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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3,600 | Adapting to the Shifting Intent of Search Queries∗ Umar Syed† Department of Computer and Information Science University of Pennsylvania Philadelphia, PA 19104 usyed@cis.upenn.edu Aleksandrs Slivkins Microsoft Research Mountain View, CA 94043 slivkins@microsoft.com Nina Mishra Microsoft Research ... | 2009 | 107 |
3,601 | Predicting the Optimal Spacing of Study: A Multiscale Context Model of Memory Michael C. Mozer⋆, Harold Pashler†, Nicholas Cepeda◦, Robert Lindsey⋆, & Ed Vul‡ ⋆Dept. of Computer Science, University of Colorado †Dept. of Psychology, UCSD ◦Dept. of Psychology, York University ‡Dept. of Brain and Cognitive S... | 2009 | 108 |
3,602 | A Game-Theoretic Approach to Hypergraph Clustering Samuel Rota Bul`o Marcello Pelillo University of Venice, Italy {srotabul,pelillo}@dsi.unive.it Abstract Hypergraph clustering refers to the process of extracting maximally coherent groups from a set of objects using high-order (rather than pairwise) sim... | 2009 | 109 |
3,603 | Entropic Graph Regularization in Non-Parametric Semi-Supervised Classification Amarnag Subramanya & Jeff Bilmes Department of Electrical Engineering, University of Washington, Seattle. {asubram,bilmes}@ee.washington.edu Abstract We prove certain theoretical properties of a graph-regularized transductive lear... | 2009 | 11 |
3,604 | Dirichlet-Bernoulli Alignment: A Generative Model for Multi-Class Multi-Label Multi-Instance Corpora Shuang-Hong Yang College of Computing Georgia Tech shy@gatech.edu Hongyuan Zha College of Computing Georgia Tech zha@cc.gatech.edu Bao-Gang Hu NLPR & LIAMA Chinese Academy of Sciences hubg@nlpr... | 2009 | 110 |
3,605 | Adaptive Design Optimization in Experiments with People Daniel R. Cavagnaro Department of Psychology Ohio State University cavagnaro.2@osu.edu Mark A. Pitt Department of Psychology Ohio State University pitt.2@osu.edu Jay I. Myung Department of Psychology Ohio State University myung.1@osu.edu ... | 2009 | 111 |
3,606 | Riffled Independence for Ranked Data Jonathan Huang, Carlos Guestrin School of Computer Science, Carnegie Mellon University {jch1,guestrin}@cs.cmu.edu Abstract Representing distributions over permutations can be a daunting task due to the fact that the number of permutations of n objects scales factorially... | 2009 | 112 |
3,607 | A Neural Implementation of the Kalman Filter Robert C. Wilson Department of Psychology Princeton University Princeton, NJ 08540 rcw2@princeton.edu Leif H. Finkel Department of Bioengineering University of Pennsylvania Philadelphia, PA 19103 Abstract Recent experimental evidence suggests that the b... | 2009 | 113 |
3,608 | An LP View of the M-best MAP problem Menachem Fromer Amir Globerson School of Computer Science and Engineering The Hebrew University of Jerusalem {fromer,gamir}@cs.huji.ac.il Abstract We consider the problem of finding the M assignments with maximum probability in a probabilistic graphical model. We show... | 2009 | 114 |
3,609 | Speeding up Magnetic Resonance Image Acquisition by Bayesian Multi-Slice Adaptive Compressed Sensing Matthias W. Seeger Saarland University and Max Planck Institute for Informatics Campus E1.4, 66123 Saarbr¨ucken, Germany mseeger@mmci.uni-saarland.de Abstract We show how to sequentially optimize magneti... | 2009 | 115 |
3,610 | Toward Provably Correct Feature Selection in Arbitrary Domains Dimitris Margaritis Department of Computer Science Iowa State University Ames, IA 50010, USA dmarg@cs.iastate.edu Abstract In this paper we address the problem of provably correct feature selection in arbitrary domains. An optimal solution t... | 2009 | 116 |
3,611 | 3D Object Recognition with Deep Belief Nets Vinod Nair and Geoffrey E. Hinton Department of Computer Science, University of Toronto 10 King’s College Road, Toronto, M5S 3G5 Canada {vnair,hinton}@cs.toronto.edu Abstract We introduce a new type of top-level model for Deep Belief Nets and evaluate it on a 3D ob... | 2009 | 117 |
3,612 | Improving Existing Fault Recovery Policies Guy Shani Department of Information Systems Engineering Ben Gurion University, Beer-Sheva, Israel shanigu@bgu.ac.il Christopher Meek Microsoft Research One Microsoft Way, Redmond, WA meek@microsoft.com Abstract An automated recovery system is a key componen... | 2009 | 118 |
3,613 | Convex Relaxation of Mixture Regression with Efficient Algorithms Novi Quadrianto, Tib´erio S. Caetano, John Lim NICTA - Australian National University Canberra, Australia {firstname.lastname}@nicta.com.au Dale Schuurmans University of Alberta Edmonton, Canada dale@cs.ualberta.ca Abstract We develop... | 2009 | 119 |
3,614 | Replacing supervised classification learning by Slow Feature Analysis in spiking neural networks Stefan Klampfl, Wolfgang Maass Institute for Theoretical Computer Science Graz University of Technology A-8010 Graz, Austria {klampfl,maass}@igi.tugraz.at Abstract It is open how neurons in the brain are able ... | 2009 | 12 |
3,615 | Hierarchical Learning of Dimensional Biases in Human Categorization Katherine Heller Department of Engineering University of Cambridge Cambridge CB2 1PZ heller@gatsby.ucl.ac.uk Adam Sanborn Gatsby Computational Neuroscience Unit University College London London WC1N 3AR asanborn@gatsby.ucl.ac.uk ... | 2009 | 120 |
3,616 | Polynomial Semantic Indexing Bing Bai(1) Jason Weston(1)(2) David Grangier(1) Ronan Collobert(1) Kunihiko Sadamasa(1) Yanjun Qi(1) Corinna Cortes(2) Mehryar Mohri(2)(3) (1)NEC Labs America, Princeton, NJ {bbai, dgrangier, collober, kunihiko, yanjun}@nec-labs.com (2) Google Research, New York, NY ... | 2009 | 121 |
3,617 | Neural Implementation of Hierarchical Bayesian Inference by Importance Sampling Lei Shi Helen Wills Neuroscience Institute University of California, Berkeley Berkeley, CA 94720 lshi@berkeley.edu Thomas L. Griffiths Department of Psychology University of California, Berkeley Berkeley, CA 94720 tom g... | 2009 | 122 |
3,618 | Discriminative Network Models of Schizophrenia Guillermo A. Cecchi, Irina Rish IBM T. J. Watson Research Center Yorktown Heights, NY, USA Benjamin Thyreau Neurospin CEA, Saclay, France Bertrand Thirion INRIA Saclay, France Marion Plaze INSERM - CEA - Univ. Paris Sud Research Unit U.797 Neuroim... | 2009 | 123 |
3,619 | Sparsistent Learning of Varying-coefficient Models with Structural Changes Mladen Kolar, Le Song and Eric P. Xing ∗ School of Computer Science, Carnegie Mellon University {mkolar,lesong,epxing}@cs.cmu.edu Abstract To estimate the changing structure of a varying-coefficient varying-structure (VCVS) model rem... | 2009 | 124 |
3,620 | Dual Averaging Method for Regularized Stochastic Learning and Online Optimization Lin Xiao Microsoft Research, Redmond, WA 98052 lin.xiao@microsoft.com Abstract We consider regularized stochastic learning and online optimization problems, where the objective function is the sum of two convex terms: one is... | 2009 | 125 |
3,621 | Analysis of SVM with Indefinite Kernels Yiming Ying† , Colin Campbell† and Mark Girolami‡ †Department of Engineering Mathematics, University of Bristol, Bristol BS8 1TR, United Kingdom ‡Department of Computer Science, University of Glasgow, S.A.W. Building, G12 8QQ, United Kingdom Abstract The recent intro... | 2009 | 126 |
3,622 | Quantification and the language of thought Charles Kemp Department of Psychology Carnegie Mellon University ckemp@cmu.edu Abstract Many researchers have suggested that the psychological complexity of a concept is related to the length of its representation in a language of thought. As yet, however, there... | 2009 | 127 |
3,623 | Exploring Functional Connectivity of the Human Brain using Multivariate Information Analysis Barry Chai1∗ Dirk B. Walther2∗ Diane M. Beck2,3† Li Fei-Fei1† 1Computer Science Department, Stanford University, Stanford, CA 94305 2Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL 61801 ... | 2009 | 128 |
3,624 | Semi-supervised Learning using Sparse Eigenfunction Bases Kaushik Sinha Dept. of Computer Science and Engineering Ohio State University Columbus, OH 43210 sinhak@cse.ohio-state.edu Mikhail Belkin Dept. of Computer Science and Engineering Ohio State University Columbus, OH 43210 mbelkin@cse.ohio-st... | 2009 | 129 |
3,625 | Free energy score-space Alessandro Perina1,3, Marco Cristani1,2, Umberto Castellani1 Vittorio Murino1,2 and Nebojsa Jojic3 {alessandro.perina, marco.cristani, umberto.castellani, vittorio.murino}@univr.it jojic@microsoft.com 1 Department of Computer Science, University of Verona, Italy 2 IIT, Italian Instit... | 2009 | 13 |
3,626 | On Learning Rotations Raman Arora University of Wisconsin-Madison Department of Electrical and Computer Engineering 1415 Engineering Drive, Madison, WI 53706 rmnarora@u.washington.edu Abstract An algorithm is presented for online learning of rotations. The proposed algorithm involves matrix exponentiate... | 2009 | 130 |
3,627 | A Gaussian Tree Approximation for Integer Least-Squares Jacob Goldberger School of Engineering Bar-Ilan University goldbej@eng.biu.ac.il Amir Leshem School of Engineering Bar-Ilan University leshema@eng.biu.ac.il Abstract This paper proposes a new algorithm for the linear least squares problem whe... | 2009 | 131 |
3,628 | Nonlinear directed acyclic structure learning with weakly additive noise models Robert E. Tillman Carnegie Mellon University Pittsburgh, PA rtillman@cmu.edu Arthur Gretton Carnegie Mellon University, MPI for Biological Cybernetics Pittsburgh, PA arthur.gretton@gmail.com Peter Spirtes Carnegie Me... | 2009 | 132 |
3,629 | FACTORIE: Probabilistic Programming via Imperatively Defined Factor Graphs Andrew McCallum, Karl Schultz, Sameer Singh Department of Computer Science University of Massachusetts Amherst Amherst, MA 01003 {mccallum, kschultz, sameer}@cs.umass.edu Abstract Discriminatively trained undirected graphical mode... | 2009 | 133 |
3,630 | Exponential Family Graph Matching and Ranking James Petterson, Tib´erio S. Caetano, Julian J. McAuley and Jin Yu NICTA, Australian National University Canberra, Australia Abstract We present a method for learning max-weight matching predictors in bipartite graphs. The method consists of performing maximum a... | 2009 | 134 |
3,631 | Extending Phase Mechanism to Differential Motion Opponency for Motion Pop-Out Yicong Meng and Bertram E. Shi Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay, Kowloon, Hong Kong {eeyicong, eebert}@ust.hk Abstract We extend the... | 2009 | 135 |
3,632 | Periodic Step-Size Adaptation for Single-Pass On-line Learning Chun-Nan Hsu1,2,∗, Yu-Ming Chang1, Han-Shen Huang1 and Yuh-Jye Lee3 1Institute of Information Science, Academia Sinica, Taipei 115, Taiwan 2USC/Information Sciences Institute, Marina del Rey, CA 90292, USA 3Department of Computer Science and Infor... | 2009 | 136 |
3,633 | Construction of Nonparametric Bayesian Models from Parametric Bayes Equations Peter Orbanz University of Cambridge and ETH Zurich p.orbanz@eng.cam.ac.uk Abstract We consider the general problem of constructing nonparametric Bayesian models on infinite-dimensional random objects, such as functions, infinite ... | 2009 | 137 |
3,634 | Adaptive Regularization for Transductive Support Vector Machine Zenglin Xu †‡ † Cluster MMCI Saarland Univ. & MPI INF Saarbrucken, Germany zlxu@mpi-inf.mpg.de Rong Jin Computer Sci. & Eng. Michigan State Univ. East Lansing, MI, U.S. rongjin@cse.msu.edu Jianke Zhu Computer Vision Lab ETH Zuri... | 2009 | 138 |
3,635 | Subject independent EEG-based BCI decoding Siamac Fazli Cristian Grozea M´arton Dan´oczy Florin Popescu Benjamin Blankertz Klaus-Robert M¨uller Abstract In the quest to make Brain Computer Interfacing (BCI) more usable, dry electrodes have emerged that get rid of the initial 30 minutes required for plac... | 2009 | 139 |
3,636 | Multi-label Prediction via Sparse Infinite CCA Piyush Rai and Hal Daum´e III School of Computing, University of Utah {piyush,hal}@cs.utah.edu Abstract Canonical Correlation Analysis (CCA) is a useful technique for modeling dependencies between two (or more) sets of variables. Building upon the recently sugge... | 2009 | 14 |
3,637 | On the Convergence of the Concave-Convex Procedure Bharath K. Sriperumbudur Department of Electrical and Computer Engineering University of California, San Diego La Jolla, CA 92093 bharathsv@ucsd.edu Gert R. G. Lanckriet Department of Electrical and Computer Engineering University of California, San D... | 2009 | 140 |
3,638 | Orthogonal Matching Pursuit from Noisy Measurements: A New Analysis∗ Alyson K. Fletcher University of California, Berkeley Berkeley, CA alyson@eecs.berkeley.edu Sundeep Rangan Qualcomm Technologies Bedminster, NJ srangan@qualcomm.com Abstract A well-known analysis of Tropp and Gilbert shows that o... | 2009 | 141 |
3,639 | Noise Characterization, Modeling, and Reduction for In Vivo Neural Recording Zhi Yang1, Qi Zhao2, Edward Keefer3,4, and Wentai Liu1 1 University of California at Santa Cruz, 2 California Institute of Technology 3 UT Southwestern Medical Center, 4 Plexon Inc yangzhi@soe.ucsc.edu Abstract Studying signal an... | 2009 | 142 |
3,640 | Adaptive Regularization of Weight Vectors Koby Crammer Department of Electrical Enginering The Technion Haifa, 32000 Israel koby@ee.technion.ac.il Alex Kulesza Department of Computer and Information Science University of Pennsylvania Philadelphia, PA 19104 kulesza@cis.upenn.edu Mark Dredze H... | 2009 | 143 |
3,641 | Posterior vs. Parameter Sparsity in Latent Variable Models João V. Graça L2F INESC-ID Lisboa, Portugal Kuzman Ganchev Ben Taskar University of Pennsylvania Philadelphia, PA, USA Fernando Pereira Google Research Mountain View, CA, USA Abstract We address the problem of learning structured unsup... | 2009 | 144 |
3,642 | 1 Reconstruction of Sparse Circuits Using Multi-neuronal Excitation (RESCUME) Tao Hu and Dmitri B. Chklovskii Janelia Farm Research Campus, HHMI 19700 Helix Drive, Ashburn, VA 20147 hut, mitya@janelia.hhmi.org Abstract One of the central problems in neuroscience is reconstructing sy... | 2009 | 145 |
3,643 | Label Selection on Graphs Andrew Guillory Department of Computer Science University of Washington guillory@cs.washington.edu Jeff Bilmes Department of Electrical Engineering University of Washington bilmes@ee.washington.edu Abstract We investigate methods for selecting sets of labeled vertices for u... | 2009 | 146 |
3,644 | A Fast, Consistent Kernel Two-Sample Test Arthur Gretton Carnegie Mellon University MPI for Biological Cybernetics arthur.gretton@gmail.com Kenji Fukumizu Inst. of Statistical Mathematics Tokyo Japan fukumizu@ism.ac.jp Zaid Harchaoui Carnegie Mellon University Pittsburgh, PA, USA zaid.harchaoui@... | 2009 | 147 |
3,645 | Robust Nonparametric Regression with Metric-Space valued Output Matthias Hein Department of Computer Science, Saarland University Campus E1 1, 66123 Saarbr¨ucken, Germany hein@cs.uni-sb.de Abstract Motivated by recent developments in manifold-valued regression we propose a family of nonparametric kernel... | 2009 | 148 |
3,646 | Kernels and learning curves for Gaussian process regression on random graphs Peter Sollich, Matthew J Urry King’s College London, Department of Mathematics London WC2R 2LS, U.K. {peter.sollich,matthew.urry}@kcl.ac.uk Camille Coti INRIA Saclay ˆIle de France, F-91893 Orsay, France Abstract We investiga... | 2009 | 149 |
3,647 | Fast subtree kernels on graphs Nino Shervashidze, Karsten M. Borgwardt Interdepartmental Bioinformatics Group Max Planck Institutes T¨ubingen, Germany {nino.shervashidze,karsten.borgwardt}@tuebingen.mpg.de Abstract In this article, we propose fast subtree kernels on graphs. On graphs with n nodes and m ed... | 2009 | 15 |
3,648 | Thresholding Procedures for High Dimensional Variable Selection and Statistical Estimation Shuheng Zhou Seminar f¨ur Statistik ETH Z¨urich CH-8092, Switzerland Abstract Given n noisy samples with p dimensions, where n ≪p, we show that the multistep thresholding procedure can accurately estimate a sparse v... | 2009 | 150 |
3,649 | Beyond Categories: The Visual Memex Model for Reasoning About Object Relationships Tomasz Malisiewicz, Alexei A. Efros Robotics Institute Carnegie Mellon University {tmalisie,efros}@cs.cmu.edu Abstract The use of context is critical for scene understanding in computer vision, where the recognition of an... | 2009 | 151 |
3,650 | Conditional Random Fields with High-Order Features for Sequence Labeling Nan Ye Wee Sun Lee Department of Computer Science National University of Singapore {yenan,leews}@comp.nus.edu.sg Hai Leong Chieu DSO National Laboratories chaileon@dso.org.sg Dan Wu Singapore MIT Alliance National Universit... | 2009 | 152 |
3,651 | Abstraction and relational learning Charles Kemp & Alan Jern Department of Psychology Carnegie Mellon University {ckemp,ajern}@cmu.edu Abstract Most models of categorization learn categories defined by characteristic features but some categories are described more naturally in terms of relations. We presen... | 2009 | 153 |
3,652 | Fast Graph Laplacian Regularized Kernel Learning via Semidefinite–Quadratic–Linear Programming Xiao-Ming Wu Dept. of IE The Chinese University of Hong Kong wxm007@ie.cuhk.edu.hk Anthony Man-Cho So Dept. of SE&EM The Chinese University of Hong Kong manchoso@se.cuhk.edu.hk Zhenguo Li Dept. of IE Th... | 2009 | 154 |
3,653 | A Data-Driven Approach to Modeling Choice Vivek F. Farias Srikanth Jagabathula Devavrat Shah∗ Abstract We visit the following fundamental problem: For a ‘generic’ model of consumer choice (namely, distributions over preference lists) and a limited amount of data on how consumers actually make decisions (suc... | 2009 | 155 |
3,654 | Anomaly Detection with Score functions based on Nearest Neighbor Graphs Manqi Zhao ECE Dept. Boston University Boston, MA 02215 mqzhao@bu.edu Venkatesh Saligrama ECE Dept. Boston University Boston, MA, 02215 srv@bu.edu Abstract We propose a novel non-parametric adaptive anomaly detection algor... | 2009 | 156 |
3,655 | Submodularity Cuts and Applications Yoshinobu Kawahara∗ The Inst. of Scientific and Industrial Res. (ISIR), Osaka Univ., Japan kawahara@ar.sanken.osaka-u.ac.jp Kiyohito Nagano Dept. of Math. and Comp. Sci., Tokyo Inst. of Technology, Japan nagano@is.titech.ac.jp Koji Tsuda Comp. Bio. Research Center,... | 2009 | 157 |
3,656 | Bayesian Sparse Factor Models and DAGs Inference and Comparison Ricardo Henao DTU Informatics Technical University of Denmark 2800 Lyngby, Denmark Bioinformatics Centre University of Copenhagen 2200 Copenhagen, Denmark rhenao@binf.ku.dk Ole Winther DTU Informatics Technical University of Denmark... | 2009 | 158 |
3,657 | A Sparse Non-Parametric Approach for Single Channel Separation of Known Sounds Paris Smaragdis Adobe Systems Inc. paris@adobe.com Madhusudana Shashanka Mars Inc. shashanka@alum.bu.edu Bhiksha Raj Carnegie Mellon University bhiksha@cs.cmu.edu Abstract In this paper we present an algorithm for sep... | 2009 | 159 |
3,658 | Directed Regression Yi-hao Kao Stanford University Stanford, CA 94305 yihaokao@stanford.edu Benjamin Van Roy Stanford University Stanford, CA 94305 bvr@stanford.edu Xiang Yan Stanford University Stanford, CA 94305 xyan@stanford.edu Abstract When used to guide decisions, linear regression ana... | 2009 | 16 |
3,659 | Data-driven calibration of linear estimators with minimal penalties Sylvain Arlot ∗ CNRS ; Willow Project-Team Laboratoire d’Informatique de l’Ecole Normale Superieure (CNRS/ENS/INRIA UMR 8548) 23, avenue d’Italie, F-75013 Paris, France sylvain.arlot@ens.fr Francis Bach † INRIA ; Willow Project-Team... | 2009 | 160 |
3,660 | fMRI-Based Inter-Subject Cortical Alignment Using Functional Connectivity Bryan R. Conroy1 Benjamin D. Singer2 James V. Haxby3∗ Peter J. Ramadge1 1 Department of Electrical Engineering, 2 Neuroscience Institute, Princeton University 3 Department of Psychology, Dartmouth College Abstract The inter-subj... | 2009 | 161 |
3,661 | Unsupervised feature learning for audio classification using convolutional deep belief networks Honglak Lee Yan Largman Peter Pham Andrew Y. Ng Computer Science Department Stanford University Stanford, CA 94305 Abstract In recent years, deep learning approaches have gained significant interest as a ... | 2009 | 162 |
3,662 | Accelerating Bayesian Structural Inference for Non-Decomposable Gaussian Graphical Models Baback Moghaddam Jet Propulsion Laboratory California Institute of Technology baback@jpl.nasa.gov Benjamin M. Marlin Department of Computer Science University of British Columbia bmarlin@cs.ubc.ca Mohammad Emti... | 2009 | 163 |
3,663 | Learning transport operators for image manifolds Benjamin J. Culpepper Department of EECS Computer Science Division University of California, Berkeley Berkeley, CA 94720 bjc@cs.berkeley.edu Bruno A. Olshausen Helen Wills Neuroscience Institute & School of Optometry University of California, Berkeley... | 2009 | 164 |
3,664 | Manifold Embeddings for Model-Based Reinforcement Learning under Partial Observability Keith Bush School of Computer Science McGill University Montreal, Canada kbush@cs.mcgill.ca Joelle Pineau School of Computer Science McGill University Montreal, Canada jpineau@cs.mcgill.ca Abstract Interesti... | 2009 | 165 |
3,665 | Ensemble Nystr¨om Method Sanjiv Kumar Google Research New York, NY sanjivk@google.com Mehryar Mohri Courant Institute and Google Research New York, NY mohri@cs.nyu.edu Ameet Talwalkar Courant Institute of Mathematical Sciences New York, NY ameet@cs.nyu.edu Abstract A crucial technique for sc... | 2009 | 166 |
3,666 | Manifold Regularization for SIR with Rate Root-n Convergence Wei Bian School of Computer Engineering Nanyang Technological University Singapore, 639798 weibian@pmail.ntu.edu.sg Dacheng Tao School of Computer Engineering Nanyang Technological University Singapore, 639798 dctao@ntu.edu.sg Abstract... | 2009 | 167 |
3,667 | STDP enables spiking neurons to detect hidden causes of their inputs Bernhard Nessler, Michael Pfeiffer, and Wolfgang Maass Institute for Theoretical Computer Science, Graz University of Technology A-8010 Graz, Austria {nessler,pfeiffer,maass}@igi.tugraz.at Abstract The principles by which spiking neurons... | 2009 | 168 |
3,668 | Locality-Sensitive Binary Codes from Shift-Invariant Kernels Maxim Raginsky Duke University Durham, NC 27708 m.raginsky@duke.edu Svetlana Lazebnik UNC Chapel Hill Chapel Hill, NC 27599 lazebnik@cs.unc.edu Abstract This paper addresses the problem of designing binary codes for high-dimensional da... | 2009 | 169 |
3,669 | Nonparametric Greedy Algorithms for the Sparse Learning Problem Han Liu and Xi Chen School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 Abstract This paper studies the forward greedy strategy in sparse nonparametric regression. For additive models, we propose an algorithm called add... | 2009 | 17 |
3,670 | Regularized Distance Metric Learning: Theory and Algorithm Rong Jin1 Shijun Wang2 Yang Zhou1 1Dept. of Computer Science & Engineering, Michigan State University, East Lansing, MI 48824 2Radiology and Imaging Sciences, National Institutes of Health, Bethesda, MD 20892 rongjin@cse.msu.edu wangshi@cc.nih.g... | 2009 | 170 |
3,671 | Time-Varying Dynamic Bayesian Networks Le Song, Mladen Kolar and Eric P. Xing School of Computer Science, Carnegie Mellon University {lesong, mkolar, epxing}@cs.cmu.edu Abstract Directed graphical models such as Bayesian networks are a favored formalism for modeling the dependency structures in complex mult... | 2009 | 171 |
3,672 | A General Projection Property for Distribution Families Yao-Liang Yu Yuxi Li Dale Schuurmans Csaba Szepesv´ari Department of Computing Science University of Alberta Edmonton, AB, T6G 2E8 Canada {yaoliang,yuxi,dale,szepesva}@cs.ualberta.ca Abstract Surjectivity of linear projections between distribut... | 2009 | 172 |
3,673 | Region-based Segmentation and Object Detection Stephen Gould1 Tianshi Gao1 Daphne Koller2 1 Department of Electrical Engineering, Stanford University 2 Department of Computer Science, Stanford University {sgould,tianshig,koller}@cs.stanford.edu Abstract Object detection and multi-class image segmentatio... | 2009 | 173 |
3,674 | Hierarchical Mixture of Classification Experts Uncovers Interactions between Brain Regions Bangpeng Yao1 Dirk B. Walther2 Diane M. Beck2,3∗ Li Fei-Fei1∗ 1Computer Science Department, Stanford University, Stanford, CA 94305 2Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL 61801 3... | 2009 | 174 |
3,675 | Unsupervised Detection of Regions of Interest Using Iterative Link Analysis Gunhee Kim School of Computer Science Carnegie Mellon University gunhee@cs.cmu.edu Antonio Torralba Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology torralba@csail.mit.edu Abstract... | 2009 | 175 |
3,676 | Fast Learning from Non-i.i.d. Observations Ingo Steinwart Information Sciences Group CCS-3 Los Alamos National Laboratory Los Alamos, NM 87545, USA ingo@lanl.gov Andreas Christmann University of Bayreuth Department of Mathematics D-95440 Bayreuth Andreas.Christmann@uni-bayreuth.de Abstract We pr... | 2009 | 176 |
3,677 | Evaluating multi-class learning strategies in a hierarchical framework for object detection Sanja Fidler Marko Boben Aleˇs Leonardis Faculty of Computer and Information Science University of Ljubljana, Slovenia {sanja.fidler, marko.boben, ales.leonardis}@fri.uni-lj.si Abstract Multi-class object learn... | 2009 | 177 |
3,678 | Optimal Scoring for Unsupervised Learning Zhihua Zhang and Guang Dai College of Computer Science & Technology Zhejiang University Hangzhou, Zhejiang, 310027 China Abstract We are often interested in casting classification and clustering problems as a regression framework, because it is feasible to achieve so... | 2009 | 178 |
3,679 | Matrix Completion from Noisy Entries Raghunandan H. Keshavan∗, Andrea Montanari∗†, and Sewoong Oh∗ Abstract Given a matrix M of low-rank, we consider the problem of reconstructing it from noisy observations of a small, random subset of its entries. The problem arises in a variety of applications, from collabo... | 2009 | 179 |
3,680 | Rethinking LDA: Why Priors Matter Hanna M. Wallach David Mimno Andrew McCallum Department of Computer Science University of Massachusetts Amherst Amherst, MA 01003 {wallach,mimno,mccallum}@cs.umass.edu Abstract Implementations of topic models typically use symmetric Dirichlet priors with fixed concen... | 2009 | 18 |
3,681 | A Generalized Natural Actor-Critic Algorithm Tetsuro Morimura†, Eiji Uchibe‡, Junichiro Yoshimoto‡, Kenji Doya‡ †: IBM Research – Tokyo, Kanagawa, Japan ‡: Okinawa Institute of Science and Technology, Okinawa, Japan tetsuro@jp.ibm.com, {uchibe,jun-y,doya}@oist.jp Abstract Policy gradient Reinforcement Learn... | 2009 | 180 |
3,682 | Efficient Moments-based Permutation Tests Chunxiao Zhou Huixia Judy Wang Dept. of Electrical and Computer Eng. Dept. of Statistics University of Illinois at Urbana-Champaign North Carolina State University ... | 2009 | 181 |
3,683 | Maximum likelihood trajectories for continuous-time Markov chains Theodore J. Perkins Ottawa Hospital Research Institute Ottawa, Ontario, Canada tperkins@ohri.ca Abstract Continuous-time Markov chains are used to model systems in which transitions between states as well as the time the system spends in ... | 2009 | 182 |
3,684 | Lower bounds on minimax rates for nonparametric regression with additive sparsity and smoothness Garvesh Raskutti1, Martin J. Wainwright1,2, Bin Yu1,2 1UC Berkeley Department of Statistics 2UC Berkeley Department of Electrical Engineering and Computer Science Abstract We study minimax rates for estimating h... | 2009 | 183 |
3,685 | Sufficient Conditions for Agnostic Active Learnable Liwei Wang Key Laboratory of Machine Perception, MOE, School of Electronics Engineering and Computer Science, Peking University, wanglw@cis.pku.edu.cn Abstract We study pool-based active learning in the presence of noise, i.e. the agnostic setting. Previo... | 2009 | 184 |
3,686 | The Ordered Residual Kernel for Robust Motion Subspace Clustering Tat-Jun Chin, Hanzi Wang and David Suter School of Computer Science The University of Adelaide, South Australia {tjchin, hwang, dsuter}@cs.adelaide.edu.au Abstract We present a novel and highly effective approach for multi-body motion segme... | 2009 | 185 |
3,687 | Learning to Rank by Optimizing NDCG Measure Hamed Valizadegan Rong Jin Computer Science and Engineering Michigan State University East Lansing, MI 48824 {valizade,rongjin}@cse.msu.edu Ruofei Zhang Jianchang Mao Advertising Sciences, Yahoo! Labs 4401 Great America Parkway, Santa Clara, CA 95054 {... | 2009 | 186 |
3,688 | Slow Learners are Fast John Langford, Alexander J. Smola, Martin Zinkevich Machine Learning, Yahoo! Labs and Australian National University 4401 Great America Pky, Santa Clara, 95051 CA {jl, maz, smola}@yahoo-inc.com Abstract Online learning algorithms have impressive convergence properties when it comes ... | 2009 | 187 |
3,689 | Tracking Dynamic Sources of Malicious Activity at Internet-Scale Shobha Venkataraman∗, Avrim Blum†, Dawn Song⋄, Subhabrata Sen∗, Oliver Spatscheck∗ ∗AT&T Labs – Research {shvenk,sen,spatsch}@research.att.com †Carnegie Mellon University avrim@cs.cmu.edu ⋄University of California, Berkeley dawnsong@cs.ber... | 2009 | 188 |
3,690 | Graph Zeta Function in the Bethe Free Energy and Loopy Belief Propagation Yusuke Watanabe The Institute of Statistical Mathematics 10-3 Midori-cho, Tachikawa Tokyo 190-8562, Japan watay@ism.ac.jp Kenji Fukumizu The Institute of Statistical Mathematics 10-3 Midori-cho, Tachikawa Tokyo 190-8562, Japan... | 2009 | 189 |
3,691 | Augmenting Feature-driven fMRI Analyses: Semi-supervised Learning and Resting State Activity Matthew B. Blaschko Visual Geometry Group Department of Engineering Science University of Oxford blaschko@robots.ox.ac.uk Jacquelyn A. Shelton Max Planck Institute for Biological Cybernetics Fakult¨at f¨ur Inf... | 2009 | 19 |
3,692 | Learning in Markov Random Fields using Tempered Transitions Ruslan Salakhutdinov Brain and Cognitive Sciences and CSAIL Massachusetts Institute of Technology rsalakhu@mit.edu Abstract Markov random fields (MRF’s), or undirected graphical models, provide a powerful framework for modeling complex dependencie... | 2009 | 190 |
3,693 | Statistical Consistency of Top-k Ranking Fen Xia Institute of Automation Chinese Academy of Sciences fen.xia@ia.ac.cn Tie-Yan Liu Microsoft Research Asia tyliu@microsoft.com Hang Li Microsoft Research Asia hanglig@microsoft.com Abstract This paper is concerned with the consistency analysis on li... | 2009 | 191 |
3,694 | Speaker Comparison with Inner Product Discriminant Functions W. M. Campbell MIT Lincoln Laboratory Lexington, MA 02420 wcampbell@ll.mit.edu Z. N. Karam DSPG, MIT RLE, Cambridge MA MIT Lincoln Laboratory, Lexington, MA zahi@mit.edu D. E. Sturim MIT Lincoln Laboratory Lexington, MA 02420 sturim@... | 2009 | 192 |
3,695 | Asymptotic Analysis of MAP Estimation via the Replica Method and Compressed Sensing∗ Sundeep Rangan Qualcomm Technologies Bedminster, NJ srangan@qualcomm.com Alyson K. Fletcher University of California, Berkeley Berkeley, CA alyson@eecs.berkeley.edu Vivek K Goyal Mass. Inst. of Tech. Cambridge, ... | 2009 | 193 |
3,696 | Statistical Models of Linear and Non–linear Contextual Interactions in Early Visual Processing Ruben Coen–Cagli AECOM Bronx, NY 10461 rcagli@aecom.yu.edu Peter Dayan GCNU, UCL 17 Queen Square, LONDON dayan@gatsby.ucl.ac.uk Odelia Schwartz AECOM Bronx, NY 10461 oschwart@aecom.yu.edu Abstract ... | 2009 | 194 |
3,697 | A joint maximum-entropy model for binary neural population patterns and continuous signals Sebastian Gerwinn Philipp Berens Matthias Bethge MPI for Biological Cybernetics and University of T¨ubingen Computational Vision and Neuroscience Spemannstrasse 41, 72076 T¨ubingen, Germany {firstname.surname}@t... | 2009 | 195 |
3,698 | Hierarchical Modeling of Local Image Features through Lp-Nested Symmetric Distributions Fabian Sinz Max Planck Institute for Biological Cybernetics Spemannstraße 41 72076 T¨ubingen, Germany fabee@tuebingen.mpg.de Eero P. Simoncelli Center for Neural Science, and Courant Institute of Mathematical Scien... | 2009 | 196 |
3,699 | Optimizing Multi-class Spatio-Spectral Filters via Bayes Error Estimation for EEG Classification Wenming Zheng Research Center for Learning Science Southeast University Nanjing, Jiangsu 210096, P.R. China wenming zheng@seu.edu.cn Zhouchen Lin Microsoft Research Asia Beijing 100190, P.R. China zhoulin... | 2009 | 197 |
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