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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1,600 | Linear Hinge Loss and Average Margin Claudio Gentile DSI, Universita' di Milano, Via Comelico 39, 20135 Milano. Italy gentile@dsi.unimi.it Manfred K. Warmuth· Computer Science Department, University of California, 95064 Santa Cruz, USA manfred@cse.ucsc.edu Abstract We describe a unif... | 1998 | 98 |
1,601 | Learning Instance-Independent Value Functions to Enhance Local Search Robert Moll Andrew G. Barto Theodore J. Perkins Department of Computer Science University of Massachusetts, Amherst, MA 01003 Richard S. Sutton AT&T Shannon Laboratory, 180 Park Avenue, Florham Park, NJ 07932 Abstract Reinf... | 1998 | 99 |
1,602 | Regular and Irregular Gallager-type Error-Correcting Codes Y. Kabashirna and T. Murayarna Dept. of Compt. IntI. & Syst. Sci. Tokyo Institute of Technology Yokohama 2268502, Japan D. Saad and R. Vicente Neural Computing Research Group Aston University Birmingham B4 7ET, UK Abstract The p... | 1999 | 1 |
1,603 | Building Predictive Models from Fractal Representations of Symbolic Sequences Peter Tioo Georg Dorffner Austrian Research Institute for Artificial Intelligence Schottengasse 3, A-101O Vienna, Austria {petert,georg}@ai.univie.ac.at Abstract We propose a novel approach for building finite memory pred... | 1999 | 10 |
1,604 | Constructing Heterogeneous Committees Using Input Feature Grouping: Application to Economic Forecasting Yuansong Liao and John Moody Department of Computer Science, Oregon Graduate Institute, P.O.Box 91000, Portland, OR 97291-1000 Abstract The committee approach has been proposed for reducing model... | 1999 | 100 |
1,605 | An Analysis of Turbo Decoding with Gaussian Densities Paat Rusmevichientong and Benjamin Van Roy Stanford University Stanford, CA 94305 {paatrus, bvr} @stanford.edu Abstract We provide an analysis of the turbo decoding algorithm (TDA) in a setting involving Gaussian densities. In this context, w... | 1999 | 101 |
1,606 | Probabilistic methods for Support Vector Machines Peter Sollich Department of Mathematics, King's College London Strand, London WC2R 2LS, U.K. Email: peter.sollich@kcl.ac.uk Abstract I describe a framework for interpreting Support Vector Machines (SVMs) as maximum a posteriori (MAP) solutions to in... | 1999 | 102 |
1,607 | Neural System Model of Human Sound Localization Craig T. Jin Department of Physiology and Department of Electrical Engineering, Univ. of Sydney, NSW 2006, Australia Simon Carlile Department of Physiology and Institute of Biomedical Research, Univ. of Sydney, NSW 2006, Australia Abstract ... | 1999 | 103 |
1,608 | A Neuromorphic VLSI System for Modeling the Neural Control of Axial Locomotion Girish N. Patel girish@ece.gatech.edu Edgar A. Brown ebrown@ece.gatech.edu Stephen P. De Weerth steved@ece.gatech.edu School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta, Ga. 30... | 1999 | 104 |
1,609 | Graded grammaticality in Prediction Fractal Machines Shan Parfitt, Peter Tiilo and Georg Dorffner Austrian Research Institute for Artificial Intelligence, Schottengasse 3, A-IOIO Vienna, Austria. { shan,petert,georg} @ai. univie. ac. at Abstract We introduce a novel method of constructing language ... | 1999 | 105 |
1,610 | Learning sparse codes with a mixture-of-Gaussians prior Bruno A. Olshausen Department of Psychology and Center for Neuroscience, UC Davis 1544 Newton Ct. Davis, CA 95616 baolshausen@ucdavis.edu K. Jarrod Millman Center for Neuroscience, UC Davis 1544 Newton Ct. Davis, CA 95616 kjmill... | 1999 | 106 |
1,611 | Semiparametric Approach to Multichannel Blind Deconvolution of Nonminimum Phase Systems L.-Q. Zhang, S. Amari and A. Cichocki Brain-style Information Systems Research Group, BSI The Institute of Physical and Chemical Research Wako shi, Saitama 351-0198, JAPAN zha@open.brain.riken.go.jp {amari,ci... | 1999 | 107 |
1,612 | Application of Blind Separation of Sources to Optical Recording of Brain Activity Holger Schoner, Martin Stetter, Ingo Schie61 Department of Computer Science Technical University of Berlin Germany {hjsch,moatl,ingos}@cs.tu-berlin.de John E. W. Mayhew University of Sheffield, UK j. e.mayhew@sheff... | 1999 | 108 |
1,613 | Spectral Cues in Human Sound Localization Craig T. Jin Department of Physiology and Department of Electrical Engineering, Univ. of Sydney, NSW 2006, Australia Simon Carlile Department of Physiology and Institute of Biomedical Research Univ. of Sydney, NSW 2006, Australia Anna Corderoy Depa... | 1999 | 109 |
1,614 | Neural Computation with Winner-Take-All as the only Nonlinear Operation Wolfgang Maass Institute for Theoretical Computer Science Technische UniversWit Graz A-8010 Graz, Austria email: maass@igi.tu-graz.ac.at http://www.cis.tu-graz.ac.atiigi/maass Abstract Everybody "knows" that neural networ... | 1999 | 11 |
1,615 | Robust Full Bayesian Methods for Neural Networks Christophe Andrieu* Cambridge University Engineering Department Cambridge CB2 1PZ England ca226@eng.cam.ac.uk J oao FG de Freitas UC Berkeley Computer Science 387 Soda Hall, Berkeley CA 94720-1776 USA jfgf@cs.berkeley.edu Abstrac... | 1999 | 110 |
1,616 | A Neurodynamical Approach to Visual Attention Gustavo Deco Siemens AG Corporate Technology Neural Computation, ZT IK 4 Otto-Hahn-Ring 6 81739 Munich, Germany Gustavo.Deco@mchp.siemens.de JosefZihl Institute of Psychology Neuropsychology Ludwig-Maximilians-University Munich Leopoldstr... | 1999 | 111 |
1,617 | Dynamics of Supervised Learning with Restricted Training Sets and Noisy Teachers A.C.C. Coolen Dept of Mathematics King's College London The Strand, London WC2R 2LS, UK tcoolen@mth.kc1.ac.uk C.W.H.Mace Dept of Mathematics King's College London The Strand, London WC2R 2LS, UK cmace@mth.k... | 1999 | 112 |
1,618 | Audio-Vision: Using Audio-Visual Synchrony to Locate Sounds John Hershey .. jhershey~cogsci.ucsd.edu Department of Cognitive Science University of California, San Diego La Jolla, CA 92093-0515 Javier Movellan movellan~cogsci.ucsd.edu Department of Cognitive Science University of Califor... | 1999 | 113 |
1,619 | Predictive Sequence Learning in Recurrent Neocortical Circuits* R.P.N.Rao Computational Neurobiology Lab and Sloan Center for Theoretical Neurobiology The Salk Institute, La Jolla, CA 92037 rao@salk.edu T. J. Sejnowski Computational Neurobiology Lab and Howard Hughes Medical Institute The ... | 1999 | 114 |
1,620 | Differentiating Functions of the Jacobian with Respect to the Weights Gary William Flake NEC Research Institute 4 Independence Way Princeton, NJ 08540 jiake@research.nj.nec.com Barak A. Pearlmutter Dept of Computer Science, FEC 313 University of New Mexico Albuquerque, NM 87131 bap@cs.u... | 1999 | 115 |
1,621 | Effects of Spatial and Temporal Contiguity on the Acquisition of Spatial Information Thea B. Ghiselli-Crippa and Paul W. Munro Department of Information Science and Telecommunications University of Pittsburgh Pittsburgh, PA 15260 tbgst@sis.pitt.edu, munro@sis.pitt.edu Abstract Spatial informatio... | 1999 | 116 |
1,622 | Agglomerative Information Bottleneck Noam Slonim Naftali Tishby* Institute of Computer Science and Center for Neural Computation The Hebrew University Jerusalem, 91904 Israel email: {noamm.tishby}(Qcs.huji.ac.il Abstract We introduce a novel distributional clustering algorithm that maximizes ... | 1999 | 117 |
1,623 | Spike-based learning rules and stabilization of persistent neural activity Xiaohui Xie and H. Sebastian Seung Dept. of Brain & Cog. Sci., MIT, Cambridge, MA 02139 {xhxie, seung}@mit.edu Abstract We analyze the conditions under which synaptic learning rules based on action potential timing can be ap... | 1999 | 118 |
1,624 | Nonlinear Discriminant Analysis using Kernel Functions Volker Roth & Volker Steinhage University of Bonn, Institut of Computer Science III Romerstrasse 164, D-53117 Bonn, Germany {roth, steinhag}@cs.uni-bonn.de Abstract Fishers linear discriminant analysis (LDA) is a classical multivariate techniqu... | 1999 | 119 |
1,625 | Support Vector Method for Multivariate Density Estimation Vladimir N. Vapnik Royal Halloway College and AT &T Labs, 100 Schultz Dr. Red Bank, NJ 07701 vlad@research.att.com Abstract Sayan Mukherjee CBCL, MIT E25-201 Cambridge, MA 02142 sayan@ai.mit.edu A new method for multivariate d... | 1999 | 12 |
1,626 | On input selection with reversible jump Markov chain Monte Carlo sampling Peter Sykacek Austrian Research Institute for Artificial Intelligence (OFAI) Schottengasse 3, A-10lO Vienna, Austria peter@ai. univie. ac. at Abstract In this paper we will treat input selection for a radial basis function ... | 1999 | 120 |
1,627 | Uniqueness of the SVM Solution Christopher J .C. Burges Advanced Technologies, Bell Laboratories, Lucent Technologies Holmdel, New Jersey burges@iucent.com David J. Crisp Centre for Sensor Signal and Information Processing, Deptartment of Electrical Engineering, University of Adelaide, ... | 1999 | 121 |
1,628 | The Parallel Problems Server: an Interactive Tool for Large Scale Machine Learning Charles Lee Isbell, Jr. isbell @research.att.com AT&T Labs 180 Park Avenue Room A255 Florham Park, NJ 07932-0971 Parry Husbands PIRHusbands@lbl.gov Lawrence Berkeley National LaboratorylNERSC 1 Cyclotron Roa... | 1999 | 122 |
1,629 | Search for Information Bearing Components in Speech Howard Hua Yang and Hynek Hermansky Department of Electrical and Computer Engineering Oregon Graduate Institute of Science and Technology 20000 NW, Walker Rd., Beaverton, OR97006, USA {hyang,hynek}@ece.ogi.edu, FAX:503 7481406 Abstract In this ... | 1999 | 123 |
1,630 | An Oscillatory Correlation Framework for Computational Auditory Scene Analysis GuyJ.Brown Department of Computer Science University of Sheffield Regent Court, 211 Portobello Street, Sheffield S 1 4DP, UK Email: g.brown@dcs.shefac.uk DeLiang L. Wang Department of Computer and Information Sc... | 1999 | 124 |
1,631 | Manifold Stochastic Dynamics for Bayesian Learning Mark Zlochin Department of Computer Science Technion - Israel Institute of Technology Technion City, Haifa 32000, Israel zmark@cs.technion.ac.il YoramBaram Department of Computer Science Technion - Israel Institute of Technology Technion C... | 1999 | 125 |
1,632 | Bifurcation Analysis of a Silicon Neuron Girish N. Patel] , Gennady s. Cymbalyuk2,3, Ronald L. Calabrese2, and Stephen P. DeWeerth1 lSchool of Electrical and Computer Engineering Georgia Institute of Technology Atlanta, Ga. 30332-0250 {girish.patel, steve.deweerth} @ece.gatech.edu 2Department of Bi... | 1999 | 126 |
1,633 | Speech Modelling Using Subspace and EM Techniques Gavin Smith Cambridge University Engineering Department Cambridge CB2 1PZ England gas1 oo3@eng.cam.ac.uk Joao FG de Freitas Computer Science Division 487 Soda Hall UC Berkeley CA 94720-1776, USA. jfgf@cs.berkeley.edu 1 Mahesan N... | 1999 | 127 |
1,634 | Optimal Kernel Shapes for Local Linear Regression Dirk Ormoneit Trevor Hastie Department of Statistics Stanford University Stanford, CA 94305-4065 ormoneit@stat.stanjord.edu Abstract Local linear regression performs very well in many low-dimensional forecasting problems. In high-dimensiona... | 1999 | 128 |
1,635 | Robust Neural Network Regression for Offline and Online Learning Thomas Briegel* Siemens AG, Corporate Technology D-81730 Munich, Germany thomas.briegel@mchp.siemens.de Volker Tresp Siemens AG, Corporate Technology D-81730 Munich, Germany volker.tresp@mchp.siemens.de Abstract We replace... | 1999 | 129 |
1,636 | Leveraged Vector Machines Yoram Singer Hebrew University singer@cs.huji.ac.il Abstract We describe an iterative algorithm for building vector machines used in classification tasks. The algorithm builds on ideas from support vector machines, boosting, and generalized additive models. The algorithm c... | 1999 | 13 |
1,637 | Neural Network Based Model Predictive Control Stephen Piche Pavilion Technologies Austin, TX 78758 spiche@pav.com Gene Boe Pavilion Technologies Austin, TX 78758 gboe@pav.com Jim Keeler Pavilion Technologies Austin, TX 78758 jkeeler@pav.com Doug Johnson Pavilion Technologies... | 1999 | 130 |
1,638 | Coastal Navigation with Mobile Robots Nicholas Roy and Sebastian Thrun School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 { nicholas. roy I sebastian. thrun } @cs.cmu.edu Abstract The problem that we address in this paper is how a mobile robot can plan in order to arrive ... | 1999 | 131 |
1,639 | Boosting with Multi-Way Branching in Decision Trees Yishay Mansour AT&T Labs-Research 180 Park Ave Florham Park NJ 07932 David McAllester {mansour, dmac }@research.att.com Abstract It is known that decision tree learning can be viewed as a form of boosting. However, existing boosting theor... | 1999 | 132 |
1,640 | Effects of Spatial and Temporal Contiguity on the Acquisition of Spatial Information Thea B. Ghiselli-Crippa and Paul W. Munro Department of Information Science and Telecommunications University of Pittsburgh Pittsburgh, PA 15260 tbgst@sis.pitt.edu, munro@sis.pitt.edu Abstract Spatial informatio... | 1999 | 133 |
1,641 | Model Selection for Support Vector Machines Olivier Chapelle*,t, Vladimir Vapnik* * AT&T Research Labs, Red Bank, NJ t LIP6, Paris, France { chapelle, vlad} @research.au.com Abstract New functionals for parameter (model) selection of Support Vector Machines are introduced based on the concepts of the ... | 1999 | 134 |
1,642 | Memory Capacity of Linear vs. Nonlinear Models of Dendritic Integration Panayiota Poirazi* Biomedical Engineering Department University of Southern California Los Angeles, CA 90089 poirazi@sc/. usc. edu Bartlett W. Mel* Biomedical Engineering Department University of Southern California Lo... | 1999 | 135 |
1,643 | State Abstraction in MAXQ Hierarchical Reinforcement Learning Thomas G. Dietterich Department of Computer Science Oregon State University Corvallis, Oregon 97331-3202 tgd@cs.orst.edu Abstract Many researchers have explored methods for hierarchical reinforcement learning (RL) with temporal abstra... | 1999 | 136 |
1,644 | Modeling High-Dimensional Discrete Data with Multi-Layer Neural Networks Yoshua Bengio Dept.IRO Universite de Montreal Montreal, Qc, Canada, H3C 317 bengioy@iro.umontreal.ca Abstract Samy Bengio * IDIAP CP 592, rue du Simplon 4, 1920 Martigny, Switzerland bengio@idiap.ch The curse... | 1999 | 137 |
1,645 | A generative model for attractor dynamics Richard S. Zemel Department of Psychology University of Arizona Tucson, AZ 85721 zemel@u.arizona.edu Michael C. Mozer Department of Computer Science University of Colorado Boulder, CO 80309-0430 mozer@colorado.edu Abstract Attractor networks,... | 1999 | 138 |
1,646 | An Environment Model for N onstationary Reinforcement Learning Samuel P. M. Choi Dit-Yan Yeung Nevin L. Zhang pmchoi~cs.ust.hk dyyeung~cs.ust.hk lzhang~cs.ust.hk Department of Computer Science, Hong Kong University of Science and Technology Clear Water Bay, Kowloon, Hong Kong Abstract R... | 1999 | 139 |
1,647 | Learning Factored Representations for Partially Observable Markov Decision Processes Department of Computer Science University of Toronto Toronto M5S 2Z9 Canada Brian Sallans Gatsby Computational Neuroscience Unit* University College London London WCIN 3AR U.K. sallans@cs.toronto.edu Abstr... | 1999 | 14 |
1,648 | Predictive Approaches For Choosing Hyperparameters in Gaussian Processes S. Sathiya Keerthi Mechanical and Production Engg. National University of Singapore S. Sundararajan Computer Science and Automation Indian Institute of Science Bangalore 560 012, India sundar@csa.iisc. ernet. in 10 Ke... | 1999 | 140 |
1,649 | Acquisition in Autoshaping Sham Kakade Peter Dayan Gatsby Computational Neuroscience Unit 17 Queen Square, London, England, WC1N 3AR. sharn@gatsby.ucl.ac.uk dayan@gatsby.ucl.ac.uk Abstract Quantitative data on the speed with which animals acquire behavioral responses during classical conditionin... | 1999 | 141 |
1,650 | Transductive Inference for Estimating Values of Functions Olivier Chapelle*, Vladimir Vapnik*,t, Jason Westontt.t,* * AT&T Research Laboratories, Red Bank, USA. t Royal Holloway, University of London, Egham, Surrey, UK. tt Barnhill BioInformatics.com, Savannah, Georgia, USA. { chapelle, vlad, weston} ... | 1999 | 142 |
1,651 | Effective Learning Requires Neuronal Remodeling of Hebbian Synapses Gal Chechik Isaac Meilijson Eytan Ruppin School of Mathematical Sciences Tel-Aviv University Tel Aviv, Israel ggal@math.tau.ac.il isaco@math.tau.ac.il ruppin@math.tau.ac.il Abstract This paper revisits the classical neuroscie... | 1999 | 143 |
1,652 | The Relevance Vector Machine Michael E. Tipping Microsoft Research St George House, 1 Guildhall Street Cambridge CB2 3NH, U.K. mtipping~microsoft.com Abstract The support vector machine (SVM) is a state-of-the-art technique for regression and classification, combining excellent generalisation ... | 1999 | 144 |
1,653 | Dual Estimation and the Unscented Transformation EricA. Wan ericwan@ece.ogi.edu Rudolph van der Merwe rudmerwe@ece.ogi.edu Alex T. Nelson atneison@ece.ogi.edu Oregon Graduate Institute of Science & Technology Department of Electrical and Computer Engineering 20000 N.W. Walker Rd., Beaverto... | 1999 | 145 |
1,654 | Learning Statistically Neutral Tasks without Expert Guidance Ton Weijters Information Technology, Eindhoven University, The Netherlands Antal van den Bosch ILK, Tilburg University, The Netherlands Abstract Eric Postma Computer Science, Universiteit Maastricht, The Netherlands ... | 1999 | 146 |
1,655 | Correctness of belief propagation in Gaussian graphical models of arbitrary topology Yair Weiss Computer Science Division UC Berkeley, 485 Soda Hall Berkeley, CA 94720-1776 Phone: 510-642-5029 yweiss@cs.berkeley.edu William T. Freeman Mitsubishi Electric Research Lab 201 Broadway Cambri... | 1999 | 147 |
1,656 | A Multi-class Linear Learning Algorithm Related to Winnow Chris Mesterhann* Rutgers Computer Science Department 110 Frelinghuysen Road Piscataway, NJ 08854 mesterha@paul.rutgers.edu Abstract In this paper, we present Committee, a new multi-class learning algorithm related to the Winnow family of... | 1999 | 148 |
1,657 | Churn Reduction in the Wireless Industry Michael C. Mozer*+, Richard Wolniewicz*, David B. Grimes*+, Eric Johnson * , Howard Kaushansky* * Athene Software + Department of Computer Science 2060 Broadway, Suite 300 University of Colorado Boulder, CO 80302 Boulder, CO 80309-0430 Abstract Comp... | 1999 | 149 |
1,658 | Variational Inference for Bayesian Mixtures of Factor Analysers Zoubin Ghahramani and Matthew J. Beal Gatsby Computational Neuroscience Unit University College London 17 Queen Square, London WC1N 3AR, England {zoubin,m.beal}Ggatsby.ucl.ac.uk Abstract We present an algorithm that infers the model... | 1999 | 15 |
1,659 | Recognizing Evoked Potentials in a Virtual Environment * Jessica D. Bayliss and Dana H. Ballard Department of Computer Science University of Rochester Rochester, NY 14627 {bayliss,dana}@cs.rochester.edu Abstract Virtual reality (VR) provides immersive and controllable experimental environments. ... | 1999 | 150 |
1,660 | Topographic Transformation as a Discrete Latent Variable Nebojsa Jojic Beckman Institute University of Illinois at Urbana www.ifp.uiuc.edu/",jojic Brendan J. Frey Computer Science University of Waterloo www.cs.uwaterloo.ca/ ... frey Abstract Invariance to topographic transformations suc... | 1999 | 16 |
1,661 | Channel Noise in Excitable Neuronal Membranes Amit Manwani; Peter N. Steinmetz and Christof Koch Computation and Neural Systems Program, M-S 139-74 California Institute of Technology Pasadena, CA 91125 { quixote,peter,koch } @klab.caltech.edu Abstract Stochastic fluctuations of voltage-gated ion ch... | 1999 | 17 |
1,662 | Efficient Approaches to Gaussian Process Classification Lehel Csato, Ernest Fokoue, Manfred Opper, Bernhard Schottky Neural Computing Research Group School of Engineering and Applied Sciences Aston University Birmingham B4 7ET, UK. {opperm,csatol}~aston.ac.uk Ole Winther Theoretical Physics II, ... | 1999 | 18 |
1,663 | Optimal sizes of dendritic and axonal arbors Dmitri B. Chklovskii Sloan Center for Theoretical Neurobiology The Salk Institute, La Jolla, CA 92037 mitya@salk.edu Abstract I consider a topographic projection between two neuronal layers with different densities of neurons. Given the number of output neu... | 1999 | 19 |
1,664 | An MEG Study of Response Latency and Variability in the Human Visual System During a Visual-Motor Integration Task Akaysha C. Tang Dept. of Psychology University of New Mexico Albuquerque, NM 87131 akaysha@unm.edu Barak A. Pearlmutter Dept. of Computer Science University of New Mexico A... | 1999 | 2 |
1,665 | v-Arc: Ensemble Learning in the Presence of Outliers G. Ratscht , B. Scholkopf1, A. Smola", K.-R. Miillert, T. Onodatt , and S. Mikat t GMD FIRST, Rudower Chaussee 5,12489 Berlin, Germany t Microsoft Research, 1 Guildhall Street, Cambridge CB2 3NH, UK * Dep. of Engineering, ANU, Canberra ACT 0200, Aus... | 1999 | 20 |
1,666 | Monte Carlo POMDPs Sebastian Thrun School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 Abstract We present a Monte Carlo algorithm for learning to act in partially observable Markov decision processes (POMDPs) with real-valued state and action spaces. Our approach uses imp... | 1999 | 21 |
1,667 | A recurrent model of the interaction between Prefrontal and Inferotemporal cortex in delay tasks ALFONSO RENART, NESTOR PARGA Departamento de F{sica Te6rica Universidad Aut6noma de Madrid Canto Blanco, 28049 Madrid, Spain http://www.ft.uam.es/neurociencialGRUPO/grup0.1!nglish.html and EDMUND ... | 1999 | 22 |
1,668 | Information Factorization in Connectionist Models of Perception Javier R. Movellan Department of Cognitive Science Institute for Neural Computation University of California San Diego James L. McClelland Center for the Neural Bases of Cognition Department of Psychology Carnegie Mellon Universi... | 1999 | 23 |
1,669 | Hierarchical Image Probability (HIP) Models Clay D. Spence and Lucas Parra Sarnoff Corporation CN5300 Princeton, NJ 08543-5300 { cspence, lparra} @samoff.com Abstract We formulate a model for probability distributions on image spaces. We show that any distribution of images can be factored exact... | 1999 | 24 |
1,670 | Reinforcement Learning for Spoken Dialogue Systems Satinder Singh AT&T Labs Michael Keams AT&T Labs Diane Litman AT&T Labs Marilyn Walker AT&T Labs {baveja,mkeams,diane, walker} @research.att.com Abstract Recently, a number of authors have proposed treating dialogue systems as Markov... | 1999 | 25 |
1,671 | Distributed Synchrony of Spiking Neurons in a Hebbian Cell Assembly David Horn Nir Levy School of Physics and Astronomy, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv 69978, Israel horn~neuron.tau.ac.il nirlevy~post.tau.ac.il Isaac Meilijson Eytan Ruppin... | 1999 | 26 |
1,672 | Image representations for facial expression coding Marian Stewart Bartlett* V.C. San Diego marni<Osalk.edu Javier R. Movellan V.C. San Diego movellan<ocogsci.ucsd.edu Paul Ekman V.C. San Francisco ekman<ocompuserve.com Gianluca Donato Di~ital Persona, Redwood City, CA glanlucad<Od... | 1999 | 27 |
1,673 | Algorithms for Independent Components Analysis and Higher Order Statistics Daniel D. Lee Bell Laboratories Lucent Technologies Murray Hill, NJ 07974 Uri Rokni and Haim Sompolinsky Racah Institute of Physics and Center for Neural Computation Hebrew University Jerusalem, 91904, Israel Abs... | 1999 | 28 |
1,674 | A SNoW-Based Face Detector Ming-Hsuan Yang Dan Roth Narendra Ahuja Department of Computer Science and the Beckman Institute University of Illinois at Urbana-Champaign Urbana, IL 61801 mhyang~vision.ai.uiuc.edu danr~cs.uiuc.edu ahuja~vision.ai.uiuc.edu Abstract A novel learning approach ... | 1999 | 29 |
1,675 | The Entropy Regularization Information Criterion Alex J. Smola Dept. of Engineering and RSISE Australian National University Canberra ACT 0200, Australia Alex.Smola@anu.edu.au Bernhard Scholkopf Microsoft Research Limited St. George House, 1 Guildhall Street Cambridge CB2 3NH bsc@micros... | 1999 | 3 |
1,676 | A Winner-Take-All Circuit with Controllable Soft Max Property Shih-Chii Lin Institute for Neuroinformatics, ETHjUNIZ Winterthurstrasse 190, CH-8057 Zurich Switzerland shih@ini.phys.ethz.ch Abstract I describe a silicon network consisting of a group of excitatory neurons and a global inhibitory n... | 1999 | 30 |
1,677 | Bayesian model selection for Support Vector machines, Gaussian processes and other kernel classifiers Matthias Seeger Institute for Adaptive and Neural Computation University of Edinburgh 5 Forrest Hill, Edinburgh EHI 2QL seeger@dai.ed.ac.uk Abstract We present a variational Bayesian method f... | 1999 | 31 |
1,678 | Kirchoff Law Markov Fields for Analog Circuit Design Richard M. Golden * RMG Consulting Inc. 2000 Fresno Road, Plano, Texas 75074 RMGCONSULT@AOL.COM, www.neural-network.com Abstract Three contributions to developing an algorithm for assisting engineers in designing analog circuits are provided i... | 1999 | 32 |
1,679 | Can VI mechanisms account for figure-ground and medial axis effects? Zhaoping Li Gatsby Computational Neuroscience Unit University College London zhaoping~gatsby.ucl.ac.uk Abstract When a visual image consists of a figure against a background, V1 cells are physiologically observed to give higher... | 1999 | 33 |
1,680 | Policy Gradient Methods for Reinforcement Learning with Function Approximation Richard S. Sutton, David McAllester, Satinder Singh, Yishay Mansour AT&T Labs - Research, 180 Park Avenue, Florham Park, NJ 07932 Abstract Function approximation is essential to reinforcement learning, but the standard a... | 1999 | 34 |
1,681 | Lower Bounds on the Complexity of Approximating Continuous Functions by Sigmoidal Neural Networks Michael Schmitt Lehrstuhl Mathematik und Informatik FakuWit ftir Mathematik Ruhr-Universitat Bochum D-44780 Bochum, Germany mschmitt@lmi.ruhr-uni-bochum.de Abstract We calculate lower bounds o... | 1999 | 35 |
1,682 | Evolv .......... JIiIIIIIIo. Bradley Tookes Dept of Comp. Sci. and Elec. Engineering University of Queensland Queensland, 4072 Australia btonkes@csee.uq. edu.au Alan Blair Department of Computer Science University of Melbourne Parkville, Victoria, 3052 Australia blair@cs.mu.oz.au Janet Wiles ... | 1999 | 36 |
1,683 | Large Margin DAGs for Multiclass Classification John C. Platt Microsoft Research 1 Microsoft Way Redmond, WA 98052 jpiatt@microsojt.com Nello Cristianini Dept. of Engineering Mathematics University of Bristol Bristol, BS8 1 TR - UK nello.cristianini@bristol.ac.uk John Shawe-Taylor ... | 1999 | 37 |
1,684 | Approximate Planning in Large POMDPs via Reusable Trajectories Michael Kearns AT&T Labs mkearns@research.att.com Yishay Mansour Tel Aviv University mansour@math.tau.ac.il Abstract AndrewY. Ng UC Berkeley ang@cs.berkeley.edu We consider the problem of reliably choosing a near-best str... | 1999 | 38 |
1,685 | Maximum entropy discrimination Tommi Jaakkola MIT AI Lab 545 Technology Sq. Cambridge, MA 02139 tommi@ai.mit.edu Marina Meila MIT AI Lab 545 Technology Sq. Cambridge, MA 02139 mmp@ai. mit. edu Abstract Tony Jebara MIT Media Lab 20 Ames St. Cambridge, MA 02139 jebara@media... | 1999 | 39 |
1,686 | 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 | 4 |
1,687 | The Relaxed Online Maximum Margin Algorithm Yi Li and Philip M. Long Department of Computer Science National University of Singapore Singapore 119260, Republic of Singapore {liyi,p/ong}@comp.nus.edu.sg Abstract We describe a new incremental algorithm for training linear threshold functions: the ... | 1999 | 40 |
1,688 | Bayesian modelling of tMRI time series Pedro A. d. F. R. H~jen-S~rensen, Lars K. Hansen and Carl Edward Rasmussen Department of Mathematical Modelling, Building 321 Technical University of Denmark DK-2800 Lyngby, Denmark phs,lkhansen,carl@imrn.dtu.dk Abstract We present a Hidden Markov Model (HMM) ... | 1999 | 41 |
1,689 | Bayesian averaging is well-temperated Lars Kai Hansen Department of Mathematical Modelling Technical University of Denmark B321 DK-2800 Lyngby, Denmark lkhansen@imm.dtu.dk Abstract Bayesian predictions are stochastic just like predictions of any other inference scheme that generalize from a fini... | 1999 | 42 |
1,690 | Policy Search via Density Estimation AndrewY. Ng Computer Science Division u.c. Berkeley Berkeley, CA 94720 ang@cs.berkeley.edu Ronald Parr Computer Science Dept. Stanford University Stanford, CA 94305 parr@cs.stanjord.edu Abstract Daphne Koller Computer Science Dept. Stanford ... | 1999 | 43 |
1,691 | Low Power Wireless Communication via Reinforcement Learning Timothy X Brown Electrical and Computer Engineering University of Colorado Boulder, CO 80309-0530 tirnxb@colorado.edu Abstract This paper examines the application of reinforcement learning to a wireless communication problem. The proble... | 1999 | 44 |
1,692 | Learning to Parse Images Geoffrey E. Hinton and Zoubin Ghahramani Gatsby Computational Neuroscience Unit University College London London, United Kingdom WC1N 3AR {hinton,zoubin}@gatsby.ucl.ac.uk Vee Whye Tah Department of Computer Science University of Toronto Toronto, Ontario, Canada M5S 3G... | 1999 | 45 |
1,693 | Robust Recognition of Noisy and Superimposed Patterns via Selective Attention Soo-Young Lee Brain Science Research Center Korea Advanced Institute of Science & Technology Yusong-gu, Taejon 305-701 Korea sylee@ee.kaist.ac.kr Abstract Michael C. Mozer Department of Computer Science Universit... | 1999 | 46 |
1,694 | Bayesian Network Induction via Local Neighborhoods Dimitris Margaritis Department of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 D.Margaritis@cs.cmu.edu Sebastian Thrun Department of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 S. Thrun@cs.cmu.ed... | 1999 | 47 |
1,695 | Spiking Boltzmann Machines Geoffrey E. Hinton Gatsby Computational Neuroscience Unit University College London London WCIN 3AR, UK hinton@gatsby. ucl. ac. uk Abstract Andrew D. Brown Department of Computer Science University of Toronto Toronto, Canada andy@cs.utoronto.ca We first sho... | 1999 | 48 |
1,696 | Actor-Critic Algorithms Vijay R. Konda John N. Tsitsiklis Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, MA, 02139. konda@mit.edu, jnt@mit.edu Abstract We propose and analyze a class of actor-critic algorithms for simulation-based optimizati... | 1999 | 49 |
1,697 | Correctness of belief propagation in Gaussian graphical models of arbitrary topology Yair Weiss Computer Science Division UC Berkeley, 485 Soda Hall Berkeley, CA 94720-1776 Phone: 510-642-5029 yweiss@cs.berkeley.edu William T. Freeman Mitsubishi Electric Research Lab 201 Broadway Cambri... | 1999 | 5 |
1,698 | Training Data Selection for Optimal Generalization in Trigonometric Polynomial Networks Masashi Sugiyama*and Hidemitsu Ogawa Department of Computer Science, Tokyo Institute of Technology, 2-12-1, O-okayama, Meguro-ku, Tokyo, 152-8552, Japan. sugi@cs. titeck. ac.jp Abstract In this paper, we cons... | 1999 | 50 |
1,699 | Image Recognition in Context: Application to Microscopic Urinalysis XuboSong* Department of Electrical and Computer Engineering Oregon Graduate Institute of Science and Technology Beaverton, OR 97006 xubosong@ece.ogi.edu Joseph Sill Department of Computation and Neural Systems California Inst... | 1999 | 51 |
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