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ORCIDNIPS 2005: Vancouver, British Columbia, Canada
- Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, NIPS 2005, December 5-8, 2005, Vancouver, British Columbia, Canada]. 2005
- Pieter Abbeel, Varun Ganapathi, Andrew Y. Ng:
Learning vehicular dynamics, with application to modeling helicopters. 1-8 - Douglas Aberdeen:
Policy-Gradient Methods for Planning. 9-16 - Felix V. Agakov, David Barber:
Kernelized Infomax Clustering. 17-24 - Misha B. Ahrens, Quentin J. M. Huys, Liam Paninski:
Large-scale biophysical parameter estimation in single neurons via constrained linear regression. 25-32 - Yasemin Altun, David A. McAllester, Mikhail Belkin:
Margin Semi-Supervised Learning for Structured Variables. 33-40 - J. Ignacio Alvarez-Hamelin, Luca Dall'Asta, Alain Barrat, Alessandro Vespignani:
Large scale networks fingerprinting and visualization using the k-core decomposition. 41-50 - Brigham S. Anderson, Andrew W. Moore:
Fast Information Value for Graphical Models. 51-58 - David Arathorn:
A Cortically-Plausible Inverse Problem Solving Method Applied to Recognizing Static and Kinematic 3D Objects. 59-66 - Andreas Argyriou, Mark Herbster, Massimiliano Pontil:
Combining Graph Laplacians for Semi-Supervised Learning. 67-74 - John V. Arthur, Kwabena Boahen:
Learning in Silicon: Timing is Everything. 75-82 - Michaël Aupetit:
Learning Topology with the Generative Gaussian Graph and the EM Algorithm. 83-90 - J. Andrew Bagnell, Andrew Y. Ng:
On Local Rewards and Scaling Distributed Reinforcement Learning. 91-98 - Chris L. Baker, Joshua B. Tenenbaum, Rebecca Saxe:
Bayesian models of human action understanding. 99-106 - Yoshua Bengio, Olivier Delalleau, Nicolas Le Roux:
The Curse of Highly Variable Functions for Local Kernel Machines. 107-114 - Yoshua Bengio, Hugo Larochelle, Pascal Vincent:
Non-Local Manifold Parzen Windows. 115-122 - Yoshua Bengio, Nicolas Le Roux, Pascal Vincent, Olivier Delalleau, Patrice Marcotte:
Convex Neural Networks. 123-130 - Gilles Blanchard, Masashi Sugiyama, Motoaki Kawanabe, Vladimir G. Spokoiny, Klaus-Robert Müller:
Non-Gaussian Component Analysis: a Semi-parametric Framework for Linear Dimension Reduction. 131-138 - Doron Blatt, Alfred O. Hero III:
From Weighted Classification to Policy Search. 139-146 - David M. Blei, John D. Lafferty:
Correlated Topic Models. 147-154 - Neil D. B. Bruce, John K. Tsotsos:
Saliency Based on Information Maximization. 155-162 - Brent Bryan, Jeff G. Schneider, Robert Nichol, Christopher J. Miller, Christopher R. Genovese, Larry A. Wasserman:
Active Learning For Identifying Function Threshold Boundaries. 163-170 - Razvan C. Bunescu, Raymond J. Mooney:
Subsequence Kernels for Relation Extraction. 171-178 - Rui M. Castro, Rebecca Willett, Robert D. Nowak:
Faster Rates in Regression via Active Learning. 179-186 - Abdullah Celik, Milutin Stanacevic, Gert Cauwenberghs:
Gradient Flow Independent Component Analysis in Micropower VLSI. 187-194 - Nicolò Cesa-Bianchi, Claudio Gentile:
Improved risk tail bounds for on-line algorithms. 195-202 - Antoni B. Chan, Nuno Vasconcelos:
Layered Dynamic Textures. 203-210 - Yixin Chen, Ya Zhang, Xiang Ji:
Size Regularized Cut for Data Clustering. 211-218 - Koby Crammer, Michael J. Kearns, Jennifer Wortman:
Learning from Data of Variable Quality. 219-226 - Márton Danóczy, Richard H. R. Hahnloser:
Efficient estimation of hidden state dynamics from spike trains. 227-234 - Sanjoy Dasgupta:
Coarse sample complexity bounds for active learning. 235-242 - Peter Dayan, Angela J. Yu:
Norepinephrine and Neural Interrupts. 243-250 - Ofer Dekel, Shai Shalev-Shwartz, Yoram Singer:
The Forgetron: A Kernel-Based Perceptron on a Fixed Budget. 259-266 - Ofer Dekel, Yoram Singer:
Data-Driven Online to Batch Conversions. 267-274 - Ricky Der, Daniel Lee:
Beyond Gaussian Processes: On the Distributions of Infinite Networks. 275-282 - Inderjit S. Dhillon, Suvrit Sra:
Generalized Nonnegative Matrix Approximations with Bregman Divergences. 283-290 - James Diebel, Sebastian Thrun:
An Application of Markov Random Fields to Range Sensing. 291-298 - Chuong B. Do, Andrew Y. Ng:
Transfer learning for text classification. 299-306 - Eizaburo Doi, Doru-Cristian Balcan, Michael S. Lewicki:
A Theoretical Analysis of Robust Coding over Noisy Overcomplete Channels. 307-314 - Guido Dornhege, Benjamin Blankertz, Matthias Krauledat, Florian Losch, Gabriel Curio, Klaus-Robert Müller:
Optimizing spatio-temporal filters for improving Brain-Computer Interfacing. 315-322 - Miroslav Dudík, Robert E. Schapire, Steven J. Phillips:
Correcting sample selection bias in maximum entropy density estimation. 323-330 - Jaety Edwards, David A. Forsyth:
Searching for Character Models. 331-338 - Austin I. Eliazar, Ronald Parr:
Hierarchical Linear/Constant Time SLAM Using Particle Filters for Dense Maps. 339-346 - Yaakov Engel, Peter Szabó, Dmitry Volkinshtein:
Learning to Control an Octopus Arm with Gaussian Process Temporal Difference Methods. 347-354 - Jason D. R. Farquhar, David R. Hardoon, Hongying Meng, John Shawe-Taylor, Sándor Szedmák:
Two view learning: SVM-2K, Theory and Practice. 355-362 - Patrick Flaherty, Michael I. Jordan, Adam P. Arkin:
Robust design of biological experiments. 363-370 - François Fleuret, Gilles Blanchard:
Pattern Recognition from One Example by Chopping. 371-378 - Nando de Freitas, Yang Wang, Maryam Mahdaviani, Dustin Lang:
Fast Krylov Methods for N-Body Learning. 251-258 - Brendan J. Frey, Delbert Dueck:
Mixture Modeling by Affinity Propagation. 379-386 - Kenji Fukumizu, Francis R. Bach, Arthur Gretton:
Statistical Convergence of Kernel CCA. 387-394 - Glenn Fung, Rómer Rosales, Balaji Krishnapuram:
Learning Rankings via Convex Hull Separation. 395-402 - Artur S. d'Avila Garcez, Luís C. Lamb, Dov M. Gabbay:
A Connectionist Model for Constructive Modal Reasoning. 403-410 - Thomas Gärtner, Quoc V. Le, Simon Burton, Alexander J. Smola, S. V. N. Vishwanathan:
Large-Scale Multiclass Transduction. 411-418 - Peter V. Gehler, Max Welling:
Products of Edge-perts. 419-426 - Tao Geng, Bernd Porr, Florentin Wörgötter:
Fast biped walking with a reflexive controller and real-time policy searching. 427-434 - Zoubin Ghahramani, Katherine A. Heller:
Bayesian Sets. 435-442 - Ran Gilad-Bachrach, Amir Navot, Naftali Tishby:
Query by Committee Made Real. 443-450 - Amir Globerson, Sam T. Roweis:
Metric Learning by Collapsing Classes. 451-458 - Sharon Goldwater, Thomas L. Griffiths, Mark Johnson:
Interpolating between types and tokens by estimating power-law generators. 459-466 - Yves Grandvalet, Johnny Mariéthoz, Samy Bengio:
A Probabilistic Interpretation of SVMs with an Application to Unbalanced Classification. 467-474 - Thomas L. Griffiths, Zoubin Ghahramani:
Infinite latent feature models and the Indian buffet process. 475-482 - Lacey Gunter, Ji Zhu:
Computing the Solution Path for the Regularized Support Vector Regression. 483-490 - Firas Hamze, Nando de Freitas:
Hot Coupling: A Particle Approach to Inference and Normalization on Pairwise Undirected Graphs. 491-498 - Xiaofei He, Deng Cai, Partha Niyogi:
Laplacian Score for Feature Selection. 507-514 - Xiaofei He, Deng Cai, Partha Niyogi:
Tensor Subspace Analysis. 499-506 - Geoffrey E. Hinton, Vinod Nair:
Inferring Motor Programs from Images of Handwritten Digits. 515-522 - Wentao Huang, Licheng Jiao, Tan Shan, Maoguo Gong:
Response Analysis of Neuronal Population with Synaptic Depression. 523-530 - Yunsong Huang, B. Keith Jenkins:
Non-iterative Estimation with Perturbed Gaussian Markov Processes. 531-538 - Jarmo Hurri:
Learning Cue-Invariant Visual Responses. 539-546 - Laurent Itti, Pierre Baldi:
Bayesian Surprise Attracts Human Attention. 547-554 - Herbert Jaeger, Mingjie Zhao, Andreas Kolling:
Efficient Estimation of OOMs. 555-562 - Viren Jain, Valentin P. Zhigulin, H. Sebastian Seung:
Representing Part-Whole Relationships in Recurrent Neural Networks. 563-570 - Rong Jin, Chris H. Q. Ding, Feng Kang:
A Probabilistic Approach for Optimizing Spectral Clustering. 571-578 - Jason K. Johnson, Dmitry M. Malioutov, Alan S. Willsky:
Walk-Sum Interpretation and Analysis of Gaussian Belief Propagation. 579-586 - Nebojsa Jojic, Vladimir Jojic, Brendan J. Frey, Christopher Meek, David Heckerman:
Using epitomes to model genetic diversity: Rational design of HIV vaccines. 587-594 - Renaud Jolivet, Alexander Rauch, Hans-Rudolf Lüscher, Wulfram Gerstner:
Integrate-and-Fire models with adaptation are good enough. 595-602 - Anatoli B. Juditsky, Alexander V. Nazin, Alexandre B. Tsybakov, Nicolas Vayatis:
Generalization Error Bounds for Aggregation by Mirror Descent with Averaging. 603-610 - Sham M. Kakade, Matthias W. Seeger, Dean P. Foster:
Worst-Case Bounds for Gaussian Process Models. 619-626 - Sham M. Kakade, Adam Kalai:
From Batch to Transductive Online Learning. 611-618 - Ashish Kapoor, Yuan (Alan) Qi, Hyungil Ahn, Rosalind W. Picard:
Hyperparameter and Kernel Learning for Graph Based Semi-Supervised Classification. 627-634 - Yan Karklin, Michael S. Lewicki:
Is Early Vision Optimized for Extracting Higher-order Dependencies? 635-642 - S. Sathiya Keerthi, Wei Chu:
A matching pursuit approach to sparse Gaussian process regression. 643-650 - Mikaela Keller, Samy Bengio, Siew Yeung Wong:
Benchmarking Non-Parametric Statistical Tests. 651-658 - Seung-Jean Kim, Alessandro Magnani, Stephen P. Boyd:
Robust Fisher Discriminant Analysis. 659-666 - Kristina Lisa Klinkner, Cosma Rohilla Shalizi, Marcelo Camperi:
Measuring Shared Information and Coordinated Activity in Neuronal Networks. 667-674 - O. Patrick Kreidl, Alan S. Willsky:
Inference with Minimal Communication: a Decision-Theoretic Variational Approach. 675-682 - Eyal Krupka, Naftali Tishby:
Generalization in Clustering with Unobserved Features. 683-690 - Jeremy Kubica, Joseph Masiero, Andrew W. Moore, Robert Jedicke, Andrew J. Connolly:
Variable KD-Tree Algorithms for Spatial Pattern Search and Discovery. 691-698 - Malte Kuss, Carl Edward Rasmussen:
Assessing Approximations for Gaussian Process Classification. 699-706 - John D. Lafferty, Larry A. Wasserman:
Rodeo: Sparse Nonparametric Regression in High Dimensions. 707-714 - Kevin J. Lang:
Fixing two weaknesses of the Spectral Method. 715-722 - Tilman Lange, Joachim M. Buhmann:
Fusion of Similarity Data in Clustering. 723-730 - François Laviolette, Mario Marchand, Mohak Shah:
A PAC-Bayes approach to the Set Covering Machine. 731-738 - Yann LeCun, Urs Muller, Jan Ben, Eric Cosatto, Beat Flepp:
Off-Road Obstacle Avoidance through End-to-End Learning. 739-746 - Dongryeol Lee, Alexander G. Gray, Andrew W. Moore:
Dual-Tree Fast Gauss Transforms. 747-754 - Jung Hoon Lee, Xiaolong Ma, Konstantin Likharev:
CMOL CrossNets: Possible Neuromorphic Nanoelectronic Circuits. 755-762 - Robert Legenstein, Wolfgang Maass:
A Criterion for the Convergence of Learning with Spike Timing Dependent Plasticity. 763-770 - Anna Levina, J. Michael Herrmann, Theo Geisel:
Dynamical Synapses Give Rise to a Power-Law Distribution of Neuronal Avalanches. 771-778 - Fan Li, Yiming Yang, Eric P. Xing:
From Lasso regression to Feature vector machine. 779-786 - Xuejun Liao, Lawrence Carin:
Radial Basis Function Network for Multi-task Learning. 792-802 - Lin Liao, Dieter Fox, Henry A. Kautz:
Location-based activity recognition. 787-794 - Ross A. Lippert, Ryan M. Rifkin:
Asymptotics of Gaussian Regularized Least Squares. 803-810 - Nicolas Loeff, Himanshu Arora, Alexander Sorokin, David A. Forsyth:
Efficient Unsupervised Learning for Localization and Detection in Object Categories. 811-818 - Aurélie C. Lozano, Sanjeev R. Kulkarni, Robert E. Schapire:
Convergence and Consistency of Regularized Boosting Algorithms with Stationary B-Mixing Observations. 819-826 - Hongjing Lu, Alan L. Yuille:
Ideal Observers for Detecting Motion: Correspondence Noise. 827-834 - Wolfgang Maass, Prashant Joshi, Eduardo D. Sontag:
Principles of real-time computing with feedback applied to cortical microcircuit models. 835-842 - Sridhar Mahadevan, Mauro Maggioni:
Value Function Approximation with Diffusion Wavelets and Laplacian Eigenfunctions. 843-850 - Uri Maoz, Elon Portugaly, Tamar Flash, Yair Weiss:
Noise and the two-thirds power Law. 851-858 - Naoki Masuda, Shun-ichi Amari:
Modeling Memory Transfer and Saving in Cerebellar Motor Learning. 859-866 - Samuel M. McClure, Mark S. Gilzenrat, Jonathan D. Cohen:
An exploration-exploitation model based on norepinepherine and dopamine activity. 867-874 - Peter McCracken, Michael H. Bowling:
Online Discovery and Learning of Predictive State Representations. 875-882 - Edward Meeds, Simon Osindero:
An Alternative Infinite Mixture Of Gaussian Process Experts. 883-890 - Keiji Miura, Masato Okada, Shun-ichi Amari:
Unbiased Estimator of Shape Parameter for Spiking Irregularities under Changing Environments. 891-898 - Ciamac Cyrus Moallemi, Benjamin Van Roy:
Consensus Propagation. 899-906 - Daichi Mochihashi, Yuji Matsumoto:
Context as Filtering. 907-914 - Baback Moghaddam, Yair Weiss, Shai Avidan:
Spectral Bounds for Sparse PCA: Exact and Greedy Algorithms. 915-922 - Michael Mozer, Michael Shettel, Shaun Vecera:
Top-Down Control of Visual Attention: A Rational Account. 923-930 - Tatsuto Murayama, Peter Davis:
Rate Distortion Codes in Sensor Networks. 931-938 - Juan José Murillo-Fuentes, Sebastian Caro, Fernando Pérez-Cruz:
Gaussian Processes for Multiuser Detection in CDMA receivers. 939-946 - Iain Murray, David J. C. MacKay, Zoubin Ghahramani, John Skilling:
Nested sampling for Potts models. 947-954 - Boaz Nadler, Stéphane Lafon, Ronald R. Coifman, Ioannis G. Kevrekidis:
Diffusion Maps, Spectral Clustering and Eigenfunctions of Fokker-Planck Operators. 955-962 - Srikantan S. Nagarajan, Hagai Attias, Kenneth E. Hild II, Kensuke Sekihara:
Stimulus Evoked Independent Factor Analysis of MEG Data with Large Background Activity. 963-970 - Yusuke Nakashita, Yoshio Mita, Tadashi Shibata:
An Analog Visual Pre-Processing Processor Employing Cyclic Line Access in Only-Nearest-Neighbor-Interconnects Architecture. 971-978 - Mukund Narasimhan, Nebojsa Jojic, Jeff A. Bilmes:
Q-Clustering. 979-986 - Vidhya Navalpakkam, Laurent Itti:
Optimal cue selection strategy. 987-994 - Amir Navot, Lavi Shpigelman, Naftali Tishby, Eilon Vaadia:
Nearest Neighbor Based Feature Selection for Regression and its Application to Neural Activity. 996-1002 - Daniel B. Neill, Andrew W. Moore, Gregory F. Cooper:
A Bayesian Spatial Scan Statistic. 1003-1010 - XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan:
Divergences, surrogate loss functions and experimental design. 1011-1018 - Yael Niv, Nathaniel D. Daw, Peter Dayan:
How fast to work: Response vigor, motivation and tonic dopamine. 1019-1026 - Guido Nolte, Andreas Ziehe, Frank C. Meinecke, Klaus-Robert Müller:
Analyzing Coupled Brain Sources: Distinguishing True from Spurious Interaction. 1027-1034 - Manfred Opper:
An Approximate Inference Approach for the PCA Reconstruction Error. 1035-1042 - Gergo Orbán, József Fiser, Richard N. Aslin, Máté Lengyel:
Bayesian model learning in human visual perception. 1043-1050 - Matthias Oster, Shih-Chii Liu:
Spiking Inputs to a Winner-take-all Network. 1051-1058 - Jason A. Palmer, David P. Wipf, Kenneth Kreutz-Delgado, Bhaskar D. Rao:
Variational EM Algorithms for Non-Gaussian Latent Variable Models. 1059-1066 - Liam Paninski:
Nonparametric inference of prior probabilities from Bayes-optimal behavior. 1067-1074 - Ofer Pasternak, Nir A. Sochen, Nathan Intrator, Yaniv Assaf:
Neuronal Fiber Delineation in Area of Edema from Diffusion Weighted MRI. 1075-1080 - Jean-Pascal Pfister, Wulfram Gerstner:
Beyond Pair-Based STDP: a Phenomenological Rule for Spike Triplet and Frequency Effects. 1081-1088 - Brian Potetz, Tai Sing Lee:
Scaling Laws in Natural Scenes and the Inference of 3D Shape. 1089-1096 - Doina Precup, Richard S. Sutton, Cosmin Paduraru, Anna Koop, Satinder Singh:
Off-policy Learning with Options and Recognizers. 1097-1104 - Maxim Raginsky, Svetlana Lazebnik:
Estimation of Intrinsic Dimensionality Using High-Rate Vector Quantization. 1105-1112 - Pradeep Ravikumar, John D. Lafferty:
Preconditioner Approximations for Probabilistic Graphical Models. 1113-1120 - Xiaofeng Ren, Charless C. Fowlkes, Jitendra Malik:
Cue Integration for Figure/Ground Labeling. 1121-1128 - Teemu Roos, Peter Grünwald, Petri Myllymäki, Henry Tirri:
Generalization to Unseen Cases. 1129-1136 - Benjamin Van Roy:
TD(0) Leads to Better Policies than Approximate Value Iteration. 1377-1384 - Michele Rucci:
Visual Encoding with Jittering Eyes. 1137-1144 - Purnamrita Sarkar, Andrew W. Moore:
Dynamic Social Network Analysis using Latent Space Models. 1145-1152 - Eric Saund:
Logic and MRF Circuitry for Labeling Occluding and Thinline Visual Contours. 1153-1159 - Ashutosh Saxena, Sung H. Chung, Andrew Y. Ng:
Learning Depth from Single Monocular Images. 1161-1168 - Rory Sayres, David Ress, Kalanit Grill-Spector:
Identifying Distributed Object Representations in Human Extrastriate Visual Cortex. 1169-1176 - André van Schaik, Richard E. Reeve, Craig T. Jin, Tara Julia Hamilton:
An aVLSI Cricket Ear Model. 1385-1392 - Michael Schmitt, Laura Martignon:
On the Accuracy of Bounded Rationality: How Far from Optimal Is Fast and Frugal?. 1177-1184 - Nicol N. Schraudolph, Douglas Aberdeen, Jin Yu:
Fast Online Policy Gradient Learning with SMD Gain Vector Adaptation. 1185-1192 - Brad Schumitsch, Sebastian Thrun, Gary R. Bradski, Kunle Olukotun:
The Information-Form Data Association Filter. 1193-1200 - Odelia Schwartz, Terrence J. Sejnowski, Peter Dayan:
A Bayesian Framework for Tilt Perception and Confidence. 1201-1208 - Clayton D. Scott, Robert D. Nowak:
Learning Minimum Volume Sets. 1209-1216 - Rafael Serrano-Gotarredona, Matthias Oster, Patrick Lichtsteiner, Alejandro Linares-Barranco, Rafael Paz-Vicente, Francisco Gomez-Rodriguez, Håvard Kolle Riis, Tobi Delbruck, Shih-Chii Liu, S. Zahnd, Adrian M. Whatley, Rodney J. Douglas, Philipp Häfliger, Gabriel Jiménez-Moreno, Antón Civit, Teresa Serrano-Gotarredona, Antonio Acosta-Jimenez, Bernabé Linares-Barranco:
AER Building Blocks for Multi-Layer Multi-Chip Neuromorphic Vision Systems. 1217-1224 - Yirong Shen, Andrew Y. Ng, Matthias W. Seeger:
Fast Gaussian Process Regression using KD-Trees. 1225-1232 - Aaron P. Shon, Keith Grochow, Aaron Hertzmann, Rajesh P. N. Rao:
Learning Shared Latent Structure for Image Synthesis and Robotic Imitation. 1233-1240 - Jorge G. Silva, Jorge S. Marques, João Miranda Lemos:
Selecting Landmark Points for Sparse Manifold Learning. 1241-1248 - Cristian Sminchisescu, Atul Kanaujia, Zhiguo Li, Dimitris N. Metaxas:
Conditional Visual Tracking in Kernel Space. 1249-1256 - Edward Lloyd Snelson, Zoubin Ghahramani:
Sparse Gaussian Processes using Pseudo-inputs. 1257-1264 - Le Song, Evian Gordon, Elly Gysels:
Phase Synchrony Rate for the Recognition of Motor Imagery in Brain-Computer Interface. 1265-1272 - Sören Sonnenburg, Gunnar Rätsch, Christin Schäfer:
A General and Efficient Multiple Kernel Learning Algorithm. 1273-1280 - Mark Steyvers, Scott D. Brown:
Prediction and Change Detection. 1281-1288 - Alan Stocker, Eero P. Simoncelli:
Sensory Adaptation within a Bayesian Framework for Perception. 1289-1296 - Erik B. Sudderth, Antonio Torralba, William T. Freeman, Alan S. Willsky:
Describing Visual Scenes using Transformed Dirichlet Processes. 1297-1304 - Masashi Sugiyama:
Active Learning for Misspecified Models. 1305-1312 - Richard S. Sutton, Eddie J. Rafols, Anna Koop:
Temporal Abstraction in Temporal-difference Networks. 1313-1320 - Jun Suzuki, Hideki Isozaki:
Sequence and Tree Kernels with Statistical Feature Mining. 1321-1328 - Brian Taba, Kwabena Boahen:
Silicon growth cones map silicon retina. 1329-1336 - Minija Tamosiunaite, Bernd Porr, Florentin Wörgötter:
Temporally changing synaptic plasticity. 1337-1344 - Benjamin Taskar, Simon Lacoste-Julien, Michael I. Jordan:
Structured Prediction via the Extragradient Method. 1345-1352 - Sebastian Thrun:
Affine Structure From Sound. 1353-1360 - Jo-Anne Ting, Aaron D'Souza, Kenji Yamamoto, Toshinori Yoshioka, Donna L. Hoffman, Lauren Sergio, Shinji Kakei, John Kalaska, Mitsuo Kawato, Peter Strick, Stefan Schaal:
Predicting EMG Data from M1 Neurons with Variational Bayesian Least Squares. 1361-1368 - Nicolas Usunier, Massih-Reza Amini, Patrick Gallinari:
Generalization error bounds for classifiers trained with interdependent data. 1369-1376 - Deepak Verma, Rajesh P. N. Rao:
Goal-Based Imitation as Probabilistic Inference over Graphical Models. 1393-1400 - Jean-Philippe Vert, Robert E. Thurman, William Stafford Noble:
Kernels for gene regulatory regions. 1401-1408 - Régis Vert, Jean-Philippe Vert:
Consistency of one-class SVM and related algorithms. 1409-1416 - Paul A. Viola, John C. Platt, Cha Zhang:
Multiple Instance Boosting for Object Detection. 1417-1424 - Martin J. Wainwright:
Estimating the wrong Markov random field: Benefits in the computation-limited setting. 1425-1432 - Michael B. Wakin, Marco F. Duarte, Shriram Sarvotham, Dror Baron, Richard G. Baraniuk:
Recovery of Jointly Sparse Signals from Few Random Projections. 1433-1440 - Jack M. Wang, David J. Fleet, Aaron Hertzmann:
Gaussian Process Dynamical Models. 1441-1448 - Xuerui Wang, Natasha Mohanty, Andrew McCallum:
Group and Topic Discovery from Relations and Their Attributes. 1449-1456 - Manfred K. Warmuth:
A Bayes Rule for Density Matrices. 1457-1464 - Kazuho Watanabe, Sumio Watanabe:
Variational Bayesian Stochastic Complexity of Mixture Models. 1465-1472 - Kilian Q. Weinberger, John Blitzer, Lawrence K. Saul:
Distance Metric Learning for Large Margin Nearest Neighbor Classification. 1473-1480 - Inna Weiner, Tomer Hertz, Israel Nelken, Daphna Weinshall:
Analyzing Auditory Neurons by Learning Distance Functions. 1481-1488 - Gabriel Y. Weintraub, C. Lanier Benkard, Benjamin Van Roy:
Oblivious Equilibrium: A Mean Field Approximation for Large-Scale Dynamic Games. 1489-1496 - Bo Wen, Kwabena Boahen:
Active Bidirectional Coupling in a Cochlear Chip. 1497-1504 - Jim Wielaard, Paul Sajda:
Neural mechanisms of contrast dependent receptive field size in V1. 1505-1512 - Christopher K. I. Williams, John A. Quinn, Neil McIntosh:
Factorial Switching Kalman Filters for Condition Monitoring in Neonatal Intensive Care. 1513-1520 - David P. Wipf, Bhaskar D. Rao:
Comparing the Effects of Different Weight Distributions on Finding Sparse Representations. 1521-1528 - K. Y. Michael Wong, David Saad, Zhuo Gao:
Message passing for task redistribution on sparse graphs. 1529-1536 - Frank D. Wood, Stefan Roth, Michael J. Black:
Modeling Neural Population Spiking Activity with Gibbs Distributions. 1537-1544 - Shipeng Yu, Kai Yu, Volker Tresp:
Soft Clustering on Graphs. 1553-1560 - Byron M. Yu, Afsheen Afshar, Gopal Santhanam, Stephen I. Ryu, Krishna V. Shenoy, Maneesh Sahani:
Extracting Dynamical Structure Embedded in Neural Activity. 1545-1552 - Alan L. Yuille:
Augmented Rescorla-Wagner and Maximum Likelihood Estimation. 1561-1568 - Gregory J. Zelinsky, Wei Zhang, Bing Yu, Xin Chen, Dimitris Samaras:
The Role of Top-down and Bottom-up Processes in Guiding Eye Movements during Visual Search. 1569-1576 - Yungang Zhang, Changshui Zhang:
Separation of Music Signals by Harmonic Structure Modeling. 1617-1624 - Jian Zhang, Zoubin Ghahramani, Yiming Yang:
Learning Multiple Related Tasks using Latent Independent Component Analysis. 1585-1592 - Lei Zhang, Dimitris Samaras, Nelly Alia-Klein, Nora D. Volkow, Rita Z. Goldstein:
Modeling Neuronal Interactivity using Dynamic Bayesian Networks. 1593-1600 - Dong Zhang, Daniel Gatica-Perez, Samy Bengio, Deb Roy:
Learning Influence among Interacting Markov Chains. 1577-1584 - Zhenyue Zhang, Hongyuan Zha:
A Domain Decomposition Method for Fast Manifold Learning. 1625-1632 - Tong Zhang, Rie Kubota Ando:
Analysis of Spectral Kernel Design based Semi-supervised Learning. 1601-1608 - Wei Zhang, Hyejin Yang, Dimitris Samaras, Gregory J. Zelinsky:
A Computational Model of Eye Movements during Object Class Detection. 1609-1616 - Long Zhu, Alan L. Yuille:
A Hierarchical Compositional System for Rapid Object Detection. 1633-1640 - Martin Zinkevich, Amy Greenwald, Michael L. Littman:
Cyclic Equilibria in Markov Games. 1641-1648 - Laurent Zwald, Gilles Blanchard:
On the Convergence of Eigenspaces in Kernel Principal Component Analysis. 1649-1656
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