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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1,800 | The Missing Link - A Probabilistic Model of Document Content and Hypertext Connectivity David Cohn Burning Glass Technologies 201 South Craig St, Suite 2W Pittsburgh, PA 15213 david. cohn @burning-glass.com Thomas Hofmann Department of Computer Science Brown University Providence, RI 02192... | 2000 | 142 |
1,801 | NIPS '00 The Use of Classifiers in Sequential Inference Vasin Punyakanok Dan Roth Department of Computer Science University of Illinois at Urbana-Champaign Urbana, IL 61801 punyakan@cs.uiuc.edu danr@cs.uiuc.edu Abstract We study the problem of combining the outcomes of several different ... | 2000 | 143 |
1,802 | Finding the Key to a Synapse Thomas Natschlager & Wolfgang Maass Institute for Theoretical Computer Science Technische Universitat Graz, Austria {tnatschl, maass}@igi.tu-graz.ac.at Abstract Experimental data have shown that synapses are heterogeneous: different synapses respond with different seque... | 2000 | 144 |
1,803 | The Unscented Particle Filter Rudolph van der Merwe Oregon Graduate Institute Electrical and Computer Engineering P.O. Box 91000,Portland,OR 97006, USA rvdmerwe@ece.ogi.edu N ando de Freitas Arnaud Doucet Cambridge University Engineering Department Cambridge CB2 1PZ, England ad2@eng.cam... | 2000 | 145 |
1,804 | Algorithms for Non-negative Matrix Factorization Daniel D. Lee* *BelJ Laboratories Lucent Technologies Murray Hill, NJ 07974 H. Sebastian Seung*t tDept. of Brain and Cog. Sci. Massachusetts Institute of Technology Cambridge, MA 02138 Abstract Non-negative matrix factorization (NMF) has ... | 2000 | 146 |
1,805 | Divisive and Subtractive Mask Effects: Linking Psychophysics and Biophysics Barbara Zenger Division of Biology Caltech 139-74 Pasadena, CA 91125 barbara@klab.caltech. edu Christof Koch Computation and Neural Systems Caltech 139-74 Pasadena, CA 91125 koch@klab.caltech.edu Abstract ... | 2000 | 147 |
1,806 | Factored Semi-Tied Covariance Matrices M.J.F. Gales Cambridge University Engineering Department Trumpington Street, Cambridge. CB2 IPZ United Kingdom mjfg@eng.cam.ac.uk Abstract A new form of covariance modelling for Gaussian mixture models and hidden Markov models is presented. This is an exten... | 2000 | 148 |
1,807 | Noise suppression based on neurophysiologically-motivated SNR estimation for robust speech recognition J iirgen Tcharz Medical Physics Group Oldenburg University 26111 Oldenburg Germany tch@medi.physik.uni-oldenburg.de Michael Kleinschmidt Medical Physics Group Oldenburg University 2... | 2000 | 149 |
1,808 | Learning Joint Statistical Models for Audio-Visual Fusion and Segregation John W. Fisher 111* Massachusetts Institute of Technology Cambridge, MA 02139 fisher@ai.mit.edu William T. Freeman Mitsubishi Electric Research Laboratory Cambridge, MA 02139 jreeman@merl.com Trevor Darrell Massac... | 2000 | 15 |
1,809 | Dendritic compartmentalization could underlie competition and attentional biasing of simultaneous visual stimuli Kevin A. Archie Neuroscience Program University of Southern California Los Angeles, CA 90089-2520 Bartlett W. Mel Department of Biomedical Engineering University of Southern Califo... | 2000 | 150 |
1,810 | Smart Vision Chip Fabricated Using Three Dimensional Integration Technology H.Kurino, M.Nakagawa, K.W.Lee, T.Nakamura, Y.Yamada, K.T.Park and M.Koyanagi Dept. of Machine Intelligence and Systems Engineering, Tohoku University 01, Aza-Aramaki, Aoba-ku, Sendai 980-8579, Japan kurino@sd.mech.toh... | 2000 | 151 |
1,811 | A productive, systematic framework for the representation of visual structure Shimon Edelman 232 Uris Hall, Dept. of Psychology Cornell University Ithaca, NY 14853-7601 se37@cornell.edu Nathan Intrator Institute for Brain and Neural Systems Box 1843, Brown University Providence, RI 02912 ... | 2000 | 152 |
1,812 | Convergence of Large Margin Separable Linear Classification Tong Zhang Mathematical Sciences Department IBM TJ. Watson Research Center Yorktown Heights, NY 10598 tzhang@watson.ibm.com Abstract Large margin linear classification methods have been successfully applied to many applications. For a l... | 2000 | 16 |
1,813 | Emergence of movement sensitive neurons' properties by learning a sparse code for natural moving images Rafal Bogacz Dept. of Computer Science University of Bristol Bristol BS8 lUB, U.K. R.Bogacz@bri.l'fol.ac.uk Malcolm W. Brown Dept. of Anatomy University of Bristol Bristol BS8 lTD, U.... | 2000 | 17 |
1,814 | High-temperature expansions for learning models of nonnegative data Oliver B. Downs Dept. of Mathematics Princeton University Princeton, NJ 08544 obdown s@p r incet on.edu Abstract Recent work has exploited boundedness of data in the unsupervised learning of new types of generative model. For... | 2000 | 18 |
1,815 | A Support Vector Method for Clustering AsaBen-Hur Faculty of IE and Management Technion, Haifa 32000, Israel Hava T. Siegelmann Lab for Inf. & Decision Systems MIT Cambridge, MA 02139, USA David Horn School of Physics and Astronomy Tel Aviv University, Tel Aviv 69978, Israel Vladimir Vapni... | 2000 | 19 |
1,816 | Redundancy and Dimensionality Reduction in Sparse-Distributed Representations of Natural Objects in Terms of Their Local Features Penio S. Penev* Laboratory of Computational Neuroscience The Rockefeller University 1230 York Avenue, New York, NY 10021 penev@rockefeller.edu http://venezia.rockefeller... | 2000 | 2 |
1,817 | Incremental and Decremental Support Vector Machine Learning Gert Cauwenberghs* CLSP, ECE Dept. Johns Hopkins University Baltimore, MD 21218 gert@jhu.edu Tomaso Poggio CBCL, BCS Dept. Massachusetts Institute of Technology Cambridge, MA 02142 tp@ai.mit.edu Abstract An on-line recurs... | 2000 | 20 |
1,818 | Active inference in concept learning Jonathan D. Nelson Department of Cogniti ve Science University of California, San Diego La Jolla, CA 92093-0515 jnelson@cogsci.ucsd.edu Abstract Javier R. Movellan Department of Cognitive Science University of California, San Diego La Jolla, CA 92093-05... | 2000 | 21 |
1,819 | The Interplay of Symbolic and Subsymbolic Processes in Anagram Problem Solving David B. Grimes and Michael C. Mozer Department of Computer Science and Institute of Cognitive Science University of Colorado, Boulder, CO 80309-0430 USA {grimes ,mo zer}@c s .co l orado .edu Abstract Although connect... | 2000 | 22 |
1,820 |
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&% ')( * + ! ,.-0/21 3547678:9;-=</?>)@;AB@DCE1 35FGFH1 3JILKM4$IONQPSR IT676B-=1 35I:4VU=<T<TWX<T/ YZ\[]_^ `a]cbO]_deGfgihij:k+lT]m^ `and$k2Z:j!opd7bOgqk2rQstfuvlObO]mk]m^ ... | 2000 | 23 |
1,821 | Sex with Support Vector Machines Baback Moghaddam Mitsubishi Electric Research Laboratory Cambridge MA 02139, USA baback<amerl.com Ming-Hsuan Yang University of Illinois at Urbana-Champaign Urbana, IL 61801 USA mhyang<avision.ai.uiuc.edu Abstract Nonlinear Support Vector Machines (SVMs) ar... | 2000 | 24 |
1,822 | Recognizing Hand-written Digits Using Hierarchical Products of Experts Guy Mayraz & Geoffrey E. Hinton Gatsby Computational Neuroscience Unit University College London 17 Queen Square, London WCIN 3AR, u.K. Abstract The product of experts learning procedure [1] can discover a set of stochastic b... | 2000 | 25 |
1,823 | APRICODD: Approximate Policy Construction using Decision Diagrams Robert St-Aubin Dept. of Computer Science University of British Columbia Vancouver, BC V6T lZA staubin@cs.ubc.ca Jesse Hoey Dept. of Computer Science University of British Columbia Vancouver, BC V6T lZA jhoey@cs.ubc.ca ... | 2000 | 26 |
1,824 | Four-Iegged Walking Gait Control Using a Neuromorphic Chip Interfaced to a Support Vector Learning Algorithm Susanne Still NEC Research Institute 4 Independence Way, Princeton NJ 08540, USA sasa@research.nj.nec.com Klaus Hepp Institute of Theoretical Physics ETH Zurich, Switzerland Abstrac... | 2000 | 27 |
1,825 | A Mathematical Programming Approach to the Kernel Fisher Algorithm Sebastian Mika*, Gunnar Ratsch*, and Klaus-Robert Miiller*+ *GMD FIRST.lDA, KekulestraBe 7, 12489 Berlin, Germany +University of Potsdam, Am Neuen Palais 10, 14469 Potsdam {mika, raetsch, klaus}@jirst.gmd.de Abstract We investigate ... | 2000 | 28 |
1,826 | Using Free Energies to Represent Q-values in a Multiagent Reinforcement Learning Task Brian Sallans Department of Computer Science University of Toronto Toronto M5S 2Z9 Canada sallam'@cs,toronto,edu Geoffrey E. Hinton Gatsby Computational Neuroscience Unit University College London London ... | 2000 | 29 |
1,827 | Who Does What? A Novel Algorithm to Determine Function Localization Ranit Aharonov-Barki Interdisciplinary Center for Neural Computation The Hebrew University, Jerusalem 91904, Israel ranit@alice.nc.huji.ac.il Isaac Meilijson and Eytan Ruppin School of Mathematical Sciences Tel-Aviv University, ... | 2000 | 3 |
1,828 | Dopamine Bonuses Sham Kakade Peter Dayan Gatsby Computational Neuroscience Unit 17 Queen Square, London, England, WC1N 3AR. sham@gat sby.ucl. ac . uk daya n@gat sby.uc l. ac .uk Abstract Substantial data support a temporal difference (TO) model of dopamine (OA) neuron activity in which the ce... | 2000 | 30 |
1,829 | Sparse Greedy Gaussian Process Regression Alex J. Smola· RSISE and Department of Engineering Australian National University Canberra, ACT, 0200 Alex.Smola@anu.edu.au Abstract Peter Bartlett RSISE Australian National University Canberra, ACT, 0200 Peter.Bartlett@anu.edu.au We prese... | 2000 | 31 |
1,830 | Large Scale Bayes Point Machines Ralf Herbrich Statistics Research Group Computer Science Department Technical University of Berlin ralfh@cs.tu-berlin.de Thore Graepel Statistics Research Group Computer Science Department Technical University of Berlin guru@cs.tu-berlin.de Abstract T... | 2000 | 32 |
1,831 | Efficient Learning of Linear Perceptrons Shai Ben-David Department of Computer Science Technion Haifa 32000, Israel shai~cs.technion.ac.il Hans Ulrich Simon Fakultat fur Mathematik Ruhr Universitat Bochum D-44780 Bochum, Germany simon~lmi.ruhr-uni-bochum.de Abstract We consider the e... | 2000 | 33 |
1,832 | Gaussianization Scott Shaobing Chen Renaissance Technologies East Setauket, NY 11733 schen@rentec.com Ramesh A. Gopinath IBM TJ. Watson Research Center Yorktown Heights, NY 10598 rameshg@us.ibm.com Abstract High dimensional data modeling is difficult mainly because the so-called "curse ... | 2000 | 34 |
1,833 | Error-correcting Codes on a Bethe-like Lattice Renato Vicente David Saad The Neural Computing Research Group Aston University, Birmingham, B4 7ET, United Kingdom {vicenter,saadd}@aston.ac.uk Yoshiyuki Kabashima Department of Computational Intelligence and Systems Science Tokyo Institute of Techn... | 2000 | 35 |
1,834 | Homeostasis in a Silicon Integrate and Fire Neuron Shih-Chii LiD Institute for Neuroinformatics, ETHIVNIZ Winterthurstrasse 190, CH-8057 Zurich Switzerland shih@ini.phys.ethz.ch Bradley A. Minch School of Electrical and Computer Engineering Cornell University Ithaca, NY 14853-5401, U.S.A. ... | 2000 | 36 |
1,835 | Incorporating Second-Order Functional Knowledge for Better Option Pricing Charles Dugas, Yoshua Bengio, Fran~ois Belisle, Claude Nadeau:Rene Garcia CIRANO, Montreal, Qc, Canada H3A 2A5 {dugas ,bengi oy,beli s lfr ,na deau c}@i ro .umontr e a l. ca garc i ar@ci rano .qc . ca Abstract Incorporating p... | 2000 | 37 |
1,836 | Spike-Timing-Dependent Learning for Oscillatory Networks Silvia Scarp etta Dept. of Physics "E.R. Caianiello" Salerno University 84081 (SA) Italy and INFM, Sezione di Salerno Italy scarpetta@na. infn. it Zhaoping Li Gatsby Compo Neurosci. Unit University College, London, WCIN 3AR United Ki... | 2000 | 38 |
1,837 | On Reversing Jensen's Inequality Tony Jebara MIT Media Lab Cambridge, MA 02139 jebam@media.mit.edu Abstract Alex Pentland MIT Media Lab Cambridge, MA 02139 sandy@media.mit.edu Jensen's inequality is a powerful mathematical tool and one of the workhorses in statistical learning. Its appl... | 2000 | 39 |
1,838 | Overfitting in Neural Nets: Backpropagation, Conjugate Gradient, and Early Stopping Rich Caruana CALD,CMU 5000 Forbes Ave. Pittsburgh, PA 15213 caruana@cs.cmu.edu Steve Lawrence NEC Research Institute 4 Independence Way Princeton, NJ 08540 lawrence@ research. nj. nec. com Abstract ... | 2000 | 4 |
1,839 | From Mixtures of Mixtures to Adaptive Transform Coding Cynthia Archer and Todd K. Leen Department of Computer Science and Engineering Oregon Graduate Institute of Science & Technology 20000 N.W. Walker Rd, Beaverton, OR 97006-1000 E-mail: archer, tleen@cse.ogi.edu Abstract We establish a princip... | 2000 | 40 |
1,840 | Algorithmic Stability and Generalization Performance Olivier Bousquet CMAP Ecole Polytechnique F-91128 Palaiseau cedex FRANCE bousquet@cmapx.polytechnique·fr Andre Elisseeff'" Barnhill Technologies 6709 Waters A venue Savannah, GA 31406 USA andre@barnhilltechnologies.com Abstra... | 2000 | 41 |
1,841 | The Kernel Trick for Distances Bernhard SchOikopf Microsoft Research 1 Guildhall Street Cambridge, UK bs@kyb.tuebingen.mpg.de Abstract A method is described which, like the kernel trick in support vector machines (SVMs), lets us generalize distance-based algorithms to operate in feature spaces, ... | 2000 | 42 |
1,842 | Higher-order Statistical Properties Arising from the Non-stationarity of Natural Signals Lucas Parra, Clay Spence Adaptive Signal and Image Processing, Sarnoff Corporation {lparra, cspence} @sarnofJ. com Paul Sajda Department of Biomedical Engineering, Columbia University ps629@columbia. edu ... | 2000 | 43 |
1,843 | Probabilistic Semantic Video Indexing Milind R. Naphade, Igor Kozintsev and Thomas Huang Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign {milind, igor,huang}@ifp.uiuc.edu Abstract We propose a novel probabilistic framework for semantic video indexing. We de... | 2000 | 44 |
1,844 | Accumulator networks: Suitors of local probability propagation Brendan J. Frey and Anitha Kannan Intelligent Algorithms Lab, University of Toronto, www. cs. toronto. edu/ "-'frey Abstract One way to approximate inference in richly-connected graphical models is to apply the sum-product algorithm (a.k.a... | 2000 | 45 |
1,845 | Sparse Representation for Gaussian Process Models Lehel Csat6 and Manfred Opper Neural Computing Research Group School of Engineering and Applied Sciences B4 7ET Birmingham, United Kingdom {csat ol, opper m} @as t on. ac .uk Abstract We develop an approach for a sparse representation for G... | 2000 | 46 |
1,846 | Hippocampally-Dependent Consolidation in a Hierarchical Model of Neocortex Szabolcs Ka1i1,2 Peter Dayan1 1 Gatsby Computational Neuroscience Unit University College London 17 Queen Square, London, England, WCIN 3AR. 2Department of Brain and Cognitive Sciences Massachusetts Institute of Technolog... | 2000 | 47 |
1,847 | Automated State Abstraction for Options using the U-Tree Algorithm Anders Jonsson, Andrew G. Barto Department of Computer Science University of Massachusetts Amherst, MA 01003 {ajonsson,barto}@cs.umass.edu Abstract Learning a complex task can be significantly facilitated by defining a hierarc... | 2000 | 48 |
1,848 | Structure learning in human causal induction Joshua B. Tenenbaum & Thomas L. Griffiths Department of Psychology Stanford University, Stanford, CA 94305 {jbt,gruffydd}@psych.stanford .edu Abstract We use graphical models to explore the question of how people learn simple causal relationships from data.... | 2000 | 49 |
1,849 | Color Opponency Constitutes A Sparse Representation For the Chromatic Structure of Natural Scenes Te-Won Lee; Thomas Wachtler and Terrence Sejnowski Institute for Neural Computation, University of California, San Diego & Computational Neurobiology Laboratory, The Salk Institute 10010 N. Torrey Pines R... | 2000 | 5 |
1,850 | Minimum Bayes Error Feature Selection for Continuous Speech Recognition George Saon and Mukund Padmanabhan IBM T. 1. Watson Research Center, Yorktown Heights, NY, 10598 E-mail: {saon.mukund}@watson.ibm.com. Phone: (914)-945-2985 Abstract We consider the problem of designing a linear transformation () ... | 2000 | 50 |
1,851 | Bayesian video shot segmentation Nuno Vasconcelos Andrew Lippman MIT Media Laboratory, 20 Ames St, E15-354, Cambridge, MA 02139, {nuno,lip}@media.mit.edu, http://www.media.mit.edurnuno Abstract Prior knowledge about video structure can be used both as a means to improve the peiformance of conten... | 2000 | 51 |
1,852 | Kernel expansions with unlabeled examples Martin Szummer MIT AI Lab & CBCL Cambridge, MA szummer@ai.mit.edu Abstract Tommi Jaakkola MIT AI Lab Cambridge, MA tommi@ai.mit.edu Modern classification applications necessitate supplementing the few available labeled examples with unlabeled ex... | 2000 | 52 |
1,853 | Universality and individuality in a neural code Elad Schneidman,1,2 Naama Brenner,3 Naftali Tishby,1,3 Rob R. de Ruyter van Steveninck,3 William Bialek3 ISchool of Computer Science and Engineering, Center for Neural Computation and 2Department of Neurobiology, Hebrew University, Jerusalem 91904, Israe... | 2000 | 53 |
1,854 | Generalized Belief Propagation Jonathan S. Yedidia MERL 201 Broadway Cambridge, MA 02139 Phone: 617-621-7544 yedidia@merl.com William T. Freeman MERL 201 Broadway Cambridge, MA 02139 Phone: 617-621-7527 freema n@merl.com Abstract Yair Weiss Computer Science Division UC Be... | 2000 | 54 |
1,855 | Multiple times cales of adaptation in a neural code Adrienne L. Fairhall, Geoffrey D. Lewen, William Bialek, and Robert R. de Ruyter van Steveninck NEe Research Institute 4 Independence Way Princeton, New Jersey 08540 adrienne!geofflbialeklruyter@ research. nj. nec. com Abstract Many neural s... | 2000 | 55 |
1,856 | Speech Denoising and Dereverberation Using Probabilistic Models Hagai Attias John C. Platt Alex Acero Li Deng Microsoft Research 1 Microsoft Way Redmond, WA 98052 {hagaia,jplatt,alexac,deng} @microsoft.com Abstract This paper presents a unified probabilistic framework for denoising and ... | 2000 | 56 |
1,857 | Modelling spatial recall, mental imagery and neglect Suzanna Becker Department of Psychology McMaster University 1280 Main Street West Hamilton,Ont. Canada L8S 4Kl becker@mcmaster.ca Neil Burgess Department of Anatomy and Institute of Cognitive Neuroscience, UCL 17 Queen Square Abstr... | 2000 | 57 |
1,858 | Adaptive Object Representation with Hierarchically-Distributed Memory Sites Bosco S. Tjan Department of Psychology University of Southern California btjan@usc.edu Abstract Theories of object recognition often assume that only one representation scheme is used within one visual-processing pathway. V... | 2000 | 58 |
1,859 | Text Classification using String Kernels HUlna Lodhi John Shawe-Taylor N ello Cristianini Chris Watkins Department of Computer Science Royal Holloway, University of London Egham, Surrey TW20 OEX, UK {huma, john, nello, chrisw}Cdcs.rhbnc.ac.uk Abstract We introduce a novel kernel for comparing... | 2000 | 59 |
1,860 | Learning Segmentation by Random Walks Marina Meila University of Washington mmp~stat.washington.edu Jianbo Shi Carnegie Mellon University jshi~cs.cmu.edu Abstract We present a new view of image segmentation by pairwise similarities. We interpret the similarities as edge flows in a Markov rand... | 2000 | 6 |
1,861 | Model Complexity, Goodness of Fit and Diminishing Returns Igor V. Cadez Information and Computer Science University of California Irvine, CA 92697-3425, U.S.A. Padhraic Smyth Information and Computer Science University of California Irvine, CA 92697-3425, U.S.A. Abstract We investigate ... | 2000 | 60 |
1,862 | Fast Training of Support Vector Classifiers F. Perez-Cruzt, P. L. Alarc6n-Dianat, A. Navia-V azquez:j:and A. Artes-Rodriguez:j:. tDpto. Teoria de la Seiial y Com., Escuela Politecnica, Universidad de Alcala. 28871-Alcala de Henares (Madrid) Spain. e-mail: fernando@tsc.uc3m.es :j:Dpto. Tecnologias de las com... | 2000 | 61 |
1,863 | Some new bounds on the generalization error of combined classifiers Vladimir Koltchinskii Department of Mathematics and Statistics University of New Mexico Albuquerque, NM 87131-1141 vlad@math.unm.edu Dmitriy Panchenko Department of Mathematics and Statistics University of New Mexico Albuq... | 2000 | 62 |
1,864 | Beyond maximum likelihood and density estimation: A sample-based criterion for unsupervised learning of complex models Sepp Hochreiter and Michael C. Mozer Department of Computer Science University of Colorado Boulder, CO 80309- 0430 {hochreit,mozer}~cs.colorado.edu Abstract The goal of many ... | 2000 | 63 |
1,865 | A Neural Probabilistic Language Model Yoshua Bengio; Rejean Ducharme and Pascal Vincent Departement d'Informatique et Recherche Operationnelle Centre de Recherche Mathematiques Universite de Montreal Montreal, Quebec, Canada, H3C 317 {bengioy,ducharme, vincentp }@iro.umontreal.ca Abstract A goal... | 2000 | 64 |
1,866 | Support Vector Novelty Detection Applied to Jet Engine Vibration Spectra Paul Hayton Department of Engineering Science University of Oxford, UK pmh@robots.ox.ac.uk Lionel Tarassenko Department of Engineering Science University of Oxford, UK lionel@robots.ox.ac.uk Bernhard SchOlkopf Micr... | 2000 | 65 |
1,867 | .N-Body. Problems in Statistical Learning Alexander G. Gray Department of Computer Science Carnegie Mellon University agray@cs.cmu.edu Andrew W. Moore Robotics Inst. and Dept. Compo Sci. Carnegie Mellon University awm@cs.cmu.edu Abstract We present efficient algorithms for all-point-pairs ... | 2000 | 66 |
1,868 | A silicon primitive for competitive learning David Usu Miguel Figueroa Computer Science and Engineering The University of Washington 114 Sieg Hall, Box 352350 Seattle, W A 98195-2350 USA hsud, miguel, diorio@cs.washington.edu Abstract Chris Diorio Competitive learning is a technique for tr... | 2000 | 67 |
1,869 | Automatic choice of dimensionality for peA Thomas P. Minka MIT Media Lab 20 Ames St, Cambridge, MA 02139 tpminka@media.mit.edu Abstract A central issue in principal component analysis (PCA) is choosing the number of principal components to be retained. By interpreting PCA as density estimation, ... | 2000 | 68 |
1,870 | Propagation Algorithms for Variational Bayesian Learning Zoubin GhahraIllani and Matthew J. Beal Gatsby Computational Neuroscience Unit University College London 17 Queen Square, London WC1N 3AR, England {zoubin,m.beal}~gatsby.ucl.ac.uk Abstract Variational approximations are becoming a widespre... | 2000 | 69 |
1,871 | A PAC-Bayesian Margin Bound for Linear Classifiers: Why SVMs work Ralf Herbrich Statistics Research Group Computer Science Department Technical University of Berlin ralfh@cs.tu-berlin.de Thore Graepel Statistics Research Group Computer Science Department Technical University of Berlin g... | 2000 | 7 |
1,872 | An Adaptive Metric Machine for Pattern Classification Carlotta Domeniconi, Jing Peng+, Dimitrios Gunopulos Dept. of Computer Science, University of California, Riverside, CA 92521 + Dept. of Computer Science, Oklahoma State University, Stillwater, OK 74078 { carlotta, dg} @cs.ucr.edu, jpeng@cs.okstate.ed... | 2000 | 70 |
1,873 | On iterative Krylov-dogleg trust-region steps for solving neural networks nonlinear least squares problems Eiji Mizutani Department of Computer Science National Tsing Hua University Hsinchu, 30043 TAIWAN R.O.C. eiji@wayne.cs.nthu.edu.tw James w. Demmel Mathematics and Computer Science Univ... | 2000 | 71 |
1,874 | Stability and noise in biochemical switches William Bialek NEC Research Instit ute 4 Independence Way Princeton, New Jersey 08540 bialek@research. nj. nec. com Abstract Many processes in biology, from the regulation of gene expression in bacteria to memory in the brain, involve switches construc... | 2000 | 72 |
1,875 | Computing with Finite and Infinite Networks Ole Winther* Theoretical Physics, Lund University SOlvegatan 14 A, S-223 62 Lund, Sweden winthe r@nimis.thep.lu. s e Abstract Using statistical mechanics results, I calculate learning curves (average generalization error) for Gaussian processes (GPs) and ... | 2000 | 73 |
1,876 | Hierarchical Memory-Based Reinforcement Learning Natalia Hernandez-Gardio} Artificial Intelligence Lab Massachusetts Institute of Technology Cambridge, MA 02139 nhg@ai.mit.edu Sridhar Mahadevan Department of Computer Science Michigan State University East Lansing, MI 48824 mahadeva@cse.... | 2000 | 74 |
1,877 | Second order approximations for probability models Hilbert J. Kappen Department of Biophysics Nijmegen University Nijmegen, the Netherlands bert@mbfys.kun.nl Abstract Wim Wiegerinck Department of Biophysics Nijmegen University Nijmegen, the Netherlands wimw@mbfys.kun.nl In this pa... | 2000 | 75 |
1,878 | Feature Selection for SVMs J. Weston t, S. Mukherjee tt , O. Chapelle*, M. Pontiltt T. Poggiott, V. Vapnik*,ttt t Barnhill Biolnformatics.com, Savannah, Georgia, USA. tt CBCL MIT, Cambridge, Massachusetts, USA. * AT&T Research Laboratories, Red Bank, USA. ttt Royal Holloway, University of London, Egha... | 2000 | 76 |
1,879 | Direct Classification with Indirect Data Timothy X Brown Interdisciplinary Telecommunications Program Dept. of Electrical and Computer Engineering University of Colorado, Boulder, 80309-0530 timxb~colorado.edu Abstract We classify an input space according to the outputs of a real-valued function... | 2000 | 77 |
1,880 | Constrained Independent Component Analysis Wei Lu and Jagath C. Rajapakse School of Computer Engineering Nanyang Technological University, Singapore 639798 email: asjagath@ntu.edu.sg Abstract The paper presents a novel technique of constrained independent component analysis (CICA) to introduce c... | 2000 | 78 |
1,881 | A Gradient-Based Boosting Algorithm for Regression Problems Richard S. Zemel Toniann Pitassi Department of Computer Science University of Toronto Abstract In adaptive boosting, several weak learners trained sequentially are combined to boost the overall algorithm performance. Recently adaptive b... | 2000 | 79 |
1,882 | Active Learning for Parameter Estimation in Bayesian Networks Simon Tong Computer Science Department Stanford University simon. tong@cs.stanford.edu Daphne Koller Computer Science Department Stanford University koller@cs.stanford.edu Abstract Bayesian networks are graphical representati... | 2000 | 8 |
1,883 | The Early Word Catches the Weights Mark A. Smith Garrison W. Cottrell Karen L. Anderson Department of Computer Science University of California at San Diego La Jolla, CA 92093 {masmith,gary,kanders}@cs.ucsd.edu Abstract The strong correlation between the frequency of words and their naming ... | 2000 | 80 |
1,884 | The Manhattan World Assumption: Regularities in scene statistics which enable Bayesian inference James M. Coughlan Smith-Kettlewell Eye Research Inst. 2318 Fillmore St. San Francisco, CA 94115 coughlan@ski.org A.L. Yuille Smith-Kettlewell Eye Research Inst. 2318 Fillmore St. San Francis... | 2000 | 81 |
1,885 | Processing of Time Series by Neural Circuits with Biologically Realistic Synaptic Dynamics Thomas NatschIager & Wolfgang Maass Institute for Theoretical Computer Science Technische Universitat Graz, Austria {tna t schl,maass}@i gi.tu-graz. ac . at Eduardo D. Sontag Dept. of Mathematics Rutgers U... | 2000 | 82 |
1,886 | Machine Learning for Video-Based Rendering Arno Schadl arno@schoedl. org Irfan Essa irjan@cc.gatech.edu Georgia Institute of Technology GVU Center / College of Computing Atlanta, GA 30332-0280, USA. Abstract We present techniques for rendering and animation of realistic scenes by analyz... | 2000 | 83 |
1,887 | Discovering Hidden Variables: A Structure-Based Approach Gal Elidan Noam Lotner Nir Friedman Hebrew University {galel,noaml,nir}@cs.huji.ac.il Abstract Daphne Koller Stanford University koller@cs.stanford.edu A serious problem in learning probabilistic models is the presence of hidden v... | 2000 | 84 |
1,888 | Natural sound statistics and divisive normalization in the auditory system Odelia Schwartz Center for Neural Science New York University odelia@cns.nyu.edu Eero P. Simoncelli Howard Hughes Medical Institute Center for Neural Science, and Courant Institute of Mathematical Sciences New York ... | 2000 | 85 |
1,889 | Regularized Winnow Methods Tong Zhang Mathematical Sciences Department IBM TJ. Watson Research Center Yorktown Heights, NY 10598 tzhang@watson.ibm.com Abstract In theory, the Winnow multiplicative update has certain advantages over the Perceptron additive update when there are many irrelevant at... | 2000 | 86 |
1,890 | FaceSync: A linear operator for measuring synchronization of video facial images and audio tracks Malcolm Slaney! Interval Research malcolm@ieee.org Michele Covell2 Interval Research covell@ieee.org Abstract FaceSync is an optimal linear algorithm that finds the degree of synchronization b... | 2000 | 87 |
1,891 | Mixtures of Gaussian Processes Volker Tresp Siemens AG, Corporate Technology, Department of Neural Computation Otto-Hahn-Ring 6,81730 Miinchen, Germany Volker. Tresp@mchp.siemens.de Abstract We introduce the mixture of Gaussian processes (MGP) model which is useful for applications in which the opt... | 2000 | 88 |
1,892 | What can a single neuron compute? Blaise Agiiera y Areas, l Adrienne L. Fairhall,2 and William Bialek2 1 Rare Books Library, Princeton University, Princeton, New Jersey 08544 2NEC Research Institute, 4 Independence Way, Princeton, New Jersey 08540 blaisea@prineeton. edu {adrienne, bialek} @researeh. nj. nee... | 2000 | 89 |
1,893 | A tighter bound for graphical models M.A.R. Leisink* and H.J. Kappent Department of Biophysics University of Nijmegen, Geert Grooteplein 21 NL 6525 EZ Nijmegen, The Netherlands {martijn,bert}Cmbfys.kun.nl Abstract We present a method to bound the partition function of a Boltzmann machine neural net... | 2000 | 9 |
1,894 | Tree-Based Modeling and Estimation of Gaussian Processes on Graphs with Cycles Martin J. Wainwright, Erik B. Sudderth, and Alan S. Willsky Laboratory for Information and Decision Systems Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge, MA 02139... | 2000 | 90 |
1,895 | Learning winner-take-all competition between groups of neurons in lateral inhibitory networks Xiaohui Xie, Richard Hahnloser and H. Sebastian Seung E25-21O, MIT, Cambridge, MA 02139 {xhxielrhlseung}@mit.edu Abstract It has long been known that lateral inhibition in neural networks can lead to a win... | 2000 | 91 |
1,896 | Foundations for a Circuit Complexity Theory of Sensory Processing* Robert A. Legenstein & Wolfgang Maass Institute for Theoretical Computer Science Technische Universitat Graz, Austria {Iegi, maass }@igi.tu-graz.ac.at Abstract We introduce total wire length as salient complexity measure for an anal... | 2000 | 92 |
1,897 | Bayes Networks on Ice: Robotic Search for Antarctic Meteorites Liam Pedersen-, Dimi Apostolopoulos, Red Whittaker Robotics Institute Carnegie Mellon University Pittsburgh, PA 15213 {pedersen+, dalv, red}@ri.cmu.edu Abstract A Bayes network based classifier for distinguishing terrestrial rocks... | 2000 | 93 |
1,898 | Learning and Tracking Cyclic Human Motion D.Ormoneit Dept. of Computer Science Stanford University Stanford, CA 94305 ormoneitOcs.stanford.edu M. J. Black Dept. of Computer Science Brown University, Box 1910 Providence, RI 02912 blackOcs.brown.edu H. Sidenbladh Royal Institute of ... | 2000 | 94 |
1,899 | The Use of MDL to Select among Computational Models of Cognition In J. Myung, Mark A. Pitt & Shaobo Zhang Vijay Balasubramanian Department of Psychology David Rittenhouse Laboratories Ohio State University University of Pennsylvania Columbus, OH 43210 Philadelphia, PA 19103 {myung.l, pitt.... | 2000 | 95 |
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