index int64 0 20.3k | text stringlengths 0 1.3M | year stringdate 1987-01-01 00:00:00 2024-01-01 00:00:00 | No stringlengths 1 4 |
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3,300 | A configurable analog VLSI neural network with spiking neurons and self-regulating plastic synapses which classifies overlapping patterns M. Giulioni∗ Italian National Inst. of Health, Rome, Italy INFN-RM2, Rome, Italy giulioni@roma2.infn.it M. Pannunzi Italian National Inst. of Health, Rome, Italy INFN... | 2007 | 62 |
3,301 | The discriminant center-surround hypothesis for bottom-up saliency Dashan Gao Vijay Mahadevan Nuno Vasconcelos Department of Electrical and Computer Engineering University of California, San Diego {dgao, vmahadev, nuno}@ucsd.edu Abstract The classical hypothesis, that bottom-up saliency is a center-su... | 2007 | 63 |
3,302 | Statistical Analysis of Semi-Supervised Regression John Lafferty Computer Science Department Carnegie Mellon University Pittsburgh, PA 15213 lafferty@cs.cmu.edu Larry Wasserman Department of Statistics Carnegie Mellon University Pittsburgh, PA 15213 larry@stat.cmu.edu Abstract Semi-supervised me... | 2007 | 64 |
3,303 | Hierarchical Apprenticeship Learning, with Application to Quadruped Locomotion J. Zico Kolter, Pieter Abbeel, Andrew Y. Ng Department of Computer Science Stanford University Stanford, CA 94305 {kolter, pabbeel, ang}@cs.stanford.edu Abstract We consider apprenticeship learning—learning from expert demons... | 2007 | 65 |
3,304 | Colored Maximum Variance Unfolding Le Song†, Alex Smola†, Karsten Borgwardt‡ and Arthur Gretton∗ †National ICT Australia, Canberra, Australia ‡University of Cambridge, Cambridge, United Kingdom ∗MPI for Biological Cybernetics, T¨ubingen, Germany {le.song,alex.smola}@nicta.com.au kmb51@eng.cam.ac.uk,arthur.g... | 2007 | 66 |
3,305 | Adaptive Embedded Subgraph Algorithms using Walk-Sum Analysis Venkat Chandrasekaran, Jason K. Johnson, and Alan S. Willsky Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology venkatc@mit.edu, jasonj@mit.edu, willsky@mit.edu Abstract We consider the estimation pr... | 2007 | 67 |
3,306 | Ultrafast Monte Carlo for Kernel Estimators and Generalized Statistical Summations Michael P. Holmes, Alexander G. Gray, and Charles Lee Isbell, Jr. College Of Computing Georgia Institute of Technology Atlanta, GA 30327 {mph, agray, isbell}@cc.gatech.edu Abstract Machine learning contains many computati... | 2007 | 68 |
3,307 | Inferring Neural Firing Rates from Spike Trains Using Gaussian Processes John P. Cunningham1, Byron M. Yu1,2,3, Krishna V. Shenoy1,2 1Department of Electrical Engineering, 2Neurosciences Program, Stanford University, Stanford, CA 94305 {jcunnin,byronyu,shenoy}@stanford.edu Maneesh Sahani3 3Gatsby Computat... | 2007 | 69 |
3,308 | Stable Dual Dynamic Programming Tao Wang∗ Daniel Lizotte Michael Bowling Dale Schuurmans Department of Computing Science University of Alberta {trysi,dlizotte,bowling,dale}@cs.ualberta.ca Abstract Recently, we have introduced a novel approach to dynamic programming and reinforcement learning that is b... | 2007 | 7 |
3,309 | People Tracking with the Laplacian Eigenmaps Latent Variable Model Zhengdong Lu CSEE, OGI, OHSU zhengdon@csee.ogi.edu Miguel ´A. Carreira-Perpi˜n´an EECS, UC Merced http://eecs.ucmerced.edu Cristian Sminchisescu University of Bonn sminchisescu.ins.uni-bonn.de Abstract Reliably recovering 3D huma... | 2007 | 70 |
3,310 | The Distribution Family of Similarity Distances Gertjan J. Burghouts∗ Arnold W. M. Smeulders Intelligent Systems Lab Amsterdam Informatics Institute University of Amsterdam Jan-Mark Geusebroek † Abstract Assessing similarity between features is a key step in object recognition and scene categorization... | 2007 | 71 |
3,311 | Congruence between model and human attention reveals unique signatures of critical visual events Robert J. Peters∗ Department of Computer Science University of Southern California Los Angeles, CA 90089 rjpeters@usc.edu Laurent Itti Departments of Neuroscience and Computer Science University of Souther... | 2007 | 72 |
3,312 | Multi-task Gaussian Process Prediction Edwin V. Bonilla, Kian Ming A. Chai, Christopher K. I. Williams School of Informatics, University of Edinburgh, 5 Forrest Hill, Edinburgh EH1 2QL, UK edwin.bonilla@ed.ac.uk, K.M.A.Chai@sms.ed.ac.uk, c.k.i.williams@ed.ac.uk Abstract In this paper we investigate multi-... | 2007 | 73 |
3,313 | Multi-Task Learning via Conic Programming Tsuyoshi Kato⋆,◦, Hisashi Kashima†, Masashi Sugiyama‡, Kiyoshi Asai⋆,⋄ ⋆Graduate School of Frontier Sciences, The University of Tokyo, ◦Institute for Bioinformatics Research and Development (BIRD), Japan Science and Technology Agency (JST) † Tokyo Research Laboratory,... | 2007 | 74 |
3,314 | Incremental Natural Actor-Critic Algorithms Shalabh Bhatnagar Department of Computer Science & Automation, Indian Institute of Science, Bangalore, India Richard S. Sutton, Mohammad Ghavamzadeh, Mark Lee Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada Abstract We present fou... | 2007 | 75 |
3,315 | Collective Inference on Markov Models for Modeling Bird Migration Daniel Sheldon M. A. Saleh Elmohamed Dexter Kozen Cornell University Ithaca, NY 14853 {dsheldon,kozen}@cs.cornell.edu saleh@cam.cornell.edu Abstract We investigate a family of inference problems on Markov models, where many sample p... | 2007 | 76 |
3,316 | EEG-Based Brain-Computer Interaction: Improved Accuracy by Automatic Single-Trial Error Detection Pierre W. Ferrez IDIAP Research Institute Centre du Parc Av. des Pr´es-Beudin 20 1920 Martigny, Switzerland pierre.ferrez@idiap.ch Jos´e del R. Mill´an IDIAP Research Institute Centre du Parc Av. des ... | 2007 | 77 |
3,317 | Invariant Common Spatial Patterns: Alleviating Nonstationarities in Brain-Computer Interfacing Benjamin Blankertz1,2 Motoaki Kawanabe2 Ryota Tomioka3 Friederike U. Hohlefeld4 Vadim Nikulin5 Klaus-Robert Müller1,2 1TU Berlin, Dept. of Computer Science, Machine Learning Laboratory, Berlin, Germany 2Frau... | 2007 | 78 |
3,318 | The Infinite Gamma-Poisson Feature Model Michalis K. Titsias School of Computer Science, University of Manchester, UK mtitsias@cs.man.ac.uk Abstract We present a probability distribution over non-negative integer valued matrices with possibly an infinite number of columns. We also derive a stochastic proces... | 2007 | 79 |
3,319 | FilterBoost: Regression and Classification on Large Datasets Joseph K. Bradley Machine Learning Department Carnegie Mellon University Pittsburgh, PA 15213 jkbradle@cs.cmu.edu Robert E. Schapire Department of Computer Science Princeton University Princeton, NJ 08540 schapire@cs.princeton.edu Abstr... | 2007 | 8 |
3,320 | A Unified Near-Optimal Estimator For Dimension Reduction in lα (0 < α ≤2) Using Stable Random Projections Ping Li Department of Statistical Science Faculty of Computing and Information Science Cornell University pingli@cornell.edu Trevor J. Hastie Department of Statistics Department of Health, Research... | 2007 | 80 |
3,321 | Continuous Time Particle Filtering for fMRI Lawrence Murray School of Informatics University of Edinburgh lawrence.murray@ed.ac.uk Amos Storkey School of Informatics University of Edinburgh a.storkey@ed.ac.uk Abstract We construct a biologically motivated stochastic differential model of the neural ... | 2007 | 81 |
3,322 | Computing Robust Counter-Strategies Michael Johanson johanson@cs.ualberta.ca Martin Zinkevich maz@cs.ualberta.ca Michael Bowling Computing Science Department University of Alberta Edmonton, AB Canada T6G2E8 bowling@cs.ualberta.ca Abstract Adaptation to other initially unknown agents often requires... | 2007 | 82 |
3,323 | Random Sampling of States in Dynamic Programming Christopher G. Atkeson and Benjamin Stephens Robotics Institute, Carnegie Mellon University cga@cmu.edu, bstephens@cmu.edu www.cs.cmu.edu/∼cga, www.cs.cmu.edu/∼bstephe1 Abstract We combine three threads of research on approximate dynamic programming: spar... | 2007 | 83 |
3,324 | Predicting human gaze using low-level saliency combined with face detection Moran Cerf Computation and Neural Systems California Institute of Technology Pasadena, CA 91125 moran@klab.caltech.edu Jonathan Harel Electrical Engineering California Institute of Technology Pasadena, CA 91125 harel@klab.... | 2007 | 84 |
3,325 | Local Algorithms for Approximate Inference in Minor-Excluded Graphs Kyomin Jung Dept. of Mathematics, MIT kmjung@mit.edu Devavrat Shah Dept. of EECS, MIT devavrat@mit.edu Abstract We present a new local approximation algorithm for computing MAP and logpartition function for arbitrary exponential famil... | 2007 | 85 |
3,326 | Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization XuanLong Nguyen SAMSI & Duke University Martin J. Wainwright UC Berkeley Michael I. Jordan UC Berkeley Abstract We develop and analyze an algorithm for nonparametric estimation of divergence functionals ... | 2007 | 86 |
3,327 | Learning with Tree-Averaged Densities and Distributions Sergey Kirshner AICML and Dept of Computing Science University of Alberta Edmonton, Alberta, Canada T6G 2E8 sergey@cs.ualberta.ca Abstract We utilize the ensemble of trees framework, a tractable mixture over superexponential number of tree-structur... | 2007 | 87 |
3,328 | Variational inference for Markov jump processes Manfred Opper Department of Computer Science Technische Universit¨at Berlin D-10587 Berlin, Germany opperm@cs.tu-berlin.de Guido Sanguinetti Department of Computer Science University of Sheffield, U.K. guido@dcs.shef.ac.uk Abstract Markov jump process... | 2007 | 88 |
3,329 | Expectation Maximization and Posterior Constraints Jo˜ao V. Grac¸a L2F INESC-ID INESC-ID Lisboa, Portugal Kuzman Ganchev Computer & Information Science University of Pennsylvania Philadelphia, PA Ben Taskar Computer & Information Science University of Pennsylvania Philadelphia, PA Abstract T... | 2007 | 89 |
3,330 | Unsupervised Feature Selection for Accurate Recommendation of High-Dimensional Image Data Sabri Boutemedjet DI, Universite de Sherbrooke 2500 boulevard de l’Universit´e Sherbrooke, QC J1K 2R1, Canada sabri.boutemedjet@usherbrooke.ca Djemel Ziou DI, Universite de Sherbrooke 2500 boulevard de l’Universi... | 2007 | 9 |
3,331 | Anytime Induction of Cost-sensitive Trees Saher Esmeir Computer Science Department Technion—Israel Institute of Technology Haifa 32000, Israel esaher@cs.technion.ac.il Shaul Markovitch Computer Science Department Technion—Israel Institute of Technology Haifa 32000, Israel shaulm@cs.technion.ac.il ... | 2007 | 90 |
3,332 | Optimal ROC Curve for a Combination of Classifiers Marco Barreno Alvaro A. C´ardenas J. D. Tygar Computer Science Division University of California at Berkeley Berkeley, California 94720 {barreno,cardenas,tygar}@cs.berkeley.edu Abstract We present a new analysis for the combination of binary classifiers... | 2007 | 91 |
3,333 | Modeling homophily and stochastic equivalence in symmetric relational data Peter D. Hoff Departments of Statistics and Biostatistics University of Washington Seattle, WA 98195-4322. hoff@stat.washington.edu Abstract This article discusses a latent variable model for inference and prediction of symmetric... | 2007 | 92 |
3,334 | On Sparsity and Overcompleteness in Image Models Pietro Berkes, Richard Turner, and Maneesh Sahani Gatsby Computational Neuroscience Unit, UCL Alexandra House, 17 Queen Square, London WC1N 3AR Abstract Computational models of visual cortex, and in particular those based on sparse coding, have enjoyed much r... | 2007 | 93 |
3,335 | A Probabilistic Approach to Language Change Alexandre Bouchard-Cˆot´e∗ Percy Liang∗ Thomas L. Griffiths† Dan Klein∗ ∗Computer Science Division †Department of Psychology University of California at Berkeley Berkeley, CA 94720 Abstract We present a probabilistic approach to language change in which wor... | 2007 | 94 |
3,336 | Learning the 2-D Topology of Images Nicolas Le Roux University of Montreal nicolas.le.roux@umontreal.ca Yoshua Bengio University of Montreal yoshua.bengio@umontreal.ca Pascal Lamblin University of Montreal lamblinp@umontreal.ca Marc Joliveau ´Ecole Centrale Paris marc.joliveau@ecp.fr Bal´azs K... | 2007 | 95 |
3,337 | A Bayesian LDA-based model for semi-supervised part-of-speech tagging Kristina Toutanova Microsoft Research Redmond, WA kristout@microsoft.com Mark Johnson Brown University Providence, RI Mark Johnson@brown.edu Abstract We present a novel Bayesian model for semi-supervised part-of-speech tagging. ... | 2007 | 96 |
3,338 | Cluster Stability for Finite Samples Ohad Shamir† and Naftali Tishby†‡ † School of Computer Science and Engineering ‡ Interdisciplinary Center for Neural Computation The Hebrew University Jerusalem 91904, Israel {ohadsh,tishby}@cs.huji.ac.il Abstract Over the past few years, the notion of stability in d... | 2007 | 97 |
3,339 | Variational Inference for Diffusion Processes C´edric Archambeau University College London c.archambeau@cs.ucl.ac.uk Manfred Opper Technical University Berlin opperm@cs.tu-berlin.de Yuan Shen Aston University y.shen2@aston.ac.uk Dan Cornford Aston University d.cornford@aston.ac.uk John Shawe-T... | 2007 | 98 |
3,340 | Augmented Functional Time Series Representation and Forecasting with Gaussian Processes Nicolas Chapados and Yoshua Bengio Department of Computer Science and Operations Research University of Montr´eal Montr´eal, Qu´ebec, Canada H3C 3J7 {chapados,bengioy}@iro.umontreal.ca Abstract We introduce a functio... | 2007 | 99 |
3,341 | Near-Minimax Recursive Density Estimation on the Binary Hypercube Maxim Raginsky Duke University Durham, NC 27708 m.raginsky@duke.edu Svetlana Lazebnik UNC Chapel Hill Chapel Hill, NC 27599 lazebnik@cs.unc.edu Rebecca Willett Duke University Durham, NC 27708 willett@duke.edu Jorge Silva Du... | 2008 | 1 |
3,342 | Asynchronous Distributed Learning of Topic Models Arthur Asuncion, Padhraic Smyth, Max Welling Department of Computer Science University of California, Irvine {asuncion,smyth,welling}@ics.uci.edu Abstract Distributed learning is a problem of fundamental interest in machine learning and cognitive science. ... | 2008 | 10 |
3,343 | Exact Convex Confidence-Weighted Learning Koby Crammer Mark Dredze Fernando Pereira∗ Department of Computer and Information Science , University of Pennsylvania Philadelphia, PA 19104 {crammer,mdredze,pereira}@cis.upenn.edu Abstract Confidence-weighted (CW) learning [6], an online learning method for line... | 2008 | 100 |
3,344 | The Conjoint Effect of Divisive Normalization and Orientation Selectivity on Redundancy Reduction in Natural Images Fabian Sinz MPI for Biological Cybernetics 72076 T¨ubingen, Germany fabee@tuebingen.mpg.de Matthias Bethge MPI for Biological Cybernetics 72076 T¨ubingen, Germany mbethge@tuebingen.mpg... | 2008 | 101 |
3,345 | Regularized Co-Clustering with Dual Supervision Vikas Sindhwani Jianying Hu Aleksandra Mojsilovic IBM Research, Yorktown Heights, NY 10598 {vsindhw, jyhu, aleksand}@us.ibm.com Abstract By attempting to simultaneously partition both the rows (examples) and columns (features) of a data matrix, Co-clusteri... | 2008 | 102 |
3,346 | Tighter Bounds for Structured Estimation Chuong B. Do, Quoc Le Stanford University {chuongdo,quocle}@cs.stanford.edu Choon Hui Teo Australian National University and NICTA choonhui.teo@anu.edu.au Olivier Chapelle, Alex Smola Yahoo! Research chap@yahoo-inc.com,alex@smola.org Abstract Large-margin s... | 2008 | 103 |
3,347 | Multi-Agent Filtering with Infinitely Nested Beliefs Luke S. Zettlemoyer MIT CSAIL Cambridge, MA 02139 lsz@csai.mit.edu Brian Milch∗ Google Inc. Mountain View, CA 94043 brian@google.com Leslie Pack Kaelbling MIT CSAIL Cambridge, MA 02139 lpk@csail.mit.edu Abstract In partially observable worl... | 2008 | 104 |
3,348 | Beyond Novelty Detection: Incongruent Events, when General and Specific Classifiers Disagree Abstract Unexpected stimuli are a challenge to any machine learning algorithm. Here we identify distinct types of unexpected events, focusing on ’incongruent events’ when ’general level’ and ’specific level’ classifiers giv... | 2008 | 105 |
3,349 | Look Ma, No Hands: Analyzing the Monotonic Feature Abstraction for Text Classification Doug Downey Electrical Engineering and Computer Science Department Northwestern University Evanston, IL 60208 ddowney@eecs.northwestern.edu Oren Etzioni Turing Center, Department of Computer Science and Engineering U... | 2008 | 106 |
3,350 | Nonparametric Regression and Classification with Joint Sparsity Constraints Han Liu John Lafferty Larry Wasserman Carnegie Mellon University Pittsburgh, PA 15213 Abstract We propose new families of models and algorithms for high-dimensional nonparametric learning with joint sparsity constraints. Our appr... | 2008 | 107 |
3,351 | Recursive Segmentation and Recognition Templates for 2D Parsing Long (Leo) Zhu CSAIL MIT leozhu@csail.mit.edu Yuanhao Chen USTC yhchen4@ustc.edu.cn Yuan Lin Shanghai Jiaotong University loirey@sjtu.edu.cn Chenxi Lin Microsoft Research Asia chenxil@microsoft.com Alan Yuille UCLA yuille@st... | 2008 | 108 |
3,352 | Predicting the Geometry of Metal Binding Sites from Protein Sequence Paolo Frasconi Universit`a degli Studi di Firenze Via di S. Marta 3, 50139 Firenze, Italy p-f@dsi.unifi.it Andrea Passerini Universit`a degli Studi di Trento Via Sommarive, 14, 38100 Povo, Italy passerini@disi.unitn.it Abstract M... | 2008 | 109 |
3,353 | Cascaded Classification Models: Combining Models for Holistic Scene Understanding Geremy Heitz Stephen Gould Department of Electrical Engineering Stanford University, Stanford, CA 94305 {gaheitz,sgould}@stanford.edu Ashutosh Saxena Daphne Koller Department of Computer Science Stanford University, Sta... | 2008 | 11 |
3,354 | Cell Assemblies in Large Sparse Inhibitory Networks of Biologically Realistic Spiking Neurons Adam Ponzi OIST, Uruma, Okinawa, Japan. adamp@oist.jp Jeff Wickens OIST, Uruma, Okinawa, Japan. wickens@oist.jp Abstract Cell assemblies exhibiting episodes of recurrent coherent activity have been observed... | 2008 | 110 |
3,355 | High-dimensional support union recovery in multivariate regression Guillaume Obozinski Department of Statistics UC Berkeley gobo@stat.berkeley.edu Martin J. Wainwright Department of Statistics Dept. of Electrical Engineering and Computer Science UC Berkeley wainwright@stat.berkeley.edu Michael I. ... | 2008 | 111 |
3,356 | Convergence and Rate of Convergence of A Manifold-Based Dimension Reduction Algorithm Andrew K. Smith, Xiaoming Huo School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta, GA 30332 andrewsmith81@gmail.com, huo@gatech.edu Hongyuan Zha College of Computing Georgia Institu... | 2008 | 112 |
3,357 | A Convex Upper Bound on the Log-Partition Function for Binary Graphical Models Laurent El Ghaoui Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley, CA 9470 elghaoui@eecs.berkeley.edu Assane Gueye Department of Electrical Engineering and Computer Science... | 2008 | 113 |
3,358 | Adaptive Forward-Backward Greedy Algorithm for Sparse Learning with Linear Models Tong Zhang Statistics Department Rutgers University, NJ tzhang@stat.rutgers.edu Abstract Consider linear prediction models where the target function is a sparse linear combination of a set of basis functions. We are interest... | 2008 | 114 |
3,359 | Reconciling Real Scores with Binary Comparisons: A Unified Logistic Model for Ranking Nir Ailon Google Research NY 111 8th Ave, 4th FL New York NY 10011 nailon@gmail.com Abstract The problem of ranking arises ubiquitously in almost every aspect of life, and in particular in Machine Learning/Information Ret... | 2008 | 115 |
3,360 | Theory of matching pursuit Zakria Hussain and John Shawe-Taylor Department of Computer Science University College London, UK {z.hussain,j.shawe-taylor}@cs.ucl.ac.uk Abstract We analyse matching pursuit for kernel principal components analysis (KPCA) by proving that the sparse subspace it produces is a sam... | 2008 | 116 |
3,361 | Policy Search for Motor Primitives in Robotics Jens Kober, Jan Peters Max Planck Institute for Biological Cybernetics Spemannstr. 38 72076 Tübingen, Germany {jens.kober,jan.peters}@tuebingen.mpg.de Abstract Many motor skills in humanoid robotics can be learned using parametrized motor primitives as done... | 2008 | 117 |
3,362 | Mortal Multi-Armed Bandits Deepayan Chakrabarti Yahoo! Research Sunnyvale, CA 94089 deepay@yahoo-inc.com Ravi Kumar Yahoo! Research Sunnyvale, CA 94089 ravikumar@yahoo-inc.com Filip Radlinski∗ Microsoft Research Cambridge, UK filiprad@microsoft.com Eli Upfal† Brown University Providence, R... | 2008 | 118 |
3,363 | Adapting to a Market Shock: Optimal Sequential Market-Making Sanmay Das Department of Computer Science Rensselaer Polytechnic Institute Troy, NY 12180 sanmay@cs.rpi.edu Malik Magdon-Ismail Department of Computer Science Rensselaer Polytechnic Institute Troy, NY 12180 magdon@cs.rpi.edu Abstract ... | 2008 | 119 |
3,364 | Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes Ben Calderhead Dept. of Computing Sci. University of Glasgow bc@dcs.gla.ac.uk Mark Girolami Dept. of Computing Sci. University of Glasgow girolami@dcs.gla.ac.uk Neil D. Lawrence School of Computer Sci. ... | 2008 | 12 |
3,365 | DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification Simon Lacoste-Julien Computer Science Division UC Berkeley Berkeley, CA 94720 Fei Sha Dept. of Computer Science University of Southern California Los Angeles, CA 90089 Michael I. Jordan Dept. of EECS and Statistics U... | 2008 | 120 |
3,366 | Understanding Brain Connectivity Patterns during Motor Imagery for Brain-Computer Interfacing Moritz Grosse-Wentrup Max Planck Institute for Biological Cybernetics Spemannstr. 38 72076 T¨ubingen, Germany moritzgw@ieee.org Abstract EEG connectivity measures could provide a new type of feature space for i... | 2008 | 121 |
3,367 | Rademacher Complexity Bounds for Non-I.I.D. Processes Mehryar Mohri Courant Institute of Mathematical Sciences and Google Research 251 Mercer Street New York, NY 10012 mohri@cims.nyu.edu Afshin Rostamizadeh Department of Computer Science Courant Institute of Mathematical Sciences 251 Mercer Street... | 2008 | 122 |
3,368 | A rational model of preference learning and choice prediction by children Christopher G. Lucas Department of Psychology University of California, Berkeley Berkeley, CA 94720, USA clucas@berkeley.edu Thomas L. Griffiths Department of Psychology University of California, Berkeley Berkeley, CA 94720, US... | 2008 | 123 |
3,369 | An ideal observer model of infant object perception Charles Kemp Department of Psychology Carnegie Mellon University ckemp@cmu.edu Fei Xu Department of Psychology University of British Columbia fei@psych.ubc.ca Abstract Before the age of 4 months, infants make inductive inferences about the motions ... | 2008 | 124 |
3,370 | Empirical performance maximization for linear rank statistics St´ephan Cl´emenc¸on Telecom Paristech (TSI) - LTCI UMR Institut Telecom/CNRS 5141 stephan.clemencon@telecom-paristech.fr Nicolas Vayatis ENS Cachan & UniverSud - CMLA UMR CNRS 8536 vayatis@cmla.ens-cachan.fr Abstract The ROC curve is known... | 2008 | 125 |
3,371 | Resolution Limits of Sparse Coding in High Dimensions∗ Alyson K. Fletcher,† Sundeep Rangan,‡ and Vivek K Goyal§ Abstract This paper addresses the problem of sparsity pattern detection for unknown ksparse n-dimensional signals observed through m noisy, random linear measurements. Sparsity pattern recovery arises... | 2008 | 126 |
3,372 | Privacy-preserving logistic regression Kamalika Chaudhuri Information Theory and Applications University of California, San Diego kamalika@soe.ucsd.edu Claire Monteleoni∗ Center for Computational Learning Systems Columbia University cmontel@ccls.columbia.edu Abstract This paper addresses the importa... | 2008 | 127 |
3,373 | Efficient Exact Inference in Planar Ising Models Nicol N. Schraudolph Dmitry Kamenetsky nips@schraudolph.org dkamen@cecs.anu.edu.au National ICT Australia, Locked Bag 8001, Canberra ACT 2601, Australia & RSISE, Australian National University, Canberra ACT 0200, Australia Abstract We give polynomial-time ... | 2008 | 128 |
3,374 | Deflation Methods for Sparse PCA Lester Mackey Computer Science Division University of California, Berkeley Berkeley, CA 94703 Abstract In analogy to the PCA setting, the sparse PCA problem is often solved by iteratively alternating between two subtasks: cardinality-constrained rank-one variance maximization... | 2008 | 129 |
3,375 | Linear Classification and Selective Sampling Under Low Noise Conditions Giovanni Cavallanti DSI, Universit`a degli Studi di Milano, Italy cavallanti@dsi.unimi.it Nicol`o Cesa-Bianchi DSI, Universit`a degli Studi di Milano, Italy cesa-bianchi@dsi.unimi.it Claudio Gentile DICOM, Universit`a dell’Insubria... | 2008 | 13 |
3,376 | Mixed Membership Stochastic Blockmodels Edoardo M. Airoldi 1,2, David M. Blei 1, Stephen E. Fienberg 3,4 & Eric P. Xing 4∗ 1 Department of Computer Science, 2 Lewis-Sigler Institute, Princeton University 3 Department of Statistics, 4 School of Computer Science, Carnegie Mellon University eairoldi@Princeton.EDU ... | 2008 | 130 |
3,377 | Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes Erik B. Sudderth and Michael I. Jordan Electrical Engineering & Computer Science, University of California, Berkeley sudderth@cs.berkeley.edu, jordan@cs.berkeley.edu Abstract We develop a statistical framework for the simultaneous, u... | 2008 | 131 |
3,378 | Shape-Based Object Localization for Descriptive Classification Geremy Heitz1,∗ Gal Elidan2,3,∗ Ben Packer2,∗ Daphne Koller2 1Department of Electrical Engineering, Stanford University 2Department of Computer Science, Stanford University 3Department of Statistics, Hebrew University, Jerusalem {gaheitz,bp... | 2008 | 132 |
3,379 | Covariance Estimation for High Dimensional Data Vectors Using the Sparse Matrix Transform Guangzhi Cao Charles A. Bouman School of Electrical and Computer Enigneering Purdue University West Lafayette, IN 47907 {gcao, bouman}@purdue.edu Abstract Covariance estimation for high dimensional vectors is a c... | 2008 | 133 |
3,380 | Characterizing neural dependencies with copula models Pietro Berkes Volen Center for Complex Systems Brandeis University, Waltham, MA 02454 berkes@brandeis.edu Frank Wood and Jonathan Pillow Gatsby Computational Neuroscience Unit, UCL London WC1N 3AR, UK {fwood,pillow}@gatsby.ucl.ac.uk Abstract Th... | 2008 | 134 |
3,381 | Learning with Consistency between Inductive Functions and Kernels Haixuan Yang1,2 Irwin King1 Michael R. Lyu1 1Department of Computer Science & Engineering The Chinese University of Hong Kong {hxyang,king,lyu}@cse.cuhk.edu.hk 2Department of Computer Science Royal Holloway University of London haixua... | 2008 | 135 |
3,382 | Syntactic Topic Models Jordan Boyd-Graber Department of Computer Science 35 Olden Street Princeton University Princeton, NJ 08540 jbg@cs.princeton.edu David Blei Department of Computer Science 35 Olden Street Princeton University Princeton, NJ 08540 blei@cs.princeton.edu Abstract We develop ... | 2008 | 136 |
3,383 | A general framework for investigating how far the decoding process in the brain can be simplified Masafumi Oizumi1, Toshiyuki Ishii2, Kazuya Ishibashi1 Toshihiko Hosoya2, Masato Okada1,2 oizumi@mns.k.u-tokyo.ac.jp tishii@brain.riken.jp,kazuya@mns.k.u-tokyo.ac.jp hosoya@brain.riken.jp, okada@k.u-tokyo.ac.jp ... | 2008 | 137 |
3,384 | Signal-to-Noise Ratio Analysis of Policy Gradient Algorithms John W. Roberts and Russ Tedrake Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge, MA 02139 Abstract Policy gradient (PG) reinforcement learning algorithms have strong (local) convergence g... | 2008 | 138 |
3,385 | Artificial Olfactory Brain for Mixture Identification Mehmet K. Muezzinoglu1 Alexander Vergara1 Ramon Huerta1 Thomas Nowotny2 Nikolai F. Rulkov1 Heny D. I. Abarbanel1 Allen Selverston1 Mikhail I. Rabinovich1 1 Institute for Nonlinear Science 2 Centre for Computational Neuroscience and Robotics Unive... | 2008 | 139 |
3,386 | On the Efficient Minimization of Classification Calibrated Surrogates Richard Nock CEREGMIA — Univ. Antilles-Guyane 97275 Schoelcher Cedex, Martinique, France rnock@martinique.univ-ag.fr Frank Nielsen LIX - Ecole Polytechnique 91128 Palaiseau Cedex, France nielsen@lix.polytechnique.fr Abstract Bartl... | 2008 | 14 |
3,387 | Robust Kernel Principal Component Analysis Minh Hoai Nguyen & Fernando De la Torre Carnegie Mellon University, Pittsburgh, PA 15213, USA. Abstract Kernel Principal Component Analysis (KPCA) is a popular generalization of linear PCA that allows non-linear feature extraction. In KPCA, data in the input space is... | 2008 | 140 |
3,388 | Relative Margin Machines Pannagadatta K Shivaswamy and Tony Jebara Department of Computer Science, Columbia University, New York, NY pks2103,jebara@cs.columbia.edu Abstract In classification problems, Support Vector Machines maximize the margin of separation between two classes. While the paradigm has been s... | 2008 | 141 |
3,389 | Bayesian Kernel Shaping for Learning Control Jo-Anne Ting1, Mrinal Kalakrishnan1, Sethu Vijayakumar2 and Stefan Schaal1,3 1Computer Science, U. of Southern California, Los Angeles, CA 90089, USA 2School of Informatics, University of Edinburgh, Edinburgh, EH9 3JZ, UK 3ATR Computational Neuroscience Labs, Kyoto 6... | 2008 | 142 |
3,390 | An Extended Level Method for Efficient Multiple Kernel Learning Zenglin Xu† Rong Jin‡ † Dept. of Computer Science & Engineering The Chinese University of Hong Kong Shatin, N.T., Hong Kong {zlxu, king, lyu}@cse.cuhk.edu.hk Irwin King† Michael R. Lyu† ‡Dept. of Computer Science & Engineering Michigan... | 2008 | 143 |
3,391 | Sparse Online Learning via Truncated Gradient John Langford Yahoo! Research jl@yahoo-inc.com Lihong Li Department of Computer Science Rutgers University lihong@cs.rutgers.edu Tong Zhang Department of Statistics Rutgers University tongz@rci.rutgers.edu Abstract We propose a general method calle... | 2008 | 144 |
3,392 | Bayesian Model of Behaviour in Economic Games Debajyoti Ray Computation and Neural Systems California Institute of Technology Pasadena, CA 91125. USA dray@caltech.edu Brooks King-Casas Computational Psychiatry Unit Baylor College of Medicine. Houston, TX 77030. USA bkcasas@cpu.bcm.tmc.edu P. Read ... | 2008 | 145 |
3,393 | Skill characterization based on betweenness ¨Ozg¨ur S¸ims¸ek∗ Andrew G. Barto Department of Computer Science University of Massachusetts Amherst, MA 01003 {ozgur|barto}@cs.umass.edu Abstract We present a characterization of a useful class of skills based on a graphical representation of an agent’s inter... | 2008 | 146 |
3,394 | Improved Moves for Truncated Convex Models M. Pawan Kumar P.H.S. Torr Dept. of Engineering Science Dept. of Computing University of Oxford Oxford Brookes University pawan@robots.ox.ac.uk philiptorr@brookes.ac.uk Abstract We consider the problem of obtaining the approximate maximum a posteriori estim... | 2008 | 147 |
3,395 | Kernelized Sorting Novi Quadrianto RSISE, ANU & SML, NICTA Canberra, ACT, Australia novi.quad@gmail.com Le Song SCS, CMU Pittsburgh, PA, USA lesong@cs.cmu.edu Alex J. Smola Yahoo! Research Santa Clara, CA, USA alex@smola.org Abstract Object matching is a fundamental operation in data analysi... | 2008 | 148 |
3,396 | Posterior Consistency of the Silverman g-prior in Bayesian Model Choice Zhihua Zhang School of Computer Science & Technology Zhejiang University, Hangzhou, China Michael I. Jordan Departments of EECS and Statistics University of California, Berkeley, CA, USA Dit-Yan Yeung Department of Computer Scienc... | 2008 | 149 |
3,397 | Unlabeled data: Now it helps, now it doesn’t Aarti Singh, Robert D. Nowak∗ Xiaojin Zhu† Department of Electrical and Computer Engineering Department of Computer Sciences University of Wisconsin - Madison University of Wisconsin - Madison Madison, WI 53706 Madison, WI 53706 {singh@cae,nowak@engr}.wisc.... | 2008 | 15 |
3,398 | Nonparametric Bayesian Learning of Switching Linear Dynamical Systems Emily B. Fox Electrical Engineering & Computer Science, Massachusetts Institute of Technology ebfox@mit.edu Erik B. Sudderth†, Michael I. Jordan†‡ †Electrical Engineering & Computer Science and ‡Statistics, University of California, Berke... | 2008 | 150 |
3,399 | Learning to use Working Memory in Partially Observable Environments through Dopaminergic Reinforcement Michael T. Todd, Yael Niv, Jonathan D. Cohen Department of Psychology & Princeton Neuroscience Institute Princeton University, Princeton, NJ 08544 {mttodd,yael,jdc}@princeton.edu Abstract ... | 2008 | 151 |
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