Tommi Jaakkola AA70 Derivative observations in Gaussian Process models of dynamic systems, by Ercan Solak, Roderick Murray-Smith, W. Leithead, D. Leith, and C. Rasmussen AA71 Multiplicative updates for nonnegative quadratic programming in support vector machines, by Fei Sha, Lawrence K. Saul, and Daniel D. Lee AA72 Location Estimation with a Differential Update Network, by A. Rahimi and T. Darrell AA73 Real-time particle filters, by Cody Kwok, Dieter Fox, and Marina Meila Applications AP01 Identity Uncertainty and Citation Matching, by Hanna Pasula, Bhaskara Marthi, Brian Milch, Stuart Russell, and Ilya Shpitser AP02 The RA Scanner: Prediction of Rheumatoid Joint Inflammation Based on Laser Imaging, by Anton Schwaighofer, Volker Tresp, Peter Mayer, Alexander K. Scheel, Gerhard Muller, and Ingolf Mesecke-von Rheinbaben AP03 Mismatch String Kernels for SVM Protein Classification, by Christina Leslie, Eleazar Eskin, Jason Weston, and William Stafford Noble AP04 Graph-driven features extraction from microarray data using diffusion kernels and kernel CCA, by Jean-Philippe Vert and Minoru Kanehisa AP05 Real-time monitoring of complex industrial processes with particle filters, by Ruben Morales-Menendez, Nando de Freitas, and David Poole AP06 A Maximum Entropy Approach To Collaborative Filtering in Dynamic, Sparse, High-Dimensional Domains, by Dmitry Y. Pavlov and David M. Pennock AP07 Prediction of Protein Topologies Using GIOHMMs and GRNNs, by Gianluca Pollastri, Pierre Baldi, Alessandro Vullo, and Paolo Frasconi AP08 Approximate Inference and Protein-Folding, by Chen Yanover and Yair Weiss AP09 Adaptive Caching by Refetching, by Robert B. Gramacy, Manfred K. Warmuth, Scott A. Brandt, and Ismail Ari AP10 Inferring a Semantic Representation of Text via Cross-Language Correlation Analysis, by Alexei Vinokourov, John Shawe-Taylor, and Nello Cristianini AP11 Improving A Page Classifier with Anchor Extraction and Link Analysis, by William W. Cohen AP12 A Hierarchical Bayesian Markovian Model for Motifs in Biopolymer Sequences, by Eric P.Xing, Michael I. Jordan, Richard M. Karp, and Stuart Russell AP13 Learning to Classify Galaxy Shapes using the EM Algorithm, by Sergey Kirshner, Igor Cadez, Padhraic Smyth, and Chandrika Kamath AP14 Name that Song: A Probabilistic Approach to Querying on Music and Text, by Eric Brochu and Nando de Freitas AP15 A Probabilistic Model for Learning Concatenative Morphology, by Matthew G. Snover and Michael R. Brent Control and Navigation CN01 Learning Attractor Landscapes for Learning Motor Primitives, by Auke Jan Ijspeert, Jun Nakanishi, and Stefan Schaal CN02 Learning a forward model of a reflex, by Bernd Porr and Florentin Woergoetter CN03 Minimax Differential Dynamic Programming:An Application to Robust Biped Walking, by Jun Morimoto and Christopher Atkeson CN04 Bias-Optimal Incremental Problem Solving, by Juergen Schmidhuber CN05 Value-directed Compression of POMDPs, by Pascal Poupart and Craig Boutilier CN06 Optimality of Reinforcement Learning Algorithms with Linear Function Approximation, by Ralf Schoknecht CN07 Speeding up the Parti-Game Algorithm, by Maxim Likhachev and Sven Koenig CN08 Reinforcement Learning to Play an Optimal Nash Equilibrium in Team Markov Games, by XiaoFeng Wang and Tuomas Sandholm CN09 Convergent Combinations of Reinforcement Learning with Linear Function Approximation, by Ralf Schoknecht and Artur Merke CN10 Approximate Linear programming for Average-Cost Dynamic Programming, by Daniela Pucci de Farias and Benjamin Van Roy CN11 A Convergent Form of Approximate Policy Iteration, by Theodore J. Perkins and Doina Precup CN12 Efficient Learning Equilibrium, by Ronen Brafman and Moshe Tennenholtz CN13 Nonparametric Representation of Policies and Value Functions: A Trajectory-Based Approach, by Chris Atkeson and Jun Morimoto CN14 Learning to Take Concurrent Actions, by Khashayar Rohanimanesh and Sridhar Mahadevan CN15 Learning in Zero-Sum Team Markov Games using Factored Value Functions, by Michail G. Lagoudakis and Ronald Parr CN16 Exponential Family PCA for Belief Compression in POMDPs, by Nicholas Roy and Geoff Gordon Cognitive Science and AI CS01 Fast Exact Inference with a Factored Model for Natural Language Parsing, by Dan Klein and Christopher D. Manning CS02 Prediction and semantic association, by Thomas L. Griffiths and Mark Steyvers CS03 Replay, Repair, and Consolidation, by Szabolcs Kali and Peter Dayan CS04 A Minimal Intervention Principle for Coordinated Movement, by Emanuel Todorov and Michael I. Jordan CS05 Categorization Under Complexity: A Unified MDL Account of Human Learning of Regular and Irregular Categories, by David Fass and Jacob Feldman CS06 Theory-based causal inference, by Joshua B. Tenenbaum and Thomas L. Griffiths CS07 How the poverty of stimulus solves the poverty of stimulus, by Willem Zuidema CS08 Bayesian Models of Inductive Generalization, by Neville E. Sanjana and Joshua B. Tenenbaum, Finalist for the Ben Wegbreit Best Student Paper Award CS09 Combining Dimensions and Features in Similarity-Based Representations, by Daniel J. Navarro and Michael D. Lee CS10 Modeling Midazolam's Effect on the Hippocampus and Recognition Memory, by Kenneth J. Malmberg, Rene Zeelenberg, and Richard. M. Shiffrin CS11 Dynamical Causal Learning, by X. Danks, T.L. Griffiths, and J. Tenenbaum CS12 Visual Development Aids the Acquisition of Motion Velocity Sensitivities, by Robert A. Jacobs and Melissa Dominguez CS13 Timing and partial observability in the dopamine system, by Nathaniel D. Daw, Aaron C. Courville, and David S. Touretzky CS14 Automatic acquisition and efficient representation of syntactic structures, by Zach Solan, Eytan Ruppin, David Horn, and Shimon Edelman Emerging Technologies IM01 Optoelectronic Implementation of a Fitzugh-Nagumo neural model, by A.R.S. Romariz and K. Wagner IM02 Circuit Model of Short-Term Synaptic Dynamics, by Shih-Chii Liu, Malte Boegerhausen, and Pascal Suter IM03 Adaptive Quantization and Density Estimation in Silicon, by David Hsu, Seth Bridges, Miguel Figueroa, and Chris Diorio IM04 Circuits for bistable spike-timing-dependent plasticity neuromorphic VLSI synapses, by Giacomo Indiveri IM05 Retinal Processing Emulation in a Programmable 2-Layer Analog Array Processor CMOS Chip, by R. Carmona, F. Jimenez-Garrido, R. Dominguez-Castro, S. Espejo, and A. Rodriguez-Vazquez IM06 Improving Transfer Rates in Brain Computer Interfacing: a Case Study, by P. Meinicke, M. Kaper, F. Hoppe, M. Heumann, and H. Ritter IM07 Combining Features for BCI, by Guido Dornhege, Benjamin Blankertz, Gabriel Curio, and Klaus-Robert Mueller IM08 classifying patterns of visual motion - a neuromorphic approach, by Jakob Heinzle and Alan Stocker IM09 Developing Topography and Ocular Dominance using two aVLSI Vision Sensors and a Neurotrophic Model of Plasticity, by Terry Elliott, and Jorg Kramer IM10 Topographic Map Formation by Silicon Growth Cones, by Brian Taba and Kwabena Boahen, Winner of the Ben Wegbreit Best Student Paper Award IM11 Spike Timing-Dependent Plasticity in the Address Domain, by R. Jacob Vogelstein, Francesco Tenore, Ralf Philipp, Miriam S. Adlerstein, David H. Goldberg, and Gert Cauwenberghs IM12 Field-Programmable Learning Arrays, by Seth Bridges, Miguel Figueroa, David Hsu, and Chris Diorio Learning Theory LT01 Data-Dependent Bounds for Bayesian Mixture Methods, by Ron Meir and Tong Zhang LT02 A Statistical Mechanics Approach to Approximate Analytical Bootstrap Averages, by Dorthe Malzahn and Manfred Opper LT03 Maximum Likelihood and the Information Bottleneck, by Noam Slonim and Yair Weiss LT04 Stable fixed points of loopy belief propagation are minima of the Bethe free energy, by Tom Heskes LT05 Concentration Inequalities for the Missing Mass and Histogram Rule Error, by David McAllester and Luis Ortiz LT06 Dyadic Classification Trees via Structural Risk Minimization, by Clayton Scott and Robert Nowak LT07 The Stability of Kernel Principal Components Analysis and its Relation to the Process Eigenspectrum, by John Shawe-Taylor and Christopher K. I. Williams LT08 Information Diffusion Kernels, by John Lafferty and Guy Lebanon LT09 Scaling of Probability-based Optimization Algorithms, by J. L. Shapiro LT10 The effect of singularities in a learning machine when the true parameters do not lie on such singularities, by Sumio Watanabe Shun-ichi Amari LT11 On the Complexity of Learning the Kernel Matrix, by Olivier Bosquet and Daniel J.L. Herrmann LT12 Rate Distortion Function in the Spin Glass State: A Toy Model, by Tatsuto Murayama and Masato Okada LT13 Conditional Models on the Ranking Poset, by Guy Lebanon and John Lafferty LT14 PAC-Bayes And Margins, by John Langford and John Shawe-Taylor LT15 A Note on the Representational Incompatability of Function Approximation and Factored Dynamics, by Eric Allender, Sanjeev Arora, Michael Kearns, Christopher Moore, and Alexander Russell LT16 Fractional Belief Propagation, by Wim Wiegerinck and Tom Heskes LT17 An Impossibility Theorem for Clustering, by Jon Kleinberg LT18 Effective dimension and Generalization of Kernel Learning, by Tong Zhang LT19 Margin Analysis of the LVQ Algorithm, by Koby Crammer, Ran Gilad-Bachrach, Amir Navot, and Naftali Tishby LT20 Margin-based algorithms for information filtering, by Nicolo' Cesa-Bianchi, Alex Conconi, and Claudio Gentile LT21 Superkernels, by Cheng Soon Ong, Alexander J. Smola, and Robert C. Williamson Neuroscience NS01 Binary coding in auditory cortex, by Michael R. DeWeese and Anthony M. Zador NS02 How linear are auditory cortical responses?, by Maneesh Sahani and Jennifer Linden NS03 Neural Decoding of Cursor Motion using a Kalman Filter, by W. Wu, M. J. Black, Y. Gao, E. Bienenstock, M. Serruya, A. Shaikhouni, and J. P. Donoghue NS04 Spikernels: Embedding Spiking Neurons in Inner-Product Spaces, by Lavi Shpigelman, Yoram Singer, Rony Paz, and Eilon Vaadia NS05 Spectro-temporal receptive fields of subthreshold responses in auditory cortex, by Christian K. Machens, Michael Wehr, and Anthony M. Zador NS06 Temporal Coherence, Natural Image Sequences, and the Visual Cortex, by Jarmo Hurri and Aapo Hyvarinen NS07 Learning in Spiking Neural Assemblies, by David Barber NS08 Expected and Unexpected Uncertainty: ACh and NE in the Neocortex, by Angela Yu and Peter Dayan NS09 Dopamine Induced Bistability Enhances Signal Processing in Spiny Neurons, by Aaron J. Gruber, Sara A. Solla, and James C. Houk NS10 Convergence properties of spike-triggered analysis techniques, by Liam Paninski NS11 Branching Law for Axons, by Dmitri B. Chklovskii and Armen Stepanyants NS12 Binary tuning is optimal for neural rate coding with high temporal resolution, by Matthias Bethge, David Rotermund, and Klaus Pawelzik NS13 An information theoretic approach to the functional classification of neurons, by Elad Schneidman, William Bialek, and Michael J. Berry NS14 Naive Bayesian Coding of Color in Primary Visual Cortex, by Javier R. Movellan, Thomas Wachtler, Thomas D. Albright, and Terrence Sejnowski NS15 A Model for Real-Time Computation in Generic Neural Microcircuits, by Wolfgang Maass, Thomas Natschlaeger, and Henry Markram NS16 Adaptation and Unsupervised Learning, by Peter Dayan, Maneesh Sahani, and Greg Deback NS17 A digital antennal lobe for pattern equalization: analysis and design, by Pietro Perona, Alex Holub, and Gilles Laurent NS18 Hidden Markov model of cortical synaptic plasticity: Derivation of the learning rule, by Michael Eisele and Kenneth D. Miller NS19 Input Selectivity of Spiking Neurons: Metaplasticity in a Unified Calcium-Dependent Learning Model, by Luk Chong Yeung, Harel Z. Shouval, and Leon N Cooper NS20 Kernel-based extraction of Slow Features: Complex cells learn disparity and translation invariance from natural images, by Alistair Bray and Dominique Martinez NS21 Maximally Informative Dimensions: Analyzing Neural Responses to Natural Signals, by Tatyana Sharpee, Nicole C. Rust, and William Bialek NS22 Dynamical constraints on computing with spike timing in the cortex, by Arunava Banerjee and Alexandre Pouget NS23 Interpreting neural response variability as Monte Carlo sampling of the posterior, by Patrik O. Hoyer and Aapo Hyvarinen NS24 A neural edge-detection model for enhanced auditory sensitivity in modulated noise, by Alon Fishbach and Bradford J. May NS25 An Estimation-Theoretic Framework for the Presentation of Multiple Stimuli, by Christian W. Eurich NS26 Evidence optimization techniques for estimating stimulus-response functions, by Maneesh Sahani and Jennifer Linden NS27 Reconstructing stimulus-driven neural networks from spike times, by Duane Q. Nykamp Speech and Signal Processing SP01 Forward-Decoding Kernel-Based Phone Sequence Recognition, by Shantanu Chakrabartty and Gert Cauwenberghs SP02 A Probabilistic Approach to Single Channel Blind Signal Separation, by Gil-Jin Jang and Te-Won Lee SP03 Real time voice processing with audiovisual feedback: toward autonomous agents with perfect pitch, by Lawrence K. Saul, Daniel D. Lee, Charles L. Isbell, and Yann LeCun SP04 Analysis of Information in Speech using Results of MANOVA, by Sachin S. Kajarekar and Hynek Hermansky SP05 Bayesian Estimation of Time-Frequency Coefficients for Audio Signal Enhancement, by Patrick J. Wolfe and Simon J. Godsill SP06 Source Separation with a Microphone Array using Graphical Models and Subband Filtering, by Hagai Attias SP07 An Asynchronous Hidden Markov Model for Audio-Visual Speech Recognition, by Samy Bengio SP08 Monaural Speech Separation, by Guoning Hu and DeLiang Wang SP09 Discriminative Binaural Sound Localization, by Udi Ben-Reuven and Yoram Singer SP10 Application of the Variational Bayesian Approach to Speech Recognition, by Shinji Watanabe, Yasuhiro Minami, Atsushi Nakamura, and Naonori Ueda Vision VS01 Learning to Perceive Transparency from the Statistics of Natural Scenes, by Anat Levin, Assaf Zomet, and Yair Weiss, Finalist for the Ben Wegbreit Best Student Paper Award VS02 Learning to Detect Natural Image Boundaries Using Brightness and Texture, by David R. Martin, Charless C. Fowlkes, and Jitendra Malik VS03 Fast transformation-invariant component analysis, by Anitha Kannan, Nebojsa Jojic, and Brendan Frey VS04 An Approach to Automatic Analysis of Spontaneous Facial Expressions, by M.S. Bartlett, G. Littlewort, B. Braathen, T.J. Sejnowski, and J.R. Movellan VS05 Bayesian Image Super-resolution, by Michael E Tipping and Christopher M Bishop VS06 A bilinear model for sparse coding, by David B. Grimes and Rajesh P. N. Rao VS07 Dynamic Structure Super-Resolution, by Amos Storkey VS08 Unsupervised Color Constancy, by Kinh Tieu and Erik Miller VS09 Recovering Articulated Model Topology from Observed Rigid Motion, by Leonid Taycher, John W. Fisher, and Trevor Darrell VS10 Optimal linear estimation of self-motion - a real-world test of a neural model, by Matthias O. Franz and Javaan S. Chahl VS11 Learning Sparse Multiscale Image Representations, by Phil A Sallee and Bruno A Olshausen VS12 Shape recipes: scene representations that refer to the image, by William T. Freeman and Antonio Torralba VS13 Recovering Intrinsic Images from a Single Image, by Marshall F Tappen, William T Freeman, and Edward H Adelson VS14 Feature Selection by Maximum Marginal Diversity, by Nuno Vasconcelos VS15 Learning Sparse Topographic Representations with Products of Student-t Distributions, by Max Welling, Simon Osindero, and Geoffrey Hinton VS16 Higher-order structure of natural images, by Yan Karklin and Michael S. Lewicki VS17 How to combine color and shape information for 3D object recognition: kernels do the trick, by B. Caputo and Gy. Dorko VS18 Concurrent Object Recognition and Segmentation by Graph Partitioning, by Stella X. Yu, Ralph Gross, and Jianbo Shi VS19 Learning About Multiple Objects in Images: Factorial Learning without Factorial Search, by Christopher K. I. Williams and Michalis K. Titsias
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