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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2,700 | Reducing Spike Train Variability: A Computational Theory Of Spike-Timing Dependent Plasticity Sander M. Bohte1,2 S.M.Bohte@cwi.nl 1Dept. Software Engineering CWI, Amsterdam, The Netherlands Michael C. Mozer2 mozer@cs.colorado.edu 2Dept. of Computer Science University of Colorado, Boulder, USA Abst... | 2004 | 88 |
2,701 | Maximum Likelihood Estimation of Intrinsic Dimension Elizaveta Levina Department of Statistics University of Michigan Ann Arbor MI 48109-1092 elevina@umich.edu Peter J. Bickel Department of Statistics University of California Berkeley CA 94720-3860 bickel@stat.berkeley.edu Abstract We propose ... | 2004 | 89 |
2,702 | Synergies between Intrinsic and Synaptic Plasticity in Individual Model Neurons Jochen Triesch Dept. of Cognitive Science, UC San Diego, La Jolla, CA, 92093-0515, USA Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany triesch@ucsd.edu Abstract This paper explores the computational conseq... | 2004 | 9 |
2,703 | Distributed Information Regularization on Graphs Adrian Corduneanu CSAIL MIT Cambridge, MA 02139 adrianc@csail.mit.edu Tommi Jaakkola CSAIL MIT Cambridge, MA 02139 tommi@csail.mit.edu Abstract We provide a principle for semi-supervised learning based on optimizing the rate of communicating label... | 2004 | 90 |
2,704 | The Convergence of Contrastive Divergences Alan Yuille Department of Statistics University of California at Los Angeles Los Angeles, CA 90095 yuille@stat.ucla.edu Abstract This paper analyses the Contrastive Divergence algorithm for learning statistical parameters. We relate the algorithm to the stochas... | 2004 | 91 |
2,705 | Log-concavity results on Gaussian process methods for supervised and unsupervised learning Liam Paninski Gatsby Computational Neuroscience Unit University College London liam@gatsby.ucl.ac.uk http://www.gatsby.ucl.ac.uk/∼liam Abstract Log-concavity is an important property in the context of optimizati... | 2004 | 92 |
2,706 | Incremental Algorithms for Hierarchical Classification∗ Nicol`o Cesa-Bianchi Universit`a di Milano Milano, Italy Claudio Gentile Universit`a dell’Insubria Varese, Italy Andrea Tironi Luca Zaniboni Universit`a di Milano Crema, Italy Abstract We study the problem of hierarchical classification whe... | 2004 | 93 |
2,707 | On Semi-Supervised Classification Balaji Krishnapuram, David Williams, Ya Xue, Alex Hartemink, Lawrence Carin Duke University, USA M´ario A. T. Figueiredo Instituto de Telecomunicac¸˜oes, Instituto Superior T´ecnico, Portugal Abstract A graph-based prior is proposed for parametric semi-supervised classificati... | 2004 | 94 |
2,708 | Joint MRI Bias Removal Using Entropy Minimization Across Images Erik G. Learned-Miller Department of Computer Science University of Massachusetts, Amherst Amherst, MA 01003 Parvez Ahammad Division of Electrical Engineering University of California, Berkeley Berkeley, CA 94720 Abstract The correcti... | 2004 | 95 |
2,709 | Chemosensory processing in a spiking model of the olfactory bulb: chemotopic convergence and center surround inhibition Baranidharan Raman and Ricardo Gutierrez-Osuna Department of Computer Science Texas A&M University College Station, TX 77840 {barani,rgutier}@cs.tamu.edu Abstract ... | 2004 | 96 |
2,710 | Using Random Forests in the Structured Language Model Peng Xu and Frederick Jelinek Center for Language and Speech Processing Department of Electrical and Computer Engineering The Johns Hopkins University {xp,jelinek}@jhu.edu Abstract In this paper, we explore the use of Random Forests (RFs) in the stru... | 2004 | 97 |
2,711 | Exploration-Exploitation Tradeoffs for Experts Algorithms in Reactive Environments Daniela Pucci de Farias Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge, MA 02139 pucci@mit.edu Nimrod Megiddo IBM Almaden Research Center 650 Harry Road, K53-B2 San Jose, CA 95120... | 2004 | 98 |
2,712 | Unsupervised Variational Bayesian Learning of Nonlinear Models Antti Honkela and Harri Valpola Neural Networks Research Centre, Helsinki University of Technology P.O. Box 5400, FI-02015 HUT, Finland {Antti.Honkela, Harri.Valpola}@hut.fi http://www.cis.hut.fi/projects/bayes/ Abstract In this paper we pre... | 2004 | 99 |
2,713 | Bayesian Surprise Attracts Human Attention Laurent Itti Department of Computer Science University of Southern California Los Angeles, California 90089-2520, USA itti@usc.edu Pierre Baldi Department of Computer Science University of California, Irvine Irvine, California 92697-3425, USA pfbaldi@ics.uc... | 2005 | 1 |
2,714 | Convex Neural Networks Yoshua Bengio, Nicolas Le Roux, Pascal Vincent, Olivier Delalleau, Patrice Marcotte Dept. IRO, Universit´e de Montr´eal P.O. Box 6128, Downtown Branch, Montreal, H3C 3J7, Qc, Canada {bengioy,lerouxni,vincentp,delallea,marcotte}@iro.umontreal.ca Abstract Convexity has recently received... | 2005 | 10 |
2,715 | Efficient Estimation of OOMs Herbert Jaeger, Mingjie Zhao, Andreas Kolling International University Bremen Bremen, Germany h.jaeger|m.zhao|a.kolling@iu-bremen.de Abstract A standard method to obtain stochastic models for symbolic time series is to train state-emitting hidden Markov models (SE-HMMs) with th... | 2005 | 100 |
2,716 | AER Building Blocks for Multi-Layer Multi-Chip Neuromorphic Vision Systems R. Serrano-Gotarredona1, M. Oster2, P. Lichtsteiner2, A. Linares-Barranco4, R. PazVicente4, F. Gómez-Rodríguez4, H. Kolle Riis3, T. Delbrück2, S. C. Liu2, S. Zahnd2, A. M. Whatley2, R. Douglas2, P. Häfliger3, G. Jimenez-Moreno4, A. Civit4, ... | 2005 | 101 |
2,717 | A Hierarchical Compositional System for Rapid Object Detection Long Zhu and Alan Yuille Department of Statistics University of California at Los Angeles Los Angeles, CA 90095 {lzhu,yuille}@stat.ucla.edu Abstract We describe a hierarchical compositional system for detecting deformable objects in images. ... | 2005 | 102 |
2,718 | Conditional Visual Tracking in Kernel Space Cristian Sminchisescu1,2,3 Atul Kanujia3 Zhiguo Li3 Dimitris Metaxas3 1TTI-C, 1497 East 50th Street, Chicago, IL, 60637, USA 2University of Toronto, Department of Computer Science, Canada 3Rutgers University, Department of Computer Science, USA crismin@cs.toro... | 2005 | 103 |
2,719 | Fast Krylov Methods for N-Body Learning Nando de Freitas Department of Computer Science University of British Columbia nando@cs.ubc.ca Yang Wang School of Computing Science Simon Fraser University ywang12@cs.sfu.ca Maryam Mahdaviani Department of Computer Science University of British Columbia m... | 2005 | 104 |
2,720 | A Connectionist Model for Constructive Modal Reasoning Artur S. d’Avila Garcez Department of Computing, City University London London EC1V 0HB, UK aag@soi.city.ac.uk Lu´ıs C. Lamb Institute of Informatics, Federal University of Rio Grande do Sul Porto Alegre RS, 91501-970, Brazil LuisLamb@acm.org Do... | 2005 | 105 |
2,721 | Spiking Inputs to a Winner-take-all Network Matthias Oster and Shih-Chii Liu Institute of Neuroinformatics University of Zurich and ETH Zurich Winterthurerstrasse 190 CH-8057 Zurich, Switzerland {mao,shih}@ini.phys.ethz.ch Abstract Recurrent networks that perform a winner-take-all computation have bee... | 2005 | 106 |
2,722 | Estimation of Intrinsic Dimensionality Using High-Rate Vector Quantization Maxim Raginsky and Svetlana Lazebnik Beckman Institute, University of Illinois 405 N Mathews Ave, Urbana, IL 61801 {maxim,slazebni}@uiuc.edu Abstract We introduce a technique for dimensionality estimation based on the notion of qua... | 2005 | 107 |
2,723 | Generalization error bounds for classifiers trained with interdependent data Nicolas Usunier, Massih-Reza Amini, Patrick Gallinari Department of Computer Science, University of Paris VI 8, rue du Capitaine Scott, 75015 Paris France {usunier, amini, gallinari}@poleia.lip6.fr Abstract In this paper we propos... | 2005 | 108 |
2,724 | Bayesian model learning in human visual perception Gerg˝o Orb´an Collegium Budapest Institute for Advanced Study 2 Szenth´aroms´ag utca, Budapest, 1014 Hungary ogergo@colbud.hu J´ozsef Fiser Department of Psychology and Volen Center for Complex Systems Brandeis University Waltham, Massachusetts ... | 2005 | 109 |
2,725 | Temporal Abstraction in Temporal-difference Networks Richard S. Sutton, Eddie J. Rafols, Anna Koop Department of Computing Science University of Alberta Edmonton, AB, Canada T6G 2E8 {sutton,erafols,anna}@cs.ualberta.ca Abstract We present a generalization of temporal-difference networks to include tempo... | 2005 | 11 |
2,726 | Active Learning For Identifying Function Threshold Boundaries Brent Bryan Center for Automated Learning and Discovery Carnegie Mellon University Pittsburgh, PA 15213 bryanba@cs.cmu.edu Jeff Schneider Robotics Institute Carnegie Mellon University Pittsburgh, PA 15213 schneide@cs.cmu.edu Robert C.... | 2005 | 110 |
2,727 | Cue Integration for Figure/Ground Labeling Xiaofeng Ren, Charless C. Fowlkes and Jitendra Malik Computer Science Division, University of California, Berkeley, CA 94720 {xren,fowlkes,malik}@cs.berkeley.edu Abstract We present a model of edge and region grouping using a conditional random field built over a sc... | 2005 | 111 |
2,728 | Inferring Motor Programs from Images of Handwritten Digits Geoffrey Hinton and Vinod Nair Department of Computer Science, University of Toronto 10 King’s College Road, Toronto, M5S 3G5 Canada {hinton,vnair}@cs.toronto.edu Abstract We describe a generative model for handwritten digits that uses two pairs ... | 2005 | 112 |
2,729 | Dual-Tree Fast Gauss Transforms Dongryeol Lee Computer Science Carnegie Mellon Univ. dongryel@cmu.edu Alexander Gray Computer Science Carnegie Mellon Univ. agray@cs.cmu.edu Andrew Moore Computer Science Carnegie Mellon Univ. awm@cs.cmu.edu Abstract In previous work we presented an efficient a... | 2005 | 113 |
2,730 | Describing Visual Scenes using Transformed Dirichlet Processes Erik B. Sudderth, Antonio Torralba, William T. Freeman, and Alan S. Willsky Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology esuddert@mit.edu, torralba@csail.mit.edu, billf@mit.edu, willsky@mit.edu ... | 2005 | 114 |
2,731 | Worst-Case Bounds for Gaussian Process Models Sham M. Kakade University of Pennsylvania Matthias W. Seeger UC Berkeley Dean P. Foster University of Pennsylvania Abstract We present a competitive analysis of some non-parametric Bayesian algorithms in a worst-case online learning setting, where no probabi... | 2005 | 115 |
2,732 | Analyzing Auditory Neurons by Learning Distance Functions Inna Weiner1 Tomer Hertz1,2 Israel Nelken2,3 Daphna Weinshall1,2 1School of Computer Science and Engineering, 2The Center for Neural Computation, 3Department of Neurobiology, The Hebrew University of Jerusalem, Jerusalem, Israel, 91904 weinerin... | 2005 | 116 |
2,733 | Cyclic Equilibria in Markov Games Martin Zinkevich and Amy Greenwald Department of Computer Science Brown University Providence, RI 02912 {maz,amy}@cs.brown.edu Michael L. Littman Department of Computer Science Rutgers, The State University of NJ Piscataway, NJ 08854–8019 mlittman@cs.rutgers.edu A... | 2005 | 117 |
2,734 | The Information-Form Data Association Filter Brad Schumitsch, Sebastian Thrun, Gary Bradski, and Kunle Olukotun Stanford AI Lab Stanford University, Stanford, CA 94305 Abstract This paper presents a new filter for online data association problems in high-dimensional spaces. The key innovation is a representa... | 2005 | 118 |
2,735 | Consistency of one-class SVM and related algorithms R´egis Vert Laboratoire de Recherche en Informatique Universit´e Paris-Sud 91405, Orsay Cedex, France Masagroup 24 Bd de l’Hˆopital 75005, Paris, France Regis.Vert@lri.fr Jean-Philippe Vert Geostatistics Center Ecole des Mines de Paris - ParisT... | 2005 | 119 |
2,736 | Robust Fisher Discriminant Analysis Seung-Jean Kim Alessandro Magnani Stephen P. Boyd Information Systems Laboratory Electrical Engineering Department, Stanford University Stanford, CA 94305-9510 sjkim@stanford.edu alem@stanford.edu boyd@stanford.edu Abstract Fisher linear discriminant analysis (L... | 2005 | 12 |
2,737 | Nested sampling for Potts models Iain Murray Gatsby Computational Neuroscience Unit University College London i.murray@gatsby.ucl.ac.uk David J.C. MacKay Cavendish Laboratory University of Cambridge mackay@mrao.cam.ac.uk Zoubin Ghahramani Gatsby Computational Neuroscience Unit University College L... | 2005 | 120 |
2,738 | Correlated Topic Models David M. Blei Department of Computer Science Princeton University John D. Lafferty School of Computer Science Carnegie Mellon University Abstract Topic models, such as latent Dirichlet allocation (LDA), can be useful tools for the statistical analysis of document collections an... | 2005 | 121 |
2,739 | Learning in Silicon: Timing is Everything John V. Arthur and Kwabena Boahen Department of Bioengineering University of Pennsylvania Philadelphia, PA 19104 {jarthur, boahen}@seas.upenn.edu Abstract We describe a neuromorphic chip that uses binary synapses with spike timing-dependent plasticity (STDP) to ... | 2005 | 122 |
2,740 | Correcting sample selection bias in maximum entropy density estimation Miroslav Dud´ık, Robert E. Schapire Princeton University Department of Computer Science 35 Olden St, Princeton, NJ 08544 {mdudik,schapire}@princeton.edu Steven J. Phillips AT&T Labs −Research 180 Park Ave, Florham Park, NJ 07932 ... | 2005 | 123 |
2,741 | Rate Distortion Codes in Sensor Networks: A System-level Analysis Tatsuto Murayama and Peter Davis NTT Communication Science Laboratories Nippon Telegraph and Telephone Corporation “Keihanna Science City”, Kyoto 619-0237, Japan {murayama,davis}@cslab.kecl.ntt.co.jp Abstract This paper provides a system-... | 2005 | 124 |
2,742 | Beyond Pair-Based STDP: a Phenomenogical Rule for Spike Triplet and Frequency Effects Jean-Pascal Pfister and Wulfram Gerstner School of Computer and Communication Sciences and Brain-Mind Institute, Ecole Polytechnique F´ed´erale de Lausanne (EPFL), CH-1015 Lausanne {jean-pascal.pfister, wulfram.gerstner}@ep... | 2005 | 125 |
2,743 | Is Early Vision Optimized for Extracting Higher-order Dependencies? Yan Karklin yan+@cs.cmu.edu Michael S. Lewicki∗ lewicki@cnbc.cmu.edu Computer Science Department & Center for the Neural Basis of Cognition Carnegie Mellon University Abstract Linear implementations of the efficient coding hypothesis... | 2005 | 126 |
2,744 | An Approximate Inference Approach for the PCA Reconstruction Error Manfred Opper Electronics and Computer Science University of Southampton Southampton, SO17 1BJ mo@ecs.soton.ac.uk Abstract The problem of computing a resample estimate for the reconstruction error in PCA is reformulated as an inference... | 2005 | 127 |
2,745 | Active Bidirectional Coupling in a Cochlear Chip Bo Wen and Kwabena Boahen Department of Bioengineering University of Pennsylvania Philadelphia, PA 19104 {wenbo,boahen}@seas.upenn.edu Abstract We present a novel cochlear model implemented in analog very large scale integration (VLSI) technology that emu... | 2005 | 128 |
2,746 | Affine Structure From Sound Sebastian Thrun Stanford AI Lab Stanford University, Stanford, CA 94305 Email: thrun@stanford.edu Abstract We consider the problem of localizing a set of microphones together with a set of external acoustic events (e.g., hand claps), emitted at unknown times and unknown location... | 2005 | 129 |
2,747 | Measuring Shared Information and Coordinated Activity in Neuronal Networks Kristina Lisa Klinkner Statistics Department University of Michigan Ann Arbor, MI 48109 kshalizi@umich.edu Cosma Rohilla Shalizi Statistics Department Carnegie Mellon University Pittsburgh, PA 15213 cshalizi@stat.cmu.edu ... | 2005 | 13 |
2,748 | Distance Metric Learning for Large Margin Nearest Neighbor Classification Kilian Q. Weinberger, John Blitzer and Lawrence K. Saul Department of Computer and Information Science, University of Pennsylvania Levine Hall, 3330 Walnut Street, Philadelphia, PA 19104 {kilianw, blitzer, lsaul}@cis.upenn.edu Abstract... | 2005 | 130 |
2,749 | Estimating the “wrong” Markov random field: Benefits in the computation-limited setting Martin J. Wainwright Department of Statistics, and Department of Electrical Engineering and Computer Science UC Berkeley, Berkeley CA 94720 wainwrig@{stat,eecs}.berkeley.edu Abstract Consider the problem of joint param... | 2005 | 131 |
2,750 | Gaussian Processes for Multiuser Detection in CDMA receivers Juan Jos´e Murillo-Fuentes, Sebastian Caro Dept. Signal Theory and Communications University of Seville {murillo,scaro}@us.es Fernando P´erez-Cruz Gatsby Computational Neuroscience University College London fernando@gatsby.ucl.ac.uk Abstra... | 2005 | 132 |
2,751 | Metric Learning by Collapsing Classes Amir Globerson School of Computer Science and Engineering, Interdisciplinary Center for Neural Computation The Hebrew University Jerusalem, 91904, Israel gamir@cs.huji.ac.il Sam Roweis Machine Learning Group Department of Computer Science University of Toronto, Ca... | 2005 | 133 |
2,752 | Walk-Sum Interpretation and Analysis of Gaussian Belief Propagation Jason K. Johnson, Dmitry M. Malioutov and Alan S. Willsky Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge, MA 02139 {jasonj,dmm,willsky}@mit.edu Abstract This paper presents a ne... | 2005 | 134 |
2,753 | Analyzing Coupled Brain Sources: Distinguishing True from Spurious Interaction Guido Nolte1, Andreas Ziehe3, Frank Meinecke1 and Klaus-Robert M¨uller1,2 1 Fraunhofer FIRST.IDA, Kekul´estr. 7, 12489 Berlin, Germany 2 Dept. of CS, University of Potsdam, August-Bebel-Strasse 89, 14482 Potsdam, Germany 3 TU Berli... | 2005 | 135 |
2,754 | Group and Topic Discovery from Relations and Their Attributes Xuerui Wang, Natasha Mohanty, Andrew McCallum Department of Computer Science University of Massachusetts Amherst, MA 01003 {xuerui,nmohanty,mccallum}@cs.umass.edu Abstract We present a probabilistic generative model of entity relationships an... | 2005 | 136 |
2,755 | Factorial Switching Kalman Filters for Condition Monitoring in Neonatal Intensive Care Christopher K. I. Williams and John Quinn School of Informatics, University of Edinburgh Edinburgh EH1 2QL, UK c.k.i.williams@ed.ac.uk john.quinn@ed.ac.uk Neil McIntosh Simpson Centre for Reproductive Health, Edin... | 2005 | 137 |
2,756 | A Criterion for the Convergence of Learning with Spike Timing Dependent Plasticity Robert Legenstein and Wolfgang Maass Institute for Theoretical Computer Science Technische Universitaet Graz A-8010 Graz, Austria {legi,maass}@igi.tugraz.at Abstract We investigate under what conditions a neuron can learn... | 2005 | 138 |
2,757 | Q-Clustering Mukund Narasimhan† Nebojsa Jojic‡ Jeff Bilmes† †Dept of Electrical Engineering, University of Washington, Seattle WA ‡Microsoft Research, Microsoft Corporation, Redmond WA {mukundn,bilmes}@ee.washington.edu and jojic@microsoft.com Abstract We show that Queyranne’s algorithm for minimizing s... | 2005 | 139 |
2,758 | Computing the Solution Path for the Regularized Support Vector Regression Lacey Gunter Department of Statistics University of Michigan Ann Arbor, MI 48109 lgunter@umich.edu Ji Zhu∗ Department of Statistics University of Michigan Ann Arbor, MI 48109 jizhu@umich.edu Abstract In this paper we der... | 2005 | 14 |
2,759 | Generalization Error Bounds for Aggregation by Mirror Descent with Averaging Anatoli Juditsky Laboratoire de Mod´elisation et Calcul - Universit´e Grenoble I B.P. 53, 38041 Grenoble, France anatoli.iouditski@imag.fr Alexander Nazin Institute of Control Sciences - Russian Academy of Science 65, Profsoyuz... | 2005 | 140 |
2,760 | Asymptotics of Gaussian Regularized Least-Squares Ross A. Lippert M.I.T., Department of Mathematics 77 Massachusetts Avenue Cambridge, MA 02139-4307 lippert@math.mit.edu Ryan M. Rifkin Honda Research Institute USA, Inc. 145 Tremont Street Boston, MA 02111 rrifkin@honda-ri.com Abstract We consi... | 2005 | 141 |
2,761 | Large scale networks fingerprinting and visualization using the k-core decomposition J. Ignacio Alvarez-Hamelin∗ LPT (UMR du CNRS 8627), Universit´e de Paris-Sud, 91405 ORSAY Cedex France Ignacio.Alvarez-Hamelin@lri.fr Luca Dall’Asta LPT (UMR du CNRS 8627), Universit´e de Paris-Sud, 91405 ORSAY Cedex... | 2005 | 142 |
2,762 | Norepinephrine and Neural Interrupts Peter Dayan Angela J. Yu Gatsby Computational Neuroscience Unit Center for Brain, Mind & Behavior University College London Green Hall, Princeton University 17 Queen Square, London WC1N 3AR, UK Princeton, NJ 08540, USA dayan@gatsby.ucl.ac.uk ajyu@princeton.edu ... | 2005 | 143 |
2,763 | Consensus Propagation Ciamac C. Moallemi Stanford University Stanford, CA 95014 USA ciamac@stanford.edu Benjamin Van Roy Stanford University Stanford, CA 95014 USA bvr@stanford.edu Abstract We propose consensus propagation, an asynchronous distributed protocol for averaging numbers across a network.... | 2005 | 144 |
2,764 | CMOL CrossNets: Possible Neuromorphic Nanoelectronic Circuits Jung Hoon Lee Xiaolong Ma Konstantin K. Likharev Stony Brook University Stony Brook, NY 11794-3800 klikharev@notes.cc.sunysb.edu Abstract Hybrid “CMOL” integrated circuits, combining... | 2005 | 145 |
2,765 | A General and Efficient Multiple Kernel Learning Algorithm S¨oren Sonnenburg∗ Fraunhofer FIRST Kekul´estr. 7 12489 Berlin Germany sonne@first.fhg.de Gunnar R¨atsch Friedrich Miescher Lab Max Planck Society Spemannstr. 39 T¨ubingen, Germany raetsch@tue.mpg.de Christin Sch¨afer Fraunhofer FIR... | 2005 | 146 |
2,766 | Stimulus Evoked Independent Factor Analysis of MEG Data with Large Background Activity S.S. Nagarajan Biomagnetic Imaging Laboratory Department of Radiology University of California, San Francisco San Francisco, CA 94122 sri@radiology.ucsf.edu H.T. Attias Golden Metallic, Inc. P.O. Box 475608 San ... | 2005 | 147 |
2,767 | Augmented Rescorla-Wagner and Maximum Likelihood estimation. Alan Yuille Department of Statistics University of California at Los Angeles Los Angeles, CA 90095 yuille@stat.ucla.edu Abstract We show that linear generalizations of Rescorla-Wagner can perform Maximum Likelihood estimation of the paramete... | 2005 | 148 |
2,768 | Laplacian Score for Feature Selection Xiaofei He1 Deng Cai2 Partha Niyogi1 1 Department of Computer Science, University of Chicago {xiaofei, niyogi}@cs.uchicago.edu 2 Department of Computer Science, University of Illinois at Urbana-Champaign dengcai2@uiuc.edu Abstract In supervised learning scenarios,... | 2005 | 149 |
2,769 | Soft Clustering on Graphs Kai Yu1, Shipeng Yu2, Volker Tresp1 1Siemens AG, Corporate Technology 2Institute for Computer Science, University of Munich kai.yu@siemens.com, volker.tresp@siemens.com spyu@dbs.informatik.uni-muenchen.de Abstract We propose a simple clustering framework on graphs encoding pairwi... | 2005 | 15 |
2,770 | Variational Bayesian Stochastic Complexity of Mixture Models Kazuho Watanabe∗ Department of Computational Intelligence and Systems Science Tokyo Institute of Technology Mail Box:R2-5, 4259 Nagatsuta, Midori-ku, Yokohama, 226-8503, Japan kazuho23@pi.titech.ac.jp Sumio Watanabe P& I Lab. Tokyo Insti... | 2005 | 150 |
2,771 | Hierarchical Linear/Constant Time SLAM Using Particle Filters for Dense Maps Austin I. Eliazar Ronald Parr Department of Computer Science Duke University Durham, NC 27708 {eliazar,parr}@cs.duke.edu Abstract We present an improvement to the DP-SLAM algorithm for simultaneous localization and mapping (S... | 2005 | 151 |
2,772 | An exploration-exploitation model based on norepinepherine and dopamine activity Samuel M. McClure*, Mark S. Gilzenrat, and Jonathan D. Cohen Center for the Study of Brain, Mind, and Behavior Princeton University Princeton, NJ 08544 smcclure@princeton.edu; mgilzen@princeton.edu; jdc@princet... | 2005 | 152 |
2,773 | Kernels for gene regulatory regions Jean-Philippe Vert Geostatistics Center Ecole des Mines de Paris - ParisTech Jean-Philippe.Vert@ensmp.fr Robert Thurman Division of Medical Genetics University of Washington rthurman@u.washington.edu William Stafford Noble Department of Genome Sciences Universit... | 2005 | 153 |
2,774 | Transfer learning for text classification Chuong B. Do Computer Science Department Stanford University Stanford, CA 94305 Andrew Y. Ng Computer Science Department Stanford University Stanford, CA 94305 Abstract Linear text classification algorithms work by computing an inner product between a test doc... | 2005 | 154 |
2,775 | The Forgetron: A Kernel-Based Perceptron on a Fixed Budget Ofer Dekel Shai Shalev-Shwartz Yoram Singer School of Computer Science & Engineering The Hebrew University, Jerusalem 91904, Israel {oferd,shais,singer}@cs.huji.ac.il Abstract The Perceptron algorithm, despite its simplicity, often performs we... | 2005 | 155 |
2,776 | Online Discovery and Learning of Predictive State Representations Peter McCracken Department of Computing Science University of Alberta Edmonton, Alberta Canada, T6G 2E8 peterm@cs.ualberta.ca Michael Bowling Department of Computing Science University of Alberta Edmonton, Alberta Canada, T6G 2E8 ... | 2005 | 156 |
2,777 | Hyperparameter and Kernel Learning for Graph Based Semi-Supervised Classification Ashish Kapoor†, Yuan (Alan) Qi‡, Hyungil Ahn† and Rosalind W. Picard† †MIT Media Laboratory, Cambridge, MA 02139 {kapoor, hiahn, picard}@media.mit.edu ‡MIT CSAIL, Cambridge, MA 02139 alanqi@csail.mit.edu Abstract There have... | 2005 | 157 |
2,778 | Fusion of Similarity Data in Clustering Tilman Lange and Joachim M. Buhmann (langet,jbuhmann)@inf.ethz.ch Institute of Computational Science, Dept. of Computer Sience, ETH Zurich, Switzerland Abstract Fusing multiple information sources can yield significant benefits to successfully accomplish learning tasks.... | 2005 | 158 |
2,779 | Sensory Adaptation within a Bayesian Framework for Perception Alan A. Stocker∗and Eero P. Simoncelli Howard Hughes Medical Institute and Center for Neural Science New York University Abstract We extend a previously developed Bayesian framework for perception to account for sensory adaptation. We first no... | 2005 | 159 |
2,780 | Efficient estimation of hidden state dynamics from spike trains M´arton G. Dan´oczy Inst. for Theoretical Biology Humboldt University, Berlin Invalidenstr. 43 10115 Berlin, Germany m.danoczy@biologie.hu-berlin.de Richard H. R. Hahnloser Inst. for Neuroinformatics UNIZH / ETHZ Winterthurerstrasse 19... | 2005 | 16 |
2,781 | Radial Basis Function Network for Multi-task Learning Xuejun Liao Department of ECE Duke University Durham, NC 27708-0291, USA xjliao@ee.duke.edu Lawrence Carin Department of ECE Duke University Durham, NC 27708-0291, USA lcarin@ee.duke.edu Abstract We extend radial basis function (RBF) networ... | 2005 | 160 |
2,782 | Prediction and Change Detection Mark Steyvers Scott Brown msteyver@uci.edu scottb@uci.edu University of California, Irvine University of California, Irvine Irvine, CA 92697 Irvine, CA 92697 Abstract We measure the ability of human observers to predict the next datum ... | 2005 | 161 |
2,783 | Gaussian Process Dynamical Models Jack M. Wang, David J. Fleet, Aaron Hertzmann Department of Computer Science University of Toronto, Toronto, ON M5S 3G4 {jmwang,hertzman}@dgp.toronto.edu, fleet@cs.toronto.edu Abstract This paper introduces Gaussian Process Dynamical Models (GPDM) for nonlinear time serie... | 2005 | 162 |
2,784 | Rodeo: Sparse Nonparametric Regression in High Dimensions John Lafferty School of Computer Science Carnegie Mellon University Larry Wasserman Department of Statistics Carnegie Mellon University Abstract We present a method for nonparametric regression that performs bandwidth selection and variable sel... | 2005 | 163 |
2,785 | Pattern Recognition from One Example by Chopping Franc¸ois Fleuret CVLAB/LCN – EPFL Lausanne, Switzerland francois.fleuret@epfl.ch Gilles Blanchard∗ Fraunhofer FIRST Berlin, Germany blanchar@first.fhg.de Abstract We investigate the learning of the appearance of an object from a single image of i... | 2005 | 164 |
2,786 | Hot Coupling: A Particle Approach to Inference and Normalization on Pairwise Undirected Graphs of Arbitrary Topology Firas Hamze Nando de Freitas Department of Computer Science University of British Columbia Abstract This paper presents a new sampling algorithm for approximating functions of variables r... | 2005 | 165 |
2,787 | Separation of Music Signals by Harmonic Structure Modeling Yun-Gang Zhang Department of Automation Tsinghua University Beijing 100084, China zyg00@mails.tsinghua.edu.cn Chang-Shui Zhang Department of Automation Tsinghua University Beijing 100084, China zcs@mail.tsinghua.edu.cn Abstract Separat... | 2005 | 166 |
2,788 | Learning Minimum Volume Sets Clayton Scott Statistics Department Rice University Houston, TX 77005 cscott@rice.edu Robert Nowak Electrical and Computer Engineering University of Wisconsin Madison, WI 53706 nowak@engr.wisc.edu Abstract Given a probability measure P and a reference measure µ, one ... | 2005 | 167 |
2,789 | Spectral Bounds for Sparse PCA: Exact and Greedy Algorithms Baback Moghaddam MERL Cambridge MA, USA baback@merl.com Yair Weiss Hebrew University Jerusalem, Israel yweiss@cs.huji.ac.il Shai Avidan MERL Cambridge MA, USA avidan@merl.com Abstract Sparse PCA seeks approximate sparse “eigenvect... | 2005 | 168 |
2,790 | A Domain Decomposition Method for Fast Manifold Learning Zhenyue Zhang Department of Mathematics Zhejiang University, Yuquan Campus, Hangzhou, 310027, P. R. China zyzhang@zju.edu.cn Hongyuan Zha Department of Computer Science Pennsylvania State University University Park, PA 16802 zha@cse.psu.edu ... | 2005 | 169 |
2,791 | From Batch to Transductive Online Learning Sham Kakade Toyota Technological Institute Chicago, IL 60637 sham@tti-c.org Adam Tauman Kalai Toyota Technological Institute Chicago, IL 60637 kalai@tti-c.org Abstract It is well-known that everything that is learnable in the difficult online setting, wher... | 2005 | 17 |
2,792 | Generalized Nonnegative Matrix Approximations with Bregman Divergences Inderjit S. Dhillon Suvrit Sra Dept. of Computer Sciences The Univ. of Texas at Austin Austin, TX 78712. {inderjit,suvrit}@cs.utexas.edu Abstract Nonnegative matrix approximation (NNMA) is a recent technique for dimensionality redu... | 2005 | 170 |
2,793 | Predicting EMG Data from M1 Neurons with Variational Bayesian Least Squares Jo-Anne Ting1, Aaron D’Souza1 Kenji Yamamoto3, Toshinori Yoshioka2 , Donna Hoffman3 Shinji Kakei4, Lauren Sergio6, John Kalaska5 Mitsuo Kawato2, Peter Strick3, Stefan Schaal1,2 1Comp. Science & Neuroscience, U.of S. California, Los A... | 2005 | 171 |
2,794 | Comparing the Effects of Different Weight Distributions on Finding Sparse Representations David Wipf and Bhaskar Rao ∗ Department of Electrical and Computer Engineering University of California, San Diego, CA 92093 dwipf@ucsd.edu, brao@ece.ucsd.edu Abstract Given a redundant dictionary of basis vectors ... | 2005 | 172 |
2,795 | Goal-Based Imitation as Probabilistic Inference over Graphical Models Deepak Verma Deptt of CSE, Univ. of Washington, Seattle WA- 98195-2350 deepak@cs.washington.edu Rajesh P. N. Rao Deptt of CSE, Univ. of Washington, Seattle WA- 98195-2350 rao@cs.washington.edu Abstract Humans are extremely adept... | 2005 | 173 |
2,796 | Policy-Gradient Methods for Planning Douglas Aberdeen Statistical Machine Learning, National ICT Australia, Canberra doug.aberdeen@anu.edu.au Abstract Probabilistic temporal planning attempts to find good policies for acting in domains with concurrent durative tasks, multiple uncertain outcomes, and limite... | 2005 | 174 |
2,797 | Message passing for task redistribution on sparse graphs K. Y. Michael Wong Hong Kong U. of Science & Technology Clear Water Bay, Hong Kong, China phkywong@ust.hk David Saad NCRG, Aston University Birmingham B4 7ET, UK D.Saad@aston.ac.uk Zhuo Gao Hong Kong U. of Science & Technology, Clear Water B... | 2005 | 175 |
2,798 | Neuronal Fiber Delineation in Area of Edema from Diffusion Weighted MRI Ofer Pasternak∗ School of Computer Science Tel-Aviv University Tel-Aviv, ISRAEL 69978 oferpas@post.tau.ac.il Nir Sochen Department of Applied Mathematics Tel-Aviv University sochen@post.tau.ac.il Nathan Intrator School of Co... | 2005 | 176 |
2,799 | A Computational Model of Eye Movements during Object Class Detection Wei Zhang† Hyejin Yang‡∗ Dimitris Samaras† Gregory J. Zelinsky†‡ Dept. of Computer Science† Dept. of Psychology‡ State University of New York at Stony Brook Stony Brook, NY 11794 {wzhang,samaras}@cs.sunysb.edu† hjyang@ic.sunysb.e... | 2005 | 177 |
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