NIPS 2005: Vancouver, British Columbia, Canada

• Pieter Abbeel, Varun Ganapathi, Andrew Y. Ng:
  Learning vehicular dynamics, with application to modeling helicopters.
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• Douglas Aberdeen:
  Policy-Gradient Methods for Planning.
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• Felix V. Agakov, David Barber:
  Kernelized Infomax Clustering.
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• Misha Ahrens, Quentin Huys, Liam Paninski:
  Large-scale biophysical parameter estimation in single neurons via constrained linear regression.
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• Yasemin Altun, David A. McAllester, Mikhail Belkin:
  Margin Semi-Supervised Learning for Structured Variables.
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• J. Ignacio Alvarez-Hamelin, Luca Dall'Asta, Alain Barrat, Alessandro Vespignani:
  Large scale networks fingerprinting and visualization using the k-core decomposition.
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• Brigham Anderson, Andrew W. Moore:
  Fast Information Value for Graphical Models.
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• David Arathorn:
  A Cortically-Plausible Inverse Problem Solving Method Applied to Recognizing Static and Kinematic 3D Objects.
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• Andreas Argyriou, Mark Herbster, Massimiliano Pontil:
  Combining Graph Laplacians for Semi-Supervised Learning.
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• John V. Arthur, Kwabena Boahen:
  Learning in Silicon: Timing is Everything.
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• Michaël Aupetit:
  Learning Topology with the Generative Gaussian Graph and the EM Algorithm.
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• Drew Bagnell, Andrew Y. Ng:
  On Local Rewards and Scaling Distributed Reinforcement Learning.
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• Chris Baker, Joshua B. Tenenbaum, Rebecca Saxe:
  Bayesian models of human action understanding.
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• Yoshua Bengio, Olivier Delalleau, Nicolas Le Roux:
  The Curse of Highly Variable Functions for Local Kernel Machines.
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• Yoshua Bengio, Hugo Larochelle, Pascal Vincent:
  Non-Local Manifold Parzen Windows.
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• Yoshua Bengio, Nicolas Le Roux, Pascal Vincent, Olivier Delalleau, Patrice Marcotte:
  Convex Neural Networks.
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• Gilles Blanchard, Masashi Sugiyama, Motoaki Kawanabe, Vladimir Spokoiny, Klaus-Robert Müller:
  Non-Gaussian Component Analysis: a Semi-parametric Framework for Linear Dimension Reduction.
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• Doron Blatt, Alfred O. Hero:
  From Weighted Classification to Policy Search.
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• David M. Blei, John D. Lafferty:
  Correlated Topic Models.
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• Neil D. B. Bruce, John K. Tsotsos:
  Saliency Based on Information Maximization.
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• Brent Bryan, Jeff G. Schneider, Robert Nichol, Christopher Miller, Christopher Genovese, Larry Wasserman:
  Active Learning For Identifying Function Threshold Boundaries.
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• Razvan C. Bunescu, Raymond J. Mooney:
  Subsequence Kernels for Relation Extraction.
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• Rui Castro, Rebecca Willett, Robert Nowak:
  Faster Rates in Regression via Active Learning.
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• Abdullah Celik, Milutin Stanacevic, Gert Cauwenberghs:
  Gradient Flow Independent Component Analysis in Micropower VLSI.
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• Nicolò Cesa-Bianchi, Claudio Gentile:
  Improved risk tail bounds for on-line algorithms.
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• Antoni B. Chan, Nuno Vasconcelos:
  Layered Dynamic Textures.
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• Yixin Chen, Ya Zhang, Xiang Ji:
  Size Regularized Cut for Data Clustering.
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• Koby Crammer, Michael S. Kearns, Jennifer Wortman:
  Learning from Data of Variable Quality.
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• Márton Danóczy, Richard H. R. Hahnloser:
  Efficient estimation of hidden state dynamics from spike trains.
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• Sanjoy Dasgupta:
  Coarse sample complexity bounds for active learning.
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• Peter Dayan, Angela J. Yu:
  Norepinephrine and Neural Interrupts.
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• Ofer Dekel, Shai Shalev-Shwartz, Yoram Singer:
  The Forgetron: A Kernel-Based Perceptron on a Fixed Budget.
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• Ofer Dekel, Yoram Singer:
  Data-Driven Online to Batch Conversions.
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• Ricky Der, Daniel Lee:
  Beyond Gaussian Processes: On the Distributions of Infinite Networks.
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• Inderjit S. Dhillon, Suvrit Sra:
  Generalized Nonnegative Matrix Approximations with Bregman Divergences.
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• James Diebel, Sebastian Thrun:
  An Application of Markov Random Fields to Range Sensing.
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• Chuong Do, Andrew Y. Ng:
  Transfer learning for text classification.
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• Eizaburo Doi, Doru-Cristian Balcan, Michael S. Lewicki:
  A Theoretical Analysis of Robust Coding over Noisy Overcomplete Channels.
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• Guido Dornhege, Benjamin Blankertz, Matthias Krauledat, Florian Losch, Gabriel Curio, Klaus-Robert Müller:
  Optimizing spatio-temporal filters for improving Brain-Computer Interfacing.
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• Miroslav Dudík, Robert E. Schapire, Steven J. Phillips:
  Correcting sample selection bias in maximum entropy density estimation.
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• Jaety Edwards, David A. Forsyth:
  Searching for Character Models.
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• Austin I. Eliazar, Ronald Parr:
  Hierarchical Linear/Constant Time SLAM Using Particle Filters for Dense Maps.
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• Yaakov Engel, Peter Szabó, Dmitry Volkinshtein:
  Learning to Control an Octopus Arm with Gaussian Process Temporal Difference Methods.
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• Jason D. R. Farquhar, David R. Hardoon, Hongying Meng, John Shawe-Taylor, Sándor Szedmák:
  Two view learning: SVM-2K, Theory and Practice.
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• Patrick Flaherty, Michael I. Jordan, Adam P. Arkin:
  Robust design of biological experiments.
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• François Fleuret, Gilles Blanchard:
  Pattern Recognition from One Example by Chopping.
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• Nando de Freitas, Yang Wang, Maryam Mahdaviani, Dustin Lang:
  Fast Krylov Methods for N-Body Learning.
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• Brendan J. Frey, Delbert Dueck:
  Mixture Modeling by Affinity Propagation.
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• Kenji Fukumizu, Francis R. Bach, Arthur Gretton:
  Statistical Convergence of Kernel CCA.
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• Glenn Fung, Rómer Rosales, Balaji Krishnapuram:
  Learning Rankings via Convex Hull Separation.
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• Artur S. d'Avila Garcez, Luís C. Lamb, Dov M. Gabbay:
  A Connectionist Model for Constructive Modal Reasoning.
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• Thomas Gärtner, Quoc V. Le, Simon Burton, Alex J. Smola, S. V. N. Vishwanathan:
  Large-Scale Multiclass Transduction.
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• Peter V. Gehler, Max Welling:
  Products of Edge-perts.
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• Tao Geng, Bernd Porr, Florentin Wörgötter:
  Fast biped walking with a reflexive controller and real-time policy searching.
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• Zoubin Ghahramani, Katherine A. Heller:
  Bayesian Sets.
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• Ran Gilad-Bachrach, Amir Navot, Naftali Tishby:
  Query by Committee Made Real.
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• Amir Globerson, Sam T. Roweis:
  Metric Learning by Collapsing Classes.
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• Sharon Goldwater, Tom Griffiths, Mark Johnson:
  Interpolating between types and tokens by estimating power-law generators.
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• Yves Grandvalet, Johnny Mariéthoz, Samy Bengio:
  A Probabilistic Interpretation of SVMs with an Application to Unbalanced Classification.
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• Tom Griffiths, Zoubin Ghahramani:
  Infinite latent feature models and the Indian buffet process.
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• Lacey Gunter, Ji Zhu:
  Computing the Solution Path for the Regularized Support Vector Regression.
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• Firas Hamze, Nando de Freitas:
  Hot Coupling: A Particle Approach to Inference and Normalization on Pairwise Undirected Graphs.
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• Xiaofei He, Deng Cai, Partha Niyogi:
  Laplacian Score for Feature Selection.
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• Xiaofei He, Deng Cai, Partha Niyogi:
  Tensor Subspace Analysis.
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• Geoff Hinton, Vinod Nair:
  Inferring Motor Programs from Images of Handwritten Digits.
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• Wentao Huang, Licheng Jiao, Tan Shan, Maoguo Gong:
  Response Analysis of Neuronal Population with Synaptic Depression.
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• Yunsong Huang, B. Keith Jenkins:
  Non-iterative Estimation with Perturbed Gaussian Markov Processes.
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• Jarmo Hurri:
  Learning Cue-Invariant Visual Responses.
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• Laurent Itti, Pierre Baldi:
  Bayesian Surprise Attracts Human Attention.
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• Herbert Jaeger, Mingjie Zhao, Andreas Kolling:
  Efficient Estimation of OOMs.
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• Viren Jain, Valentin P. Zhigulin, H. Sebastian Seung:
  Representing Part-Whole Relationships in Recurrent Neural Networks.
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• Rong Jin, Chris H. Q. Ding, Feng Kang:
  A Probabilistic Approach for Optimizing Spectral Clustering.
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• Jason K. Johnson, Dmitry M. Malioutov, Alan S. Willsky:
  Walk-Sum Interpretation and Analysis of Gaussian Belief Propagation.
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• Nebojsa Jojic, Vladimir Jojic, Brendan J. Frey, Christopher Meek, David Heckerman:
  Using epitomes to model genetic diversity: Rational design of HIV vaccines.
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• Renaud Jolivet, Alexander Rauch, Hans-Rudolf Lüscher, Wulfram Gerstner:
  Integrate-and-Fire models with adaptation are good enough.
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• Anatoli Juditsky, Alexander V. Nazin, Alexandre B. Tsybakov, Nicolas Vayatis:
  Generalization Error Bounds for Aggregation by Mirror Descent with Averaging.
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• Sham M. Kakade, Matthias Seeger, Dean P. Foster:
  Worst-Case Bounds for Gaussian Process Models.
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• Sham M. Kakade, Adam Kalai:
  From Batch to Transductive Online Learning.
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• Ashish Kapoor, Yuan (Alan) Qi, Hyungil Ahn, Rosalind W. Picard:
  Hyperparameter and Kernel Learning for Graph Based Semi-Supervised Classification.
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• Yan Karklin, Michael S. Lewicki:
  Is Early Vision Optimized for Extracting Higher-order Dependencies?
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• S. Sathiya Keerthi, Wei Chu:
  A matching pursuit approach to sparse Gaussian process regression.
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• Mikaela Keller, Samy Bengio, Siew Yeung Wong:
  Benchmarking Non-Parametric Statistical Tests.
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• Seung-Jean Kim, Alessandro Magnani, Stephen P. Boyd:
  Robust Fisher Discriminant Analysis.
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• Kristina Klinkner, Cosma Rohilla Shalizi, Marcelo Camperi:
  Measuring Shared Information and Coordinated Activity in Neuronal Networks.
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• O. Patrick Kreidl, Alan S. Willsky:
  Inference with Minimal Communication: a Decision-Theoretic Variational Approach.
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• Eyal Krupka, Naftali Tishby:
  Generalization in Clustering with Unobserved Features.
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• Jeremy Kubica, Joseph Masiero, Andrew W. Moore, Robert Jedicke, Andrew Connolly:
  Variable KD-Tree Algorithms for Spatial Pattern Search and Discovery.
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• Malte Kuss, Carl Edward Rasmussen:
  Assessing Approximations for Gaussian Process Classification.
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• John D. Lafferty, Larry A. Wasserman:
  Rodeo: Sparse Nonparametric Regression in High Dimensions.
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• Kevin Lang:
  Fixing two weaknesses of the Spectral Method.
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• Tilman Lange, Joachim M. Buhmann:
  Fusion of Similarity Data in Clustering.
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• François Laviolette, Mario Marchand, Mohak Shah:
  A PAC-Bayes approach to the Set Covering Machine.
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• Yann LeCun, Urs Muller, Jan Ben, Eric Cosatto, Beat Flepp:
  Off-Road Obstacle Avoidance through End-to-End Learning.
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• Dongryeol Lee, Alexander G. Gray, Andrew W. Moore:
  Dual-Tree Fast Gauss Transforms.
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• Jung Hoon Lee, Xiaolong Ma, Konstantin Likharev:
  CMOL CrossNets: Possible Neuromorphic Nanoelectronic Circuits.
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• Robert A. Legenstein, Wolfgang Maass:
  A Criterion for the Convergence of Learning with Spike Timing Dependent Plasticity.
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• Anna Levina, J. Michael Herrmann, Theo Geisel:
  Dynamical Synapses Give Rise to a Power-Law Distribution of Neuronal Avalanches.
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• Fan Li, Yiming Yang, Eric Xing:
  From Lasso regression to Feature vector machine.
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• Xuejun Liao, Lawrence Carin:
  Radial Basis Function Network for Multi-task Learning.
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• Lin Liao, Dieter Fox, Henry A. Kautz:
  Location-based activity recognition.
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• Ross A. Lippert, Ryan M. Rifkin:
  Asymptotics of Gaussian Regularized Least Squares.
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• Nicolas Loeff, Himanshu Arora, Alexander Sorokin, David A. Forsyth:
  Efficient Unsupervised Learning for Localization and Detection in Object Categories.
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• Aurelie C. Lozano, Sanjeev Kulkarni, Robert E. Schapire:
  Convergence and Consistency of Regularized Boosting Algorithms with Stationary B-Mixing Observations.
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• Hongjing Lu, Alan L. Yuille:
  Ideal Observers for Detecting Motion: Correspondence Noise.
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• Wolfgang Maass, Prashant Joshi, Eduardo D. Sontag:
  Principles of real-time computing with feedback applied to cortical microcircuit models.
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• Sridhar Mahadevan, Mauro Maggioni:
  Value Function Approximation with Diffusion Wavelets and Laplacian Eigenfunctions.
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• Uri Maoz, Elon Portugaly, Tamar Flash, Yair Weiss:
  Noise and the two-thirds power Law.
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• Naoki Masuda, Shun-ichi Amari:
  Modeling Memory Transfer and Saving in Cerebellar Motor Learning.
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• Samuel McClure, Mark S. Gilzenrat, Jonathan D. Cohen:
  An exploration-exploitation model based on norepinepherine and dopamine activity.
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• Peter McCracken, Michael H. Bowling:
  Online Discovery and Learning of Predictive State Representations.
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• Edward Meeds, Simon Osindero:
  An Alternative Infinite Mixture Of Gaussian Process Experts.
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• Keiji Miura, Masato Okada, Shun-ichi Amari:
  Unbiased Estimator of Shape Parameter for Spiking Irregularities under Changing Environments.
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• Ciamac Cyrus Moallemi, Benjamin Van Roy:
  Consensus Propagation.
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• Daichi Mochihashi, Yuji Matsumoto:
  Context as Filtering.
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• Baback Moghaddam, Yair Weiss, Shai Avidan:
  Spectral Bounds for Sparse PCA: Exact and Greedy Algorithms.
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• Michael Mozer, Michael Shettel, Shaun Vecera:
  Top-Down Control of Visual Attention: A Rational Account.
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• Tatsuto Murayama, Peter Davis:
  Rate Distortion Codes in Sensor Networks.
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• Juan José Murillo-Fuentes, Sebatian Caro, Fernando Pérez-Cruz:
  Gaussian Processes for Multiuser Detection in CDMA receivers.
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• Iain Murray, David MacKay, Zoubin Ghahramani, John Skilling:
  Nested sampling for Potts models.
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• Boaz Nadler, Stephane Lafon, Ronald R. Coifman, Ioannis G. Kevrekidis:
  Diffusion Maps, Spectral Clustering and Eigenfunctions of Fokker-Planck Operators.
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• Srikantan S. Nagarajan, Hagai Attias, Kenneth E. Hild II, Kensuke Sekihara:
  Stimulus Evoked Independent Factor Analysis of MEG Data with Large Background Activity.
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• Yusuke Nakashita, Yoshio Mita, Tadashi Shibata:
  An Analog Visual Pre-Processing Processor.
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• Mukund Narasimhan, Nebojsa Jojic, Jeff Bilmes:
  Q-Clustering.
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• Vidhya Navalpakkam, Laurent Itti:
  Optimal cue selection strategy.
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• Amir Navot, Lavi Shpigelman, Naftali Tishby, Eilon Vaadia:
  Nearest Neighbor Based Feature Selection for Regression and its Application to Neural Activity.
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• Daniel B. Neill, Andrew W. Moore, Gregory F. Cooper:
  A Bayesian Spatial Scan Statistic.
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• XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan:
  Divergences, surrogate loss functions and experimental design.
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• Yael Niv, Nathaniel D. Daw, Peter Dayan:
  How fast to work: Response vigor, motivation and tonic dopamine.
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• Guido Nolte, Andreas Ziehe, Frank C. Meinecke, Klaus-Robert Müller:
  Analyzing Coupled Brain Sources: Distinguishing True from Spurious Interaction.
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• Manfred Opper:
  An Approximate Inference Approach for the PCA Reconstruction Error.
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• Gergo Orbán, József Fiser, Richard N. Aslin, Máté Lengyel:
  Bayesian model learning in human visual perception.
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• Matthias Oster, Shih-Chii Liu:
  Spiking Inputs to a Winner-take-all Network.
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• Jason A. Palmer, David P. Wipf, Kenneth Kreutz-Delgado, Bhaskar D. Rao:
  Variational EM Algorithms for Non-Gaussian Latent Variable Models.
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• Liam Paninski:
  Nonparametric inference of prior probabilities from Bayes-optimal behavior.
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• Ofer Pasternak, Nir A. Sochen, Nathan Intrator, Yaniv Assaf:
  Neuronal Fiber Delineation in Area of Edema from Diffusion Weighted MRI.
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• Jean-Pascal Pfister, Wulfram Gerstner:
  Beyond Pair-Based STDP: a Phenomenological Rule for Spike Triplet and Frequency Effects.
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• Brian Potetz, Tai Sing Lee:
  Scaling Laws in Natural Scenes and the Inference of 3D Shape.
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• Doina Precup, Richard S. Sutton, Cosmin Paduraru, Anna Koop, Satinder P. Singh:
  Off-policy Learning with Options and Recognizers.
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• Maxim Raginsky, Svetlana Lazebnik:
  Estimation of Intrinsic Dimensionality Using High-Rate Vector Quantization.
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• Pradeep D. Ravikumar, John Lafferty:
  Preconditioner Approximations for Probabilistic Graphical Models.
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• Xiaofeng Ren, Charless Fowlkes, Jitendra Malik:
  Cue Integration for Figure/Ground Labeling.
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• Teemu Roos, Peter Grünwald, Petri Myllymäki, Henry Tirri:
  Generalization to Unseen Cases.
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• Benjamin Van Roy:
  TD(0) Leads to Better Policies than Approximate Value Iteration.
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• Michele Rucci:
  Visual Encoding with Jittering Eyes.
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• Purnamrita Sarkar, Andrew W. Moore:
  Dynamic Social Network Analysis using Latent Space Models.
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• Eric Saund:
  Logic and MRF Circuitry for Labeling Occluding and Thinline Visual Contours.
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• Ashutosh Saxena, Sung H. Chung, Andrew Y. Ng:
  Learning Depth from Single Monocular Images.
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• Rory Sayres, David Ress, Kalanit Grill Spector:
  Identifying Distributed Object Representations in Human Extrastriate Visual Cortex.
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• André van Schaik, Richard Reeve, Craig T. Jin, Tara Julia Hamilton:
  An aVLSI Cricket Ear Model.
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• Michael Schmitt, Laura Martignon:
  On the Accuracy of Bounded Rationality: How Far from Optimal Is Fast and Frugal?.
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• Nicol N. Schraudolph, Douglas Aberdeen, Jin Yu:
  Fast Online Policy Gradient Learning with SMD Gain Vector Adaptation.
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• Brad Schumitsch, Sebastian Thrun, Gary R. Bradski, Kunle Olukotun:
  The Information-Form Data Association Filter.
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• Odelia Schwartz, Terrence J. Sejnowski, Peter Dayan:
  A Bayesian Framework for Tilt Perception and Confidence.
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• Clayton Scott, Robert D. Nowak:
  Learning Minimum Volume Sets.
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• Rafael Serrano-Gotarredona, Matthias Oster, Patrick Lichtsteiner, Alejandro Linares-Barranco, R. Paz-Vicente, F. Gomez-Rodriguez, Håvard Kolle Riis, Tobi Delbrück, Shih-Chii Liu, S. Zahnd, Adrian M. Whatley, Rodney J. Douglas, Philipp Häfliger, Gabriel Jiménez-Moreno, Antón Civit, Teresa Serrano-Gotarredona, A. Acosta-Jimenez, Bernabé Linares-Barranco:
  AER Building Blocks for Multi-Layer Multi-Chip Neuromorphic Vision Systems.
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• Yirong Shen, Andrew Y. Ng, Matthias Seeger:
  Fast Gaussian Process Regression using KD-Trees.
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• Aaron P. Shon, Keith Grochow, Aaron Hertzmann, Rajesh P. N. Rao:
  Learning Shared Latent Structure for Image Synthesis and Robotic Imitation.
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• Jorge Silva, Jorge S. Marques, João Miranda Lemos:
  Selecting Landmark Points for Sparse Manifold Learning.
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• Cristian Sminchisescu, Atul Kanaujia, Zhiguo Li, Dimitris N. Metaxas:
  Conditional Visual Tracking in Kernel Space.
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• Edward Snelson, Zoubin Ghahramani:
  Sparse Gaussian Processes using Pseudo-inputs.
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• Le Song, Evian Gordon, Elly Gysels:
  Phase Synchrony Rate for the Recognition of Motor Imagery in Brain-Computer Interface.
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• Sören Sonnenburg, Gunnar Rätsch, Christin Schäfer:
  A General and Efficient Multiple Kernel Learning Algorithm.
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• Mark Steyvers, Scott Brown:
  Prediction and Change Detection.
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• Alan Stocker, Eero P. Simoncelli:
  Sensory Adaptation within a Bayesian Framework for Perception.
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• Erik B. Sudderth, Antonio B. Torralba, William T. Freeman, Alan S. Willsky:
  Describing Visual Scenes using Transformed Dirichlet Processes.
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• Masashi Sugiyama:
  Active Learning for Misspecified Models.
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• Richard S. Sutton, Eddie J. Rafols, Anna Koop:
  Temporal Abstraction in Temporal-difference Networks.
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• Jun Suzuki, Hideki Isozaki:
  Sequence and Tree Kernels with Statistical Feature Mining.
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• Brian Taba, Kwabena Boahen:
  Silicon growth cones map silicon retina.
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• Minija Tamosiunaite, Bernd Porr, Florentin Wörgötter:
  Temporally changing synaptic plasticity.
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• Benjamin Taskar, Simon Lacoste-Julien, Michael I. Jordan:
  Structured Prediction via the Extragradient Method.
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• Sebastian Thrun:
  Affine Structure From Sound.
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• 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.
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• Nicolas Usunier, Massih-Reza Amini, Patrick Gallinari:
  Generalization error bounds for classifiers trained with interdependent data.
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• Deepak Verma, Rajesh P. N. Rao:
  Goal-Based Imitation as Probabilistic Inference over Graphical Models.
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• Jean-Philippe Vert, Robert Thurman, William Stafford Noble:
  Kernels for gene regulatory regions.
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• Régis Vert, Jean-Philippe Vert:
  Consistency of one-class SVM and related algorithms.
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• Paul A. Viola, John Platt, Cha Zhang:
  Multiple Instance Boosting for Object Detection.
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• Martin J. Wainwright:
  Estimating the wrong Markov random field: Benefits in the computation-limited setting.
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• Michael B. Wakin, Marco F. Duarte, Shriram Sarvotham, Dror Baron, Richard G. Baraniuk:
  Recovery of Jointly Sparse Signals from Few Random Projections.
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• Jack M. Wang, David J. Fleet, Aaron Hertzmann:
  Gaussian Process Dynamical Models.
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• Xuerui Wang, Natasha Mohanty, Andrew McCallum:
  Group and Topic Discovery from Relations and Their Attributes.
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• Manfred K. Warmuth:
  A Bayes Rule for Density Matrices.
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• Kazuho Watanabe, Sumio Watanabe:
  Variational Bayesian Stochastic Complexity of Mixture Models.
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• Kilian Q. Weinberger, John Blitzer, Lawrence K. Saul:
  Distance Metric Learning for Large Margin Nearest Neighbor Classification.
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• Inna Weiner, Tomer Hertz, Israel Nelken, Daphna Weinshall:
  Analyzing Auditory Neurons by Learning Distance Functions.
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• Gabriel Y. Weintraub, C. Lanier Benkard, Benjamin Van Roy:
  Oblivious Equilibrium: A Mean Field Approximation for Large-Scale Dynamic Games.
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• Bo Wen, Kwabena Boahen:
  Active Bidirectional Coupling in a Cochlear Chip.
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• Jim Wielaard, Paul Sajda:
  Neural mechanisms of contrast dependent receptive field size in V1.
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• Christopher K. I. Williams, John A. Quinn, Neil McIntosh:
  Factorial Switching Kalman Filters for Condition Monitoring in Neonatal Intensive Care.
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• David P. Wipf, Bhaskar D. Rao:
  Comparing the Effects of Different Weight Distributions on Finding Sparse Representations.
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• K. Y. Michael Wong, David Saad, Zhuo Gao:
  Message passing for task redistribution on sparse graphs.
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• Frank Wood, Stefan Roth, Michael J. Black:
  Modeling Neural Population Spiking Activity with Gibbs Distributions.
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• Shipeng Yu, Kai Yu, Volker Tresp:
  Soft Clustering on Graphs.
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• Byron Yu, Afsheen Afshar, Gopal Santhanam, Stephen I. Ryu, Krishna Shenoy, Maneesh Sahani:
  Extracting Dynamical Structure Embedded in Neural Activity.
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• Alan L. Yuille:
  Augmented Rescorla-Wagner and Maximum Likelihood Estimation.
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• 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.
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• Yungang Zhang, Changshui Zhang:
  Separation of Music Signals by Harmonic Structure Modeling.
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• Jian Zhang, Zoubin Ghahramani, Yiming Yang:
  Learning Multiple Related Tasks using Latent Independent Component Analysis.
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• Lei Zhang, Dimitris Samaras, Nelly Alia-Klein, Nora Volkow, Rita Goldstein:
  Modeling Neuronal Interactivity using Dynamic Bayesian Networks.
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• Dong Zhang, Daniel Gatica-Perez, Samy Bengio, Deb Roy:
  Learning Influence among Interacting Markov Chains.
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• Zhenyue Zhang, Hongyuan Zha:
  A Domain Decomposition Method for Fast Manifold Learning.
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• Tong Zhang, Rie Ando:
  Analysis of Spectral Kernel Design based Semi-supervised Learning.
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• Wei Zhang, Hyejin Yang, Dimitris Samaras, Gregory J. Zelinsky:
  A Computational Model of Eye Movements during Object Class Detection.
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• Long Zhu, Alan L. Yuille:
  A Hierarchical Compositional System for Rapid Object Detection.
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• Martin Zinkevich, Amy R. Greenwald, Michael L. Littman:
  Cyclic Equilibria in Markov Games.
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• Laurent Zwald, Gilles Blanchard:
  On the Convergence of Eigenspaces in Kernel Principal Component Analysis.
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