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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3,100 | Comparative Gene Prediction using Conditional Random Fields Jade P. Vinson∗† jpvinson@broad.mit.edu David DeCaprio∗ daved@broad.mit.edu Matthew D. Pearson mdp@broad.mit.edu Stacey Luoma sluoma@broad.mit.edu James E. Galagan jgalag@broad.mit.edu The Broad Institute of MIT and Harvard Cambridge,... | 2006 | 78 |
3,101 | Learning annotated hierarchies from relational data Daniel M. Roy, Charles Kemp, Vikash K. Mansinghka, and Joshua B. Tenenbaum CSAIL, Dept. of Brain & Cognitive Sciences, MIT, Cambridge, MA 02139 {droy, ckemp, vkm, jbt}@mit.edu Abstract The objects in many real-world domains can be organized into hierarchies,... | 2006 | 79 |
3,102 | Predicting spike times from subthreshold dynamics of a neuron Ryota Kobayashi Department of Physics Kyoto University Kyoto 606-8502, Japan kobayashi@ton.scphys.kyoto-u.ac.jp Shigeru Shinomoto Department of Physics Kyoto University Kyoto 606-8502, Japan shinomoto@scphys.kyoto-u.ac.jp Abstract I... | 2006 | 8 |
3,103 | Single Channel Speech Separation Using Factorial Dynamics John R. Hershey Trausti Kristjansson Steven Rennie Peder A. Olsen IBM Thomas J. Watson Research Center Yorktown Heights, NY 10598 Abstract Human listeners have the extraordinary ability to hear and recognize speech even when more than one per... | 2006 | 80 |
3,104 | A Small World Threshold for Economic Network Formation Eyal Even-Dar Computer and Information Science University of Pennsylvania Philadelphia, PA 19104 evendar@seas.upenn.edu Michael Kearns Computer and Information Science University of Pennsylvania Philadelphia, PA 19104 mkearns@cis.upenn.edu A... | 2006 | 81 |
3,105 | iLSTD: Eligibility Traces and Convergence Analysis Alborz Geramifard Michael Bowling Martin Zinkevich Richard S. Sutton Department of Computing Science University of Alberta Edmonton, Alberta {alborz,bowling,maz,sutton}@cs.ualberta.ca Abstract We present new theoretical and empirical results with th... | 2006 | 82 |
3,106 | Sparse Kernel Orthonormalized PLS for feature extraction in large data sets Jer´onimo Arenas-Garc´ıa, Kaare Brandt Petersen and Lars Kai Hansen Informatics and Mathematical Modelling Technical University of Denmark DK-2800 Kongens Lyngby, Denmark {jag,kbp,lkh}@imm.dtu.dk Abstract In this paper we are pr... | 2006 | 83 |
3,107 | Data Integration for Classification Problems Employing Gaussian Process Priors Mark Girolami Department of Computing Science University of Glasgow Scotland, UK girolami@dcs.gla.ac.uk Mingjun Zhong IRISA, Campus de Beaulieu F-35042 Rennes Cedex France zmingjun@irisa.fr Abstract By adopting Gauss... | 2006 | 84 |
3,108 | Stochastic Relational Models for Discriminative Link Prediction Kai Yu NEC Laboratories America Cupertino, CA 95014 Wei Chu CCLS, Columbia University New York, NY 10115 Shipeng Yu, Volker Tresp, Zhao Xu Siemens AG, Corporate Research & Technology, 81739 Munich, Germany Abstract We introduce a Ga... | 2006 | 85 |
3,109 | Adaptive Spatial Filters with predefined Region of Interest for EEG based Brain-Computer-Interfaces Moritz Grosse-Wentrup Institute of Automatic Control Engineering Technische Universit¨at M¨unchen 80333 M¨unchen, Germany moritz@tum.de Klaus Gramann Department Psychology Ludwig-Maximilians-Universit¨at... | 2006 | 86 |
3,110 | A recipe for optimizing a time-histogram Hideaki Shimazaki Department of Physics, Graduate School of Science Kyoto University Kyoto 606-8502, Japan shimazaki@ton.scphys.kyoto-u.ac.jp Shigeru Shinomoto Department of Physics, Graduate School of Science Kyoto University Kyoto 606-8502, Japan shinomoto@... | 2006 | 87 |
3,111 | Inducing Metric Violations in Human Similarity Judgements Julian Laub1, Jakob Macke2, Klaus-Robert Müller1,3 and Felix A. Wichmann2 1 Fraunhofer FIRST.IDA, Kekulestr. 7, 12489 Berlin, Germany 2 Max Planck Institut for Biological Cybernetics, Spemannstr. 38, 72076 Tübingen, Germany 3 University of Potsdam, Dep... | 2006 | 88 |
3,112 | Nonnegative Sparse PCA Ron Zass and Amnon Shashua ∗ Abstract We describe a nonnegative variant of the ”Sparse PCA” problem. The goal is to create a low dimensional representation from a collection of points which on the one hand maximizes the variance of the projected points and on the other uses only par... | 2006 | 89 |
3,113 | Blind source separation for over-determined delayed mixtures Lars Omlor, Martin Giese∗ Laboratory for Action Representation and Learning Department of Cognitive Neurology, Hertie Institute for Clinical Brain Research University of T¨ubingen, Germany Abstract Blind source separation, i.e. the extraction ... | 2006 | 9 |
3,114 | Context dependent amplification of both rate and event-correlation in a VLSI network of spiking neurons Elisabetta Chicca, Giacomo Indiveri and Rodney J. Douglas Institute of Neuroinformatics University - ETH Zurich Winterthurerstrasse 190, CH-8057 Zurich, Switzerland chicca,giacomo,rjd@ini.phys.ethz.ch ... | 2006 | 90 |
3,115 | PAC-Bayes Bounds for the Risk of the Majority Vote and the Variance of the Gibbs Classifier Alexandre Lacasse, Franc¸ois Laviolette and Mario Marchand D´epartement IFT-GLO Universit´e Laval Qu´ebec, Canada Firstname.Secondname@ift.ulaval.ca Pascal Germain D´epartement IFT-GLO Universit´e Laval Qu´ebec,... | 2006 | 91 |
3,116 | Generalized Regularized Least-Squares Learning with Predefined Features in a Hilbert Space Wenye Li, Kin-Hong Lee, Kwong-Sak Leung Department of Computer Science and Engineering The Chinese University of Hong Kong Shatin, Hong Kong, China {wyli, khlee, ksleung}@cse.cuhk.edu.hk Abstract Kernel-based regul... | 2006 | 92 |
3,117 | Map-Reduce for Machine Learning on Multicore Cheng-Tao Chu ∗ chengtao@stanford.edu Sang Kyun Kim ∗ skkim38@stanford.edu Yi-An Lin ∗ ianl@stanford.edu YuanYuan Yu ∗ yuanyuan@stanford.edu Gary Bradski ∗† garybradski@gmail Andrew Y. Ng ∗ ang@cs.stanford.edu Kunle Olukotun ∗ kunle@cs.stanford.ed... | 2006 | 93 |
3,118 | Natural Actor-Critic for Road Traffic Optimisation Silvia Richter Albert-Ludwigs-Universit¨at Freiburg, Germany si.richter@web.de Douglas Aberdeen National ICT Australia Canberra, Australia doug.aberdeen@anu.edu.au Jin Yu National ICT Australia Canberra, Australia. jin.yu@anu.edu.au Abstract ... | 2006 | 94 |
3,119 | Implicit Surfaces with Globally Regularised and Compactly Supported Basis Functions Christian Walder†⋆, Bernhard Sch¨olkopf† & Olivier Chapelle† † Max Planck Institute for Biological Cybernetics, 72076 T¨ubingen, Germany ⋆The University of Queensland, Brisbane, Queensland 4072, Australia first.last@tuebingen.... | 2006 | 95 |
3,120 | Multiple Instance Learning for Computer Aided Diagnosis Glenn Fung, Murat Dundar, Balaji Krishnapuram, R. Bharat Rao CAD & Knowledge Solutions, Siemens Medical Solutions USA, Malvern, PA 19355 {glenn.fung, murat.dundar, balaji.krishnapuram, bharat.rao}@siemens.com Abstract Many computer aided diagnosis (CAD... | 2006 | 96 |
3,121 | A selective attention multi–chip system with dynamic synapses and spiking neurons Chiara Bartolozzi Institute of neuroinformatics UNI-ETH Zurich Wintherthurerstr. 190, 8057, Switzerland chiara@ini.phys.ethz.ch Giacomo Indiveri Institute of neuroinformatics UNI-ETH Zurich Wintherthurerstr. 190, 8057,... | 2006 | 97 |
3,122 | Real-time adaptive information-theoretic optimization of neurophysiology experiments∗ Jeremy Lewi† School of Bioengineering Georgia Institute of Technology jlewi@gatech.edu Robert Butera School of Electrical and Computer Engineering Georgia Institute of Technology rbutera@ece.gatech.edu Liam Paninsk... | 2006 | 98 |
3,123 | High-Dimensional Graphical Model Selection Using ℓ1-Regularized Logistic Regression Martin J. Wainwright Pradeep Ravikumar John D. Lafferty Department of Statistics Machine Learning Dept. Computer Science Dept. Department of EECS Carnegie Mellon Univ. Machine Learning Dept. Univ. of California, Ber... | 2006 | 99 |
3,124 | An Analysis of Convex Relaxations for MAP Estimation M. Pawan Kumar V. Kolmogorov P.H.S. Torr Dept. of Computing Computer Science Dept. of Computing Oxford Brookes University University College London Oxford Brookes University pkmudigonda@brookes.ac.uk vnk@adastral.ucl.ac.uk philiptorr@brookes.a... | 2007 | 1 |
3,125 | Efficient Principled Learning of Thin Junction Trees Anton Chechetka Carlos Guestrin Carnegie Mellon University Abstract We present the first truly polynomial algorithm for PAC-learning the structure of bounded-treewidth junction trees – an attractive subclass of probabilistic graphical models that permits ... | 2007 | 10 |
3,126 | Sparse Overcomplete Latent Variable Decomposition of Counts Data Madhusudana Shashanka Mars, Incorporated Hackettstown, NJ shashanka@cns.bu.edu Bhiksha Raj Mitsubishi Electric Research Labs Cambridge, MA bhiksha@merl.com Paris Smaragdis Adobe Systems Newton, MA paris@adobe.com Abstract An ... | 2007 | 100 |
3,127 | Modelling motion primitives and their timing in biologically executed movements Ben H Williams School of Informatics University of Edinburgh 5 Forrest Hill, EH1 2QL, UK ben.williams@ed.ac.uk Marc Toussaint TU Berlin Franklinstr. 28/29, FR 6-9 10587 Berlin, Germany mtoussai@cs.tu-berlin.de Amos J... | 2007 | 101 |
3,128 | Subspace-Based Face Recognition in Analog VLSI Gonzalo Carvajal, Waldo Valenzuela and Miguel Figueroa Department of Electrical Engineering, Universidad de Concepción Casilla 160-C, Correo 3, Concepción, Chile {gcarvaja, waldovalenzuela, miguel.figueroa}@udec.cl Abstract We describe an analog-VLSI neural ne... | 2007 | 102 |
3,129 | Efficient multiple hyperparameter learning for log-linear models Chuong B. Do Chuan-Sheng Foo Andrew Y. Ng Computer Science Department Stanford University Stanford, CA 94305 {chuongdo,csfoo,ang}@cs.stanford.edu Abstract In problems where input features have varying amounts of noise, using distinct ... | 2007 | 103 |
3,130 | Discovering Weakly-Interacting Factors in a Complex Stochastic Process Charlie Frogner School of Engineering and Applied Sciences Harvard University Cambridge, MA 02138 frogner@seas.harvard.edu Avi Pfeffer School of Engineering and Applied Sciences Harvard University Cambridge, MA 02138 avi@eecs.h... | 2007 | 104 |
3,131 | Stability Bounds for Non-i.i.d. Processes Mehryar Mohri Courant Institute of Mathematical Sciences and Google Research 251 Mercer Street New York, NY 10012 mohri@cims.nyu.edu Afshin Rostamizadeh Department of Computer Science Courant Institute of Mathematical Sciences 251 Mercer Street New York, N... | 2007 | 105 |
3,132 | Evaluating Search Engines by Modeling the Relationship Between Relevance and Clicks Ben Carterette∗ Center for Intelligent Information Retrieval University of Massachusetts Amherst Amherst, MA 01003 carteret@cs.umass.edu Rosie Jones Yahoo! Research 3333 Empire Ave Burbank, CA 91504 jonesr@yahoo-in... | 2007 | 106 |
3,133 | Adaptive Bayesian Inference Umut A. Acar∗ Toyota Tech. Inst. Chicago, IL umut@tti-c.org Alexander T. Ihler U.C. Irvine Irvine, CA ihler@ics.uci.edu Ramgopal R. Mettu† Univ. of Massachusetts Amherst, MA mettu@ecs.umass.edu ¨Ozg¨ur S¨umer Uni. of Chicago Chicago, IL osumer@cs.uchicago.edu ... | 2007 | 107 |
3,134 | Markov Chain Monte Carlo with People Adam N. Sanborn Psychological and Brain Sciences Indiana University Bloomington, IN 47045 asanborn@indiana.edu Thomas L. Griffiths Department of Psychology University of California Berkeley, CA 94720 tom griffiths@berkeley.edu Abstract Many formal models of co... | 2007 | 108 |
3,135 | Abstract The peak location in a population of phase-tuned neurons has been shown to be a more reliable estimator for disparity than the peak location in a population of position-tuned neurons. Unfortunately, the disparity range covered by a phasetuned population is limited by phase wraparound. Thus, a single popu... | 2007 | 109 |
3,136 | Regret Minimization in Games with Incomplete Information Martin Zinkevich maz@cs.ualberta.ca Michael Johanson johanson@cs.ualberta.ca Michael Bowling Computing Science Department University of Alberta Edmonton, AB Canada T6G2E8 bowling@cs.ualberta.ca Carmelo Piccione Computing Science Department... | 2007 | 11 |
3,137 | Locality and low-dimensions in the prediction of natural experience from fMRI Franc¸ois G. Meyer Center for the Study of Brain, Mind and Behavior, Program in Applied and Computational Mathematics Princeton University fmeyer@colorado.edu Greg J. Stephens Center for the Study of Brain, Mind and Behavior, ... | 2007 | 110 |
3,138 | Configuration Estimates Improve Pedestrian Finding Duan Tran∗ U.Illinois at Urbana-Champaign Urbana, IL 61801 USA ddtran2@uiuc.edu D.A. Forsyth U.Illinois at Urbana-Champaign Urbana, IL 61801 USA daf@uiuc.edu Abstract Fair discriminative pedestrian finders are now available. In fact, these pedestrian ... | 2007 | 111 |
3,139 | A General Boosting Method and its Application to Learning Ranking Functions for Web Search Zhaohui Zheng† Hongyuan Zha⋆Tong Zhang† Olivier Chapelle† Keke Chen† Gordon Sun† †Yahoo! Inc. 701 First Avene Sunnyvale, CA 94089 {zhaohui,tzhang,chap,kchen,gzsun}@yahoo-inc.com ⋆College of Computing Georgia Insti... | 2007 | 112 |
3,140 | Fixing Max-Product: Convergent Message Passing Algorithms for MAP LP-Relaxations Amir Globerson Tommi Jaakkola Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge, MA 02139 gamir,tommi@csail.mit.edu Abstract We present a novel message passing algorith... | 2007 | 113 |
3,141 | GRIFT: A graphical model for inferring visual classification features from human data Michael G. Ross Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Cambridge, MA 02139 mgross@mit.edu Andrew L. Cohen Psychology Department University of Massachusetts Amherst Amherst, ... | 2007 | 114 |
3,142 | An in-silico Neural Model of Dynamic Routing through Neuronal Coherence Devarajan Sridharan∗†, Brian Percival∗‡, John Arthur♮and Kwabena Boahen♮ † Program in Neurosciences, ‡ Department of Electrical Engineering and ♮Department of Bioengineering Stanford University ∗These authors contributed equally {ds... | 2007 | 115 |
3,143 | A general agnostic active learning algorithm Sanjoy Dasgupta UC San Diego dasgupta@cs.ucsd.edu Daniel Hsu UC San Diego djhsu@cs.ucsd.edu Claire Monteleoni UC San Diego cmontel@cs.ucsd.edu Abstract We present an agnostic active learning algorithm for any hypothesis class of bounded VC dimension u... | 2007 | 116 |
3,144 | Simplified Rules and Theoretical Analysis for Information Bottleneck Optimization and PCA with Spiking Neurons Lars Buesing, Wolfgang Maass Institute for Theoretical Computer Science Graz University of Technology A-8010 Graz, Austria {lars,maass}@igi.tu-graz.at Abstract We show that under suitable assu... | 2007 | 117 |
3,145 | Hidden Common Cause Relations in Relational Learning Ricardo Silva∗ Gatsby Computational Neuroscience Unit UCL, London, UK WC1N 3AR rbas@gatsby.ucl.ac.uk Wei Chu Center for Computational Learning Systems Columbia University, New York, NY 10115 chuwei@cs.columbia.edu Zoubin Ghahramani Department of... | 2007 | 118 |
3,146 | Modeling image patches with a directed hierarchy of Markov random fields Simon Osindero and Geoffrey Hinton Department of Computer Science, University of Toronto 6, King’s College Road, M5S 3G4, Canada osindero,hinton@cs.toronto.edu Abstract We describe an efficient learning procedure for multilayer generat... | 2007 | 119 |
3,147 | A Bayesian Model of Conditioned Perception Alan A. Stocker∗and Eero P. Simoncelli Howard Hughes Medical Institute, Center for Neural Science, and Courant Institute of Mathematical Sciences New York University New York, NY-10003, U.S.A. We argue that in many circumstances, human observers evaluate sensory ... | 2007 | 12 |
3,148 | The Generalized FITC Approximation Andrew Naish-Guzman & Sean Holden Computer Laboratory University of Cambridge Cambridge, CB3 0FD. United Kingdom {agpn2,sbh11}@cl.cam.ac.uk Abstract We present an efficient generalization of the sparse pseudo-input Gaussian process (SPGP) model developed by Snelson and Gh... | 2007 | 120 |
3,149 | Cooled and Relaxed Survey Propagation for MRFs Hai Leong Chieu1,2, Wee Sun Lee2 1Singapore MIT Alliance 2Department of Computer Science National University of Singapore haileong@nus.edu.sg,leews@comp.nus.edu.sg Yee-Whye Teh Gatsby Computational Neuroscience Unit University College London ywteh@gatsby.... | 2007 | 121 |
3,150 | A Spectral Regularization Framework for Multi-Task Structure Learning Andreas Argyriou Department of Computer Science University College London Gower Street, London WC1E 6BT, UK a.argyriou@cs.ucl.ac.uk Charles A. Micchelli Department of Mathematics and Statistics SUNY Albany 1400 Washington Avenue ... | 2007 | 122 |
3,151 | CPR for CSPs: A Probabilistic Relaxation of Constraint Propagation Luis E. Ortiz ECE Dept, Univ. of Puerto Rico, Mayag¨uez, PR 00681-9042 leortiz@ece.uprm.edu Abstract This paper proposes constraint propagation relaxation (CPR), a probabilistic approach to classical constraint propagation that provides anot... | 2007 | 123 |
3,152 | Theoretical Analysis of Learning with Reward-Modulated Spike-Timing-Dependent Plasticity Robert Legenstein, Dejan Pecevski, Wolfgang Maass Institute for Theoretical Computer Science Graz University of Technology A-8010 Graz, Austria {legi,dejan,maass}@igi.tugraz.at Abstract Reward-modulated spike-timi... | 2007 | 124 |
3,153 | A New View of Automatic Relevance Determination David Wipf and Srikantan Nagarajan, ∗ Biomagnetic Imaging Lab, UC San Francisco {david.wipf, sri}@mrsc.ucsf.edu Abstract Automatic relevance determination (ARD) and the closely-related sparse Bayesian learning (SBL) framework are effective tools for pruning la... | 2007 | 125 |
3,154 | Sequential Hypothesis Testing under Stochastic Deadlines Peter I. Frazier ORFE Princeton University Princeton, NJ 08544 pfrazier@princeton.edu Angela J. Yu CSBMB Princeton University Princeton, NJ 08544 ajyu@princeton.edu Abstract Most models of decision-making in neuroscience assume an infinit... | 2007 | 126 |
3,155 | Discriminative Log-Linear Grammars with Latent Variables Slav Petrov and Dan Klein Computer Science Department, EECS Division University of California at Berkeley, Berkeley, CA, 94720 {petrov, klein}@cs.berkeley.edu Abstract We demonstrate that log-linear grammars with latent variables can be practically ... | 2007 | 127 |
3,156 | HM-BiTAM: Bilingual Topic Exploration, Word Alignment, and Translation Bing Zhao IBM T. J. Watson Research zhaob@us.ibm.com Eric P. Xing Carnegie Mellon University epxing@cs.cmu.edu Abstract We present a novel paradigm for statistical machine translation (SMT), based on a joint modeling of word alig... | 2007 | 128 |
3,157 | Optimistic Linear Programming gives Logarithmic Regret for Irreducible MDPs Ambuj Tewari Computer Science Division Univeristy of California, Berkeley Berkeley, CA 94720, USA ambuj@cs.berkeley.edu Peter L. Bartlett Computer Science Division and Department of Statistics University of California, Berkele... | 2007 | 129 |
3,158 | Scan Strategies for Adaptive Meteorological Radars Victoria Manfredi, Jim Kurose Department of Computer Science University of Massachusetts Amherst, MA USA {vmanfred,kurose}@cs.umass.edu Abstract We address the problem of adaptive sensor control in dynamic resourceconstrained sensor networks. We focus on ... | 2007 | 13 |
3,159 | TrueSkill Through Time: Revisiting the History of Chess Pierre Dangauthier INRIA Rhone Alpes Grenoble, France pierre.dangauthier@imag.fr Ralf Herbrich Microsoft Research Ltd. Cambridge, UK rherb@microsoft.com Tom Minka Microsoft Research Ltd. Cambridge, UK minka@microsoft.com Thore Graepel ... | 2007 | 130 |
3,160 | Topmoumoute online natural gradient algorithm Nicolas Le Roux University of Montreal nicolas.le.roux@umontreal.ca Pierre-Antoine Manzagol University of Montreal manzagop@iro.umontreal.ca Yoshua Bengio University of Montreal yoshua.bengio@umontreal.ca Abstract Guided by the goal of obtaining an opt... | 2007 | 131 |
3,161 | Learning the structure of manifolds using random projections Yoav Freund ∗ UC San Diego Sanjoy Dasgupta † UC San Diego Mayank Kabra UC San Diego Nakul Verma UC San Diego Abstract We present a simple variant of the k-d tree which automatically adapts to intrinsic low dimensional structure in data... | 2007 | 132 |
3,162 | Learning Monotonic Transformations for Classification Andrew G. Howard Department of Computer Science Columbia University New York, NY 10027 ahoward@cs.columbia.edu Tony Jebara Department of Computer Science Columbia University New York, NY 10027 jebara@cs.columbia.edu Abstract A discriminative... | 2007 | 133 |
3,163 | Combined discriminative and generative articulated pose and non-rigid shape estimation Leonid Sigal Alexandru Balan Michael J. Black Department of Computer Science Brown University Providence, RI 02912 {ls, alb, black}@cs.brown.edu Abstract Estimation of three-dimensional articulated human pose and ... | 2007 | 134 |
3,164 | Multiple-Instance Active Learning Burr Settles Mark Craven University of Wisconsin Madison, WI 5713 USA {bsettles@cs,craven@biostat}.wisc.edu Soumya Ray Oregon State University Corvallis, OR 97331 USA sray@eecs.oregonstate.edu Abstract We present a framework for active learning in the multiple-ins... | 2007 | 135 |
3,165 | Semi-Supervised Multitask Learning Qiuhua Liu, Xuejun Liao, and Lawrence Carin Department of Electrical and Computer Engineering Duke University Durham, NC 27708-0291, USA Abstract A semi-supervised multitask learning (MTL) framework is presented, in which M parameterized semi-supervised classifiers, each ... | 2007 | 136 |
3,166 | Contraction Properties of VLSI Cooperative Competitive Neural Networks of Spiking Neurons Emre Neftci1, Elisabetta Chicca1, Giacomo Indiveri1, Jean-Jacques Slotine2, Rodney Douglas1 1Institute of Neuroinformatics, UNI|ETH, Zurich 2Nonlinear Systems Laboratory, MIT, Cambridge, Massachusetts, 02139 emre@ini.phy... | 2007 | 137 |
3,167 | Mining Internet-Scale Software Repositories Erik Linstead, Paul Rigor, Sushil Bajracharya, Cristina Lopes and Pierre Baldi Donald Bren School of Information and Computer Science University of California, Irvine Irvine, CA 92697-3435 {elinstea,prigor,sbajrach,lopes,pfbaldi}@ics.uci.edu Abstract Large repos... | 2007 | 138 |
3,168 | Optimal models of sound localization by barn owls Brian J. Fischer Division of Biology California Institute of Technology Pasadena, CA fischerb@caltech.edu Abstract Sound localization by barn owls is commonly modeled as a matching procedure where localization cues derived from auditory inputs are compar... | 2007 | 139 |
3,169 | The Tradeoffs of Large Scale Learning L´eon Bottou NEC laboratories of America Princeton, NJ 08540, USA leon@bottou.org Olivier Bousquet Google Z¨urich 8002 Zurich, Switzerland olivier.bousquet@m4x.org Abstract This contribution develops a theoretical framework that takes into account the effect o... | 2007 | 14 |
3,170 | Discriminative K-means for Clustering Jieping Ye Arizona State University Tempe, AZ 85287 jieping.ye@asu.edu Zheng Zhao Arizona State University Tempe, AZ 85287 zhaozheng@asu.edu Mingrui Wu MPI for Biological Cybernetics T¨ubingen, Germany mingrui.wu@tuebingen.mpg.de Abstract We present a th... | 2007 | 140 |
3,171 | Heterogeneous Component Analysis Shigeyuki Oba1, Motoaki Kawanabe2, Klaus Robert M¨uller3,2, and Shin Ishii4,1 1. Graduate School of Information Science, Nara Institute of Science and Technology, Japan 2. Fraunhofer FIRST.IDA, Germany 3. Department of Computer Science, Technical University Berlin, Germany 4. ... | 2007 | 141 |
3,172 | An Analysis of Inference with the Universum Fabian H. Sinz Max Planck Institute for biological Cybernetics Spemannstrasse 41, 72076, T¨ubingen, Germany fabee@tuebingen.mpg.de Olivier Chapelle Yahoo! Research Santa Clara, California chap@yahoo-inc.com Alekh Agarwal University of California Berkeley ... | 2007 | 142 |
3,173 | Exponential Family Predictive Representations of State David Wingate Computer Science and Engineering University of Michigan wingated@umich.edu Satinder Singh Computer Science and Engineering University of Michigan baveja@umich.edu Abstract In order to represent state in controlled, partially obse... | 2007 | 143 |
3,174 | One-Pass Boosting Zafer Barutcuoglu zbarutcu@cs.princeton.edu Philip M. Long plong@google.com Rocco A. Servedio rocco@cs.columbia.edu Abstract This paper studies boosting algorithms that make a single pass over a set of base classifiers. We first analyze a one-pass algorithm in the setting of boosting... | 2007 | 144 |
3,175 | The Value of Labeled and Unlabeled Examples when the Model is Imperfect Kaushik Sinha Dept. of Computer Science and Engineering Ohio State University Columbus, OH 43210 sinhak@cse.ohio-state.edu Mikahil Belkin Dept. of Computer Science and Engineering Ohio State University Columbus, OH 43210 mbelk... | 2007 | 145 |
3,176 | On Higher-Order Perceptron Algorithms ∗ Cristian Brotto DICOM, Universit`a dell’Insubria cristian.brotto@gmail.com Claudio Gentile DICOM, Universit`a dell’Insubria claudio.gentile@uninsubria.it Fabio Vitale DICOM, Universit`a dell’Insubria fabiovdk@yahoo.com Abstract A new algorithm for on-line le... | 2007 | 146 |
3,177 | Adaptive Online Gradient Descent Peter L. Bartlett Division of Computer Science Department of Statistics UC Berkeley Berkeley, CA 94709 bartlett@cs.berkeley.edu Elad Hazan IBM Almaden Research Center 650 Harry Road San Jose, CA 95120 hazan@us.ibm.com Alexander Rakhlin ∗ Division of Computer Sc... | 2007 | 147 |
3,178 | Fast Variational Inference for Large-scale Internet Diagnosis John C. Platt Emre Kıcıman Microsoft Research 1 Microsoft Way Redmond, WA 98052 {jplatt,emrek,dmaltz}@microsoft.com David A. Maltz Abstract Web servers on the Internet need to maintain high reliability, but the cause of intermittent fai... | 2007 | 148 |
3,179 | Learning and using relational theories Charles Kemp, Noah D. Goodman & Joshua B. Tenenbaum Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139 {ckemp,ndg,jbt}@mit.edu Abstract Much of human knowledge is organized into sophisticated systems that are often called intuitive theories. We propos... | 2007 | 149 |
3,180 | Inferring Elapsed Time from Stochastic Neural Processes Misha B. Ahrens and Maneesh Sahani Gatsby Computational Neuroscience Unit, UCL Alexandra House, 17 Queen Square, London, WC1N 3AR {ahrens, maneesh}@gatsby.ucl.ac.uk Abstract Many perceptual processes and neural computations, such as speech recognitio... | 2007 | 15 |
3,181 | The Infinite Markov Model Daichi Mochihashi ∗ NTT Communication Science Laboratories Hikaridai 2-4, Keihanna Science City Kyoto, Japan 619-0237 daichi@cslab.kecl.ntt.co.jp Eiichiro Sumita ATR / NICT Hikaridai 2-2, Keihanna Science City Kyoto, Japan 619-0288 eiichiro.sumita@atr.jp Abstract We pres... | 2007 | 150 |
3,182 | Retrieved context and the discovery of semantic structure Vinayak A. Rao, Marc W. Howard∗ Syracuse University Department of Psychology 430 Huntington Hall Syracuse, NY 13244 vrao@gatsby.ucl.ac.uk, marc@memory.syr.edu Abstract Semantic memory refers to our knowledge of facts and relationships between c... | 2007 | 151 |
3,183 | Active Preference Learning with Discrete Choice Data Eric Brochu, Nando de Freitas and Abhijeet Ghosh Department of Computer Science University of British Columbia Vancouver, BC, Canada {ebrochu, nando, ghosh}@cs.ubc.ca Abstract We propose an active learning algorithm that learns a continuous valuation mo... | 2007 | 152 |
3,184 | Bayesian binning beats approximate alternatives: estimating peristimulus time histograms Dominik Endres, Mike Oram, Johannes Schindelin and Peter F¨oldi´ak School of Psychology University of St. Andrews KY16 9JP, UK {dme2,mwo,js108,pf2}@st-andrews.ac.uk Abstract The peristimulus time histogram (PSTH) an... | 2007 | 153 |
3,185 | Online Linear Regression and Its Application to Model-Based Reinforcement Learning Alexander L. Strehl∗ Yahoo! Research New York, NY strehl@yahoo-inc.com Michael L. Littman Department of Computer Science Rutgers University Piscataway, NJ USA mlittman@cs.rutgers.edu Abstract We provide a provably... | 2007 | 154 |
3,186 | Transfer Learning using Kolmogorov Complexity: Basic Theory and Empirical Evaluations M. M. Hassan Mahmud Department of Computer Science University of Illinois at Urbana-Champaign mmmahmud@uiuc.edu Sylvian R. Ray Department of Computer Science University of Illinois at Urbana-Champaign ray@cs.uiuc.edu... | 2007 | 155 |
3,187 | McRank: Learning to Rank Using Multiple Classification and Gradient Boosting Ping Li ∗ Dept. of Statistical Science Cornell University pingli@cornell.edu Christopher J.C. Burges Microsoft Research Microsoft Corporation cburges@microsoft.com Qiang Wu Microsoft Research Microsoft Corporation qian... | 2007 | 156 |
3,188 | A Randomized Algorithm for Large Scale Support Vector Learning Krishnan S. Department of Computer Science and Automation, Indian Institute of Science, Bangalore-12 krishi@csa.iisc.ernet.in Chiranjib Bhattacharyya Department of Computer Science and Automation, Indian Institute of Science, Bangalore-12 chir... | 2007 | 157 |
3,189 | Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation Masashi Sugiyama Tokyo Institute of Technology sugi@cs.titech.ac.jp Shinichi Nakajima Nikon Corporation nakajima.s@nikon.co.jp Hisashi Kashima IBM Research hkashima@jp.ibm.com Paul von B¨unau Tech... | 2007 | 158 |
3,190 | The Price of Bandit Information for Online Optimization Varsha Dani Department of Computer Science University of Chicago Chicago, IL 60637 varsha@cs.uchicago.edu Thomas P. Hayes Toyota Technological Institute Chicago, IL 60637 hayest@tti-c.org Sham M. Kakade Toyota Technological Institute Chic... | 2007 | 159 |
3,191 | A Learning Framework for Nearest Neighbor Search Lawrence Cayton Department of Computer Science University of California, San Diego lcayton@cs.ucsd.edu Sanjoy Dasgupta Department of Computer Science University of California, San Diego dasgupta@cs.ucsd.edu Abstract Can we leverage learning techniques... | 2007 | 16 |
3,192 | Iterative Non-linear Dimensionality Reduction by Manifold Sculpting Mike Gashler, Dan Ventura, and Tony Martinez ∗ Brigham Young University Provo, UT 84604 Abstract Many algorithms have been recently developed for reducing dimensionality by projecting data onto an intrinsic non-linear manifold. Unfortunat... | 2007 | 160 |
3,193 | Support Vector Machine Classification with Indefinite Kernels Ronny Luss ORFE, Princeton University Princeton, NJ 08544 rluss@princeton.edu Alexandre d’Aspremont ORFE, Princeton University Princeton, NJ 08544 aspremon@princeton.edu Abstract In this paper, we propose a method for support vector machi... | 2007 | 161 |
3,194 | Learning with Transformation Invariant Kernels Christian Walder Max Planck Institute for Biological Cybernetics 72076 T¨ubingen, Germany christian.walder@tuebingen.mpg.de Olivier Chapelle Yahoo! Research Santa Clara, CA chap@yahoo-inc.com Abstract This paper considers kernels invariant to translatio... | 2007 | 162 |
3,195 | A Probabilistic Model for Generating Realistic Lip Movements from Speech Gwenn Englebienne School of Computer Science University of Manchester ge@cs.man.ac.uk Tim F. Cootes Imaging Science and Biomedical Engineering University of Manchester Tim.Cootes@manchester.ac.uk Magnus Rattray School of Comp... | 2007 | 163 |
3,196 | Automatic Generation of Social Tags for Music Recommendation Douglas Eck∗ Sun Labs, Sun Microsystems Burlington, Mass, USA douglas.eck@umontreal.ca Paul Lamere Sun Labs, Sun Microsystems Burlington, Mass, USA paul.lamere@sun.com Thierry Bertin-Mahieux Sun Labs, Sun Microsystems Burlington, Mass,... | 2007 | 164 |
3,197 | Learning to classify complex patterns using a VLSI network of spiking neurons Srinjoy Mitra†, Giacomo Indiveri† and Stefano Fusi †∇ †Institute of Neuroinformatics, UZH|ETH, Zurich ∇Center for Theoretical Neuroscience, Columbia University, New York srinjoy|giacomo|fusi@ini.phys.ethz.ch Abstract We propose ... | 2007 | 165 |
3,198 | Efficient Convex Relaxation for Transductive Support Vector Machine Zenglin Xu Dept. of Computer Science & Engineering The Chinese University of Hong Kong Shatin, N.T., Hong Kong zlxu@cse.cuhk.edu.hk Rong Jin Dept. of Computer Science & Engineering Michigan State University East Lansing, MI, 48824 ... | 2007 | 166 |
3,199 | Message Passing for Max-weight Independent Set Sujay Sanghavi LIDS, MIT sanghavi@mit.edu Devavrat Shah Dept. of EECS, MIT devavrat@mit.edu Alan Willsky Dept. of EECS, MIT willsky@mit.edu Abstract We investigate the use of message-passing algorithms for the problem of finding the max-weight indepe... | 2007 | 167 |
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