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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1,900 | Active Support Vector Machine Classification o. L. Mangasarian Computer Sciences Dept. University of Wisconsin 1210 West Dayton Street Madison, WI 53706 olvi@cs.wisc.edu David R. Musicant Dept. of Mathematics and Computer Science Carleton College One North College Street Northfield, ... | 2000 | 96 |
1,901 | Decomposition of Reinforcement Learning for Admission Control of Self-Similar Call Arrival Processes Jakob Carlstrom Department of Electrical Engineering, Technion, Haifa 32000, Israel jakob@ee . technion . ac . il Abstract This paper presents predictive gain scheduling, a technique for simplifying... | 2000 | 97 |
1,902 | Generalizable Singular Value Decomposition for Ill-posed Datasets Ulrik Kjerns Lars K. Hansen Department of Mathematical Modelling Technical University of Denmark DK-2800 Kgs. Lyngby, Denmark uk, lkhansen@imm. dtu. dk Abstract Stephen C. Strother PET Imaging Service VA medical center ... | 2000 | 98 |
1,903 | Vicinal Risk Minimization Olivier Chapelle, Jason Weston* , Leon Bottou and Vladimir Vapnik AT&T Research Labs, 100 Schultz drive, Red Bank, NJ, USA * Barnhill BioInformatics.com, Savannah, GA, USA. {chapelle, weston,leonb, vlad}@research.att.com Abstract The Vicinal Risk Minimization principle establ... | 2000 | 99 |
1,904 | Means. Correlations and Bounds M.A.R. Leisink and H.J. Kappen Department of Biophysics University of Nijmegen, Geert Grooteplein 21 NL 6525 EZ Nijmegen, The Netherlands {martijn,bert}@mbfys.kun.nl Abstract The partition function for a Boltzmann machine can be bounded from above and below. We can... | 2001 | 1 |
1,905 | Group Redundancy Measures Reveal Redundancy Reduction in the Auditory Pathway Gal Chechik Amir Globerson Naftali Tishby School of Computer Science and Engineering and The Interdisciplinary Center for Neural Computation Hebrew University of Jerusalem, Israel ggal@cs.huji.ac.il Michael J. An... | 2001 | 10 |
1,906 | Global Coordination of Local Linear Models Sam Roweis , Lawrence K. Saul , and Geoffrey E. Hinton Department of Computer Science, University of Toronto Department of Computer and Information Science, University of Pennsylvania Abstract High dimensional data that lies on or near a low dimensional ... | 2001 | 100 |
1,907 | Correlation Codes in Neuronal Populations Maoz Shamir and Haim Sompolinsky Racah Institute of Physics and Center for Neural Computation, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
Abstract Population codes often rely on the tun... | 2001 | 101 |
1,908 | Grammatical Bigrams Mark A. Paskin Computer Science Division University of California, Berkeley Berkeley, CA 94720 paskin@cs.berkeley.edu Abstract Unsupervised learning algorithms have been derived for several statistical models of English grammar, but their computational complexity makes applying ... | 2001 | 102 |
1,909 | The Steering Approach for Multi-Criteria Reinforcement Learning Shie Mannor and Nahum Shimkin Department of Electrical Engineering Technion, Haifa 32000, Israel {shie,shimkin}@{tx,ee}.technion.ac.il Abstract We consider the problem of learning to attain multiple goals in a dynamic environment, which is in... | 2001 | 103 |
1,910 | Stabilizing Value Function with the Xin Wang Department of Computer Science Oregon State University Corvallis, OR, 97331 wangxi@cs. orst.edu Thomas G Dietterich Department of Computer Science Oregon State University Corvallis, OR, 97331 tgd@cs. orst. edu Abstract We address the problem of non-... | 2001 | 104 |
1,911 | Tree-based reparameterization for approximate inference on loopy graphs Martin J. Wainwright, Tommi Jaakkola, and Alan S. Will sky Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge, MA 02139 mjwain@mit.edu tommi@ai.mit.edu willsky@mit.edu ... | 2001 | 105 |
1,912 | Cobot: A Social Reinforcement Learning Agent Charles Lee Isbell, Jr. Christian R. Shelton AT&T Labs-Research Stanford University Michael Kearns Satinder Singh Peter Stone University of Pennsylvania Syntek Capital AT&T Labs-Research Abstract We report on the use of reinforcement learning with Cob... | 2001 | 106 |
1,913 | Semi-Supervised MarginBoost F. d'Alche-Buc LIP6,UMR CNRS 7606, Universite P. et M. Curie 75252 Paris Cedex, France florence. dAlche@lip6.fr Yves Grandvalet Heudiasyc, UMR CNRS 6599, Universite de Technologie de Compiegne, BP 20.529, 60205 Compiegne cedex, France Yves. Grandvalet@hds.utc.fr... | 2001 | 107 |
1,914 | ALGONQUIN - Learning dynamic noise models from noisy speech for robust speech recognition Brendan J. Freyl, Trausti T. Kristjanssonl , Li Deng2 , Alex Acero2 1 Probabilistic and Statistical Inference Group, University of Toronto http://www.psi.toronto.edu 2 Speech Technology Group, Microsoft Research ... | 2001 | 108 |
1,915 | Adaptive N earest Neighbor Classification using Support Vector Machines Carlotta Domeniconi, Dimitrios Gunopulos Dept. of Computer Science, University of California, Riverside, CA 92521 { carlotta, dg} @cs.ucr.edu Abstract The nearest neighbor technique is a simple and appealing method to address c... | 2001 | 109 |
1,916 | Probabilistic Inference of Hand Motion from Neural Activity in Motor Cortex Y. Gao M. J. Black E. Bienenstock S. Shoham J. P. Donoghue Division of Applied Mathematics, Brown University, Providence, RI 02912 Dept. of Computer Science, Brown University, Box 1910, Providence, RI 02... | 2001 | 11 |
1,917 | Fast, large-scale transformation-invariant clustering Brendan J. Frey Machine Learning Group University of Toronto www.psi.toronto.edu/∼frey Nebojsa Jojic Vision Technology Group Microsoft Research www.ifp.uiuc.edu/∼jojic Abstract In previous work on “transformed mixtures of Gaussians” and “tran... | 2001 | 110 |
1,918 | A Rotation and Translation Invariant Discrete Saliency Network Lance R. Williams Dept. of Computer Science Univ. of New Mexico Albuquerque, NM 87131 John W. Zweck Dept. of CS and EE Univ. of Maryland Baltimore County Baltimore, MD 21250 Abstract We describe a neural network which enhances and comp... | 2001 | 111 |
1,919 | A theory of neural integration in the head-direction system Richard H.R. Hahnloser , Xiaohui Xie and H. Sebastian Seung Howard Hughes Medical Institute Dept. of Brain and Cognitive Sciences Massachusetts Institute of Technology Cambridge, MA 02139 rhahnloser|xhxie|seung @mit.edu Abstract In... | 2001 | 112 |
1,920 | (Not) Bounding the True Error John Langford Department of Computer Science Carnegie-Mellon University Pittsburgh, PA 15213 jcl+@cs.cmu.edu Rich Caruana Department of Computer Science Cornell University Ithaca, NY 14853 caruana@cs.cornell.edu Abstract We present a new approach to bounding the tru... | 2001 | 113 |
1,921 | Novel iteration schemes for the Cluster Variation Method Hilbert J. Kappen Department of Biophysics Nijmegen University Nijmegen, the Netherlands bert©mbfys.kun.nl Wim Wiegerinck Department of Biophysics Nijmegen University Nijmegen, the Netherlands wimw©mbfys.kun.nl Abstract The ... | 2001 | 114 |
1,922 | Infinite Mixtures of Gaussian Process Experts Carl Edward Rasmussen and Zoubin Ghahramani Gatsby Computational Neuroscience Unit University College London 17 Queen Square, London WC1N 3AR, England edward,zoubin@gatsby.ucl.ac.uk http://www.gatsby.ucl.ac.uk Abstract We present an extension to the Mixture o... | 2001 | 115 |
1,923 | Relative Density Nets: A New Way to Combine Backpropagation with HMM's Andrew D. Brown Department of Computer Science University of Toronto Toronto, Canada M5S 3G4 andy@cs.utoronto.ca Abstract Geoffrey E. Hinton Gatsby Unit, UCL London, UK WCIN 3AR hinton@gatsby.ucl.ac.uk Logistic un... | 2001 | 116 |
1,924 | Neural Implementation of Bayesian Inference in Population Codes Si Wu Computer Science Department Sheffield University, UK Shun-ichi Amari Lab. for Mathematic Neuroscience, RIKEN Brain Science Institute, JAPAN Abstract This study investigates a population decoding paradigm, in which the es... | 2001 | 117 |
1,925 | TAP Gibbs Free Energy, Belief Propagation and Sparsity Lehel Csat´o and Manfred Opper Neural Computing Research Group School of Engineering and Applied Science Aston University, Birmingham B4 7ET, UK. [csatol,opperm]@aston.ac.uk Ole Winther Center for Biological Sequence Analysis, BioCentrum Technical... | 2001 | 118 |
1,926 | Quantizing Density Estimators Peter Meinicke Neuroinformatics Group University of Bielefeld Bielefeld, Germany pmeinick@techfak.uni-bielefeld.de Helge Ritter Neuroinformatics Group University of Bielefeld Bielefeld, Germany helge@techfak.uni-bielefeld.de Abstract We suggest a nonparametric frame... | 2001 | 119 |
1,927 | The Noisy Euclidean Traveling Salesman Problem and Learning Mikio L. Braun, Joachim M. Buhmann braunm@cs.uni-bonn.de, jb@cs.uni-bonn.de Institute for Computer Science, Dept. III, University of Bonn R6merstraBe 164, 53117 Bonn, Germany Abstract We consider noisy Euclidean traveling salesman probl... | 2001 | 12 |
1,928 | The Concave-Convex Procedure (CCCP) A. L. Yuille and Anand Rangarajan * Smith-Kettlewell Eye Research Institute, 2318 Fillmore Street, San Francisco, CA 94115, USA. Tel. (415) 345-2144. Fax. (415) 345-8455. Email yuille@ski.org * Prof. Anand Rangarajan. Dept. of CISE, Univ. of Florida Room 301, CSE... | 2001 | 120 |
1,929 | Active Learning in the Drug Discovery Process Manfred K. Warmuth , Gunnar R¨atsch , Michael Mathieson , Jun Liao , Christian Lemmen Computer Science Dep., Univ. of Calif. at Santa Cruz FHG FIRST, Kekul´estr. 7, Berlin, Germany DuPont Pharmaceuticals,150 California S... | 2001 | 121 |
1,930 | Intransitive Likelihood-Ratio Classifiers Jeff Bilmes and Gang Ji Department of Electrical Engineering University of Washington Seattle, WA 98195-2500 bilmes,gji @ee.washington.edu Marina Meil˘a Department of Statistics University of Washington Seattle, WA 98195-4322 mmp@stat.washington.edu... | 2001 | 122 |
1,931 | Transform-invariant image decomposition with similarity templates Chris Stauffer, Erik Miller, and Kinh Tieu MIT Artificial Intelligence Lab Massachusetts Institute of Technology Cambridge, MA 02139 {stauffer,emiller,tieu}@ai.mit.edu Abstract Recent work has shown impressive transform-invariant modeling ... | 2001 | 123 |
1,932 | Activity Driven Adaptive Stochastic Resonance Gregor Wenning and Klaus Oberrnayer Department of Electrical Engineering and Computer Science Technical University of Berlin Franklinstr. 28/29, 10587 Berlin {grewe, oby}@cs.tu-berlin.de Abstract Cortical neurons might be considered as threshold elem... | 2001 | 124 |
1,933 | Reinforcement Learning Memory Bram Bakker Dept. of Psychology, Leiden University / IDSIA P.O. Box 9555; 2300 RB, Leiden; The Netherlands bbakker@fsw.leidenuniv.nl Abstract This paper presents reinforcement learning with a Long ShortTerm Memory recurrent neural network: RL-LSTM. Model-free RL-LSTM using ... | 2001 | 125 |
1,934 | ADynamic HMM for On-line Segmentation of Sequential Data Jens Kohlmorgen* Fraunhofer FIRST.IDA Kekulestr. 7 12489 Berlin, Germany jek@first·fraunhofer.de Steven Lemm Fraunhofer FIRST.IDA Kekulestr. 7 12489 Berlin, Germany lemm@first·fraunhofer.de Abstract We propose a novel method... | 2001 | 126 |
1,935 | The Intelligent Surfer: Probabilistic Combination of Link and Content Information in PageRank Matthew Richardson Pedro Domingos Department of Computer Science and Engineering University of Washington Box 352350 Seattle, WA 98195-2350, USA {mattr, pedrod}@cs.washington.edu Abstract ... | 2001 | 127 |
1,936 | Incremental A S. Koenig and M. Likhachev Georgia Institute of Technology College of Computing Atlanta, GA 30312-0280 skoenig, mlikhach @cc.gatech.edu Abstract Incremental search techniques find optimal solutions to series of similar search tasks much faster than is possible by solving each search ... | 2001 | 128 |
1,937 | Grouping and dimensionality reduction by locally linear embedding Marzia Polito Division of Physics, Mathematics and Astronomy California Institute of Technology Pasadena, CA, 91125 polito@caltech.edu Pietro Perona Division of Engeneering and Applied Mathematics California Institute of Techno... | 2001 | 129 |
1,938 | Incorporating Invariances in Nonlinear Support Vector Machines Olivier Chapelle olivier.chapelle@lip6.fr LIP6, Paris, France Biowulf Technologies Bernhard Scholkopf bernhard.schoelkopf@tuebingen.mpg.de Max-Planck-Institute, Tiibingen, Germany Biowulf Technologies Abstract The choice of ... | 2001 | 13 |
1,939 | Partially labeled classification with Markov random walks Martin Szummer MIT AI Lab & CBCL Cambridge, MA 02139 szummer@ai.mit.edu Tommi Jaakkola MIT AI Lab Cambridge, MA 02139 tommi@ai.mit.edu Abstract To classify a large number of unlabeled examples we combine a limited number of labeled examples ... | 2001 | 130 |
1,940 | Dynamic Time-Alignment Kernel in Support Vector Machine Hiroshi Shimodaira School of Information Science, Japan Advanced Institute of Science and Technology sim@jaist.ac.jp Ken-ichi Noma School of Information Science, Japan Advanced Institute of Science and Technology knoma@jaist.ac.jp Mitsuru N... | 2001 | 131 |
1,941 | Associative memory in realistic neuronal networks P.E. Latham* Department of Neurobiology University of California at Los Angeles Los Angeles, CA 90095 pel@ucla.edu Abstract Almost two decades ago, Hopfield [1] showed that networks of highly reduced model neurons can exhibit multiple attracti... | 2001 | 132 |
1,942 | A General Greedy Approximation Algorithm with Applications Tong Zhang IBM T.J. Watson Research Center Yorktown Heights, NY 10598 tzhang@watson.ibm.com Abstract Greedy approximation algorithms have been frequently used to obtain sparse solutions to learning problems. In this paper, we present a general ... | 2001 | 133 |
1,943 | Reducing multiclass to binary by coupling probability estimates Bianca Zadrozny Department of Computer Science and Engineering University of California, San Diego La Jolla, CA 92093-0114 zadrozny@cs.ucsd.edu Abstract This paper presents a method for obtaining class membership probability estimates for m... | 2001 | 134 |
1,944 | Linear Time Inference in Hierarchical HMMs Kevin P. Murphy and Mark A. Paskin Computer Science Department University of California Berkeley, CA 94720-1776 murphyk,paskin @cs.berkeley.edu Abstract The hierarchical hidden Markov model (HHMM) is a generalization of the hidden Markov model (HMM) that ... | 2001 | 135 |
1,945 | Sequential noise compensation by sequential Monte Carlo method Kaisheng Yao and Satoshi Nakamura ATR Spoken Language Translation Research Laboratories 2-2-2, Hikaridai Seika-cho, Souraku-gun, Kyoto, 619-0288, Japan E-mail: {kaisheng.yao, satoshi.nakamura}@slt.atr.co.jp Abstract We present a sequential Mon... | 2001 | 136 |
1,946 | Direct value-approxiIllation for factored MDPs Dale Schuurmans and ReIn Patrascll Department of Computer Science University of Waterloo {dale, rpatrasc} @cs.'Uwaterloo.ca Abstract We present a simple approach for computing reasonable policies for factored Markov decision processes (MDPs), when the optimal... | 2001 | 137 |
1,947 | Perceptual Metamers in Stereoscopic Vision Benjamin T. Backus* Department of Psychology University of Pennsylvania Philadelphia, PA 19104-6196 backus@psych.upenn.edu Abstract Theories of cue combination suggest the possibility of constructing visual stimuli that evoke different patterns of neural acti... | 2001 | 138 |
1,948 | Scaling laws and local minima in Hebbian ICA Magnus Rattray and Gleb Basalyga Department of Computer Science, University of Manchester, Manchester M13 9PL, UK. magnus@cs.man.ac.uk, basalygg@cs.man.ac.uk Abstract We study the dynamics of a Hebbian ICA algorithm extracting a single non-Gaussian component from... | 2001 | 139 |
1,949 | Sampling Techniques for Kernel Methods Dimitris Achlioptas Microsoft Research optas@microsoft.com Frank McSherry University of Washington mcsherry@cs.washington.edu Bernhard Sch¨olkopf Biowulf Technologies NY bs@conclu.de Abstract We propose randomized techniques for speeding up Kernel Principal ... | 2001 | 14 |
1,950 | Online Learning with Kernels Jyrki Kivinen Alex J. Smola Robert C. Williamson Research School of Information Sciences and Engineering Australian National University Canberra, ACT 0200 Abstract We consider online learning in a Reproducing Kernel Hilbert Space. Our method is computationally efficient and... | 2001 | 140 |
1,951 | 3 state neurons for contextual processing Adam Kepecs* and Sridhar Raghavachari Volen Center for Complex Systems Brandeis University Waltham MA 02454 {kepecs,sraghava}@brandeis.edu Abstract Neurons receive excitatory inputs via both fast AMPA and slow NMDA type receptors. We find that neurons re... | 2001 | 141 |
1,952 | Convolution Kernels for Natural Language Michael Collins AT&T Labs–Research 180 Park Avenue, New Jersey, NJ 07932 mcollins@research.att.com Nigel Duffy Department of Computer Science University of California at Santa Cruz nigeduff@cse.ucsc.edu Abstract We describe the application of kernel methods t... | 2001 | 142 |
1,953 | On the Convergence of Leveraging Gunnar R¨atsch, Sebastian Mika and Manfred K. Warmuth RSISE, Australian National University, Canberra, ACT 0200 Australia Fraunhofer FIRST, Kekul´estr. 7, 12489 Berlin, Germany University of California at Santa Cruz, CA 95060, USA raetsch@csl.anu.edu.au, mika@first.fhg.de, manf... | 2001 | 143 |
1,954 | A Quantitative Model of Counterfactual Reasoning Daniel Yarlett Division of Informatics University of Edinburgh Edinburgh, Scotland dany@cogsci.ed.ac.uk Michael Ramscar Division of Informatics University of Edinburgh Edinburgh, Scotland michael@dai.ed.ac.uk Abstract In this paper we explore tw... | 2001 | 144 |
1,955 | A Neural Oscillator Model of Auditory Selective Attention Stuart N. Wrigley and Guy J. Brown Department of Computer Science, University of Sheffield, Regent Court, 211 Portobello Street, Sheffield S1 4DP, UK. s.wrigley@dcs.shef.ac.uk, g.brown@dcs.shef.ac.uk Abstract A model of auditory grouping is descr... | 2001 | 145 |
1,956 | Self-regulation Mechanism of Temporally Asymmetric Hebbian Plasticity Narihisa Matsumoto Graduate School of Science and Engineering Saitama University: RIKEN Brain Science Institute Saitama 351-0198, Japan xmatumo@brain.riken.go.jp Masato Okada RIKEN Brain Science Institute Saitama 351-0198, Japan ... | 2001 | 146 |
1,957 | An Efficient, Exact Algorithm for Solving Tree-Structured Graphical Games Michael L. Littman AT&T Labs- Research Florham Park, NJ 07932-0971 mlittman©research.att.com Michael Kearns Department of Computer & Information Science University of Pennsylvania Philadelphia, PA 19104-6389 mkearns©... | 2001 | 147 |
1,958 | Causal Categorization with Bayes Nets Bob Rehder Department of Psychology New York University New York, NY 10012 bob .rehder@nyu.edu Abstract A theory of categorization is presented in which knowledge of causal relationships between category features is represented as a Bayesian network. Refe... | 2001 | 148 |
1,959 | KLD-Sampling: Adaptive Particle Filters Dieter Fox Department of Computer Science & Engineering University of Washington Seattle, WA 98195 Email: fox@cs.washington.edu Abstract Over the last years, particle filters have been applied with great success to a variety of state estimation problems. We present... | 2001 | 149 |
1,960 | Reinforcement Learning and Time Perception a Model of Animal Experiments J. L. Shapiro Department of Computer Science University of Manchester Manchester, M13 9PL U.K. jls@cs.man.ac.uk Abstract John Wearden Department of Psychology University of Manchester Manchester, M13 9PL U.K. ... | 2001 | 15 |
1,961 | Unsupervised Learning of Human Motion Models Yang Song, Luis Goncalves, and Pietro Perona California Institute of Technology, 136-93, Pasadena, CA 9112 5, USA yangs,luis,perona @vision.caltech.edu Abstract This paper presents an unsupervised learning algorithm that can derive the probabilistic depen... | 2001 | 150 |
1,962 | Orientational and geometric determinants place and headNeil Burgess & Tom Hartley Institute of Cognitive Neuroscience & Department of Anatomy, UCL 17 Queen Square, London WCIN 3AR, UK n. burgess@ucl.ac.uk. t.hartley@ucl.ac.uk Abstract We present a model of the firing of place and head-direction cells in ... | 2001 | 151 |
1,963 | Covariance Kernels from Bayesian Generative Models Matthias Seeger Institute for Adaptive and Neural Computation University of Edinburgh 5 Forrest Hill, Edinburgh EH1 2QL seeger@dai.ed.ac.uk Abstract We propose the framework of mutual information kernels for learning covariance kernels, as us... | 2001 | 152 |
1,964 | Modeling Temporal Structure in Classical Conditioning Aaron C. Courville1,3 and David S. Touretzky 2,3 1 Robotics Institute, 2Computer Science Department 3Center for the Neural Basis of Cognition Carnegie Mellon University, Pittsburgh, PA 15213-3891 { aarone, dst} @es.emu.edu Abstract The Tempor... | 2001 | 153 |
1,965 | Modeling the Modulatory Effect of Attention on Human Spatial Vision Laurent Itti Computer Science Department, Hedco Neuroscience Building HNB-30A, University of Southern California, Los Angeles, CA 90089-2520, U.S.A. J oehen Braun nstitute of Neuroscience and School of Computing, University of Plym... | 2001 | 154 |
1,966 | Approximate Dynamic Programming via Linear Programming Daniela P. de Farias Department of Management Science and Engineering Stanford University Stanford, CA 94305 pucci@stanford.edu Benjamin Van Roy Department of Management Science and Engineering Stanford University Stanford, CA 94305 ... | 2001 | 155 |
1,967 | A New Discriminative Kernel From Probabilistic Models K. Tsuda,*tM. Kawanabe,* G. Ratsch,§*S. Sonnenburg,* and K.-R. Muller*+ t AIST CBRC, 2-41-6, Aomi, Koto-ku, Tokyo, 135-0064, Japan * Fraunhofer FIRST, Kekulestr. 7, 12489 Berlin, Germany § Australian National U ni versi ty, Research School for Informa... | 2001 | 156 |
1,968 | Kernel Feature Spaces and Nonlinear Blind Source Separation Stefan Harmeling1∗, Andreas Ziehe1, Motoaki Kawanabe1, Klaus-Robert Müller1,2 1Fraunhofer FIRST.IDA, Kekuléstr. 7, 12489 Berlin, Germany 2University of Potsdam, Department of Computer Science, August-Bebel-Strasse 89, 14482 Potsdam, Germany {harmel... | 2001 | 157 |
1,969 | The Unified Propagation and Scaling Algorithm Yee Whye Teh Department of Computer Science University of Toronto 10 King’s College Road Toronto M5S 3G4 Canada ywteh@cs.toronto.edu Max Welling Gatsby Computational Neuroscience Unit University College London 17 Queen Square London WC1N 3AR U.K. well... | 2001 | 158 |
1,970 | Receptive field structure of flow detectors for heading perception Jaap A. B eintema Dept. Zoology & Neurobiology Ruhr University Bochum, Germany, 44780 beintema@neurobiologie.ruhr-uni-bochum.de Albert V. van den Berg Dept. of Neuro-ethology, Helmholtz Institute, Utrecht University, The Netherla... | 2001 | 159 |
1,971 | Hyperbolic Self-Organizing Maps for Semantic Navigation J¨org Ontrup Neuroinformatics Group Faculty of Technology Bielefeld University D-33501 Bielefeld, Germany jontrup@techfak.uni-bielefeld.de Helge Ritter Neuroinformatics Group Faculty of Technology Bielefeld University D-33501 Bielefeld, Ger... | 2001 | 16 |
1,972 | 1 Bayesian morphometry of hippocampal cells suggests same-cell somatodendritic repulsion Giorgio A. Ascoli * Alexei Samsonovich Krasnow Institute for Advanced Study at George Mason University Fairfax, VA 22030-4444 ascoli@gmu.edu asamsono@gmu.edu Abstract ... | 2001 | 160 |
1,973 | Entropy and Inference, Revisited Ilya Nemenman,1,2 Fariel Shafee,3 and William Bialek1,3 1NEC Research Institute, 4 Independence Way, Princeton, New Jersey 08540 2Institute for Theoretical Physics, University of California, Santa Barbara, CA 93106 3Department of Physics, Princeton University, Princeton, New Jer... | 2001 | 161 |
1,974 | Audio-Visual Sound Separation Via Hidden Markov Models John Hershey Department of Cognitive Science University of California San Diego jhershey@cogsci.ucsd.edu Michael Casey Mitsubishi Electric Research Labs Cambridge, Massachussets casey@merl.com Abstract It is well known that under no... | 2001 | 162 |
1,975 | Spectral Relaxation for K-means Clustering Hongyuan Zha & Xiaofeng He Dept. of Compo Sci. & Eng. The Pennsylvania State University University Park, PA 16802 {zha,xhe}@cse.psu.edu Chris Ding & Horst Simon NERSC Division Lawrence Berkeley National Lab. UC Berkeley, Berkeley, CA 94720 {chq... | 2001 | 163 |
1,976 | Gaussian Process Regression with Mismatched Models Peter Sollich Department of Mathematics, King's College London Strand, London WC2R 2LS, U.K. Email peter.sollich@kcl.ac . uk Abstract Learning curves for Gaussian process regression are well understood when the 'student' model happens to match the ... | 2001 | 164 |
1,977 | Kernel Machines and Boolean Functions Adam Kowalczyk Telstra Research Laboratories Telstra, Clayton, VIC 3168 a.kowalczyk@trl.oz.au Alex J. Smola, Robert C. Williamson RSISE, MLG and TelEng ANU, Canberra, ACT, 0200 Alex.Smola, Bob.Williamson @anu.edu.au Abstract We give results about the learn... | 2001 | 165 |
1,978 | Efficient Resources Allocation for Markov Decision Processes Remi Munos CMAP, Ecole Polytechnique, 91128 Palaiseau, France http://www.cmap.polytechnique.fr/....munos remi.munos@polytechnique.fr Abstract It is desirable that a complex decision-making problem in an uncertain world be adequately modeled by a... | 2001 | 166 |
1,979 | Information Geometrical Framework for Analyzing Belief Propagation Decoder Shiro Ikeda Kyushu Inst. of Tech., & PRESTO, JST Wakamatsu, Kitakyushu, Fukuoka, 808-0196 Japan shiro@brain.kyutech.ac.jp Toshiyuki Tanaka Tokyo Metropolitan Univ. Hachioji, Tokyo, 192-0397 Japan tanaka@eei.metro-u.ac.jp Shun... | 2001 | 167 |
1,980 | Speech Recognition using SVMs Nathan Smith Cambridge University Engineering Dept Cambridge, CB2 1PZ, U.K. ndsl 002@eng.cam.ac.uk Mark Gales Cambridge University Engineering Dept Cambridge, CB2 1PZ, U.K. mjfg@eng.cam.ac.uk Abstract An important issue in applying SVMs to speech recogni... | 2001 | 168 |
1,981 | Learning Discriminative Feature Transforms to Low Dimensions in Low Dimensions Kari Torkkola Motorola Labs, 7700 South River Parkway, MD ML28, Tempe AZ 85284, USA Kari.Torkkola@motorola.com http://members.home.net/torkkola Abstract The marriage of Renyi entropy with Parzen density estimation has been sh... | 2001 | 169 |
1,982 | Probabilistic Abstraction Hierarchies Eran Segal Computer Science Dept. Stanford University eran@cs.stanford.edu Daphne Koller Computer Science Dept. Stanford University koller@cs.stanford.edu Dirk Ormoneit Computer Science Dept. Stanford University ormoneit@cs.stanford.edu Abstract Many dom... | 2001 | 17 |
1,983 | Information-Geometrical Significance of Sparsity in Gallager Codes Toshiyuki Tanaka Department of Electronics and Information Engineering Tokyo Metropolitan University Tokyo 192-0397, Japan tanaka@eei.metro-u.ac.jp Shiro Ikeda Kyushu Institute of Technology & JST Fukuoka 808-0196, Japan shiro@brain.k... | 2001 | 170 |
1,984 | Adaptive Sparseness Using Jeffreys Prior M´ario A. T. Figueiredo Institute of Telecommunications, and Department of Electrical and Computer Engineering. Instituto Superior T´ecnico 1049-001 Lisboa, Portugal mtf @lx.it.pt Abstract In this paper we introduce a new sparseness inducing prior which does not ... | 2001 | 171 |
1,985 | An Efficient Clustering Algorithm Using Stochastic Association Model and Its Implementation Using Nanostructures Takashi Morie, Tomohiro Matsuura, Makoto Nagata, and Atsushi Iwata Graduate School of Advanced Sciences of Matter, Hiroshima University Higashi-hiroshima, 739-8526 Japan. http://www.dsl.hiroshima-... | 2001 | 172 |
1,986 | Grammar Transfer in a Second Order Recurrent Neural Network Michiro N egishi Department of Psychology Rutgers University 101 Warren St. Smith Hall #301 Newark, NJ 07102 negishi@psychology.rutgers.edu Stephen Jose Hanson Psychology Department Rutgers University 101 Warren St. Smith Hall ... | 2001 | 173 |
1,987 | Prod uct Analysis: Learning to model observations as products of hidden variables Brendan J. Freyl, Anitha Kannanl , Nebojsa Jojic2 1 Machine Learning Group, University of Toronto, www.psi.toronto.edu 2 Vision Technology Group, Microsoft Research Abstract Factor analysis and principal components an... | 2001 | 174 |
1,988 | Matching Free Trees with Replicator Equations Marcello Pelillo Dipartimento di Informatica Universit`a Ca’ Foscari di Venezia Via Torino 155, 30172 Venezia Mestre, Italy E-mail: pelillo@dsi.unive.it Abstract Motivated by our recent work on rooted tree matching, in this paper we provide a solution to the... | 2001 | 175 |
1,989 | Multiplicative Updates for Classification by Mixture Models Lawrence K. Saul and Daniel D. Lee Department of Computer and Information Science Department of Electrical Engineering University of Pennsylvania, Philadelphia, PA 19104 Abstract We investigate a learning algorithm for the classificat... | 2001 | 176 |
1,990 | The Infinite Hidden Markov Model Matthew J. Beal Zoubin Ghahramani Carl Edward Rasmussen Gatsby Computational Neuroscience Unit University College London 17 Queen Square, London WC1N 3AR, England http://www.gatsby.ucl.ac.uk m.beal,zoubin,edward @gatsby.ucl.ac.uk Abstract We show that it is poss... | 2001 | 177 |
1,991 | EM-DD: An Improved Multiple-Instance Learning Technique Qi Zhang Department of Computer Science Washington University St. Louis, MO 63130-4899 qz@cs. wustl. edu Sally A. Goldman Department of Computer Science Washington University St. Louis, MO 63130-4899 sg@cs. wustl. edu Abstract ... | 2001 | 178 |
1,992 | Fast Parameter Estimation Using Green's Functions K. Y. Michael Wong Department of Physics Hong Kong University of Science and Technology Clear Water Bay, Hong Kong phkywong@ust.hk FuIi Li Department of Applied Physics Xian Jiaotong University Xian, China 710049 flli@xjtu. edu. en ... | 2001 | 179 |
1,993 | Fast and Robust Classification using Asymmetric AdaBoost and a Detector Cascade Paul Viola and Michael Jones Mistubishi Electric Research Lab Cambridge, MA viola@merl.com and mjones@merl.com Abstract This paper develops a new approach for extremely fast detection in domains where the distribution of positi... | 2001 | 18 |
1,994 | Information-geometric decomposition spike analysis Hiroyuki Nakahara; Shun-ichi Amari Lab. for Mathematical Neuroscience, RIKEN Brain Science Institute 2-1 Hirosawa, Wako, Saitama, 351-0198 Japan {him, amari} @brain.riken.go.jp Abstract We present an information-geometric measure to systematically ... | 2001 | 180 |
1,995 | Learning Body Pose via Specialized Maps Romer Rosales Department of Computer Science Boston University, Boston, MA 02215 rrosales@cs.bu.edu Stan Sclaroff Department of Computer Science Boston University, Boston, MA 02215 sclaroff@cs.bu.edu Abstract A nonlinear supervised learning model, th... | 2001 | 181 |
1,996 | Circuits for VLSI Implementation of Temporally-Asymmetric Hebbian Learning Adria Bofill Alan F. Murray DanlOn P. Thompson Dept. of Electrical Engineering The University of Edinburgh Edinburgh, EH93JL, UK adria. bofill@ee.ed.ac. uk alan. murray@ee.ed.ac.uk damon. thompson @ee.ed.ac. uk ... | 2001 | 182 |
1,997 | Model Based Population Tracking and Automatic Detection of Distribution Changes Igor V. Cadez ∗ Dept. of Information and Computer Science, University of California, Irvine, CA 92612 icadez@ics.uci.edu P. S. Bradley digiMine, Inc. 10500 NE 8th Street, Bellevue, WA 98004-4332 paulb@digimine.com Ab... | 2001 | 183 |
1,998 | Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering Mikhail Belkin and Partha Niyogi Depts. of Mathematics and Computer Science The University of Chicago Hyde Park, Chicago, IL 60637. (misha@math.uchicago.edu,niyogi@cs.uchicago.edu) Abstract Drawing on the correspondence bet... | 2001 | 184 |
1,999 | A Bayesian Model Predicts Human Parse Preference and Reading Times in Sentence Processing Srini Narayanan Daniel Jurafsky SRI International and ICSI Berkeley University of Colorado, Boulder snarayan@cs.berkeley.edu jurafsky@colorado.edu Abstract Narayanan and Jurafsky (1998) proposed that human lang... | 2001 | 185 |
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