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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1,700 | Bayesian Map Learning in Dynamic Environments Kevin P. Murphy Computer Science Division University of California Berkeley, CA 94720-1776 murphyk@cs.berkeley.edu Abstract We consider the problem of learning a grid-based map using a robot with noisy sensors and actuators. We compare two approac... | 1999 | 52 |
1,701 | Better Generative Models for Sequential Data Problems: Bidirectional Recurrent Mixture Density Networks Mike Schuster ATR Interpreting Telecommunications Research Laboratories 2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-02, JAPAN gustl@itl.atr.co.jp Abstract This paper describes bidirectiona... | 1999 | 53 |
1,702 | Statistical Dynamics of Batch Learning s. Li and K. Y. Michael Wong Department of Physics, Hong Kong University of Science and Technology Clear Water Bay, Kowloon, Hong Kong {phlisong, phkywong}@ust.hk Abstract An important issue in neural computing concerns the description of learning dynamics wit... | 1999 | 54 |
1,703 | Scale Mixtures of Gaussians and the Statistics of Natural Images Martin J. Wainwright Stochastic Systems Group Electrical Engineering & CS MIT, Building 35-425 Cambridge, MA 02139 mjwain@mit.edu Eero P. Simoncelli Ctr. for Neural Science, and Courant Inst. of Mathematical Sciences New Y... | 1999 | 55 |
1,704 | Independent Factor Analysis with Temporally Structured Sources Hagai Attias hagai@gatsby.ucl.ac.uk Gatsby Unit, University College London 17 Queen Square London WCIN 3AR, U.K. Abstract We present a new technique for time series analysis based on dynamic probabilistic networks. In this approach, ... | 1999 | 56 |
1,705 | Managing Uncertainty in Cue Combination Zhiyong Yang Deparbnent of Neurobiology, Box 3209 Duke University Medical Center Durham, NC 27710 zhyyang@duke.edu Abstract Richard S. Zemel Deparbnent of Psychology University of Arizona Tucson, AZ 85721 zemel@u.arizona.edu We develop a hierar... | 1999 | 57 |
1,706 | Potential Boosters ? Nigel Duffy Department of Computer Science University of California Santa Cruz, CA 95064 nigedufJ@cse. ucsc. edu David Helmbold Department of Computer Science University of California Santa Cruz, CA 95064 dph@~se . ucsc. edu Abstract Recent interpretations of the... | 1999 | 58 |
1,707 | Resonance in a Stochastic Neuron Model with Delayed Interaction Toru Ohira* Sony Computer Science Laboratory 3-14-13 Higashi-gotanda Shinagawa, Tokyo 141, Japan ohira@csl.sony.co.jp Yuzuru Sato Institute of Physics, Graduate School of Arts and Science, University of Tokyo 3-8-1 Komaba, Meg... | 1999 | 59 |
1,708 | Population Decoding Based on an Unfaithful Model s. Wu, H. Nakahara, N. Murata and S. Amari RIKEN Brain Science Institute Hirosawa 2-1, Wako-shi, Saitama, Japan {phwusi, hiro, mura, amari}@brain.riken.go.jp Abstract We study a population decoding paradigm in which the maximum likelihood inference i... | 1999 | 6 |
1,709 | Wiring optimization in the brain Dmitri B. Chklovskii Sloan Center for Theoretical Neurobiology The Salk Institute La Jolla, CA 92037 mitya@salk.edu Charles F. Stevens Howard Hughes Medical Institute and Molecular Neurobiology Lab The Salk Institute Abstract La Jolla, CA 92037 ste... | 1999 | 60 |
1,710 | Learning from user feedback in image retrieval systems Nuno Vasconcelos Andrew Lippman MIT Media Laboratory, 20 Ames St, E15-354, Cambridge, MA 02139, {nuno,lip} @media.mit.edu, http://www.media.mit.edwnuno Abstract We formulate the problem of retrieving images from visual databases as a prob... | 1999 | 61 |
1,711 | Online Independent Component Analysis With Local Learning Rate Adaptation Nicol N. Schraudolph nic<Didsia.ch Xavier Giannakopoulos xavier<Didsia.ch IDSIA, Corso Elvezia 36 6900 Lugano, Switzerland http://www.idsia.ch/ Abstract Stochastic meta-descent (SMD) is a new technique for online ada... | 1999 | 62 |
1,712 | A Variational Bayesian Framework for Graphical Models Hagai Attias hagai@gatsby.ucl.ac.uk Gatsby Unit, University College London 17 Queen Square London WC1N 3AR, U.K. Abstract This paper presents a novel practical framework for Bayesian model averaging and model selection in probabilistic gra... | 1999 | 63 |
1,713 | Algebraic Analysis for Non-Regular Learning Machines Sumio Watanabe Precision and Intelligence Laboratory Tokyo Institute of Technology 4259 Nagatsuta, Midori-ku, Yokohama 223 Japan swatanab@pi. titech. ac.jp Abstract Hierarchical learning machines are non-regular and non-identifiable statist... | 1999 | 64 |
1,714 | Model selection in clustering by uniform convergence bounds* Joachim M. Buhmann and Marcus Held Institut flir Informatik III, RomerstraBe 164, D-53117 Bonn, Germany {jb,held}@cs.uni-bonn.de Abstract Unsupervised learning algorithms are designed to extract structure from data samples. Reliable and r... | 1999 | 65 |
1,715 | U nmixing Hyperspectral Data Lucas Parra, Clay Spence, Paul Sajda Sarnoff Corporation, CN-5300, Princeton, NJ 08543, USA {lparra, cspence,psajda} @sarnoff.com Andreas Ziehe, Klaus-Robert Miiller GMD FIRST.lDA, Kekulestr. 7, 12489 Berlin, Germany {ziehe,klaus}@first.gmd.de Abstract In hyperspectr... | 1999 | 66 |
1,716 | Some Theoretical Results Concerning the Convergence of Compositions of Regularized Linear Functions Tong Zhang Mathematical Sciences Department IBM T.1. Watson Research Center Yorktown Heights, NY 10598 tzhang@watson.ibm.com Abstract Recently, sample complexity bounds have been derived for pr... | 1999 | 67 |
1,717 | Inference for the Generalization Error Claude Nadeau CIRANO 2020, University, Montreal, Qc, Canada, H3A 2A5 jcnadeau@altavista.net Yoshua Bengio CIRANO and Dept. IRO Universite de Montreal Montreal, Qc, Canada, H3C 3J7 bengioy@iro.umontreal.ca Abstract In order to to compare learning... | 1999 | 68 |
1,718 | LTD Facilitates Learning In a Noisy Environment Paul Munro School of Information Sciences University of Pittsburgh Pittsburgh PA 15260 pwm+@pitt.edu Abstract Gerardina Hernandez Intelligent Systems Program University of Pittsburgh Pittsburgh PA 15260 gehst5+@pitt.edu Long-term pot... | 1999 | 69 |
1,719 | Broadband Direction-Of-Arrival Estimation Based On Second Order Statistics Justinian Rosca Joseph 6 Ruanaidh Alexander Jourjine Scott Rickard {rosca,oruanaidh,jourjine,rickard}@scr.siemens.com Siemens Corporate Research, Inc. 755 College Rd E Princeton, NJ 08540 Abstract N wideband sour... | 1999 | 7 |
1,720 | An Oeulo-Motor System with Multi-Chip Neuromorphie Analog VLSI Control Oliver Landolt* CSEMSA 2007 Neuchatel / Switzerland E-mail: landolt@caltech.edu Steve Gyger CSEMSA 2007 Neuchatel / Switzerland E-mail: steve.gyger@csem.ch Abstract A system emulating the functionality of a moving ey... | 1999 | 70 |
1,721 | Understanding stepwise generalization of Support Vector Machines: a toy model Sebastian Risau-Gusman and Mirta B. Gordon DRFMCjSPSMS CEA Grenoble, 17 avo des Martyrs 38054 Grenoble cedex 09, France Abstract In this article we study the effects of introducing structure in the input distribution of t... | 1999 | 71 |
1,722 | A Geometric Interpretation of v-SVM Classifiers David J. Crisp Centre for Sensor Signal and Information Processing, Deptartment of Electrical Engineering, University of Adelaide, South Australia dcrisp@eleceng.adelaide.edu.au Abstract Christopher J.C. Burges Advanced Technologies, Bell ... | 1999 | 72 |
1,723 | Robust Learning of Chaotic Attractors Rembrandt Bakker* Chemical Reactor Engineering Delft Univ. of Technology r.bakker@stm.tudelft·nl Floris Takens Dept. Mathematics University of Groningen F. Takens@math.rug.nl Jaap C. Schouten Chemical Reactor Engineering Eindhoven Univ. of Technolog... | 1999 | 73 |
1,724 | Greedy importance sampling Dale Schuurmans Department of Computer Science University of Waterloo dale@cs.uwaterloo.ca Abstract I present a simple variation of importance sampling that explicitly searches for important regions in the target distribution. I prove that the technique yields unbiased estim... | 1999 | 74 |
1,725 | Recurrent cortical competition: Strengthen or weaken? Peter Adorjan*, Lars Schwabe, Christian Piepenbrock* , and Klaus Obennayer Dept. of Compo Sci., FR2-I, Technical University Berlin Franklinstrasse 28/29 10587 Berlin, Germany adorjan@epigenomics.com, {schwabe, oby} @cs.tu-berlin.de, piepenbrock@... | 1999 | 75 |
1,726 | Constrained Hidden Markov Models Sam Roweis roweis@gatsby.ucl.ac.uk Gatsby Unit, University College London Abstract By thinking of each state in a hidden Markov model as corresponding to some spatial region of a fictitious topology space it is possible to naturally define neighbouring states as those ... | 1999 | 76 |
1,727 | Approximate inference algorithms for two-layer Bayesian networks AndrewY. Ng Computer Science Division UC Berkeley Berkeley, CA 94720 ang@cs.berkeley.edu Michael I. Jordan Computer Science Division and Department of Statistics UC Berkeley Berkeley, CA 94720 jordan@cs.berkeley.edu ... | 1999 | 77 |
1,728 | Perceptual Organization Based on Temporal Dynamics Xiuwen Liu and DeLiang L. Wang Department of Computer and Information Science Center for Cognitive Science The Ohio State University, Columbus, OR 43210-1277 Email: {liux, dwang}@cis.ohio-state.edu Abstract A figure-ground segregation network is... | 1999 | 78 |
1,729 | Learning Informative Statistics: A Nonparametric Approach John W. Fisher III, Alexander T. IhIer, and Paul A. Viola Massachusetts Institute of Technology 77 Massachusetts Ave., 35-421 Cambridge, MA 02139 {jisher,ihler,viola}@ai.mit.edu Abstract We discuss an information theoretic approach for ca... | 1999 | 79 |
1,730 | Emergence of Topography and Complex Cell Properties from Natural Images using Extensions of ICA Aapo Hyviirinen and Patrik Hoyer Neural Networks Research Center Helsinki University of Technology P.O. Box 5400, FIN-02015 HUT, Finland aapo.hyvarinen~hut.fi, patrik.hoyer~hut.fi http://www.cis.hut.f... | 1999 | 8 |
1,731 | Rules and Similarity in Concept Learning Joshua B. Tenenbaum Department of Psychology Stanford University, Stanford, CA 94305 jbt@psych.stanford.edu Abstract This paper argues that two apparently distinct modes of generalizing concepts - abstracting rules and computing similarity to exemplars - should... | 1999 | 80 |
1,732 | Support Vector Method for Novelty Detection Bernhard Scholkopf*, Robert Williamson§, Alex Smola§, John Shawe-Taylort, John Platt* * Microsoft Research Ltd., 1 Guildhall Street, Cambridge, UK § Department of Engineering, Australian National University, Canberra 0200 t Royal Holloway, University of London,... | 1999 | 81 |
1,733 | Generalized Model Selection For Unsupervised Learning In High Dimensions Shivakumar Vaithyanathan IBM Almaden Research Center 650 Harry Road San Jose, CA 95136 Shiv@almaden.ibm.com Byron Dom IBM Almaden Research Center 650 Harry Road San Jose, CA 95136 dom@almaden.ibm.com Abstract ... | 1999 | 82 |
1,734 | An Improved Decomposition Algorithm for Regression Support Vector Machines Pavel Laskov Department of Computer and Information Sciences University of Delaware Newark, DE 19718 laskov@asel. udel. edu Abstract A new decomposition algorithm for training regression Support Vector Machines (SVM) i... | 1999 | 83 |
1,735 | An Analog VLSI Model of Periodicity Extraction Andre van Schaik Computer Engineering Laboratory J03, University of Sydney, NSW 2006 Sydney, Australia andre@ee.usyd.edu.au Abstract This paper presents an electronic system that extracts the periodicity of a sound. It uses three analogue VLSI bu... | 1999 | 84 |
1,736 | From Coexpression to Coregulation: An Approach to Inferring Transcriptional Regulation among Gene Classes from Large-Scale Expression Data Eric Mjolsness Jet Propulsion Laboratory California Institute of Technology Pasadena CA 91109-8099 mjolsness@jpl.nasa.gov Rebecca Castaiio Jet Propulsi... | 1999 | 85 |
1,737 | Data Visualization and Feature Selection: New Algorithms for Nongaussian Data Howard Hua Yang and John Moody Oregon Graduate Institute of Science and Technology 20000 NW, Walker Rd., Beaverton, OR97006, USA hyang@ece.ogi.edu, moody@cse.ogi.edu, FAX:503 7481406 Abstract Data visualization and featur... | 1999 | 86 |
1,738 | An Information-Theoretic Framework for Understanding Saccadic Eye Movements Tai Sing Lee * Department of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 tai@es.emu.edu Abstract Stella X. Yu Robotics Institute Carnegie Mellon University Pittsburgh, PA 15213 stella@enb... | 1999 | 87 |
1,739 | Noisy Neural Networks and Generalizations Hava T. Siegelmann Industrial Eng. and Management, Mathematics Technion - lIT Haifa 32000, Israel iehava@ie.technion.ac.il Alexander Roitershtein Mathematics Technion - lIT Haifa 32000, Israel roiterst@math.technion.ac.il Asa Ben-Hur Indus... | 1999 | 88 |
1,740 | The Nonnegative Boltzmann Machine Oliver B. Downs Hopfield Group Schultz Building Princeton University Princeton, NJ 08544 obdowns@princeton.edu David J.e. MacKay Cavendish Laboratory Madingley Road Cambridge, CB3 OHE United Kingdom mackay@mrao.cam.ac.uk Daniel D. Lee Bell Labo... | 1999 | 89 |
1,741 | Reinforcement Learning Using Approximate Belief States Andres Rodriguez * Artificial Intelligence Center SRI International 333 Ravenswood Avenue, Menlo Park, CA 94025 rodriguez@ai.sri.com Abstract Ronald Parr, Daphne Koller Computer Science Department Stanford University Stanford, CA 94... | 1999 | 9 |
1,742 | Boosting Algorithms as Gradient Descent Llew Mason Research School of Information Sciences and Engineering Australian National University Canberra, ACT, 0200, Australia lmason@syseng.anu.edu.au Peter Bartlett Research School of Information Sciences and Engineering Australian National Unive... | 1999 | 90 |
1,743 | Local probability propagation for factor analysis Brendan J. Frey Computer Science, University of Waterloo, Waterloo, Ontario, Canada Abstract Ever since Pearl's probability propagation algorithm in graphs with cycles was shown to produce excellent results for error-correcting decoding a few years ... | 1999 | 91 |
1,744 | A MCMC approach to Hierarchical Mixture Modelling Christopher K. I. Williams Institute for Adaptive and Neural Computation Division of Informatics, University of Edinburgh 5 Forrest Hill, Edinburgh EHI 2QL, Scotland, UK ckiw@dai.ed.ac.uk http://anc.ed.ac.uk Abstract There are many hierarchica... | 1999 | 92 |
1,745 | The Infinite Gaussian Mixture Model Carl Edward Rasmussen Department of Mathematical Modelling Technical University of Denmark Building 321, DK-2800 Kongens Lyngby, Denmark carl@imm.dtu.dk http://bayes.imm.dtu.dk Abstract In a Bayesian mixture model it is not necessary a priori to limit the number ... | 1999 | 93 |
1,746 | Reconstruction of Sequential Data with Probabilistic Models and Continuity Constraints Miguel A. Carreira-Perpifian Dept. of Computer Science, University of Sheffield, UK miguel@dcs.shefac.uk Abstract We consider the problem of reconstructing a temporal discrete sequence of multidimensional real ve... | 1999 | 94 |
1,747 | Learning the Similarity of Documents: An Information-Geometric Approach to Document Retrieval and Categorization Thomas Hofmann Department of Computer Science Brown University, Providence, RI hofmann@cs.brown.edu, www.cs.brown.edu/people/th Abstract The project pursued in this paper is to develo... | 1999 | 95 |
1,748 | Bayesian Reconstruction of 3D Human Motion from Single-Camera Video Nicholas R. Howe Department of Computer Science Cornell University Ithaca, NY 14850 nihowe@cs.comell.edu Michael E. Leventon Artificial Intelligence Lab Massachusetts Institute of Technology Cambridge, MA 02139 leventon... | 1999 | 96 |
1,749 | Mixture Density Estimation Jonathan Q. Li Department of Statistics Yale University P.O. Box 208290 New Haven, CT 06520 Qiang.Li@aya.yale. edu Abstract Andrew R. Barron Department of Statistics Yale University P.O. Box 208290 New Haven, CT 06520 Andrew. Barron@yale. edu Gaussian... | 1999 | 97 |
1,750 | Information Capacity and Robustness of Stochastic Neuron Models Elad Schneidman Idan Segev N aftali Tishby Institute of Computer Science, Department of Neurobiology and Center for Neural Computation, Hebrew University Jerusalem 91904, Israel { elads, tishby} @cs.huji.ac.il, idan@lobster.ls... | 1999 | 98 |
1,751 | Bayesian Transduction Thore Graepel, Ralf Herbrich and Klaus Obermayer Department of Computer Science Technical University of Berlin Franklinstr. 28/29, 10587 Berlin, Germany {graepeI2, raith, oby} @cs.tu-berlin.de Abstract Transduction is an inference principle that takes a training sample and aim... | 1999 | 99 |
1,752 | Reinforcement Learning with Function Approximation Converges to a Region Geoffrey J. Gordon ggordon@es.emu.edu Abstract Many algorithms for approximate reinforcement learning are not known to converge. In fact, there are counterexamples showing that the adjustable weights in some algorithms may osc... | 2000 | 1 |
1,753 | Occam·s Razor Carl Edward Rasmussen Department of Mathematical Modelling Technical University of Denmark Building 321, DK-2800 Kongens Lyngby, Denmark carl@imm . dtu . dk http : //bayes . imm . dtu . dk Zoubin Ghahramani Gatsby Computational Neuroscience Unit University College London 17 Q... | 2000 | 10 |
1,754 | Sparse Kernel Principal Component Analysis Michael E. Tipping Microsoft Research St George House, 1 Guildhall St Cambridge CB2 3NH, U.K. mtipping~microsoft.com Abstract 'Kernel' principal component analysis (PCA) is an elegant nonlinear generalisation of the popular linear data analysis method, ... | 2000 | 100 |
1,755 | Sparsity of data representation of optimal kernel machine and leave-one-out estimator A. Kowalczyk Chief Technology Office, Telstra 770 Blackburn Road, Clayton, Vic. 3168, Australia (adam.kowalczy k@team.telstra.com) Abstract Vapnik's result that the expectation of the generalisation error ofthe op... | 2000 | 101 |
1,756 | Rate-coded Restricted Boltzmann Machines for Face Recognition Vee WhyeTeh Department of Computer Science University of Toronto Toronto M5S 2Z9 Canada ywteh@cs.toronto.edu Geoffrey E. Hinton Gatsby Computational Neuroscience UnitUniversity College London London WCIN 3AR u.K. hinton@ gatsby.... | 2000 | 102 |
1,757 | Shape Context: A new descriptor for shape matching and object recognition Serge Belongie, Jitendra Malik and Jan Puzicha Department of Electrical Engineering and Computer Sciences University of California at Berkeley Berkeley, CA 94720, USA {sjb, malik,puzicha} @cs.berkeley.edu Abstract We devel... | 2000 | 103 |
1,758 | Position Variance, Recurrence and Perceptual Learning Zhaoping Li Peter Dayan Gatsby Computational Neuroscience Unit 17 Queen Square, London, England, WCIN 3AR. zhaoping@gat sby.ucl. a c.uk da ya n@gat sby.ucl. ac .uk Abstract Stimulus arrays are inevitably presented at different positions on... | 2000 | 104 |
1,759 | Permitted and Forbidden Sets in Symmetric Threshold-Linear Networks Richard H.R. Hahnloser and H. Sebastian Seung Dept. of Brain & Cog. Sci., MIT Cambridge, MA 02139 USA rh~ai.mit.edu, seung~mit.edu Abstract Ascribing computational principles to neural feedback circuits is an important problem i... | 2000 | 105 |
1,760 | Competition and Arbors in Ocular Dominance Peter Dayan Gatsby Computational Neuroscience Unit, UCL 17 Queen Square, London, England, WCIN 3AR. da ya n @gat sby.uc l.a c .uk Abstract Hebbian and competitive Hebbian algorithms are almost ubiquitous in modeling pattern formation in cortical developmen... | 2000 | 106 |
1,761 | Learning Switching Linear Models of Human Motion Vladimir Pavlovic and James M. Rehg Compaq - Cambridge Research Lab Cambridge, MA 02139 {vladimir.pavlovic,jim.rehg}@compaq.com Abstract John MacCormick Compaq - System Research Center Palo Alto, CA 94301 {john.maccormick} @compaq.com The... | 2000 | 107 |
1,762 | New Approaches Towards Robust and Adaptive Speech Recognition Herve Bourlard, Samy Bengio and Katrin Weber IDIAP P.O. Box 592, rue du Simplon 4 1920 Martigny, Switzerland { bourlard, bengio, weber} @idiap. ch Abstract In this paper, we discuss some new research directions in automatic speech ... | 2000 | 108 |
1,763 | Place Cells and Spatial Navigation based on 2d Visual Feature Extraction, Path Integration, and Reinforcement Learning A. Arleo* F. Smeraldi S. Hug W. Gerstner Centre for Neuro-Mimetic Systems, MANTRA Swiss Federal Institute of Technology Lausanne, CH-1015 Lausanne EPFL, Switzerland Abstra... | 2000 | 109 |
1,764 | Exact Solutions to Time-Dependent MDPs Justin A. Boyan· ITA Software Building 400 One Kendall Square Cambridge, MA 02139 jab@itasoftware.com Michael L. Littman AT&T Labs-Research and Duke University 180 Park Ave. Room A275 Florham Park, NJ 07932-0971 USA mlittman@research.att. com ... | 2000 | 11 |
1,765 | Kernel-Based Reinforcement Learning in Average-Cost Problems: An Application to Optimal Portfolio Choice Dirk Ormoneit Department of Computer Science Stanford University Stanford, CA 94305-9010 ormoneit@cs.stanford.edu Abstract Peter Glynn EESOR Stanford University Stanford, CA 94305... | 2000 | 110 |
1,766 | A New Approximate Maximal Margin Classification Algorithm Claudio Gentile DSI, Universita' di Milano, Via Comelico 39, 20135 Milano, Italy gentile@dsi.unimi.it Abstract A new incremental learning algorithm is described which approximates the maximal margin hyperplane w.r.t. norm p ~ 2 for a s... | 2000 | 111 |
1,767 | Ensemble Learning and Linear Response Theory for leA Pedro A.d.F.R. Hfljen-Sflrensenl , Ole Winther2 , Lars Kai Hansenl 1 Department of Mathematical Modelling, Technical University of Denmark B321 DK-2800 Lyngby, Denmark, phs , l kha n sen@imrn. dtu. dk 2Theoretical Physics, Lund University, SOlvegatan 1... | 2000 | 112 |
1,768 | Regularization with Dot-Product Kernels Alex J. SIDola, Zoltan L. Ovari, and Robert C. WilliaIDson Department of Engineering Australian National University Canberra, ACT, 0200 Abstract In this paper we give necessary and sufficient conditions under which kernels of dot product type k(x, y) = k(x . ... | 2000 | 113 |
1,769 | From Margin To Sparsity Thore Graepel, Ralf Herbrich Computer Science Department Technical University of Berlin Berlin, Germany {guru, ralfh)@cs.tu-berlin.de Robert C. Williamson Department of Engineering Australian National University Canberra, Australia Bob. Williamson@anu.edu.au Abst... | 2000 | 114 |
1,770 | On a Connection between Kernel PCA and Metric Multidimensional Scaling Christopher K. I. WilliaIns Division of Informatics The University of Edinburgh 5 Forrest Hill, Edinburgh EH1 2QL, UK c.k.i.williams~ed.ac.uk http://anc.ed.ac.uk Abstract In this paper we show that the kernel peA algorithm... | 2000 | 115 |
1,771 | One Microphone Source Separation Sam T. Roweis Gatsby Unit, University College London roweis@gatsby.ucl. a c.uk Abstract Source separation, or computational auditory scene analysis, attempts to extract individual acoustic objects from input which contains a mixture of sounds from different sources,... | 2000 | 116 |
1,772 | Interactive Parts Model: an Application to Recognition of On-line Cursive Script Predrag Neskovic, Philip C Davis' and Leon N Cooper Physics Department and Institute for Brain and Neural Systems Brown University, Providence, RI 02912 Abstract In this work, we introduce an Interactive Parts (IP) model ... | 2000 | 117 |
1,773 | A new model of spatial representations multimodal brain areas. . In Sophie Deneve Department of Brain and cognitive Science University of Rochester Rochester, NY 14620. sdeneve@bcs.rochester.edu Jean-Rene Duhamel Institut des Sciences Cognitives C.N.R.S Bron, France 69675 jrd@isc.... | 2000 | 118 |
1,774 | Feature Correspondence: A Markov Chain Monte Carlo Approach Frank Dellaert, Steven M. Seitz, Sebastian Thrun, and Charles Thorpe Department of Computer Science &Robotics Institute Carnegie Mellon University Pittsburgh, PA 15213 {dellaert,seitz,thrun,cet }@cs.cmu.edu Abstract When trying to recov... | 2000 | 119 |
1,775 | An Information Maximization Approach to Overcomplete and Recurrent Representations Oren Shriki and Haim Sompolinsky Racah Institute of Physics and Center for Neural Computation Hebrew University Jerusalem, 91904, Israel Abstract Daniel D. Lee Bell Laboratories Lucent Technologies Murray... | 2000 | 12 |
1,776 | Stagewise processing in error-correcting codes and image restoration K. Y. Michael Wong Department of Physics, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong phkywong@ust.hk Hidetoshi Nishimori Department of Physics, Tokyo Institute of Technology, Oh-Okayama,... | 2000 | 120 |
1,777 | The Kernel Gibbs Sampler Thore Graepel Statistics Research Group Computer Science Department Technical University of Berlin Berlin, Germany guru@cs.tu-berlin.de Ralf Herbrich Statistics Research Group Computer Science Department Technical University of Berlin Berlin, Germany ralfh@cs... | 2000 | 121 |
1,778 | Learning Sparse Image Codes using a Wavelet Pyramid Architecture Bruno A. Olshausen Department of Psychology and Center for Neuroscience, UC Davis 1544 Newton Ct. Davis, CA 95616 baolshausen@uedavis.edu Phil Sallee Department of Computer Science UC Davis Davis, CA 95616 sallee@es.ued... | 2000 | 122 |
1,779 | Weak Learners and Improved Rates of Convergence in Boosting Shie Mannor and Ron Meir Department of Electrical Engineering Technion, Haifa 32000, Israel {shie,rmeir }@{techunix,ee}.technion.ac.il Abstract The problem of constructing weak classifiers for boosting algorithms is studied. We present an ... | 2000 | 123 |
1,780 | Keeping flexible active contours on track using Metropolis updates Trausti T. Kristjansson University of Waterloo ttkri stj @uwater l oo . ca Abstract Brendan J. Frey University of Waterloo f r ey@uwater l oo . ca Condensation, a form of likelihood-weighted particle filtering, has been suc... | 2000 | 124 |
1,781 | Data clustering by Markovian relaxation and the Information Bottleneck Method N aft ali Tishby and N oam Slonim School of Computer Science and Engineering and Center for Neural Computation * The Hebrew University, Jerusalem, 91904 Israel email: {tishby.noamm}ees.huji.ae.il Abstract We introdu... | 2000 | 125 |
1,782 | Balancing Multiple Sources of Reward in Reinforcement Learning Christian R. Shelton Artificial Intelligence Lab Massachusetts Institute of Technology Cambridge, MA 02139 cshelton@ai.mit.edu Abstract For many problems which would be natural for reinforcement learning, the reward signal is not ... | 2000 | 126 |
1,783 | Temporally Dependent Plasticity: An Information Theoretic Account Gal Chechik and N aft ali Tishby School of Computer Science and Engineering and the Interdisciplinary Center for Neural Computation The Hebrew University, Jerusalem, Israel {ggal,tishby}@cs.huji.ac.il Abstract The paradigm o... | 2000 | 127 |
1,784 | Analysis of Bit Error Probability of Direct-Sequence CDMA Multiuser Demodulators Toshiyuki Tanaka Department of Electronics and Information Engineering Tokyo Metropolitan University Hachioji, Tokyo 192-0397, Japan tanaka@eeLmetro-u.ac.jp Abstract We analyze the bit error probability of multiu... | 2000 | 128 |
1,785 | A variational mean-field theory for sigmoidal belief networks c. Bhattacharyya Computer Science and Automation Indian Institute of Science Bangalore, India, 560012 cbchiru@csa.iisc.ernet.in S. Sathiya Keerthi Mechanical and Production Engineering National University of Singapore mpessk@gup... | 2000 | 129 |
1,786 | A Linear Programming Approach to Novelty Detection Colin Campbell Dept. of Engineering Mathematics, Bristol University, Bristol Bristol, BS8 1 TR, United Kingdon C. Campbell@bris.ac.uk Kristin P. Bennett Dept. of Mathematical Sciences Rensselaer Polytechnic Institute Troy, New York 1218... | 2000 | 13 |
1,787 | Robust Reinforcement Learning J un Morimoto Graduate School of Information Science N ara Institute of Science and Technology; Kawato Dynamic Brain Project, JST 2-2 Hikaridai Seika-cho Soraku-gun Kyoto 619-0288 JAPAN xmorimo@erato.atr.co.jp Kenji Doya ATR International; CREST, JST 2-2 Hi... | 2000 | 130 |
1,788 | Explaining Away in Weight Space Peter Dayan Sham Kakade Gatsby Computational Neuroscience Unit, UCL 17 Queen Square London WCIN 3AR daya n @gat sby.ucl. ac . uk sham@gat sby.u cl. ac .uk Abstract Explaining away has mostly been considered in terms of inference of states in belief networks. We... | 2000 | 131 |
1,789 | Improved Output Coding for Classification Using Continuous Relaxation Koby Crammer and Yoram Singer School of Computer Science & Engineering The Hebrew University, Jerusalem 91904, Israel {kobi cs ,sing e r }@ cs.huji.ac .il Abstract Output coding is a general method for solving multiclass problems... | 2000 | 132 |
1,790 | Learning curves for Gaussian processes regression: A framework for good approximations Dorthe Malzahn Manfred Opper Neural Computing Research Group School of Engineering and Applied Science Aston University, Birmingham B4 7ET, United Kingdom. [malzahnd.opperm]~aston.ac.uk Abstract Based on... | 2000 | 133 |
1,791 | Sequentially fitting "inclusive" trees for inference in noisy-OR networks Brendan J. Frey!, Relu Patrascul , Tommi S. Jaakkola\ Jodi Moranl 1 Intelligent Algorithms Lab University of Toronto www.cs.toronto.edu/~frey 2 Computer Science and Electrical Engineering Massachusetts Institute of Technol... | 2000 | 134 |
1,792 | Whence Sparseness? C. van Vreeswijk Gatsby Computational Neuroscience Unit University College London 17 Queen Square, London WCIN 3AR, United Kingdom Abstract It has been shown that the receptive fields of simple cells in VI can be explained by assuming optimal encoding, provided that an extra constra... | 2000 | 135 |
1,793 | Periodic Component Analysis: An Eigenvalue Method for Representing Periodic Structure in Speech Lawrence K. Saul and Jont B. Allen {lsaul,jba}@research.att.com AT&T Labs, 180 Park Ave, Florham Park, NJ 07932 Abstract An eigenvalue method is developed for analyzing periodic structure in speech. S... | 2000 | 136 |
1,794 | Partially Observable SDE Models for Image Sequence Recognition Tasks Javier R. Movellan Institute for Neural Computation University of California San Diego Paul Mineiro Department of Cognitive Science University of California San Diego R. J. Williams Department of Mathematics University of... | 2000 | 137 |
1,795 | Combining ICA and top-down attention for robust speech recognition Un-Min Bae and Soo-Young Lee Department of Electrical Engineering and Computer Science and Brain Science Research Center Korea Advanced Institute of Science and Technology 373-1 Kusong-dong, Yusong-gu, Taejon, 305-701, Korea bum@neu... | 2000 | 138 |
1,796 | A comparison of Image Processing Techniques for Visual Speech Recognition Applications Michael S. Gray Computational Neurobiology Laboratory The Salk Institute San Diego, CA 92186-5800 Terrence J. Sejnowski Computational Neurobiology Laboratory The Salk Institute San Diego, CA 92186-... | 2000 | 139 |
1,797 | Programmable Reinforcement Learning Agents David Andre and Stuart J. Russell Computer Science Division, UC Berkeley, CA 94702 { dandre,russell}@cs.berkeley.edu Abstract We present an expressive agent design language for reinforcement learning that allows the user to constrain the policies considered by t... | 2000 | 14 |
1,798 | Learning continuous distributions: Simulations with field theoretic priors lIya Nemenman1,2 and William Bialek2 1 Department of Physics, Princeton University, Princeton, New Jersey 08544 2NEC Research Institute, 4 Independence Way, Princeton, New Jersey 08540 nemenman@research.nj.nec.com, bialek@research... | 2000 | 140 |
1,799 | Algebraic Information Geometry for Learning Machines with Singularities Sumio Watanabe Precision and Intelligence Laboratory Tokyo Institute of Technology 4259 Nagatsuta, Midori-ku, Yokohama, 226-8503 Japan swatanab@pi.titech.ac.jp Abstract Algebraic geometry is essential to learning theory. In ... | 2000 | 141 |
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