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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4,100 | Variational Bounds for Mixed-Data Factor Analysis Mohammad Emtiyaz Khan University of British Columbia Vancouver, BC, Canada V6T 1Z4 emtiyaz@cs.ubc.ca Guillaume Bouchard Xerox Research Center Europe 38240 Meylan, France guillaume.bouchard@xerox.com Benjamin M. Marlin University of British Columbia ... | 2010 | 59 |
4,101 | On Herding and the Perceptron Cycling Theorem Andrew E. Gelfand, Yutian Chen, Max Welling Department of Computer Science University of California, Irvine {agelfand,yutianc,welling}@ics.uci.edu Laurens van der Maaten Department of CSE, UC San Diego PRB Lab, Delft University of Tech. lvdmaaten@gmail.com ... | 2010 | 6 |
4,102 | Copula Bayesian Networks Gal Elidan Department of Statistics Hebrew University Jerusalem, 91905, Israel galel@huji.ac.il Abstract We present the Copula Bayesian Network model for representing multivariate continuous distributions, while taking advantage of the relative ease of estimating univariate dist... | 2010 | 60 |
4,103 | Evidence-Specific Structures for Rich Tractable CRFs Anton Chechetka Carnegie Mellon University antonc@cs.cmu.edu Carlos Guestrin Carnegie Mellon University guestrin@cs.cmu.edu Abstract We present a simple and effective approach to learning tractable conditional random fields with structure that depends o... | 2010 | 61 |
4,104 | Beyond Actions: Discriminative Models for Contextual Group Activities Tian Lan School of Computing Science Simon Fraser University tla58@sfu.ca Yang Wang Department of Computer Science University of Illinois at Urbana-Champaign yangwang@uiuc.edu Weilong Yang School of Computing Science Simon Fra... | 2010 | 62 |
4,105 | Decoding Ipsilateral Finger Movements from ECoG Signals in Humans Yuzong Liu1, Mohit Sharma2, Charles M. Gaona2, Jonathan D. Breshears3, Jarod Roland 3, Zachary V. Freudenburg1, Kilian Q. Weinberger1, and Eric C. Leuthardt2,3 1Department of Computer Science and Engineering, Washington University in St. Louis ... | 2010 | 63 |
4,106 | New Adaptive Algorithms for Online Classification Francesco Orabona DSI Universit`a degli Studi di Milano Milano, 20135 Italy orabona@dsi.unimi.it Koby Crammer Department of Electrical Enginering The Technion Haifa, 32000 Israel koby@ee.technion.ac.il Abstract We propose a general framework to on... | 2010 | 64 |
4,107 | Phoneme Recognition with Large Hierarchical Reservoirs Fabian Triefenbach Azarakhsh Jalalvand Benjamin Schrauwen Jean-Pierre Martens Department of Electronics and Information Systems Ghent University Sint-Pietersnieuwstraat 41, 9000 Gent, Belgium fabian.triefenbach@elis.ugent.be Abstract Automatic... | 2010 | 65 |
4,108 | Learning Multiple Tasks using Manifold Regularization Arvind Agarwal∗ Hal Daum´e III∗ Department of Computer Science University of Maryland College Park, MD 20740 arvinda@cs.umd.edu hal@umiacs.umd.edu Samuel Gerber Scientific Computing and Imaging Institute University of Utah Salt Lake City, Utah... | 2010 | 66 |
4,109 | Group Sparse Coding with a Laplacian Scale Mixture Prior Pierre J. Garrigues IQ Engines, Inc. Berkeley, CA 94704 pierre.garrigues@gmail.com Bruno A. Olshausen Helen Wills Neuroscience Institute School of Optometry University of California, Berkeley Berkeley, CA 94720 baolshausen@berkeley.edu Abs... | 2010 | 67 |
4,110 | Fractionally Predictive Spiking Neurons Sander M. Bohte CWI, Life Sciences Amsterdam, The Netherlands S.M.Bohte@cwi.nl Jaldert O. Rombouts CWI, Life Sciences Amsterdam, The Netherlands J.O.Rombouts@cwi.nl Abstract Recent experimental work has suggested that the neural firing rate can be interpreted a... | 2010 | 68 |
4,111 | Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models Han Liu Kathryn Roeder Larry Wasserman Carnegie Mellon University Pittsburgh, PA 15213 Abstract A challenging problem in estimating high-dimensional graphical models is to choose the regularization parameter in a ... | 2010 | 69 |
4,112 | Tiled convolutional neural networks Quoc V. Le, Jiquan Ngiam, Zhenghao Chen, Daniel Chia, Pang Wei Koh, Andrew Y. Ng Computer Science Department, Stanford University {quocle,jngiam,zhenghao,danchia,pangwei,ang}@cs.stanford.edu Abstract Convolutional neural networks (CNNs) have been successfully applied to man... | 2010 | 7 |
4,113 | More data means less inference: A pseudo-max approach to structured learning David Sontag Microsoft Research Ofer Meshi Hebrew University Tommi Jaakkola CSAIL, MIT Amir Globerson Hebrew University Abstract The problem of learning to predict structured labels is of key importance in many applicat... | 2010 | 70 |
4,114 | Lifted Inference Seen from the Other Side : The Tractable Features Abhay Jha Vibhav Gogate Alexandra Meliou Dan Suciu Computer Science & Engineering University of Washington Washington, WA 98195 {abhaykj,vgogate,ameli,suciu}@cs.washington.edu Abstract Lifted Inference algorithms for representations th... | 2010 | 71 |
4,115 | Predictive State Temporal Difference Learning Byron Boots Machine Learning Department Carnegie Mellon University Pittsburgh, PA 15213 beb@cs.cmu.edu Geoffrey J. Gordon Machine Learning Department Carnegie Mellon University Pittsburgh, PA 15213 ggordon@cs.cmu.edu Abstract We propose a new approac... | 2010 | 72 |
4,116 | Identifying Dendritic Processing Aurel A. Lazar Department of Electrical Engineering Columbia University New York, NY 10027 aurel@ee.columbia.edu Yevgeniy B. Slutskiy∗ Department of Electrical Engineering Columbia University New York, NY 10027 ys2146@columbia.edu Abstract In system identification... | 2010 | 73 |
4,117 | On a Connection between Importance Sampling and the Likelihood Ratio Policy Gradient Jie Tang and Pieter Abbeel Department of Electrical Engineering and Computer Science University of California, Berkeley Berkeley, CA 94709 {jietang, pabbeel}@eecs.berkeley.edu Abstract Likelihood ratio policy gradient m... | 2010 | 74 |
4,118 | Functional Geometry Alignment and Localization of Brain Areas Georg Langs, Polina Golland Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology Cambridge, MA 02139, USA langs@csail.mit.edu, polina@csail.mit.edu Yanmei Tie, Laura Rigolo, Alexandra J. Golby Department of Neu... | 2010 | 75 |
4,119 | Multi-View Active Learning in the Non-Realizable Case Wei Wang and Zhi-Hua Zhou National Key Laboratory for Novel Software Technology Nanjing University, Nanjing 210093, China {wangw,zhouzh}@lamda.nju.edu.cn Abstract The sample complexity of active learning under the realizability assumption has been we... | 2010 | 76 |
4,120 | Epitome driven 3-D Diffusion Tensor image segmentation: on extracting specific structures∗ Kamiya Motwani†§ Nagesh Adluru§ Chris Hinrichs†§ Andrew Alexander‡ Vikas Singh§† †Computer Sciences §Biostatistics & Medical Informatics ‡Medical Physics University of Wisconsin University of Wisconsin Univ... | 2010 | 77 |
4,121 | Over-complete representations on recurrent neural networks can support persistent percepts Shaul Druckmann Janelia Farm Research Campus Howard Hughes Medical Institute Ashburn, VA 20147 druckmanns@janelia.hhmi.org Dmitri B. Chklovskii Janelia Farm Research Campus Howard Hughes Medical Institute Ashb... | 2010 | 78 |
4,122 | Non-Stochastic Bandit Slate Problems Satyen Kale Yahoo! Research Santa Clara, CA skale@yahoo-inc.com Lev Reyzin∗ Georgia Inst. of Technology Atlanta, GA lreyzin@cc.gatech.edu Robert E. Schapire† Princeton University Princeton, NJ schapire@cs.princeton.edu Abstract We consider bandit problems... | 2010 | 79 |
4,123 | Decomposing Isotonic Regression for Efficiently Solving Large Problems Ronny Luss Dept. of Statistics and OR Tel Aviv University ronnyluss@gmail.com Saharon Rosset Dept. of Statistics and OR Tel Aviv University saharon@post.tau.ac.il Moni Shahar Dept. of Electrical Eng. Tel Aviv University moni... | 2010 | 8 |
4,124 | Large Margin Learning of Upstream Scene Understanding Models Jun Zhu† Li-Jia Li‡ Fei-Fei Li‡ Eric P. Xing† †{junzhu,epxing}@cs.cmu.edu ‡{lijiali,feifeili}@cs.stanford.edu †School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213 ‡Department of Computer Science, Stanford University,... | 2010 | 80 |
4,125 | Switched Latent Force Models for Movement Segmentation Mauricio A. ´Alvarez 1, Jan Peters 2, Bernhard Sch¨olkopf 2, Neil D. Lawrence 3,4 1 School of Computer Science, University of Manchester, Manchester, UK M13 9PL 2 Max Planck Institute for Biological Cybernetics, T¨ubingen, Germany 72076 3 School of Comput... | 2010 | 81 |
4,126 | Adaptive Multi-Task Lasso: with Application to eQTL Detection Seunghak Lee, Jun Zhu and Eric P. Xing School of Computer Science, Carnegie Mellon University {seunghak,junzhu,epxing}@cs.cmu.edu Abstract To understand the relationship between genomic variations among population and complex diseases, it is es... | 2010 | 82 |
4,127 | Categories and Functional Units: An Infinite Hierarchical Model for Brain Activations Danial Lashkari Ramesh Sridharan Polina Golland Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge, MA 02139 {danial, rameshvs, polina}@csail.mit.edu Abstract We p... | 2010 | 83 |
4,128 | Random Projection Trees Revisited Aman Dhesi∗ Department of Computer Science Princeton University Princeton, New Jersey, USA. adhesi@princeton.edu Purushottam Kar Department of Computer Science and Engineering Indian Institute of Technology Kanpur, Uttar Pradesh, INDIA. purushot@cse.iitk.ac.in Abs... | 2010 | 84 |
4,129 | Joint Analysis of Time-Evolving Binary Matrices and Associated Documents 1Eric Wang, 1Dehong Liu, 1Jorge Silva, 2David Dunson and 1Lawrence Carin 1Electrical and Computer Engineering Department, Duke University 2Statistics Department, Duke University {eric.wang,dehong.liu,jg.silva,lawrence.carin}@duke.edu d... | 2010 | 85 |
4,130 | Discriminative Clustering by Regularized Information Maximization Ryan Gomes gomes@vision.caltech.edu Andreas Krause krausea@caltech.edu Pietro Perona perona@vision.caltech.edu California Institute of Technology Pasadena, CA 91106 Abstract Is there a principled way to learn a probabilistic discrim... | 2010 | 86 |
4,131 | Learning to localise sounds with spiking neural networks Dan F. M. Goodman D´epartment d’Etudes Cognitive Ecole Normale Sup´erieure 29 Rue d’Ulm Paris 75005, France dan.goodman@ens.fr Romain Brette D´epartment d’Etudes Cognitive Ecole Normale Sup´erieure 29 Rue d’Ulm Paris 75005, France romain... | 2010 | 87 |
4,132 | Dynamic Infinite Relational Model for Time-varying Relational Data Analysis Katsuhiko Ishiguro Tomoharu Iwata Naonori Ueda NTT Communication Science Laboratories Kyoto, 619-0237 Japan {ishiguro,iwata,ueda}@cslab.kecl.ntt.co.jp Joshua Tenenbaum MIT Boston, MA. jbt@mit.edu Abstract We propose a n... | 2010 | 88 |
4,133 | Exact learning curves for Gaussian process regression on large random graphs Matthew J. Urry Department of Mathematics King’s College London London, WC2R 2LS, U.K. matthew.urry@kcl.ac.uk Peter Sollich Department of Mathematics King’s College London London, WC2R 2LS, U.K. peter.sollich@kcl.ac.uk ... | 2010 | 89 |
4,134 | Learning Kernels with Radiuses of Minimum Enclosing Balls Kun Gai Guangyun Chen Changshui Zhang State Key Laboratory on Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology (TNList) Department of Automation, Tsinghua University, Beijing 100084, China {ga... | 2010 | 9 |
4,135 | Sparse Instrumental Variables (SPIV) for Genome-Wide Studies Felix V. Agakov Public Health Sciences University of Edinburgh felixa@aivalley.com Paul McKeigue Public Health Sciences University of Edinburgh paul.mckeigue@ed.ac.uk Jon Krohn WTCHG, Oxford jon.krohn@magd.ox.ac.uk Amos Storkey Sch... | 2010 | 90 |
4,136 | Natural Policy Gradient Methods with Parameter-based Exploration for Control Tasks Atsushi Miyamae†‡, Yuichi Nagata†, Isao Ono†, Shigenobu Kobayashi† †: Department of Computational Intelligence and Systems Science Tokyo Institute of Technology, Kanagawa, Japan ‡: Research Fellow of the Japan Society for the P... | 2010 | 91 |
4,137 | Kernel Descriptors for Visual Recognition Liefeng Bo University of Washington Seattle WA 98195, USA Xiaofeng Ren Intel Labs Seattle Seattle WA 98105, USA Dieter Fox University of Washington & Intel Labs Seattle Seattle WA 98195 & 98105, USA Abstract The design of low-level image features is critic... | 2010 | 92 |
4,138 | Computing Marginal Distributions over Continuous Markov Networks for Statistical Relational Learning Matthias Br¨ocheler, Lise Getoor University of Maryland, College Park College Park, MD 20742 {matthias, getoor}@cs.umd.edu Abstract Continuous Markov random fields are a general formalism to model joint pro... | 2010 | 93 |
4,139 | Gaussian Process Preference Elicitation Edwin V. Bonilla, Shengbo Guo, Scott Sanner NICTA & ANU, Locked Bag 8001, Canberra ACT 2601, Australia {edwin.bonilla, shengbo.guo, scott.sanner}@nicta.com.au Abstract Bayesian approaches to preference elicitation (PE) are particularly attractive due to their ability ... | 2010 | 94 |
4,140 | A Theory of Multiclass Boosting Indraneel Mukherjee Robert E. Schapire Princeton University, Department of Computer Science, Princeton, NJ 08540 {imukherj,schapire}@cs.princeton.edu Abstract Boosting combines weak classifiers to form highly accurate predictors. Although the case of binary classification is ... | 2010 | 95 |
4,141 | Hashing Hyperplane Queries to Near Points with Applications to Large-Scale Active Learning Prateek Jain Algorithms Research Group Microsoft Research, Bangalore, India prajain@microsoft.com Sudheendra Vijayanarasimhan Department of Computer Science University of Texas at Austin svnaras@cs.utexas.edu ... | 2010 | 96 |
4,142 | Bootstrapping Apprenticeship Learning Abdeslam Boularias Department of Empirical Inference Max-Planck Institute for Biological Cybernetics 72076 T¨ubingen, Germany abdeslam.boularias@tuebingen.mpg.de Brahim Chaib-Draa Department of Computer Science Laval University Quebec G1V 0A6, Canada chaib@damas... | 2010 | 97 |
4,143 | Co-regularization Based Semi-supervised Domain Adaptation Hal Daum´e III Department of Computer Science University of Maryland CP, MD, USA hal@umiacs.umd.edu Abhishek Kumar Department of Computer Science University of Maryland CP, MD, USA abhishek@umiacs.umd.edu Avishek Saha School Of Computing Un... | 2010 | 98 |
4,144 | Structured sparsity-inducing norms through submodular functions Francis Bach INRIA - Willow project-team Laboratoire d’Informatique de l’Ecole Normale Sup´erieure Paris, France francis.bach@ens.fr Abstract Sparse methods for supervised learning aim at finding good linear predictors from as few variable... | 2010 | 99 |
4,145 | Emergence of Multiplication in a Biophysical Model of a Wide-Field Visual Neuron for Computing Object Approaches: Dynamics, Peaks, & Fits Matthias S. Keil∗ Department of Basic Psychology University of Barcelona E-08035 Barcelona, Spain matskeil@ub.edu Abstract Many species show avoidance reactions in ... | 2011 | 1 |
4,146 | Rapid Deformable Object Detection using Dual-Tree Branch-and-Bound Iasonas Kokkinos Center for Visual Computing Ecole Centrale de Paris iasonas.kokkinos@ecp.fr Abstract In this work we use Branch-and-Bound (BB) to efficiently detect objects with deformable part models. Instead of evaluating the classifier s... | 2011 | 10 |
4,147 | Fast and Accurate k-llleans For Large Datasets Michael Shindler School of EECS Oregon State University shindler@eecs.oregonstate.edu Alex Wong Department of Computer Science UC Los Angeles alexw@seas.ucla.edu Adam Meyerson Google, Inc. Mountain View, CA awmeyerson@google.com Abstract Cluster... | 2011 | 100 |
4,148 | Message-Passing for Approximate MAP Inference with Latent Variables Jiarong Jiang Dept. of Computer Science University of Maryland, CP jiarong@umiacs.umd.edu Piyush Rai School of Computing University of Utah piyush@cs.utah.edu Hal Daum´e III Dept. of Computer Science University of Maryland, CP ... | 2011 | 101 |
4,149 | A More Powerful Two-Sample Test in High Dimensions using Random Projection Miles E. Lopes1 Laurent Jacob1 Martin J. Wainwright1,2 Departments of Statistics1 and EECS2 University of California, Berkeley Berkeley, CA 94720-3860 {mlopes,laurent,wainwrig}@stat.berkeley.edu Abstract We consider the hypot... | 2011 | 102 |
4,150 | Kernel Bayes’ Rule Kenji Fukumizu The Institute of Statistical Mathematics, Tokyo fukumizu@ism.ac.jp Le Song College of Computing Georgia Institute of Technology lsong@cc.gatech.edu Arthur Gretton Gatsby Unit, UCL MPI for Intelligent Systems arthur.gretton@gmail.com Abstract A nonparametric ... | 2011 | 103 |
4,151 | ShareBoost: Efficient Multiclass Learning with Feature Sharing Shai Shalev-Shwartz⇤ Yonatan Wexler† Amnon Shashua‡ Abstract Multiclass prediction is the problem of classifying an object into a relevant target class. We consider the problem of learning a multiclass predictor that uses only few features, a... | 2011 | 104 |
4,152 | Heavy-tailed Distances for Gradient Based Image Descriptors Yangqing Jia and Trevor Darrell UC Berkeley EECS and ICSI {jiayq,trevor}@eecs.berkeley.edu Abstract Many applications in computer vision measure the similarity between images or image patches based on some statistics such as oriented gradients. T... | 2011 | 105 |
4,153 | An Application of Tree-Structured Expectation Propagation for Channel Decoding Pablo M. Olmos∗, Luis Salamanca∗, Juan J. Murillo-Fuentes∗, Fernando P´erez-Cruz† ∗Dept. of Signal Theory and Communications, University of Sevilla 41092 Sevilla Spain {olmos,salamanca,murillo}@us.es † Dept. of Signal Theory and ... | 2011 | 106 |
4,154 | PAC-Bayesian Analysis of Contextual Bandits Yevgeny Seldin1,4 Peter Auer2 Franc¸ois Laviolette3 John Shawe-Taylor4 Ronald Ortner2 1Max Planck Institute for Intelligent Systems, T¨ubingen, Germany 2Chair for Information Technology, Montanuniversit¨at Leoben, Austria 3D´epartement d’informatique, Universit´e Lava... | 2011 | 107 |
4,155 | Fast and Balanced: Efficient Label Tree Learning for Large Scale Object Recognition Jia Deng1,2, Sanjeev Satheesh1, Alexander C. Berg3, Li Fei-Fei1 Computer Science Department, Stanford University1 Computer Science Department, Princeton University2 Computer Science Department, Stony Brook University3 Abstrac... | 2011 | 108 |
4,156 | Divide-and-Conquer Matrix Factorization Lester Mackeya Ameet Talwalkara Michael I. Jordana, b a Department of Electrical Engineering and Computer Science, UC Berkeley b Department of Statistics, UC Berkeley Abstract This work introduces Divide-Factor-Combine (DFC), a parallel divide-andconquer framework f... | 2011 | 109 |
4,157 | Probabilistic Modeling of Dependencies Among Visual Short-Term Memory Representations A. Emin Orhan Robert A. Jacobs Department of Brain & Cognitive Sciences University of Rochester Rochester, NY 14627 {eorhan,robbie}@bcs.rochester.edu Abstract Extensive evidence suggests that items are not encoded in... | 2011 | 11 |
4,158 | Non-conjugate Variational Message Passing for Multinomial and Binary Regression David A. Knowles Department of Engineering University of Cambridge Thomas P. Minka Microsoft Research Cambridge, UK Abstract Variational Message Passing (VMP) is an algorithmic implementation of the Variational Bayes (VB) ... | 2011 | 110 |
4,159 | Im2Text: Describing Images Using 1 Million Captioned Photographs Vicente Ordonez Girish Kulkarni Tamara L Berg Stony Brook University Stony Brook, NY 11794 {vordonezroma or tlberg}@cs.stonybrook.edu Abstract We develop and demonstrate automatic image description methods using a large captioned photo... | 2011 | 111 |
4,160 | Modelling Genetic Variations with Fragmentation-Coagulation Processes Yee Whye Teh, Charles Blundell and Lloyd T. Elliott Gatsby Computational Neuroscience Unit, UCL 17 Queen Square, London WC1N 3AR, United Kingdom {ywteh,c.blundell,elliott}@gatsby.ucl.ac.uk Abstract We propose a novel class of Bayesian n... | 2011 | 112 |
4,161 | Uniqueness of Belief Propagation on Signed Graphs Yusuke Watanabe∗ The Institute of Statistical Mathematics 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, Japan watay@ism.ac.jp Abstract While loopy Belief Propagation (LBP) has been utilized in a wide variety of applications with empirical success, it comes wit... | 2011 | 113 |
4,162 | Improving Topic Coherence with Regularized Topic Models David Newman University of California, Irvine newman@uci.edu Edwin V. Bonilla Wray Buntine NICTA & Australian National University {edwin.bonilla, wray.buntine}@nicta.com.au Abstract Topic models have the potential to improve search and browsing... | 2011 | 114 |
4,163 | Beating SGD: Learning SVMs in Sublinear Time Elad Hazan Tomer Koren Technion, Israel Institute of Technology Haifa, Israel 32000 {ehazan@ie,tomerk@cs}.technion.ac.il Nathan Srebro Toyota Technological Institute Chicago, Illinois 60637 nati@ttic.edu Abstract We present an optimization approach for ... | 2011 | 115 |
4,164 | Inferring spike-timing-dependent plasticity from spike train data Ian H. Stevenson and Konrad P. Kording Department of Physical Medicine and Rehabilitation Northwestern University {i-stevenson, kk}@northwestern.edu Abstract Synaptic plasticity underlies learning and is thus central for development, memory... | 2011 | 116 |
4,165 | Bayesian Spike-Triggered Covariance Analysis Il Memming Park Center for Perceptual Systems University of Texas at Austin Austin, TX 78712, USA memming@austin.utexas.edu Jonathan W. Pillow Center for Perceptual Systems University of Texas at Austin Austin, TX 78712, USA pillow@mail.utexas.edu Abstr... | 2011 | 117 |
4,166 | Adaptive Hedge Tim van Erven Department of Mathematics VU University De Boelelaan 1081a 1081 HV Amsterdam, the Netherlands tim@timvanerven.nl Peter Gr¨unwald Centrum Wiskunde & Informatica (CWI) Science Park 123, P.O. Box 94079 1090 GB Amsterdam, the Netherlands pdg@cwi.nl Wouter M. Koolen CWI... | 2011 | 118 |
4,167 | Matrix Completion for Multi-label Image Classification Ricardo S. Cabral†,‡ Fernando De la Torre‡ ‡Carnegie Mellon University, Pittsburgh, PA João P. Costeira†, Alexandre Bernardino† †ISR - Instituto Superior Técnico, Lisboa, Portugal rscabral@cmu.edu, ftorre@cs.cmu.edu, {jpc,alex}@isr.ist.utl.pt Abstr... | 2011 | 119 |
4,168 | Bayesian Bias Mitigation for Crowdsourcing Fabian L. Wauthier University of California, Berkeley flw@cs.berkeley.edu Michael I. Jordan University of California, Berkeley jordan@cs.berkeley.edu Abstract Biased labelers are a systemic problem in crowdsourcing, and a comprehensive toolbox for handling th... | 2011 | 12 |
4,169 | Continuous-Time Regression Models for Longitudinal Networks Duy Q. Vu Department of Statistics Pennsylvania State University University Park, PA 16802 dqv100@stat.psu.edu Arthur U. Asuncion∗ Department of Computer Science University of California, Irvine Irvine, CA 92697 asuncion@ics.uci.edu Dav... | 2011 | 120 |
4,170 | Stochastic convex optimization with bandit feedback Alekh Agarwal Department of EECS UC Berkeley alekh@cs.berkeley.edu Dean P. Foster Department of Statistics University of Pennysylvania dean.foster@gmail.com Daniel Hsu Microsoft Research New England dahsu@microsoft.com Sham M. Kakade Depa... | 2011 | 121 |
4,171 | Online Learning: Stochastic, Constrained, and Smoothed Adversaries Alexander Rakhlin Department of Statistics University of Pennsylvania rakhlin@wharton.upenn.edu Karthik Sridharan Toyota Technological Institute at Chicago karthik@ttic.edu Ambuj Tewari Computer Science Department University of Tex... | 2011 | 122 |
4,172 | Similarity-based Learning via Data Driven Embeddings Purushottam Kar Indian Institute of Technology Kanpur, INDIA purushot@cse.iitk.ac.in Prateek Jain Microsoft Research India Bangalore, INDIA prajain@microsoft.com Abstract We consider the problem of classification using similarity/distance functio... | 2011 | 123 |
4,173 | Maximum Margin Multi-Label Structured Prediction Christoph H. Lampert IST Austria (Institute of Science and Technology Austria) Am Campus 1, 3400 Klosterneuburg, Austria http://www.ist.ac.at/∼chl chl@ist.ac.at Abstract We study multi-label prediction for structured output sets, a problem that occurs, fo... | 2011 | 124 |
4,174 | Active Ranking using Pairwise Comparisons Kevin G. Jamieson University of Wisconsin Madison, WI 53706, USA kgjamieson@wisc.edu Robert D. Nowak University of Wisconsin Madison, WI 53706, USA nowak@engr.wisc.edu Abstract This paper examines the problem of ranking a collection of objects using pairwise... | 2011 | 125 |
4,175 | Selecting Receptive Fields in Deep Networks Adam Coates Department of Computer Science Stanford University Stanford, CA 94305 acoates@cs.stanford.edu Andrew Y. Ng Department of Computer Science Stanford University Stanford, CA 94305 ang@cs.stanford.edu Abstract Recent deep learning and unsupervi... | 2011 | 126 |
4,176 | Learning Auto-regressive Models from Sequence and Non-sequence Data Tzu-Kuo Huang Machine Learning Department Carnegie Mellon University tzukuoh@cs.cmu.edu Jeff Schneider Robotics Institute Carnegie Mellon University schneide@cs.cmu.edu Abstract Vector Auto-regressive models (VAR) are useful tools... | 2011 | 127 |
4,177 | Multi-View Learning of Word Embeddings via CCA Paramveer S. Dhillon Dean Foster Lyle Ungar Computer & Information Science Statistics Computer & Information Science University of Pennsylvania, Philadelphia, PA, U.S.A {dhillon|ungar}@cis.upenn.edu, foster@wharton.upenn.edu Abstract Recently, there has... | 2011 | 128 |
4,178 | Projection onto A Nonnegative Max-Heap Jun Liu Arizona State University Tempe, AZ 85287, USA j.liu@asu.edu Liang Sun Arizona State University Tempe, AZ 85287, USA sun.liang@asu.edu Jieping Ye Arizona State University Tempe, AZ 85287, USA jieping.ye@asu.edu Abstract We consider the problem of... | 2011 | 129 |
4,179 | Phase transition in the family of p-resistances Morteza Alamgir Max Planck Institute for Intelligent Systems T¨ubingen, Germany morteza@tuebingen.mpg.de Ulrike von Luxburg Max Planck Institute for Intelligent Systems T¨ubingen, Germany ulrike.luxburg@tuebingen.mpg.de Abstract We study the family of ... | 2011 | 13 |
4,180 | Learning to Learn with Compound HD Models Ruslan Salakhutdinov Department of Statistics, University of Toronto rsalakhu@utstat.toronto.edu Joshua B. Tenenbaum Brain and Cognitive Sciences, MIT jbt@mit.edu Antonio Torralba CSAIL, MIT torralba@mit.edu Abstract We introduce HD (or “Hierarchical-Deep”... | 2011 | 130 |
4,181 | Object Detection with Grammar Models Ross B. Girshick Dept. of Computer Science University of Chicago Chicago, IL 60637 rbg@cs.uchicago.edu Pedro F. Felzenszwalb School of Engineering and Dept. of Computer Science Brown University Providence, RI 02912 pff@brown.edu David McAllester TTI-Chicago... | 2011 | 131 |
4,182 | Inductive reasoning about chimeric creatures Charles Kemp Department of Psychology Carnegie Mellon University ckemp@cmu.edu Abstract Given one feature of a novel animal, humans readily make inferences about other features of the animal. For example, winged creatures often fly, and creatures that eat fish ... | 2011 | 132 |
4,183 | Empirical models of spiking in neural populations Jakob H. Macke Gatsby Computational Neuroscience Unit University College London, UK jakob@gatsby.ucl.ac.uk Lars B¨using Gatsby Computational Neuroscience Unit University College London, UK lars@gatsby.ucl.ac.uk John P. Cunningham Department of Engine... | 2011 | 133 |
4,184 | An Exact Algorithm for F-Measure Maximization Krzysztof Dembczy´nski Institute of Computing Science Pozna´n University of Technology Pozna´n, 60-695 Poland kdembczynski@cs.put.poznan.pl Willem Waegeman Mathematical Modelling, Statistics and Bioinformatics, Ghent University Ghent, 9000 Belgium willem... | 2011 | 134 |
4,185 | Exploiting spatial overlap to efficiently compute appearance distances between image windows Bogdan Alexe ETH Zurich Viviana Petrescu ETH Zurich Vittorio Ferrari ETH Zurich Abstract We present a computationally efficient technique to compute the distance of highdimensional appearance descriptor vectors ... | 2011 | 135 |
4,186 | Signal Estimation Under Random Time-Warpings and Nonlinear Signal Alignment Sebastian Kurtek Anuj Srivastava Wei Wu Department of Statistics Florida State University, Tallahassee, FL 32306 skurtek,anuj,wwu@stat.fsu.edu Abstract While signal estimation under random amplitudes, phase shifts, and additiv... | 2011 | 136 |
4,187 | Multi-armed bandits on implicit metric spaces Aleksandrs Slivkins Microsoft Research Silicon Valley Mountain View, CA 94043 slivkins at microsoft.com Abstract The multi-armed bandit (MAB) setting is a useful abstraction of many online learning tasks which focuses on the trade-off between exploration and e... | 2011 | 137 |
4,188 | Predicting response time and error rates in visual search Bo Chen Caltech bchen3@caltech.edu Vidhya Navalpakkam Yahoo! Research nvidhya@yahoo-inc.com Pietro Perona Caltech perona@caltech.edu Abstract A model of human visual search is proposed. It predicts both response time (RT) and error rate... | 2011 | 138 |
4,189 | Sequence learning with hidden units in spiking neural networks Johanni Brea, Walter Senn and Jean-Pascal Pfister Department of Physiology University of Bern B¨uhlplatz 5 CH-3012 Bern, Switzerland {brea, senn, pfister}@pyl.unibe.ch Abstract We consider a statistical framework in which recurrent networks... | 2011 | 139 |
4,190 | On the Analysis of Multi-Channel Neural Spike Data Bo Chen, David E. Carlson and Lawrence Carin Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708 {bc69, dec18, lcarin}@duke.edu Abstract Nonparametric Bayesian methods are developed for analysis of multi-channel spike-train ... | 2011 | 14 |
4,191 | Convergent Fitted Value Iteration with Linear Function Approximation Daniel J. Lizotte David R. Cheriton School of Computer Science University of Waterloo Waterloo, ON N2L 3G1 Canada dlizotte@uwaterloo.ca Abstract Fitted value iteration (FVI) with ordinary least squares regression is known to diverge.... | 2011 | 140 |
4,192 | Learning in Hilbert vs. Banach Spaces: A Measure Embedding Viewpoint Bharath K. Sriperumbudur Gatsby Unit University College London bharath@gatsby.ucl.ac.uk Kenji Fukumizu The Institute of Statistical Mathematics, Tokyo fukumizu@ism.ac.jp Gert R. G. Lanckriet Dept. of ECE UC San Diego gert@ece... | 2011 | 141 |
4,193 | Predicting Dynamic Difficulty Olana Missura and Thomas G¨artner University of Bonn and Fraunhofer IAIS Schloß Birlinghoven 52757 Sankt Augustin, Germany {olana.missura,thomas.gaertner}@uni-bonn.de Abstract Motivated by applications in electronic games as well as teaching systems, we investigate the p... | 2011 | 142 |
4,194 | Optimistic Optimization of a Deterministic Function without the Knowledge of its Smoothness R´emi Munos SequeL project, INRIA Lille – Nord Europe, France remi.munos@inria.fr Abstract We consider a global optimization problem of a deterministic function f in a semimetric space, given a finite budget of n eval... | 2011 | 143 |
4,195 | Robust Multi-Class Gaussian Process Classification Daniel Hern´andez-Lobato ICTEAM - Machine Learning Group Universit´e catholique de Louvain Place Sainte Barbe, 2 Louvain-La-Neuve, 1348, Belgium danielhernandezlobato@gmail.com Jos´e Miguel Hern´andez-Lobato Department of Engineering University of Camb... | 2011 | 144 |
4,196 | Practical Variational Inference for Neural Networks Alex Graves Department of Computer Science University of Toronto, Canada graves@cs.toronto.edu Abstract Variational methods have been previously explored as a tractable approximation to Bayesian inference for neural networks. However the approaches propo... | 2011 | 145 |
4,197 | Penalty Decomposition Methods for Rank Minimization ∗ Zhaosong Lu † Yong Zhang ‡ Abstract In this paper we consider general rank minimization problems with rank appearing in either objective function or constraint. We first show that a class of matrix optimization problems can be solved as lower dimensional ... | 2011 | 146 |
4,198 | Accelerated Adaptive Markov Chain for Partition Function Computation∗ Stefano Ermon, Carla P. Gomes Dept. of Computer Science Cornell University Ithaca NY 14853, U.S.A. Ashish Sabharwal IBM Watson Research Ctr. Yorktown Heights NY 10598, U.S.A. Bart Selman Dept. of Computer Science Cornell Unive... | 2011 | 147 |
4,199 | On Strategy Stitching in Large Extensive Form Multiplayer Games Richard Gibson and Duane Szafron Department of Computing Science, University of Alberta Edmonton, Alberta, T6G 2E8, Canada {rggibson | dszafron}@ualberta.ca Abstract Computing a good strategy in a large extensive form game often demands an ex... | 2011 | 148 |
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