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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4,000 | On the Theory of Learning with Privileged Information Dmitry Pechyony NEC Laboratories Princeton, NJ 08540, USA pechyony@nec-labs.com Vladimir Vapnik NEC Laboratories Princeton, NJ 08540, USA vlad@nec-labs.com Abstract In Learning Using Privileged Information (LUPI) paradigm, along with the standa... | 2010 | 231 |
4,001 | Accounting for network effects in neuronal responses using L1 regularized point process models Ryan C. Kelly∗ Computer Science Department Center for the Neural Basis of Cognition Carnegie Mellon University Pittsburgh, PA 15213 rkelly@cs.cmu.edu Matthew A. Smith University of Pittsburgh Center for th... | 2010 | 232 |
4,002 | Probabilistic latent variable models for distinguishing between cause and effect Joris M. Mooij MPI for Biological Cybernetics T¨ubingen, Germany joris.mooij@tuebingen.mpg.de Oliver Stegle MPI for Biological Cybernetics T¨ubingen, Germany oliver.stegle@tuebingen.mpg.de Dominik Janzing MPI for Biol... | 2010 | 233 |
4,003 | Two-layer Generalization Analysis for Ranking Using Rademacher Average Wei Chen∗ Chinese Academy of Sciences chenwei@amss.ac.cn Tie-Yan Liu Microsoft Research Asia tyliu@micorsoft.com Zhiming Ma Chinese Academy of Sciences mazm@amt.ac.cn Abstract This paper is concerned with the generalization a... | 2010 | 234 |
4,004 | Learning from Candidate Labeling Sets Luo Jie Idiap Research Institute and EPF Lausanne jluo@idiap.ch Francesco Orabona DSI, Universit`a degli Studi di Milano orabona@dsi.unimi.it Abstract In many real world applications we do not have access to fully-labeled training data, but only to a list of possi... | 2010 | 235 |
4,005 | Direct Loss Minimization for Structured Prediction David McAllester TTI-Chicago mcallester@ttic.edu Tamir Hazan TTI-Chicago tamir@ttic.edu Joseph Keshet TTI-Chicago jkeshet@ttic.edu Abstract In discriminative machine learning one is interested in training a system to optimize a certain desired mea... | 2010 | 236 |
4,006 | Variational Inference over Combinatorial Spaces Alexandre Bouchard-Cˆot´e∗ Michael I. Jordan∗,† ∗Computer Science Division †Department of Statistics University of California at Berkeley Abstract Since the discovery of sophisticated fully polynomial randomized algorithms for a range of #P problems [1, 2,... | 2010 | 237 |
4,007 | Latent Variable Models for Predicting File Dependencies in Large-Scale Software Development Diane J. Hu1, Laurens van der Maaten1,2, Youngmin Cho1, Lawrence K. Saul1, Sorin Lerner1 1Dept. of Computer Science & Engineering, University of California, San Diego 2Pattern Recognition & Bioinformatics Lab, Delft Univ... | 2010 | 238 |
4,008 | Avoiding False Positive in Multi-Instance Learning Yanjun Han, Qing Tao, Jue Wang Institute of Automation, Chinese Academy of Sciences Beijing, 100190, China yanjun.han, qing.tao, jue.wang@ia.ac.cn Abstract In multi-instance learning, there are two kinds of prediction failure, i.e., false negative and fal... | 2010 | 239 |
4,009 | Why are some word orders more common than others? A uniform information density account Luke Maurits, Amy Perfors & Daniel Navarro School of Psychology, University of Adelaide, Adelaide, South Australia, 5000 {luke.maurits, amy.perfors, daniel.navarro}@adelaide.edu.au Abstract Languages vary widely in m... | 2010 | 24 |
4,010 | Individualized ROI Optimization via Maximization of Group-wise Consistency of Structural and Functional Profiles 1, 2*Kaiming Li, 1Lei Guo, 3Carlos Faraco, 2Dajiang Zhu, 2Fan Deng, 1Tuo Zhang, 1Xi Jiang, 1Degang Zhang, 1Hanbo Chen, 1Xintao Hu, 3Steve Miller, 2Tianming Liu 1School of Automation, Northwest... | 2010 | 240 |
4,011 | On the Convexity of Latent Social Network Inference Seth A. Myers Institute for Computational and Mathematical Engineering Stanford University samyers@stanford.edu Jure Leskovec Department of Computer Science Stanford University jure@cs.stanford.edu Abstract In many real-world scenarios, it is nea... | 2010 | 241 |
4,012 | Global seismic monitoring as probabilistic inference Nimar S. Arora Department of Computer Science University of California, Berkeley Berkeley, CA 94720 nimar@cs.berkeley.edu Stuart Russell Department of Computer Science University of California, Berkeley Berkeley, CA 94720 russell@cs.berkeley.edu ... | 2010 | 242 |
4,013 | Gaussian sampling by local perturbations George Papandreou Department of Statistics University of California, Los Angeles gpapan@stat.ucla.edu Alan L. Yuille Depts. of Statistics, Computer Science & Psychology University of California, Los Angeles yuille@stat.ucla.edu Abstract We present a technique... | 2010 | 243 |
4,014 | Hallucinations in Charles Bonnet Syndrome Induced by Homeostasis: a Deep Boltzmann Machine Model David P. Reichert, Peggy Series and Amos J. Storkey School of Informatics, University of Edinburgh 10 Crichton Street, Edinburgh, EH8 9AB {d.p.reichert@sms., pseries@inf., a.storkey@} ed.ac.uk Abstract The Cha... | 2010 | 244 |
4,015 | Moreau-Yosida Regularization for Grouped Tree Structure Learning Jun Liu Computer Science and Engineering Arizona State University J.Liu@asu.edu Jieping Ye Computer Science and Engineering Arizona State University Jieping.Ye@asu.edu Abstract We consider the tree structured group Lasso where the st... | 2010 | 245 |
4,016 | Pose-Sensitive Embedding by Nonlinear NCA Regression Graham W. Taylor, Rob Fergus, George Williams, Ian Spiro and Christoph Bregler Courant Institute of Mathematics, New York University New York, USA 10003 gwtaylor,fergus,spiro,bregler@cs.nyu.edu Abstract This paper tackles the complex problem of visually... | 2010 | 246 |
4,017 | Synergies in learning words and their referents Mark Johnson Department of Computing Macquarie University Sydney, NSW 2109 Mark.Johnson@mq.edu.au Katherine Demuth Department of Linguistics Macquarie University Sydney, NSW 2109 Katherine.Demuth@mq.edu.au Michael Frank Department of Psychology S... | 2010 | 247 |
4,018 | Approximate inference in continuous time Gaussian-Jump processes Manfred Opper Fakult¨at Elektrotechnik und Informatik Technische Universit¨at Berlin Berlin, Germany opperm@cs.tu-berlin.de Andreas Ruttor Fakult¨at Elektrotechnik und Informatik Technische Universit¨at Berlin Berlin, Germany andreas... | 2010 | 248 |
4,019 | Empirical Bernstein Inequalities for U-Statistics Thomas Peel LIF, Aix-Marseille Universit´e 39, rue F. Joliot Curie F-13013 Marseille, France thomas.peel@lif.univ-mrs.fr Sandrine Anthoine LATP, Aix-Marseille Universit´e, CNRS 39, rue F. Joliot Curie F-13013 Marseille, France anthoine@cmi.univ-mrs.f... | 2010 | 249 |
4,020 | Getting lost in space: Large sample analysis of the commute distance Ulrike von Luxburg Agnes Radl Max Planck Institute for Biological Cybernetics, T¨ubingen, Germany {ulrike.luxburg,agnes.radl}@tuebingen.mpg.de Matthias Hein Saarland University, Saarbr¨ucken, Germany hein@cs.uni-sb.de Abstract The ... | 2010 | 25 |
4,021 | Active Estimation of F-Measures Christoph Sawade, Niels Landwehr, and Tobias Scheffer University of Potsdam Department of Computer Science August-Bebel-Strasse 89, 14482 Potsdam, Germany {sawade, landwehr, scheffer}@cs.uni-potsdam.de Abstract We address the problem of estimating the Fα-measure of a given ... | 2010 | 250 |
4,022 | Inductive Regularized Learning of Kernel Functions Prateek Jain Microsoft Research Bangalore Bangalore, India prajain@microsoft.com Brian Kulis UC Berkeley EECS and ICSI Berkeley, CA, USA kulis@eecs.berkeley.edu Inderjit Dhillon UT Austin Dept. of Computer Sciences Austin, TX, USA inderjit@cs.ut... | 2010 | 251 |
4,023 | Active Learning Applied to Patient-Adaptive Heartbeat Classification Jenna Wiens CSAIL, MIT jwiens@csail.mit.edu John V. Guttag CSAIL, MIT guttag@csail.mit.edu Abstract While clinicians can accurately identify different types of heartbeats in electrocardiograms (ECGs) from different patients, researche... | 2010 | 252 |
4,024 | Large-Scale Matrix Factorization with Missing Data under Additional Constraints Kaushik Mitra ∗† Department of Electrical and Computer Engineering and UMIACS University of Maryland, College Park, MD 20742 kmitra@umiacs.umd.edu Sameer Sheorey† Toyota Technological Institute, Chicago ssameer@ttic.edu Ra... | 2010 | 253 |
4,025 | Probabilistic Deterministic Infinite Automata David Pfau Nicholas Bartlett Frank Wood Columbia University, New York, NY 10027, USA {pfau@neurotheory,{bartlett,fwood}@stat}.columbia.edu Abstract We propose a novel Bayesian nonparametric approach to learning with probabilistic deterministic finite automata (P... | 2010 | 254 |
4,026 | Brain covariance selection: better individual functional connectivity models using population prior Ga¨el Varoquaux⋆ Parietal, INRIA NeuroSpin, CEA, France gael.varoquaux@normalesup.org Alexandre Gramfort Parietal, INRIA NeuroSpin, CEA, France alexandre.gramfort@inria.fr Jean-Baptiste Poline LNAO,... | 2010 | 255 |
4,027 | Word Features for Latent Dirichlet Allocation James Petterson1, Alex Smola2, Tiberio Caetano1, Wray Buntine1, Shravan Narayanamurthy3 1NICTA and ANU, Canberra, ACT, Australia 2Yahoo! Research, Santa Clara, CA, USA 3Yahoo! Research, Bangalore, India Abstract We extend Latent Dirichlet Allocation (LDA) by exp... | 2010 | 256 |
4,028 | A Primal-Dual Message-Passing Algorithm for Approximated Large Scale Structured Prediction Tamir Hazan TTI Chicago hazan@ttic.edu Raquel Urtasun TTI Chicago rurtasun@ttic.edu Abstract In this paper we propose an approximated structured prediction framework for large scale graphical models and derive... | 2010 | 257 |
4,029 | Efficient algorithms for learning kernels from multiple similarity matrices with general convex loss functions Achintya Kundu Dept. of Computer Science & Automation, Indian Institute of Science, Bangalore. achintya@csa.iisc.ernet.in Vikram Tankasali Dept. of Computer Science & Automation, Indian Instit... | 2010 | 258 |
4,030 | Online Learning: Random Averages, Combinatorial Parameters, and Learnability Alexander Rakhlin Department of Statistics University of Pennsylvania Karthik Sridharan Toyota Technological Institute at Chicago Ambuj Tewari Computer Science Department University of Texas at Austin Abstract We develo... | 2010 | 259 |
4,031 | Multiparty Differential Privacy via Aggregation of Locally Trained Classifiers Manas A. Pathak Carnegie Mellon University Pittsburgh, PA manasp@cs.cmu.edu Shantanu Rane Mitsubishi Electric Research Labs Cambridge, MA rane@merl.com Bhiksha Raj Carnegie Mellon University Pittsburgh, PA bhiksha@cs... | 2010 | 26 |
4,032 | Variable margin losses for classifier design Hamed Masnadi-Shirazi Statistical Visual Computing Laboratory, University of California, San Diego La Jolla, CA 92039 hmasnadi@ucsd.edu Nuno Vasconcelos Statistical Visual Computing Laboratory, University of California, San Diego La Jolla, CA 92039 nuno@uc... | 2010 | 260 |
4,033 | Nonparametric Density Estimation for Stochastic Optimization with an Observable State Variable Lauren A. Hannah Duke University Durham, NC 27701 lh140@duke.edu Warren B. Powell Princeton University Princeton, NJ 08544 powell@princeton.edu David M. Blei Princeton University Princeton, NJ 08544 ... | 2010 | 261 |
4,034 | 1 Mixture of time-warped trajectory models for movement decoding Elaine A. Corbett, Eric J. Perreault and Konrad P. Körding Northwestern University Chicago, IL 60611 ecorbett@u.northwestern.edu Abstract Applications of Brain-Machine-Interfaces typi... | 2010 | 262 |
4,035 | A Discriminative Latent Model of Image Region and Object Tag Correspondence Yang Wang∗ Department of Computer Science University of Illinois at Urbana-Champaign yangwang@uiuc.edu Greg Mori School of Computing Science Simon Fraser University mori@cs.sfu.ca Abstract We propose a discriminative laten... | 2010 | 263 |
4,036 | Lower Bounds on Rate of Convergence of Cutting Plane Methods Xinhua Zhang Dept. of Computing Science University of Alberta xinhua2@ualberta.ca Ankan Saha Dept. of Computer Science University of Chicago ankans@cs.uchicago.edu S.V. N. Vishwanathan Dept. of Statistics and Dept. of Computer Science ... | 2010 | 264 |
4,037 | Self-Paced Learning for Latent Variable Models M. Pawan Kumar Benjamin Packer Daphne Koller Computer Science Department Stanford University {pawan,bpacker,koller}@cs.stanford.edu Abstract Latent variable models are a powerful tool for addressing several tasks in machine learning. However, the algorith... | 2010 | 265 |
4,038 | Learning Efficient Markov Networks Vibhav Gogate William Austin Webb Pedro Domingos Department of Computer Science & Engineering University of Washington Seattle, WA 98195. USA {vgogate,webb,pedrod}@cs.washington.edu Abstract We present an algorithm for learning high-treewidth Markov networks where inf... | 2010 | 266 |
4,039 | Multi-Stage Dantzig Selector Ji Liu, Peter Wonka, Jieping Ye Arizona State University {ji.liu,peter.wonka,jieping.ye}@asu.edu Abstract We consider the following sparse signal recovery (or feature selection) problem: given a design matrix X ∈Rn×m (m ≫n) and a noisy observation vector y ∈Rn satisfying y = X... | 2010 | 267 |
4,040 | Batch Bayesian Optimization via Simulation Matching Javad Azimi, Alan Fern, Xiaoli Z. Fern School of EECS, Oregon State University {azimi, afern, xfern}@eecs.oregonstate.edu Abstract Bayesian optimization methods are often used to optimize unknown functions that are costly to evaluate. Typically, these me... | 2010 | 268 |
4,041 | Inference with Multivariate Heavy-Tails in Linear Models Danny Bickson and Carlos Guestrin Machine Learning Department Carnegie Mellon University Pittsburgh, PA 15213 {bickson,guestrin}@cs.cmu.edu Abstract Heavy-tailed distributions naturally occur in many real life problems. Unfortunately, it is typica... | 2010 | 269 |
4,042 | Convex Multiple-Instance Learning by Estimating Likelihood Ratio Fuxin Li and Cristian Sminchisescu Institute for Numerical Simulation, University of Bonn {fuxin.li,cristian.sminchisescu}@ins.uni-bonn.de Abstract We propose an approach to multiple-instance learning that reformulates the problem as a convex ... | 2010 | 27 |
4,043 | Towards Holistic Scene Understanding: Feedback Enabled Cascaded Classification Models Congcong Li, Adarsh Kowdle, Ashutosh Saxena, Tsuhan Chen Cornell University, Ithaca, NY. {cl758,apk64}@cornell.edu, asaxena@cs.cornell.edu, tsuhan@ece.cornell.edu Abstract In many machine learning domains (such as scene u... | 2010 | 270 |
4,044 | Learning the context of a category Daniel J. Navarro School of Psychology University of Adelaide Adelaide, SA 5005, Australia daniel.navarro@adelaide.edu.au Abstract This paper outlines a hierarchical Bayesian model for human category learning that learns both the organization of objects into categories... | 2010 | 271 |
4,045 | Learning via Gaussian Herding Koby Crammer Department of Electrical Enginering The Technion Haifa, 32000 Israel koby@ee.technion.ac.il Daniel D. Lee Dept. of Electrical and Systems Engineering University of Pennsylvania Philadelphia, PA 19104 ddlee@seas.upenn.edu Abstract We introduce a new fami... | 2010 | 272 |
4,046 | Monte-Carlo Planning in Large POMDPs David Silver MIT, Cambridge, MA 02139 davidstarsilver@gmail.com Joel Veness UNSW, Sydney, Australia jveness@gmail.com Abstract This paper introduces a Monte-Carlo algorithm for online planning in large POMDPs. The algorithm combines a Monte-Carlo update of the agen... | 2010 | 273 |
4,047 | Spatial and anatomical regularization of SVM for brain image analysis R´emi Cuingnet CRICM (UPMC/Inserm/CNRS), Paris, France Inserm - LIF (UMR S 678), Paris, France remi.cuingnet@imed.jussieu.fr Marie Chupin CRICM, Paris, France marie.chupin@upmc.fr Habib Benali Inserm - LIF, Paris, France habib.b... | 2010 | 274 |
4,048 | Divisive Normalization: Justification and Effectiveness as Efficient Coding Transform Siwei Lyu ∗ Computer Science Department University at Albany, State University of New York Albany, NY 12222, USA Abstract Divisive normalization (DN) has been advocated as an effective nonlinear efficient coding transform f... | 2010 | 275 |
4,049 | Rescaling, thinning or complementing? On goodness-of-fit procedures for point process models and Generalized Linear Models Felipe Gerhard Brain Mind Institute Ecole Polytechnique F´ed´erale de Lausanne 1015 Lausanne EPFL, Switzerland felipe.gerhard@epfl.ch Wulfram Gerstner Brain Mind Institute Ecole ... | 2010 | 276 |
4,050 | Active Instance Sampling via Matrix Partition Yuhong Guo Department of Computer & Information Sciences Temple University Philadelphia, PA 19122 yuhong@temple.edu Abstract Recently, batch-mode active learning has attracted a lot of attention. In this paper, we propose a novel batch-mode active learning app... | 2010 | 277 |
4,051 | Functional form of motion priors in human motion perception Hongjing Lu 1,2 hongjing@ucla.edu Tungyou Lin 3 tungyoul@math.ucla.edu Alan L. F. Lee 1 alanlee@ucla.edu Luminita Vese 3 lvese@math.ucla.edu Alan Yuille 1,2,4 yuille@stat.ucla.edu Department of Psychology1, Statistics2, Mathematics3 and... | 2010 | 278 |
4,052 | The LASSO risk: asymptotic results and real world examples Mohsen Bayati Stanford University bayati@stanford.edu Jos´e Bento Stanford University jbento@stanford.edu Andrea Montanari Stanford University montanar@stanford.edu Abstract We consider the problem of learning a coefficient vector x0 ∈RN ... | 2010 | 279 |
4,053 | Short-term memory in neuronal networks through dynamical compressed sensing Surya Ganguli Sloan-Swartz Center for Theoretical Neurobiology, UCSF, San Francisco, CA 94143 surya@phy.ucsf.edu Haim Sompolinsky Interdisciplinary Center for Neural Computation, Hebrew University, Jerusalem 91904, Israel and Cent... | 2010 | 28 |
4,054 | Layered Image Motion with Explicit Occlusions, Temporal Consistency, and Depth Ordering Deqing Sun, Erik B. Sudderth, and Michael J. Black Department of Computer Science, Brown University {dqsun,sudderth,black}@cs.brown.edu Abstract Layered models are a powerful way of describing natural scenes containing ... | 2010 | 280 |
4,055 | Towards Property-Based Classification of Clustering Paradigms Margareta Ackerman, Shai Ben-David, and David Loker D.R.C. School of Computer Science University of Waterloo, Canada {mackerma, shai, dloker}@cs.uwaterloo.ca Abstract Clustering is a basic data mining task with a wide variety of applications. No... | 2010 | 281 |
4,056 | Implicit encoding of prior probabilities in optimal neural populations Deep Ganguli and Eero P. Simoncelli Howard Hughes Medical Institute, and Center for Neural Science New York University New York, NY 10003 {dganguli,eero}@cns.nyu.edu Optimal coding provides a guiding principle for understanding the r... | 2010 | 282 |
4,057 | Distributed Dual Averaging in Networks John C. Duchi1 Alekh Agarwal1 Martin J. Wainwright1,2 Department of Electrical Engineering and Computer Science1 and Department of Statistics2 University of California, Berkeley Berkeley, CA 94720-1776 {jduchi,alekh,wainwrig}@eecs.berkeley.edu Abstract The goal o... | 2010 | 283 |
4,058 | Probabilistic Inference and Differential Privacy Oliver Williams Microsoft Research Mountain View, CA 94043 olliew@microsoft.com Frank McSherry Microsoft Research Mountain View, CA 94043 mcsherry@microsoft.com Abstract We identify and investigate a strong connection between probabilistic inference ... | 2010 | 284 |
4,059 | Copula Processes Andrew Gordon Wilson∗ Department of Engineering University of Cambridge agw38@cam.ac.uk Zoubin Ghahramani† Department of Engineering University of Cambridge zoubin@eng.cam.ac.uk Abstract We define a copula process which describes the dependencies between arbitrarily many random var... | 2010 | 285 |
4,060 | Learning invariant features using the Transformed Indian Buffet Process Joseph L. Austerweil Department of Psychology University of California, Berkeley Berkeley, CA 94720 Joseph.Austerweil@gmail.com Thomas L. Griffiths Department of Psychology University of California, Berkeley Berkeley, CA 94720 ... | 2010 | 286 |
4,061 | An Inverse Power Method for Nonlinear Eigenproblems with Applications in 1-Spectral Clustering and Sparse PCA Matthias Hein Thomas B¨uhler Saarland University, Saarbr¨ucken, Germany {hein,tb}@cs.uni-saarland.de Abstract Many problems in machine learning and statistics can be formulated as (generalized) ... | 2010 | 287 |
4,062 | Fast detection of multiple change-points shared by many signals using group LARS Jean-Philippe Vert and Kevin Bleakley Mines ParisTech CBIO, Institut Curie, INSERM U900 {firstname.lastname}@mines-paristech.fr Abstract We present a fast algorithm for the detection of multiple change-points when each is fre... | 2010 | 288 |
4,063 | Learning To Count Objects in Images Victor Lempitsky Visual Geometry Group University of Oxford Andrew Zisserman Visual Geometry Group University of Oxford Abstract We propose a new supervised learning framework for visual object counting tasks, such as estimating the number of cells in a microscopic ... | 2010 | 289 |
4,064 | The Multidimensional Wisdom of Crowds Peter Welinder1 Steve Branson2 Serge Belongie2 Pietro Perona1 1 California Institute of Technology, 2 University of California, San Diego {welinder,perona}@caltech.edu {sbranson,sjb}@cs.ucsd.edu Abstract Distributing labeling tasks among hundreds or thousands of a... | 2010 | 29 |
4,065 | Robust PCA via Outlier Pursuit Huan Xu Electrical and Computer Engineering University of Texas at Austin huan.xu@mail.utexas.edu Constantine Caramanis Electrical and Computer Engineering University of Texas at Austin cmcaram@ece.utexas.edu Sujay Sanghavi Electrical and Computer Engineering Univers... | 2010 | 290 |
4,066 | Multi-label Multiple Kernel Learning by Stochastic Approximation: Application to Visual Object Recognition Serhat S. Bucak∗ bucakser@cse.msu.edu Rong Jin∗ rongjin@cse.msu.edu Anil K. Jain∗† jain@cse.msu.edu Dept. of Comp. Sci. & Eng.∗ Michigan State University East Lansing, MI 48824,U.S.A. Dept. o... | 2010 | 291 |
4,067 | Learning sparse dynamic linear systems using stable spline kernels and exponential hyperpriors Alessandro Chiuso Department of Management and Engineering University of Padova Vicenza, Italy alessandro.chiuso@unipd.it Gianluigi Pillonetto∗ Department of Information Engineering University of Padova Pa... | 2010 | 292 |
4,068 | Generative Local Metric Learning for Nearest Neighbor Classification Yung-Kyun Noh1,2 Byoung-Tak Zhang2 Daniel D. Lee1 1GRASP Lab, University of Pennsylvania, Philadelphia, PA 19104, USA 2Biointelligence Lab, Seoul National University, Seoul 151-742, Korea nohyung@seas.upenn.edu, btzhang@snu.ac.kr, ddlee@s... | 2010 | 3 |
4,069 | Boosting Classifier Cascades Mohammad J. Saberian Statistical Visual Computing Laboratory, University of California, San Diego La Jolla, CA 92039 saberian@ucsd.edu Nuno Vasconcelos Statistical Visual Computing Laboratory, University of California, San Diego La Jolla, CA 92039 nuno@ucsd.edu Abstract... | 2010 | 30 |
4,070 | Implicitly Constrained Gaussian Process Regression for Monocular Non-Rigid Pose Estimation Mathieu Salzmann ICSI & EECS, UC Berkeley TTI Chicago salzmann@ttic.edu Raquel Urtasun TTI Chicago rurtasun@ttic.edu Abstract Estimating 3D pose from monocular images is a highly ambiguous problem. Physical co... | 2010 | 31 |
4,071 | Effects of Synaptic Weight Diffusion on Learning in Decision Making Networks Kentaro Katahira1,2,3, Kazuo Okanoya1,3 and Masato Okada1,2,3 1ERATO Okanoya Emotional Information Project, Japan Science Technology Agency 2Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba 277-8561, Japan ... | 2010 | 32 |
4,072 | Interval Estimation for Reinforcement-Learning Algorithms in Continuous-State Domains Martha White Department of Computing Science University of Alberta whitem@cs.ualberta.ca Adam White Department of Computing Science University of Alberta awhite@cs.ualberta.ca Abstract The reinforcement learning ... | 2010 | 33 |
4,073 | Object Bank: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification Li-Jia Li*1, Hao Su*1, Eric P. Xing2, Li Fei-Fei1 1 Computer Science Department, Stanford University 2 Machine Learning Department, Carnegie Mellon University Abstract Robust low-level image features have... | 2010 | 34 |
4,074 | Using body-anchored priors for identifying actions in single images Leonid Karlinsky Michael Dinerstein Shimon Ullman Department of Computer Science Weizmann Institute of Science Rehovot 76100, Israel {leonid.karlinsky, michael.dinerstein, shimon.ullman} @weizmann.ac.il Abstract This paper presents ... | 2010 | 35 |
4,075 | Reward Design via Online Gradient Ascent Jonathan Sorg Computer Science and Eng. University of Michigan jdsorg@umich.edu Satinder Singh Computer Science and Eng. University of Michigan baveja@umich.edu Richard L. Lewis Department of Psychology University of Michigan rickl@umich.edu Abstract ... | 2010 | 36 |
4,076 | Universal Consistency of Multi-Class Support Vector Classification Tobias Glasmachers Dalle Molle Institute for Artificial Intelligence (IDSIA), 6928 Manno-Lugano, Switzerland tobias@idsia.ch Abstract Steinwart was the first to prove universal consistency of support vector machine classification. His proof an... | 2010 | 37 |
4,077 | Supervised Clustering Pranjal Awasthi Carnegie Mellon University pawasthi@cs.cmu.edu Reza Bosagh Zadeh Stanford University rezab@stanford.edu Abstract Despite the ubiquity of clustering as a tool in unsupervised learning, there is not yet a consensus on a formal theory, and the vast majority of work i... | 2010 | 38 |
4,078 | Inferring Stimulus Selectivity from the Spatial Structure of Neural Network Dynamics Kanaka Rajan Lewis-Sigler Institute for Integrative Genomics Carl Icahn Laboratories # 262, Princeton University Princeton NJ 08544 USA krajan@princeton.edu L. F. Abbott Department of Neuroscience Department of Physio... | 2010 | 39 |
4,079 | Relaxed Clipping: A Global Training Method for Robust Regression and Classification Yaoliang Yu, Min Yang, Linli Xu, Martha White, Dale Schuurmans University of Alberta, Dept. Computing Science, Edmonton AB T6G 2E8, Canada {yaoliang,myang2,linli,whitem,dale}@cs.ualberta.ca Abstract Robust regression and clas... | 2010 | 4 |
4,080 | Distributionally Robust Markov Decision Processes Huan Xu ECE, University of Texas at Austin huan.xu@mail.utexas.edu Shie Mannor Department of Electrical Engineering, Technion, Israel shie@ee.technion.ac.il Abstract We consider Markov decision processes where the values of the parameters are uncertain... | 2010 | 40 |
4,081 | Empirical Risk Minimization with Approximations of Probabilistic Grammars Shay B. Cohen Language Technologies Institute School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213, USA scohen@cs.cmu.edu Noah A. Smith Language Technologies Institute School of Computer Science Carnegi... | 2010 | 41 |
4,082 | MAP Estimation for Graphical Models by Likelihood Maximization Akshat Kumar Department of Computer Science University of Massachusetts Amherst, MA akshat@cs.umass.edu Shlomo Zilberstein Department of Computer Science University of Massachusetts Amherst, MA shlomo@cs.umass.edu Abstract Computin... | 2010 | 42 |
4,083 | Identifying graph-structured activation patterns in networks James Sharpnack Machine Learning Department, Statistics Department Carnegie Mellon University Pittsburgh, PA 15213 jsharpna@andrew.cmu.edu Aarti Singh Machine Learning Department Carnegie Mellon University Pittsburgh, PA 15213 aartisingh... | 2010 | 43 |
4,084 | Size Matters: Metric Visual Search Constraints from Monocular Metadata Mario Fritz UC Berkeley EECS & ICSI Kate Saenko UC Berkeley EECS & ICSI Trevor Darrell UC Berkeley EECS & ICSI Abstract Metric constraints are known to be highly discriminative for many objects, but if training is limited to data... | 2010 | 44 |
4,085 | Near–Optimal Bayesian Active Learning with Noisy Observations Daniel Golovin Caltech Andreas Krause Caltech Debajyoti Ray Caltech Abstract We tackle the fundamental problem of Bayesian active learning with noise, where we need to adaptively select from a number of expensive tests in order to identif... | 2010 | 45 |
4,086 | Probabilistic Belief Revision with Structural Constraints Peter B. Jones MIT Lincoln Laboratory Lexington, MA 02420 jonep@ll.mit.edu Venkatesh Saligrama Dept. of ECE Boston University Boston, MA 02215 srv@bu.edu Sanjoy K. Mitter Dept. of EECS MIT Cambridge, MA 02139 mitter@mit.edu Abstra... | 2010 | 46 |
4,087 | Structured Determinantal Point Processes Alex Kulesza Ben Taskar Department of Computer and Information Science University of Pennsylvania Philadelphia, PA 19104 {kulesza,taskar}@cis.upenn.edu Abstract We present a novel probabilistic model for distributions over sets of structures— for example, sets ... | 2010 | 47 |
4,088 | b-Bit Minwise Hashing for Estimating Three-Way Similarities Ping Li Dept. of Statistical Science Cornell University Arnd Christian K¨onig Microsoft Research Microsoft Corporation Wenhao Gui Dept. of Statistical Science Cornell University Abstract Computing1 two-way and multi-way set similarities i... | 2010 | 48 |
4,089 | Feature Transitions with Saccadic Search: Size, Color, and Orientation Are Not Alike Stella X. Yu Computer Science Department Boston College Chestnut Hill, MA 02467 stella.yu@bc.edu Abstract Size, color, and orientation have long been considered elementary features whose attributes are extracted in pa... | 2010 | 49 |
4,090 | Linear readout from a neural population with partial correlation data Adrien Wohrer(1), Ranulfo Romo(2), Christian Machens(1) (1) Group for Neural Theory Laboratoire de Neurosciences Cognitives ´Ecole Normale Suprieure 75005 Paris, France {adrien.wohrer,christian.machens}@ens.fr (2) Instituto de Fisiolo... | 2010 | 5 |
4,091 | Auto-Regressive HMM Inference with Incomplete Data for Short-Horizon Wind Forecasting Chris Barber EE and Computer Science University of Wisconsin-Milwaukee, USA Joseph Bockhorst EE and Computer Science University of Wisconsin-Milwaukee, USA Paul Roebber Atmospheric Science University of Wisconsin-M... | 2010 | 50 |
4,092 | Link Discovery using Graph Feature Tracking Emile Richard ENS Cachan - CMLA & MilleMercis, France r.emile.richard@gmail.com Nicolas Baskiotis ENS Cachan - CMLA nicolas.baskiotis@lip6.com Theodoros Evgeniou Technology Management and Decision Sciences, INSEAD Bd de Constance, Fontainebleau 77300, Fran... | 2010 | 51 |
4,093 | A VLSI Implementation of the Adaptive Exponential Integrate-and-Fire Neuron Model Sebastian Millner, Andreas Gr¨ubl, Karlheinz Meier, Johannes Schemmel and Marc-Olivier Schwartz Kirchhoff-Institut f¨ur Physik Ruprecht-Karls-Universit¨at Heidelberg smillner@kip.uni-heidelberg.de Abstract We describe an a... | 2010 | 52 |
4,094 | Sparse Inverse Covariance Selection via Alternating Linearization Methods Katya Scheinberg Department of ISE Lehigh University katyas@lehigh.edu Shiqian Ma, Donald Goldfarb Department of IEOR Columbia University {sm2756,goldfarb}@columbia.edu Abstract Gaussian graphical models are of great inter... | 2010 | 53 |
4,095 | Constructing Skill Trees for Reinforcement Learning Agents from Demonstration Trajectories George Konidaris† Scott Kuindersma†‡ Andrew Barto† Roderic Grupen‡ Autonomous Learning Laboratory† Laboratory for Perceptual Robotics‡ Computer Science Department, University of Massachusetts Amherst {gdk, scott... | 2010 | 54 |
4,096 | Trading off Mistakes and Don’t-Know Predictions Amin Sayedi∗ Tepper School of Business CMU Pittsburgh, PA 15213 ssayedir@cmu.edu Morteza Zadimoghaddam† CSAIL MIT Cambridge, MA 02139 morteza@mit.edu Avrim Blum‡ Department of Computer Science CMU Pittsburgh, PA 15213 avrim@cs.cmu.edu Abstr... | 2010 | 55 |
4,097 | Evaluation of Rarity of Fingerprints in Forensics Chang Su and Sargur Srihari Department of Computer Science and Engineering University at Buffalo Amherst, NY 14260 {changsu,srihari}@buffalo.edu Abstract A method for computing the rarity of latent fingerprints represented by minutiae is given. It allows ... | 2010 | 56 |
4,098 | (RF)2 — Random Forest Random Field Nadia Payet and Sinisa Todorovic School of Electrical Engineering and Computer Science Oregon State University payetn@onid.orst.edu, sinisa@eecs.oregonstate.edu Abstract We combine random forest (RF) and conditional random field (CRF) into a new computational framework, c... | 2010 | 57 |
4,099 | Online Learning in the Manifold of Low-Rank Matrices Uri Shalit∗, Daphna Weinshall Computer Science Dept. and ICNC The Hebrew University of Jerusalem uri.shalit@mail.huji.ac.il daphna@cs.huji.ac.il Gal Chechik Google Research and The Gonda Brain Research Center Bar Ilan University gal@google.com ... | 2010 | 58 |
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