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NIPS 2004: Vancouver, British Columbia, Canada
- Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, NIPS 2004, December 13-18, 2004, Vancouver, British Columbia, Canada]. 2004
- Michael H. Bowling:
Convergence and No-Regret in Multiagent Learning. 209-216 - Pierre Baldi, Jianlin Cheng, Alessandro Vullo:
Large-Scale Prediction of Disulphide Bond Connectivity. 97-104 - Jacob Goldberger, Sam T. Roweis:
Hierarchical Clustering of a Mixture Model. 505-512 - Daniela Pucci de Farias, Nimrod Megiddo:
Exploration-Exploitation Tradeoffs for Experts Algorithms in Reactive Environments. 409-416 - Tong Zhang:
Class-size Independent Generalization Analsysis of Some Discriminative Multi-Category Classification. 1625-1632 - Elizaveta Levina, Peter J. Bickel:
Maximum Likelihood Estimation of Intrinsic Dimension. 777-784 - Alexander T. Ihler, John W. Fisher III, Alan S. Willsky:
Message Errors in Belief Propagation. 609-616 - Alexei A. Efros, Volkan Isler, Jianbo Shi, Mirkó Visontai:
Seeing through water. 393-400 - Daniel B. Neill, Andrew W. Moore, Francisco Pereira, Tom M. Mitchell:
Detecting Significant Multidimensional Spatial Clusters. 969-976 - Amnon Shashua, Tamir Hazan:
Algebraic Set Kernels with Application to Inference Over Local Image Representations. 1257-1264 - Jacob Goldberger, Sam T. Roweis, Geoffrey E. Hinton, Ruslan Salakhutdinov:
Neighbourhood Components Analysis. 513-520 - Nicolò Cesa-Bianchi, Claudio Gentile, Andrea Tironi, Luca Zaniboni:
Incremental Algorithms for Hierarchical Classification. 233-240 - Guy Shani, Ronen I. Brafman:
Resolving Perceptual Aliasing In The Presence Of Noisy Sensors. 1249-1256 - Mario Marchand, Mohak Shah:
PAC-Bayes Learning of Conjunctions and Classification of Gene-Expression Data. 881-888 - Lavi Shpigelman, Koby Crammer, Rony Paz, Eilon Vaadia, Yoram Singer:
A Temporal Kernel-Based Model for Tracking Hand Movements from Neural Activities. 1273-1280 - John M. Winn, Andrew Blake:
Generative Affine Localisation and Tracking. 1505-1512 - Sajama, Alon Orlitsky:
Semi-parametric Exponential Family PCA. 1177-1184 - Ingo Steinwart, Don R. Hush, Clint Scovel:
Density Level Detection is Classification. 1337-1344 - Ingo Steinwart, Clint Scovel:
Fast Rates to Bayes for Kernel Machines. 1345-1352 - Tobias Blaschke, Laurenz Wiskott:
Nonlinear Blind Source Separation by Integrating Independent Component Analysis and Slow Feature Analysis. 177-184 - Ting Liu, Andrew W. Moore, Alexander G. Gray, Ke Yang:
An Investigation of Practical Approximate Nearest Neighbor Algorithms. 825-832 - Hans Peter Graf, Eric Cosatto, Léon Bottou, Igor Durdanovic, Vladimir Vapnik:
Parallel Support Vector Machines: The Cascade SVM. 521-528 - Isabelle Guyon, Steve R. Gunn, Asa Ben-Hur, Gideon Dror:
Result Analysis of the NIPS 2003 Feature Selection Challenge. 545-552 - Jinbo Bi, Tong Zhang:
Support Vector Classification with Input Data Uncertainty. 161-168 - Dashan Gao, Nuno Vasconcelos:
Discriminant Saliency for Visual Recognition from Cluttered Scenes. 481-488 - Corinna Cortes, Mehryar Mohri:
Confidence Intervals for the Area Under the ROC Curve. 305-312 - Balaji Krishnapuram, David Williams, Ya Xue, Alexander J. Hartemink, Lawrence Carin, Mário A. T. Figueiredo:
On Semi-Supervised Classification. 721-728 - Jieping Ye, Ravi Janardan, Qi Li:
Two-Dimensional Linear Discriminant Analysis. 1569-1576 - Tao Xiong, Jieping Ye, Qi Li, Ravi Janardan, Vladimir Cherkassky:
Efficient Kernel Discriminant Analysis via QR Decomposition. 1529-1536 - Phillip Boyle, Marcus R. Frean:
Dependent Gaussian Processes. 217-224 - Einat Klein, Rachel Mislovaty, Ido Kanter, Andreas Ruttor, Wolfgang Kinzel:
Synchronization of neural networks by mutual learning and its application to cryptography. 689-696 - Andras Ferencz, Erik G. Learned-Miller, Jitendra Malik:
Learning Hyper-Features for Visual Identification. 425-432 - Jian Zhang, Zoubin Ghahramani, Yiming Yang:
A Probabilistic Model for Online Document Clustering with Application to Novelty Detection. 1617-1624 - Sander M. Bohté, Michael C. Mozer:
Reducing Spike Train Variability: A Computational Theory Of Spike-Timing Dependent Plasticity. 201-208 - Hideto Kazawa, Tomonori Izumitani, Hirotoshi Taira, Eisaku Maeda:
Maximal Margin Labeling for Multi-Topic Text Categorization. 649-656 - Aharon Bar-Hillel, Adam Spiro, Eran Stark:
Spike Sorting: Bayesian Clustering of Non-Stationary Data. 105-112 - Jongmin Kim, John J. Hopfield, Erik Winfree:
Neural Network Computation by In Vitro Transcriptional Circuits. 681-688 - Johannes Mohr, Klaus Obermayer:
A Topographic Support Vector Machine: Classification Using Local Label Configurations. 929-936 - Neil D. Lawrence, Michael I. Jordan:
Semi-supervised Learning via Gaussian Processes. 753-760 - Alan L. Yuille:
The Rescorla-Wagner Algorithm and Maximum Likelihood Estimation of Causal Parameters. 1585-1592 - Alan L. Yuille:
The Convergence of Contrastive Divergences. 1593-1600 - Chiranjib Bhattacharyya, Pannagadatta K. Shivaswamy, Alexander J. Smola:
A Second Order Cone programming Formulation for Classifying Missing Data. 153-160 - Moray Allan, Christopher K. I. Williams:
Harmonising Chorales by Probabilistic Inference. 25-32 - Yoshitatsu Matsuda, Kazunori Yamaguchi:
Linear Multilayer Independent Component Analysis for Large Natural Scenes. 897-904 - Ofer Dekel, Shai Shalev-Shwartz, Yoram Singer:
The Power of Selective Memory: Self-Bounded Learning of Prediction Suffix Trees. 345-352 - Ulrike von Luxburg, Olivier Bousquet, Mikhail Belkin:
Limits of Spectral Clustering. 857-864 - Felix Schürmann, Karlheinz Meier, Johannes Schemmel:
Edge of Chaos Computation in Mixed-Mode VLSI - A Hard Liquid. 1201-1208 - Peter M. Todd, Anja Dieckmann:
Heuristics for Ordering Cue Search in Decision Making. 1393-1400 - Jan Erik Solem, Fredrik Kahl:
Surface Reconstruction using Learned Shape Models. 1305-1312 - Juan José del Coz, Gustavo F. Bayón, Jorge Díez, Oscar Luaces, Antonio Bahamonde, Carlos Sañudo:
Trait Selection for Assessing Beef Meat Quality Using Non-linear SVM. 321-328 - Volker Roth:
Outlier Detection with One-class Kernel Fisher Discriminants. 1169-1176 - Philip M. Long, Xinyu Wu:
Mistake Bounds for Maximum Entropy Discrimination. 833-840 - Nicolò Cesa-Bianchi, Claudio Gentile, Luca Zaniboni:
Worst-Case Analysis of Selective Sampling for Linear-Threshold Algorithms. 241-248 - Michael Fink:
Object Classification from a Single Example Utilizing Class Relevance Metrics. 449-456 - Wolfgang Maass, Robert Legenstein, Nils Bertschinger:
Methods for Estimating the Computational Power and Generalization Capability of Neural Microcircuits. 865-872 - Antti Honkela, Harri Valpola:
Unsupervised Variational Bayesian Learning of Nonlinear Models. 593-600 - Fei Sha, Lawrence K. Saul:
Real-Time Pitch Determination of One or More Voices by Nonnegative Matrix Factorization. 1233-1240 - Liam Paninski:
Log-concavity Results on Gaussian Process Methods for Supervised and Unsupervised Learning. 1025-1032 - Xiaojin Zhu, Jaz S. Kandola, Zoubin Ghahramani, John D. Lafferty:
Nonparametric Transforms of Graph Kernels for Semi-Supervised Learning. 1641-1648 - Oliver Williams, Andrew Blake, Roberto Cipolla:
The Variational Ising Classifier (VIC) Algorithm for Coherently Contaminated Data. 1497-1504 - Tzu-Kuo Huang, Chih-Jen Lin, Ruby C. Weng:
A Generalized Bradley-Terry Model: From Group Competition to Individual Skill. 601-608 - Constanze Hofstoetter, Manuel Gil, Kynan Eng, Giacomo Indiveri, Matti Mintz, Jörg Kramer, Paul F. M. J. Verschure:
The Cerebellum Chip: an Analog VLSI Implementation of a Cerebellar Model of Classical Conditioning. 577-584 - Saharon Rosset:
Following Curved Regularized Optimization Solution Paths. 1153-1160 - Robert Fergus, Andrew Zisserman, Pietro Perona:
Sampling Methods for Unsupervised Learning. 433-440 - Matthias O. Franz, Bernhard Schölkopf:
Implicit Wiener Series for Higher-Order Image Analysis. 465-472 - Robert Jenssen, Deniz Erdogmus, José Carlos Príncipe, Torbjørn Eltoft:
The Laplacian PDF Distance: A Cost Function for Clustering in a Kernel Feature Space. 625-632 - Peter Sollich, Christopher K. I. Williams:
Using the Equivalent Kernel to Understand Gaussian Process Regression. 1313-1320 - Fabio Aiolli, Alessandro Sperduti:
Learning Preferences for Multiclass Problems. 17-24 - Taku Kudo, Eisaku Maeda, Yuji Matsumoto:
An Application of Boosting to Graph Classification. 729-736 - Massimiliano Pavan, Marcello Pelillo:
Efficient Out-of-Sample Extension of Dominant-Set Clusters. 1057-1064 - Bernd Fischer, Volker Roth, Joachim M. Buhmann, Jonas Grossmann, Sacha Baginsky, Wilhelm Gruissem, Franz F. Roos, Peter Widmayer:
A Hidden Markov Model for de Novo Peptide Sequencing. 457-464 - Nils Bertschinger, Thomas Natschläger, Robert Legenstein:
At the Edge of Chaos: Real-time Computations and Self-Organized Criticality in Recurrent Neural Networks. 145-152 - Gökhan H. Bakir, Léon Bottou, Jason Weston:
Breaking SVM Complexity with Cross-Training. 81-88 - Nathan Srebro, Noga Alon, Tommi S. Jaakkola:
Generalization Error Bounds for Collaborative Prediction with Low-Rank Matrices. 1321-1328 - Maria-Florina Balcan, Avrim Blum, Ke Yang:
Co-Training and Expansion: Towards Bridging Theory and Practice. 89-96 - Sophie Denève:
Bayesian inference in spiking neurons. 353-360 - Andrew Y. Ng, H. Jin Kim:
Stable adaptive control with online learning. 977-984 - Rob Powers, Yoav Shoham:
New Criteria and a New Algorithm for Learning in Multi-Agent Systems. 1089-1096 - Jennifer Listgarten, Radford M. Neal, Sam T. Roweis, Andrew Emili:
Multiple Alignment of Continuous Time Series. 817-824 - Sunita Sarawagi, William W. Cohen:
Semi-Markov Conditional Random Fields for Information Extraction. 1185-1192 - Shai Avidan, Moshe Butman:
The power of feature clustering: An application to object detection. 57-64 - Wolf Kienzle, Gökhan H. Bakir, Matthias O. Franz, Bernhard Schölkopf:
Face Detection - Efficient and Rank Deficient. 673-680 - Dan Pelleg, Andrew W. Moore:
Active Learning for Anomaly and Rare-Category Detection. 1073-1080 - Changjiang Yang, Ramani Duraiswami, Larry S. Davis:
Efficient Kernel Machines Using the Improved Fast Gauss Transform. 1561-1568 - Dori Peleg, Ron Meir:
A Feature Selection Algorithm Based on the Global Minimization of a Generalization Error Bound. 1065-1072 - Thomas Navin Lal, Thilo Hinterberger, Guido Widman, Michael Schröder, N. Jeremy Hill, Wolfgang Rosenstiel, Christian Erich Elger, Bernhard Schölkopf, Niels Birbaumer:
Methods Towards Invasive Human Brain Computer Interfaces. 737-744 - Bernhard Schölkopf, Joachim Giesen, Simon Spalinger:
Kernel Methods for Implicit Surface Modeling. 1193-1200 - Liam Paninski:
Variational Minimax Estimation of Discrete Distributions under KL Loss. 1033-1040 - Balázs Kégl, Ligen Wang:
Boosting on Manifolds: Adaptive Regularization of Base Classifiers. 665-672 - Wojtek Kowalczyk, Nikos Vlassis:
Newscast EM. 713-720 - Sean O'Rourke, Gal Chechik, Robin Friedman, Eleazar Eskin:
Discrete profile alignment via constrained information bottleneck. 1009-1016 - Jean-Philippe Vert, Yoshihiro Yamanishi:
Supervised Graph Inference. 1433-1440 - Peng Xu, Frederick Jelinek:
Using Random Forests in the Structured Language Model. 1545-1552 - Shyam Visweswaran, Gregory F. Cooper:
Instance-Specific Bayesian Model Averaging for Classification. 1449-1456 - Le Lu, Gregory D. Hager, Laurent Younes:
A Three Tiered Approach for Articulated Object Action Modeling and Recognition. 841-848 - Giorgio Giacinto, Fabio Roli:
Instance-Based Relevance Feedback for Image Retrieval. 489-496 - N. Jeremy Hill, Thomas Navin Lal, Karin Bierig, Niels Birbaumer, Bernhard Schölkopf:
An Auditory Paradigm for Brain-Computer Interfaces. 569-576 - John Blitzer, Kilian Q. Weinberger, Lawrence K. Saul, Fernando Pereira:
Hierarchical Distributed Representations for Statistical Language Modeling. 185-192 - Rasmus Kongsgaard Olsson, Lars Kai Hansen
:
A Harmonic Excitation State-Space Approach to Blind Separation of Speech. 993-1000 - Finn Årup Nielsen:
Mass Meta-analysis in Talairach Space. 985-992 - Tomoharu Iwata, Kazumi Saito, Naonori Ueda, Sean Stromsten, Thomas L. Griffiths, Joshua B. Tenenbaum:
Parametric Embedding for Class Visualization. 617-624 - Sanjoy Dasgupta:
Analysis of a greedy active learning strategy. 337-344 - Marcelo A. Montemurro, Stefano Panzeri:
Optimal Information Decoding from Neuronal Populations with Specific Stimulus Selectivity. 937-944 - Yves Grandvalet, Yoshua Bengio:
Semi-supervised Learning by Entropy Minimization. 529-536 - Ofer Shai, Brendan J. Frey, Quaid Morris, Qun Pan, Christine Misquitta, Benjamin J. Blencowe:
Probabilistic Inference of Alternative Splicing Events in Microarray Data. 1241-1248 - Miguel E. Figueroa, Seth Bridges, Chris Diorio:
On-Chip Compensation of Device-Mismatch Effects in Analog VLSI Neural Networks. 441-448 - Andrew McCallum, Ben Wellner:
Conditional Models of Identity Uncertainty with Application to Noun Coreference. 905-912 - Sham M. Kakade, Michael J. Kearns, Luis E. Ortiz, Robin Pemantle, Siddharth Suri:
Economic Properties of Social Networks. 633-640 - Clayton D. Scott, Robert D. Nowak:
On the Adaptive Properties of Decision Trees. 1225-1232 - Anthony J. Bell, Lucas C. Parra:
Maximising Sensitivity in a Spiking Network. 121-128 - Frank DiMaio, Jude W. Shavlik, George N. Phillips:
Pictorial Structures for Molecular Modeling: Interpreting Density Maps. 369-376 - Dengyong Zhou, Bernhard Schölkopf, Thomas Hofmann:
Semi-supervised Learning on Directed Graphs. 1633-1640 - Pierre Moreels, Pietro Perona:
Common-Frame Model for Object Recognition. 953-960 - Koji Tsuda, Gunnar Rätsch, Manfred K. Warmuth:
Matrix Exponential Gradient Updates for On-line Learning and Bregman Projection. 1425-1432 - Jaety Edwards, Yee Whye Teh, David A. Forsyth, Roger Bock, Michael Maire, Grace Vesom:
Making Latin Manuscripts Searchable using gHMMs. 385-392 - Chakra Chennubhotla, Allan D. Jepson:
Hierarchical Eigensolver for Transition Matrices in Spectral Methods. 273-280 - Jongwoo Lim, David A. Ross, Ruei-Sung Lin, Ming-Hsuan Yang:
Incremental Learning for Visual Tracking. 793-800 - Lihi Zelnik-Manor, Pietro Perona:
Self-Tuning Spectral Clustering. 1601-1608 - Ruei-Sung Lin, David A. Ross, Jongwoo Lim, Ming-Hsuan Yang:
Adaptive Discriminative Generative Model and Its Applications. 801-808 - Dragomir Anguelov, Praveen Srinivasan, Hoi-Cheung Pang, Daphne Koller, Sebastian Thrun, James Davis:
The Correlated Correspondence Algorithm for Unsupervised Registration of Nonrigid Surfaces. 33-40 - Joris M. Mooij, Hilbert J. Kappen:
Validity Estimates for Loopy Belief Propagation on Binary Real-world Networks. 945-952 - Trevor Hastie, Saharon Rosset, Robert Tibshirani, Ji Zhu:
The Entire Regularization Path for the Support Vector Machine. 561-568 - Marco Cuturi, Jean-Philippe Vert:
Semigroup Kernels on Finite Sets. 329-336 - Nathan Srebro, Jason D. M. Rennie, Tommi S. Jaakkola:
Maximum-Margin Matrix Factorization. 1329-1336 - Jochen Triesch:
Synergies between Intrinsic and Synaptic Plasticity in Individual Model Neurons. 1417-1424 - Fredrik Bissmarck, Hiroyuki Nakahara, Kenji Doya, Okihide Hikosaka:
Responding to Modalities with Different Latencies. 169-176 - Tanzeem Choudhury, Sumit Basu:
Modeling Conversational Dynamics as a Mixed-Memory Markov Process. 281-288 - Saharon Rosset, Ji Zhu, Hui Zou, Trevor Hastie:
A Method for Inferring Label Sampling Mechanisms in Semi-Supervised Learning. 1161-1168 - Tamara L. Berg, Alexander C. Berg, Jaety Edwards, David A. Forsyth:
Whos In the Picture. 137-144 - Odelia Schwartz, Terrence J. Sejnowski, Peter Dayan:
Assignment of Multiplicative Mixtures in Natural Images. 1217-1224 - Margarita Osadchy, Matthew L. Miller, Yann LeCun:
Synergistic Face Detection and Pose Estimation with Energy-Based Models. 1017-1024 - Jing Wang, Zhenyue Zhang, Hongyuan Zha:
Adaptive Manifold Learning. 1473-1480 - Aaron C. Courville, Nathaniel D. Daw, David S. Touretzky:
Similarity and Discrimination in Classical Conditioning: A Latent Variable Account. 313-320 - Rajesh P. N. Rao:
Hierarchical Bayesian Inference in Networks of Spiking Neurons. 1113-1120 - Angela J. Yu, Peter Dayan:
Inference, Attention, and Decision in a Bayesian Neural Architecture. 1577-1584 - Peter L. Bartlett, Michael Collins, Benjamin Taskar, David A. McAllester:
Exponentiated Gradient Algorithms for Large-margin Structured Classification. 113-120 - Khashayar Rohanimanesh, Robert Platt Jr., Sridhar Mahadevan, Roderic A. Grupen:
Coarticulation in Markov Decision Processes. 1137-1144 - Francis R. Bach, Michael I. Jordan:
Blind One-microphone Speech Separation: A Spectral Learning Approach. 65-72 - Amir Globerson, Gal Chechik, Fernando C. N. Pereira, Naftali Tishby:
Euclidean Embedding of Co-Occurrence Data. 497-504 - Michael P. Holmes, Charles Lee Isbell Jr.:
Schema Learning: Experience-Based Construction of Predictive Action Models. 585-592 - Taro Toyoizumi, Jean-Pascal Pfister, Kazuyuki Aihara, Wulfram Gerstner:
Spike-timing Dependent Plasticity and Mutual Information Maximization for a Spiking Neuron Model. 1409-1416 - Lorenzo Rosasco, Andrea Caponnetto, Ernesto De Vito, Francesca Odone, Umberto De Giovannini:
Learning, Regularization and Ill-Posed Inverse Problems. 1145-1152 - Thomas L. Griffiths, Mark Steyvers, David M. Blei, Joshua B. Tenenbaum:
Integrating Topics and Syntax. 537-544 - Alexandre d'Aspremont, Laurent El Ghaoui, Michael I. Jordan, Gert R. G. Lanckriet:
A Direct Formulation for Sparse PCA Using Semidefinite Programming. 41-48 - R. Jacob Vogelstein, Udayan Mallik, Eugenio Culurciello, Gert Cauwenberghs, Ralph Etienne-Cummings:
Saliency-Driven Image Acuity Modulation on a Reconfigurable Array of Spiking Silicon Neurons. 1457-1464 - Hyun-Jin Park, Te-Won Lee:
Modeling Nonlinear Dependencies in Natural Images using Mixture of Laplacian Distribution. 1041-1048 - François Rivest, Yoshua Bengio, John Kalaska:
Brain Inspired Reinforcement Learning. 1129-1136 - Mukund Narasimhan, Jeff A. Bilmes:
Optimal sub-graphical models. 961-968 - Yuanqing Lin, Daniel D. Lee:
Bayesian Regularization and Nonnegative Deconvolution for Time Delay Estimation. 809-816 - Xiang Yan, Persi Diaconis, Paat Rusmevichientong, Benjamin Van Roy:
Solitaire: Man Versus Machine. 1553-1560 - Pradeep Shenoy, Rajesh P. N. Rao:
Dynamic Bayesian Networks for Brain-Computer Interfaces. 1265-1272 - Laurent Zwald, Régis Vert, Gilles Blanchard, Pascal Massart:
Kernel Projection Machine: a New Tool for Pattern Recognition. 1649-1656 - Máté Lengyel, Peter Dayan:
Rate- and Phase-coded Autoassociative Memory. 769-776 - Pascal Poupart, Craig Boutilier:
VDCBPI: an Approximate Scalable Algorithm for Large POMDPs. 1081-1088 - Daniela Pucci de Farias, Benjamin Van Roy:
A Cost-Shaping LP for Bellman Error Minimization with Performance Guarantees. 417-424 - David C. Parkes, Satinder Singh, Dimah Yanovsky:
Approximately Efficient Online Mechanism Design. 1049-1056 - Shivani Agarwal, Thore Graepel, Ralf Herbrich, Dan Roth:
A Large Deviation Bound for the Area Under the ROC Curve. 9-16 - Yoshua Bengio, Martin Monperrus:
Non-Local Manifold Tangent Learning. 129-136 - Shantanu Chakrabartty, Gert Cauwenberghs:
Sub-Microwatt Analog VLSI Support Vector Machine for Pattern Classification and Sequence Estimation. 249-256 - Antonio Torralba, Kevin P. Murphy, William T. Freeman:
Contextual Models for Object Detection Using Boosted Random Fields. 1401-1408 - Manfred Opper, Ole Winther:
Expectation Consistent Free Energies for Approximate Inference. 1001-1008 - Baranidharan Raman, Ricardo Gutierrez-Osuna:
Chemosensory Processing in a Spiking Model of the Olfactory Bulb: Chemotopic Convergence and Center Surround Inhibition. 1105-1112 - Scott Gaffney, Padhraic Smyth:
Joint Probabilistic Curve Clustering and Alignment. 473-480 - Richard S. Zemel, Quentin J. M. Huys, Rama Natarajan, Peter Dayan:
Probabilistic Computation in Spiking Populations. 1609-1616 - Dustin Lang, Nando de Freitas:
Beat Tracking the Graphical Model Way. 745-752 - Vladimir Koltchinskii, Manel Martínez-Ramón, Stefan Posse:
Optimal Aggregation of Classifiers and Boosting Maps in Functional Magnetic Resonance Imaging. 705-712 - Satinder Singh, Andrew G. Barto, Nuttapong Chentanez:
Intrinsically Motivated Reinforcement Learning. 1281-1288 - Max Welling, Michal Rosen-Zvi, Geoffrey E. Hinton:
Exponential Family Harmoniums with an Application to Information Retrieval. 1481-1488 - Joseph Bockhorst, Mark Craven:
Markov Networks for Detecting Overalpping Elements in Sequence Data. 193-200 - David H. Stern, Thore Graepel, David J. C. MacKay:
Modelling Uncertainty in the Game of Go. 1353-1360 - Eizaburo Doi, Michael S. Lewicki:
Sparse Coding of Natural Images Using an Overcomplete Set of Limited Capacity Units. 377-384 - Erik G. Learned-Miller, Parvez Ahammad:
Joint MRI Bias Removal Using Entropy Minimization Across Images. 761-768 - Yee Whye Teh, Michael I. Jordan, Matthew J. Beal, David M. Blei:
Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes. 1385-1392 - Anton Schwaighofer, Volker Tresp, Kai Yu:
Learning Gaussian Process Kernels via Hierarchical Bayes. 1209-1216 - Inderjit S. Dhillon, Suvrit Sra, Joel A. Tropp:
Triangle Fixing Algorithms for the Metric Nearness Problem. 361-368 - Omid Madani, David M. Pennock, Gary William Flake:
Co-Validation: Using Model Disagreement on Unlabeled Data to Validate Classification Algorithms. 873-880 - Michael D. Colagrosso, Michael C. Mozer:
Theories of Access Consciousness. 289-296 - Francis R. Bach, Romain Thibaux, Michael I. Jordan:
Computing regularization paths for learning multiple kernels. 73-80 - Eyal Even-Dar, Sham M. Kakade, Yishay Mansour:
Experts in a Markov Decision Process. 401-408 - Miguel Á. Carreira-Perpiñán, Richard S. Zemel:
Proximity Graphs for Clustering and Manifold Learning. 225-232 - Felix A. Wichmann, Arnulf B. A. Graf, Eero P. Simoncelli, Heinrich H. Bülthoff, Bernhard Schölkopf:
Machine Learning Applied to Perception: Decision Images for Gender Classification. 1489-1496 - Kosuke Hamaguchi, Masato Okada, Kazuyuki Aihara:
Theory of localized synfire chain: characteristic propagation speed of stable spike pattern. 553-560 - Erik Aurell, Uri Gordon, Scott Kirkpatrick:
Comparing Beliefs, Surveys, and Random Walks. 49-56 - Charles A. Micchelli, Massimiliano Pontil:
Kernels for Multi--task Learning. 921-928 - Ariadna Quattoni, Michael Collins, Trevor Darrell:
Conditional Random Fields for Object Recognition. 1097-1104 - K. Y. Michael Wong, S. W. Lim, Zhuo Gao:
Multi-agent Cooperation in Diverse Population Games. 1521-1528 - David P. Wipf, Bhaskar D. Rao:
L_0-norm Minimization for Basis Selection. 1513-1520 - Sham M. Kakade, Andrew Y. Ng:
Online Bounds for Bayesian Algorithms. 641-648 - Robert D. Kleinberg:
Nearly Tight Bounds for the Continuum-Armed Bandit Problem. 697-704 - Evan C. Smith, Michael S. Lewicki:
Learning Efficient Auditory Codes Using Spikes Predicts Cochlear Filters. 1289-1296 - Linli Xu, James Neufeld, Bryce Larson, Dale Schuurmans:
Maximum Margin Clustering. 1537-1544 - Roland Memisevic, Geoffrey E. Hinton:
Multiple Relational Embedding. 913-920 - S. V. N. Vishwanathan, Alexander J. Smola:
Binet-Cauchy Kernels. 1441-1448 - Richard S. Sutton, Brian Tanner:
Temporal-Difference Networks. 1377-1384 - Kumar Chellapilla, Patrice Y. Simard:
Using Machine Learning to Break Visual Human Interaction Proofs (HIPs). 265-272 - Alan Stocker, Eero P. Simoncelli:
Constraining a Bayesian Model of Human Visual Speed Perception. 1361-1368 - Olivier Chapelle, Zaïd Harchaoui:
A Machine Learning Approach to Conjoint Analysis. 257-264 - Adrian Corduneanu, Tommi S. Jaakkola:
Distributed Information Regularization on Graphs. 297-304 - Haidong Wang, Eran Segal, Asa Ben-Hur, Daphne Koller, Douglas L. Brutlag:
Identifying Protein-Protein Interaction Sites on a Genome-Wide Scale. 1465-1472 - Tim K. Marks, John R. Hershey, J. Cooper Roddey, Javier R. Movellan:
Joint Tracking of Pose, Expression, and Texture using Conditionally Gaussian Filters. 889-896 - Zhengdong Lu, Todd K. Leen:
Semi-supervised Learning with Penalized Probabilistic Clustering. 849-856 - Laura Walker Renninger, James M. Coughlan, Preeti Verghese, Jitendra Malik:
An Information Maximization Model of Eye Movements. 1121-1128 - Maxim Likhachev, Geoffrey J. Gordon, Sebastian Thrun:
Planning for Markov Decision Processes with Sparse Stochasticity. 785-792 - Balázs Kégl:
Generalization Error and Algorithmic Convergence of Median Boosting. 657-664 - Erik B. Sudderth, Michael I. Mandel, William T. Freeman, Alan S. Willsky:
Distributed Occlusion Reasoning for Tracking with Nonparametric Belief Propagation. 1369-1376 - Rion Snow, Daniel Jurafsky, Andrew Y. Ng:
Learning Syntactic Patterns for Automatic Hypernym Discovery. 1297-1304 - Pieter Abbeel, Andrew Y. Ng:
Learning first-order Markov models for control. 1-8
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