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2,300 | Categorization Under Complexity: A Unified MDL Account of Human Learning of Regular and Irregular Categories David Fass Department ofPsychology Center for Cognitive Science Rutgers University Piscataway, NJ 08854 dfass@ruccs.rutgers.edu Jacob Feldman* Department ofPsychology Center for Cognitive S... | 2002 | 92 |
2,301 | Learning with Multiple Labels Rong Jin* *School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213, USA rong@es.emu.edu Zoubin Ghahramanit* tGatsby Computational Neuroscience Unit University College London London WCIN 3AR, UK zoubin@gatsby.ucl.ae.uk Abstract In this ... | 2002 | 93 |
2,302 | Branching Law for Axons Dmitri B. Chklovskii and Armen Stepanyants Cold Spring Harbor Laboratory 1 Bungtown Rd. Cold Spring Harbor, NY 11724 mitya@cshl.edu stepanya@cshl.edu Abstract What determines the caliber of axonal branches? We pursue the hypothesis that the axonal caliber has evolved t... | 2002 | 94 |
2,303 | Dyadic Classification Trees via Structural Risk Minimization Clayton Scott and Robert Nowak Department of Electrical and Computer Engineering Rice University Houston, TX 77005 cscott,nowak @rice.edu Abstract Classification trees are one of the most popular types of classifiers, with ease of imple... | 2002 | 95 |
2,304 | shorter argument and much tighter than previous margin bounds. There are two mathematical flavors of margin bound dependent upon the weights Wi of the vote and the features Xi that the vote is taken over. 1. Those ([12], [1]) with a bound on Li w~ and Li x~ ("bib" bounds). 2. Those ([11], [6]) with a bound on L... | 2002 | 96 |
2,305 | ynamic Causal Learning Thomas L. Griffiths Department of Psychology Stanford University Stanford, CA 94305-2130 gruffydd@psych.stanford.edu David Danks Institute for Human & Machine Cognition University of West Florida Pensacola, FL 32501 ddanks@ai.uwf.edu Joshua B. Tenenbaum Department of Bra... | 2002 | 97 |
2,306 | Maximally Informative Dimensions: Analyzing Neural Responses to Natural Signals Tatyana Sharpee , Nicole C. Rust , and William Bialek Sloan–Swartz Center for Theoretical Neurobiology, Department of Physiology University of California at San Francisco, San Francisco, California 94143–0444 ... | 2002 | 98 |
2,307 | Fast Exact Inference with a Factored Model for Natural Language Parsing Dan Klein Department of Computer Science Stanford University Stanford, CA 94305-9040 klein@cs.stanford.edu Christopher D. Manning Department of Computer Science Stanford University Stanford, CA 94305-9040 manning@cs.stanford.e... | 2002 | 99 |
2,308 | Decoding V1 Neuronal Activity using Particle Filtering with Volterra Kernels Ryan Kelly Center for the Neural Basis of Cognition Carnegie-Mellon University Pittsburgh, PA 15213 rkelly@cs.cmu.edu Tai Sing Lee Center for the Neural Basis of Cognition Carnegie-Mellon University Pittsburgh, PA 15213 t... | 2003 | 1 |
2,309 | A Kullback-Leibler Divergence Based Kernel for SVM Classification in Multimedia Applications Pedro J. Moreno Purdy P. Ho Hewlett-Packard Cambridge Research Laboratory Cambridge, MA 02142, USA {pedro.moreno,purdy.ho}@hp.com Nuno Vasconcelos UCSD ECE Department 9500 Gilman Drive, MC 0407 La Jolla, CA... | 2003 | 10 |
2,310 | Approximate Policy Iteration with a Policy Language Bias Alan Fern and SungWook Yoon and Robert Givan Electrical and Computer Engineering, Purdue University, W. Lafayette, IN 47907 Abstract We explore approximate policy iteration, replacing the usual costfunction learning step with a learning step in policy s... | 2003 | 100 |
2,311 | Information Bottleneck for Gaussian Variables Gal Chechik∗ Amir Globerson∗ Naftali Tishby Yair Weiss {ggal,gamir,tishby,yweiss}@cs.huji.ac.il School of Computer Science and Engineering and The Interdisciplinary Center for Neural Computation The Hebrew University of Jerusalem, 91904, Israel ∗Both aut... | 2003 | 101 |
2,312 | Laplace Propagation Alex J. Smola, S.V.N. Vishwanathan Machine Learning Group ANU and National ICT Australia Canberra, ACT, 0200 {smola, vishy}@axiom.anu.edu.au Eleazar Eskin Department of Computer Science Hebrew University Jerusalem Jerusalem, Israel, 91904 eeskin@cs.columbia.edu Abstract We pr... | 2003 | 102 |
2,313 | Error Bounds for Transductive Learning via Compression and Clustering Philip Derbeko Ran El-Yaniv Ron Meir Technion - Israel Institute of Technology {philip,rani}@cs.technion.ac.il rmeir@ee.technion.ac.il Abstract This paper is concerned with transductive learning. Although transduction appears to be ... | 2003 | 103 |
2,314 | Approximate Expectation Tom Heskes, Onno Zoeter, and Wim Wiegerinck SNN, University of Nijmegen Geert Grooteplein 21, 6525 EZ, Nijmegen, The Netherlands Abstract We discuss the integration of the expectation-maximization (EM) algorithm for maximum likelihood learning of Bayesian networks with belief propaga... | 2003 | 104 |
2,315 | A Fast Multi-Resolution Method for Detection of Significant Spatial Disease Clusters Daniel B. Neill Department of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 neill@cs.cmu.edu Andrew W. Moore Department of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 awm@cs.... | 2003 | 105 |
2,316 | Computing Gaussian Mixture Models with EM using Equivalence Constraints Noam Shental Computer Science & Eng. Center for Neural Computation Hebrew University of Jerusalem Jerusalem, Israel 91904 fenoam@cs.huji.ac.il Aharon Bar-Hillel Computer Science & Eng. Center for Neural Computation Hebrew Univ... | 2003 | 106 |
2,317 | Convex Methods for Transduction Tijl De Bie ESAT-SCD/SISTA, K.U.Leuven Kasteelpark Arenberg 10 3001 Leuven, Belgium tijl.debie@esat.kuleuven.ac.be Nello Cristianini Department of Statistics, U.C.Davis 360 Kerr Hall One Shields Ave. Davis, CA-95616 nello@support-vector.net Abstract The 2-class tr... | 2003 | 107 |
2,318 | Kernel Dimensionality Reduction for Supervised Learning Kenji Fukumizu Institute of Statistical Mathematics Tokyo 106-8569 Japan fukumizu@ism.ac.jp Francis R. Bach CS Division University of California Berkeley, CA 94720, USA fbach@cs.berkeley.edu Michael I. Jordan CS Division and Statistics ... | 2003 | 108 |
2,319 | Learning with Local and Global Consistency Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal, Jason Weston, and Bernhard Sch¨olkopf Max Planck Institute for Biological Cybernetics, 72076 Tuebingen, Germany {firstname.secondname}@tuebingen.mpg.de Abstract We consider the general problem of learning from label... | 2003 | 109 |
2,320 | A Model for Learning the Semantics of Pictures V. Lavrenko, R. Manmatha, J. Jeon Center for Intelligent Information Retrieval Computer Science Department, University of Massachusetts Amherst {lavrenko,manmatha,jeon}@cs.umass.edu Abstract We propose an approach to learning the semantics of images which all... | 2003 | 11 |
2,321 | Max-Margin Markov Networks Ben Taskar Carlos Guestrin Daphne Koller {btaskar,guestrin,koller}@cs.stanford.edu Stanford University Abstract In typical classification tasks, we seek a function which assigns a label to a single object. Kernel-based approaches, such as support vector machines (SVMs), which m... | 2003 | 110 |
2,322 | Factorization with uncertainty and missing data: exploiting temporal coherence Amit Gruber and Yair Weiss School of Computer Science and Engineering The Hebrew University of Jerusalem 91904 Jerusalem, Israel {amitg,yweiss}@cs.huji.ac.il Abstract The problem of “Structure From Motion” is a central prob... | 2003 | 111 |
2,323 | Sequential Bayesian Kernel Regression Jaco Vermaak, Simon J. Godsill, Arnaud Doucet Cambridge University Engineering Department Cambridge, CB2 1PZ, U.K. {jv211, sjg, ad2}@eng.cam.ac.uk Abstract We propose a method for sequential Bayesian kernel regression. As is the case for the popular Relevance Vector M... | 2003 | 112 |
2,324 | PAC-Bayesian Generic Chaining Jean-Yves Audibert ∗ Universit´e Paris 6 Laboratoire de Probabilit´es et Mod`eles al´eatoires 175 rue du Chevaleret 75013 Paris - France jyaudibe@ccr.jussieu.fr Olivier Bousquet Max Planck Institute for Biological Cybernetics Spemannstrasse 38 D-72076 T¨ubingen - German... | 2003 | 113 |
2,325 | Ambiguous model learning made unambiguous with 1/f priors G. S. Atwal Department of Physics Princeton University Princeton, NJ 08544 gatwal@princeton.edu William Bialek Department of Physics Princeton University Princeton, NJ 08544 wbialek@princeton.edu Abstract What happens to the optimal int... | 2003 | 114 |
2,326 | Generalised Propagation for Fast Fourier Transforms with Partial or Missing Data Amos J Storkey School of Informatics, University of Edinburgh 5 Forrest Hill, Edinburgh UK a.storkey@ed.ac.uk Abstract Discrete Fourier transforms and other related Fourier methods have been practically implementable due to... | 2003 | 115 |
2,327 | Can We Learn to Beat the Best Stock Allan Borodin1 Ran El-Yaniv2 Vincent Gogan1 Department of Computer Science University of Toronto1 Technion - Israel Institute of Technology2 {bor,vincent}@cs.toronto.edu rani@cs.technion.ac.il Abstract A novel algorithm for actively trading stocks is presented. Wh... | 2003 | 116 |
2,328 | An MCMC-Based Method of Comparing Connectionist Models in Cognitive Science Woojae Kim, Daniel J. Navarro∗, Mark A. Pitt, In Jae Myung Department of Psychology Ohio State University fkim.1124, navarro.20, pitt.2, myung.1g@osu.edu Abstract Despite the popularity of connectionist models in cognitive science... | 2003 | 117 |
2,329 | Dynamical Modeling with Kernels for Nonlinear Time Series Prediction Liva Ralaivola Laboratoire d’Informatique de Paris 6 Universit´e Pierre et Marie Curie 8, rue du capitaine Scott F-75015 Paris, FRANCE liva.ralaivola@lip6.fr Florence d’Alch´e–Buc Laboratoire d’Informatique de Paris 6 Universit´e P... | 2003 | 118 |
2,330 | Learning Non-Rigid 3D Shape from 2D Motion Lorenzo Torresani Stanford University ltorresa@cs.stanford.edu Aaron Hertzmann University of Toronto hertzman@dgp.toronto.edu Christoph Bregler New York University chris.bregler@nyu.edu Abstract This paper presents an algorithm for learning the time-varyi... | 2003 | 119 |
2,331 | The Diffusion Mediated Biochemical Signal Relay Channel Peter J. Thomas∗, Donald J. Spencer† Computational Neurobiology Laboratory (Terrence J. Sejnowski, Director) Salk Institute for Biological Studies La Jolla, CA 92037 Sierra K. Hampton, Peter Park, Joseph P. Zurkus Department of Electrical and Compu... | 2003 | 12 |
2,332 | Discriminative Fields for Modeling Spatial Dependencies in Natural Images Sanjiv Kumar and Martial Hebert The Robotics Institute Carnegie Mellon University Pittsburgh, PA 15213 {skumar,hebert}@ri.cmu.edu Abstract In this paper we present Discriminative Random Fields (DRF), a discriminative framework for... | 2003 | 120 |
2,333 | Fast Algorithms for Large-State-Space HMMs with Applications to Web Usage Analysis Pedro F. Felzenszwalb1, Daniel P. Huttenlocher2, Jon M. Kleinberg2 1AI Lab, MIT, Cambridge MA 02139 2Computer Science Dept., Cornell University, Ithaca NY 14853 Abstract In applying Hidden Markov Models to the analysis of mas... | 2003 | 121 |
2,334 | A probabilistic model of auditory space representation in the barn owl Brian J. Fischer Dept. of Electrical and Systems Eng. Washington University in St. Louis St. Louis, MO 63110 fischerb@pcg.wustl.edu Charles H. Anderson Department of Anatomy and Neurbiology Washington University in St. Louis St. ... | 2003 | 122 |
2,335 | Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data Neil D. Lawrence Department of Computer Science, University of Sheffield, Regent Court, 211 Portobello Street, Sheffield, S1 4DP, U.K. neil@dcs.shef.ac.uk Abstract In this paper we introduce a new underlying probabilistic... | 2003 | 123 |
2,336 | Tree-structured approximations by expectation propagation Thomas Minka Department of Statistics Carnegie Mellon University Pittsburgh, PA 15213 USA minka@stat.cmu.edu Yuan Qi Media Laboratory Massachusetts Institute of Technology Cambridge, MA 02139 USA yuanqi@media.mit.edu Abstract Approximat... | 2003 | 124 |
2,337 | Boosting versus Covering Kohei Hatano∗ Tokyo Institute of Technology hatano@is.titech.ac.jp Manfred K. Warmuth UC Santa Cruz manfred@cse.ucsc.edu Abstract We investigate improvements of AdaBoost that can exploit the fact that the weak hypotheses are one-sided, i.e. either all its positive (or negati... | 2003 | 125 |
2,338 | Learning Near-Pareto-Optimal Conventions in Polynomial Time Xiaofeng Wang ECE Department Carnegie Mellon University Pittsburgh, PA 15213 xiaofeng@andrew.cmu.edu Tuomas Sandholm CS Department Carnegie Mellon University Pittsburgh, PA 15213 sandholm@cs.cmu.edu Abstract We study how to learn to p... | 2003 | 126 |
2,339 | Using the Forest to See the Trees: A Graphical Model Relating Features, Objects, and Scenes Kevin Murphy MIT AI lab Cambridge, MA 02139 murphyk@ai.mit.edu Antonio Torralba MIT AI lab Cambridge, MA 02139 torralba@ai.mit.edu William T. Freeman MIT AI lab Cambridge, MA 02139 wtf@ai.mit.edu Abst... | 2003 | 127 |
2,340 | Approximability of Probability Distributions Alina Beygelzimer∗ IBM T. J. Watson Research Center Hawthorne, NY 10532 beygel@cs.rochester.edu Irina Rish IBM T. J. Watson Research Center Hawthorne, NY 10532 rish@us.ibm.com Abstract We consider the question of how well a given distribution can be appro... | 2003 | 128 |
2,341 | Gene Expression Clustering with Functional Mixture Models Darya Chudova, Department of Computer Science University of California, Irvine Irvine CA 92697-3425 dchudova@ics.uci.edu Christopher Hart Division of Biology California Institute of Technology Pasadena, CA 91125 hart@caltech.edu Eric Mjol... | 2003 | 129 |
2,342 | Margin Maximizing Loss Functions Saharon Rosset Watson Research Center IBM Yorktown, NY, 10598 srosset@us.ibm.com Ji Zhu Department of Statistics University of Michigan Ann Arbor, MI, 48109 jizhu@umich.edu Trevor Hastie Department of Statistics Stanford University Stanford, CA, 94305 hasti... | 2003 | 13 |
2,343 | Large Scale Online Learning. L´eon Bottou NEC Labs America Princeton NJ 08540 leon@bottou.org Yann Le Cun NEC Labs America Princeton NJ 08540 yann@lecun.com Abstract We consider situations where training data is abundant and computing resources are comparatively scarce. We argue that suitably desi... | 2003 | 130 |
2,344 | Plasticity Kernels and Temporal Statistics Peter Dayan1 Michael Hausser2 Michael London1·2 1 GCNU, 2WIBR, Dept of Physiology UCL, Gower Street, London dayan@gats5y.ucl.ac.uk {m.hausser,m.london}@ucl.ac.uk Abstract Computational mysteries surround the kernels relating the magnitude and sign of... | 2003 | 131 |
2,345 | Phonetic Speaker Recognition with Support Vector Machines W. M. Campbell, J. P. Campbell, D. A. Reynolds, D. A. Jones, and T. R. Leek MIT Lincoln Laboratory Lexington, MA 02420 wcampbell,jpc,dar,daj,tleek@ll.mit.edu Abstract A recent area of significant progress in speaker recognition is the use of high ... | 2003 | 132 |
2,346 | The IM Algorithm : A variational approach to Information Maximization David Barber Felix Agakov Institute for Adaptive and Neural Computation : www.anc.ed.ac.uk Edinburgh University, EH1 2QL, U.K. Abstract The maximisation of information transmission over noisy channels is a common, albeit generally com... | 2003 | 133 |
2,347 | Variational Linear Response Manfred Opper(1) Ole Winther(2) (1) Neural Computing Research Group, School of Engineering and Applied Science, Aston University, Birmingham B4 7ET, United Kingdom (2) Informatics and Mathematical Modelling, Technical University of Denmark, R. Petersens Plads, Building 321, DK-28... | 2003 | 134 |
2,348 | Circuit Optimization Predicts Dynamic Networks for Chemosensory Orientation in the Nematode Caenorhabditis elegans Nathan A. Dunn John S. Conery Dept. of Computer Science University of Oregon Eugene, OR 97403 {ndunn,conery}@cs.uoregon.edu Shawn R. Lockery Institute of Neuroscience University of Or... | 2003 | 135 |
2,349 | Learning to Find Pre-Images G¨okhan H. Bakır, Jason Weston and Bernhard Sch¨olkopf Max Planck Institute for Biological Cybernetics Spemannstraße 38, 72076 T¨ubingen, Germany {gb,weston,bs}@tuebingen.mpg.de Abstract We consider the problem of reconstructing patterns from a feature map. Learning algorithms ... | 2003 | 136 |
2,350 | Sparse Representation and Its Applications in Blind Source Separation Yuanqing Li, Andrzej Cichocki, Shun-ichi Amari, Sergei Shishkin RIKEN Brain Science Institute, Saitama, 3510198, Japan Jianting Cao Department of Electronic Engineering Saitama Institute of Technology Saitama, 3510198, Japan Fanji Gu ... | 2003 | 137 |
2,351 | Probabilistic Inference in Human Sensorimotor Processing Konrad P. K¨ording Institute of Neurology UCL London London WC1N 3BG,UK konrad@koerding.com Daniel M. Wolpert Institute of Neurology UCL London London WC1N 3BG,UK wolpert@ion.ucl.ac.uk Abstract When we learn a new motor skill, we ha... | 2003 | 138 |
2,352 | From Algorithmic to Subjective Randomness Thomas L. Griffiths & Joshua B. Tenenbaum {gruffydd,jbt}@mit.edu Massachusetts Institute of Technology Cambridge, MA 02139 Abstract We explore the phenomena of subjective randomness as a case study in understanding how people discover structure embedded in noise. ... | 2003 | 139 |
2,353 | Inferring State Sequences for Non-linear Systems with Embedded Hidden Markov Models Radford M. Neal, Matthew J. Beal, and Sam T. Roweis Department of Computer Science University of Toronto Toronto, Ontario, Canada M5S 3G3 {radford,beal,roweis}@cs.utoronto.ca Abstract We describe a Markov chain method fo... | 2003 | 14 |
2,354 | Eye micro-movements improve stimulus detection beyond the Nyquist limit in the peripheral retina Matthias H. Hennig and Florentin W¨org¨otter Computational Neuroscience Psychology University of Stirling FK9 4LR Stirling, UK {hennig,worgott}@cn.stir.ac.uk Abstract Even under perfect fixation the human... | 2003 | 140 |
2,355 | Mechanism of neural interference by transcranial magnetic stimulation: network or single neuron? Yoichi Miyawaki RIKEN Brain Science Institute Wako, Saitama 351-0198, JAPAN yoichi miyawaki@brain.riken.jp Masato Okada RIKEN Brain Science Institute PRESTO, JST Wako, Saitama 351-0198, JAPAN okada@bra... | 2003 | 141 |
2,356 | Efficient Multiscale Sampling from Products of Gaussian Mixtures Alexander T. Ihler, Erik B. Sudderth, William T. Freeman, and Alan S. Willsky Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology ihler@mit.edu, esuddert@mit.edu, billf@ai.mit.edu, willsky@mit.edu Abs... | 2003 | 142 |
2,357 | Markov Models for Automated ECG Interval Analysis Nicholas P. Hughes, Lionel Tarassenko and Stephen J. Roberts Department of Engineering Science University of Oxford Oxford, 0X1 3PJ, UK {nph,lionel,sjrob}@robots.ox.ac.uk Abstract We examine the use of hidden Markov and hidden semi-Markov models for auto... | 2003 | 143 |
2,358 | A Mixed-Signal VLSI for Real-Time Generation of Edge-Based Image Vectors Masakazu Yagi, Hideo Yamasaki, and Tadashi Shibata* Department of Electronic Engineering *Department of Frontier Informatics The University of Tokyo 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan mgoat@dent.osaka-u.ac.jp, ... | 2003 | 144 |
2,359 | Autonomous helicopter flight via Reinforcement Learning Andrew Y. Ng Stanford University Stanford, CA 94305 H. Jin Kim, Michael I. Jordan, and Shankar Sastry University of California Berkeley, CA 94720 Abstract Autonomous helicopter flight represents a challenging control problem, with complex, noisy,... | 2003 | 145 |
2,360 | Insights from Machine Learning Applied to Human Visual Classification Arnulf B. A. Graf and Felix A. Wichmann Max Planck Institute for Biological Cybernetics Spemannstraße 38 72076 T¨ubingen, Germany {arnulf.graf, felix.wichmann}@tuebingen.mpg.de Abstract We attempt to understand visual classification in ... | 2003 | 146 |
2,361 | Classification with Hybrid Generative/Discriminative Models Rajat Raina, Yirong Shen, Andrew Y. Ng Computer Science Department Stanford University Stanford, CA 94305 Andrew McCallum Department of Computer Science University of Massachusetts Amherst, MA 01003 Abstract Although discriminatively train... | 2003 | 147 |
2,362 | Fast Feature Selection from Microarray Expression Data via Multiplicative Large Margin Algorithms Claudio Gentile DICOM, Universit`a dell’Insubria Via Mazzini, 5, 21100 Varese, Italy gentile@dsi.unimi.it Abstract New feature selection algorithms for linear threshold functions are described which combine... | 2003 | 148 |
2,363 | Feature Selection in Clustering Problems Volker Roth and Tilman Lange ETH Zurich, Institut f. Computational Science Hirschengraben 84, CH-8092 Zurich Tel: +41 1 6323179 {vroth, tilman.lange}@inf.ethz.ch Abstract A novel approach to combining clustering and feature selection is presented. It implements a w... | 2003 | 149 |
2,364 | Self-calibrating Probability Forecasting Vladimir Vovk Computer Learning Research Centre Department of Computer Science Royal Holloway, University of London Egham, Surrey TW20 0EX, UK vovk@cs.rhul.ac.uk Glenn Shafer Rutgers School of Business Newark and New Brunswick 180 University Avenue Newark, ... | 2003 | 15 |
2,365 | Optimal Manifold Representation of Data: An Information Theoretic Approach Denis Chigirev and William Bialek Department of Physics and the Lewis-Sigler Institute for Integrative Genomics Princeton University, Princeton, New Jersey 08544 chigirev,wbialek@princeton.edu Abstract We introduce an information t... | 2003 | 150 |
2,366 | Invariant Pattern Recognition by Semidefinite Programming Machines Thore Graepel Microsoft Research Ltd. Cambridge, UK thoreg@microsoft.com Ralf Herbrich Microsoft Research Ltd. Cambridge, UK rherb@microsoft.com Abstract Knowledge about local invariances with respect to given pattern transformati... | 2003 | 151 |
2,367 | Impact of an Energy Normalization Transform on the Performance of the LF-ASD Brain Computer Interface Zhou Yu1 Steven G. Mason2 Gary E. Birch1,2 1 Dept. of Electrical and Computer Engineering University of British Columbia 2356 Main Mall Vancouver, B.C. Canada V6T 1Z4 2 Neil Sq... | 2003 | 152 |
2,368 | Subject-Independent Magnetoencephalographic Source Localization by a Multilayer Perceptron Sung C. Jun Biological and Quantum Physics Group MS-D454, Los Alamos National Laboratory Los Alamos, NM 87545, USA jschan@lanl.gov Barak A. Pearlmutter Hamilton Institute NUI Maynooth Maynooth, Co. Kildare, Ir... | 2003 | 153 |
2,369 | Image Reconstruction by Linear Programming Koji Tsuda∗†and Gunnar R¨atsch∗‡ ∗Max Planck Institute for Biological Cybernetics Spemannstr. 38, 72076 T¨ubingen, Germany †AIST CBRC, 2-43 Aomi, Koto-ku, Tokyo, 135-0064, Japan ‡Fraunhofer FIRST, Kekul´estr. 7, 12489 Berlin, Germany {koji.tsuda,gunnar.raetsch}@tue... | 2003 | 154 |
2,370 | Extreme Components Analysis Max Welling Department of Computer Science University of Toronto 10 King’s College Road Toronto, M5S 3G5 Canada welling@cs.toronto.edu Felix Agakov, Christopher K. I. Williams Institute for Adaptive and Neural Computation School of Informatics University of Edinburgh 5 ... | 2003 | 155 |
2,371 | Bayesian Color Constancy with Non-Gaussian Models Charles Rosenberg Computer Science Department Carnegie Mellon University Pittsburgh, PA 15213 chuck@cs.cmu.edu Thomas Minka Statistics Department Carnegie Mellon University Pittsburgh, PA 15213 minka@stat.cmu.edu Alok Ladsariya Computer Science... | 2003 | 156 |
2,372 | All learning is local: Multi-agent learning in global reward games Yu-Han Chang MIT CSAIL Cambridge, MA 02139 ychang@csail.mit.edu Tracey Ho LIDS, MIT Cambridge, MA 02139 trace@mit.edu Leslie Pack Kaelbling MIT CSAIL Cambridge, MA 02139 lpk@csail.mit.edu Abstract In large multiagent games,... | 2003 | 157 |
2,373 | Automatic Annotation of Everyday Movements Deva Ramanan and D. A. Forsyth Computer Science Division University of California, Berkeley Berkeley, CA 94720 ramanan@cs.berkeley.edu, daf@cs.berkeley.edu Abstract This paper describes a system that can annotate a video sequence with: a description of the appe... | 2003 | 158 |
2,374 | A classification-based cocktail-party processor Nicoleta Roman, DeLiang Wang Guy J. Brown Department of Computer and Information Department of Computer Science Science and Center for Cognitive Science University of Sheffield The Ohio State University 211 ... | 2003 | 159 |
2,375 | Synchrony Detection by Analogue VLSI Neurons with Bimodal STDP Synapses Adria Bofill-i-Petit The University of Edinburgh Edinburgh, EH9 3JL Scotland adria.bofill@ee.ed.ac.uk Alan F. Murray The University of Edinburgh Edinburgh, EH9 3JL Scotland alan.murray@ee.ed.ac.uk Abstract We present test r... | 2003 | 16 |
2,376 | Linear Dependent Dimensionality Reduction Nathan Srebro Tommi Jaakkola Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge, MA 02139 nati@mit.edu,tommi@ai.mit.edu Abstract We formulate linear dimensionality reduction as a semi-parametric estimation p... | 2003 | 160 |
2,377 | Clustering with the Connectivity Kernel Bernd Fischer, Volker Roth and Joachim M. Buhmann Institute of Computational Science Swiss Federal Institute of Technology Zurich CH-8092 Zurich, Switzerland {bernd.fischer, volker.roth,jbuhmann}@inf.ethz.ch Abstract Clustering aims at extracting hidden structure in... | 2003 | 161 |
2,378 | Attractive People: Assembling Loose-Limbed Models using Non-parametric Belief Propagation Leonid Sigal Department of Computer Science Brown University Providence, RI 02912 ls@cs.brown.edu Michael Isard Microsoft Research Silicon Valley Mountain View, CA 94043 misard@microsoft.com Benjamin H. Sigel... | 2003 | 162 |
2,379 | Out-of-Sample Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering Yoshua Bengio, Jean-Franc¸ois Paiement, Pascal Vincent Olivier Delalleau, Nicolas Le Roux and Marie Ouimet D´epartement d’Informatique et Recherche Op´erationnelle Universit´e de Montr´eal Montr´eal, Qu´ebec, Canada, H3C 3J7 ... | 2003 | 163 |
2,380 | Learning Spectral Clustering Francis R. Bach Computer Science University of California Berkeley, CA 94720 fbach@cs.berkeley.edu Michael I. Jordan Computer Science and Statistics University of California Berkeley, CA 94720 jordan@cs.berkeley.edu Abstract Spectral clustering refers to a class of t... | 2003 | 164 |
2,381 | Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Model∗ Jonathan W. Pillow, Liam Paninski, and Eero P. Simoncelli Howard Hughes Medical Institute Center for Neural Science New York University {pillow, liam, eero}@cns.nyu.edu Abstract Recent work has examined the estimation of mode... | 2003 | 165 |
2,382 | Prediction on Spike Data Using Kernel Algorithms Jan Eichhorn, Andreas Tolias, Alexander Zien, Malte Kuss, Carl Edward Rasmussen, Jason Weston, Nikos Logothetis and Bernhard Sch¨olkopf Max Planck Institute for Biological Cybernetics 72076 T¨ubingen, Germany first.last@tuebingen.mpg.de Abstract We report... | 2003 | 166 |
2,383 | An MDP-Based Approach to Online Mechanism Design David C. Parkes Division of Engineering and Applied Sciences Harvard University parkes@eecs.harvard.edu Satinder Singh Computer Science and Engineering University of Michigan baveja@umich.edu Abstract Online mechanism design (MD) considers the probl... | 2003 | 167 |
2,384 | Probabilistic Inference of Speech Signals from Phaseless Spectrograms Kannan Achan, Sam T. Roweis, Brendan J. Frey Machine Learning Group University of Toronto Abstract Many techniques for complex speech processing such as denoising and deconvolution, time/frequency warping, multiple speaker separation, a... | 2003 | 168 |
2,385 | Denoising and untangling graphs using degree priors Quaid D Morris, Brendan J Frey, and Christopher J Paige University of Toronto Electrical and Computer Engineering 10 King’s College Road, Toronto, Ontario, M5S 3G4 Canada {quaid, frey}@psi.utoronto.ca, paige@uhnres.utoronto.ca Abstract This paper add... | 2003 | 169 |
2,386 | Semi-supervised protein classification using cluster kernels Jason Weston∗ Max Planck Institute for Biological Cybernetics, 72076 T¨ubingen, Germany weston@tuebingen.mpg.de Christina Leslie Department of Computer Science, Columbia University cleslie@cs.columbia.edu Dengyong Zhou, Andre Elisseeff Ma... | 2003 | 17 |
2,387 | Learning a Distance Metric from Relative Comparisons Matthew Schultz and Thorsten Joachims Department of Computer Science Cornell University Ithaca, NY 14853 {schultz,tj}@cs.cornell.edu Abstract This paper presents a method for learning a distance metric from relative comparison such as “A is closer to ... | 2003 | 170 |
2,388 | Locality Preserving Projections Xiaofei He Department of Computer Science The University of Chicago Chicago, IL 60637 xiaofei@cs.uchicago.edu Partha Niyogi Department of Computer Science The University of Chicago Chicago, IL 60637 niyogi@cs.uchicago.edu Abstract Many problems in information proc... | 2003 | 171 |
2,389 | Unsupervised Color Decomposition of Histologically Stained Tissue Samples A. Rabinovich Department of Computer Science University of California, San Diego amrabino@ucsd.edu S. Agarwal Department of Computer Science University of California, San Diego sagarwal@cs.ucsd.edu C. A. Laris Q3DM, Inc. c... | 2003 | 172 |
2,390 | Minimising Contrastive Divergence in Noisy, Mixed-mode VLSI Neurons Hsin Chen, Patrice Fleury and Alan F. Murray School of Engineering and Electronics Edinburgh University Mayfield Rd., Edinburgh EH9 3JL, UK {hc, pcdf, afm}@ee.ed.ac.uk Abstract This paper presents VLSI circuits with continuous-valued p... | 2003 | 173 |
2,391 | Eye Movements for Reward Maximization Nathan Sprague Computer Science Department University of Rochester Rochester, NY 14627 sprague@cs.rochester.edu Dana Ballard Computer Science Department University of Rochester Rochester, NY 14627 dana@cs.rochester.edu Abstract Recent eye tracking studies in... | 2003 | 174 |
2,392 | Estimating Internal Variables and Parameters of a Learning Agent by a Particle Filter Kazuyuki Samejima Kenji Doya Department of Computational Neurobiology ATR Computational Neuroscience laboratories; “Creating the Brain”, CREST, JST. “Keihan-na Science City”, Kyoto, 619-0288, Japan {samejima, doya}@atr... | 2003 | 175 |
2,393 | ICA-Based Clustering of Genes from Microarray Expression Data Su-In Lee* and Serafim Batzoglou§ *Department of Electrical Engineering §Department of Computer Science Stanford University, Stanford, CA 94305 silee@stanford.edu, serafim@cs.stanford.edu Abstract We propose an unsupervised method... | 2003 | 176 |
2,394 | On the concentration of expectation and approximate inference in layered networks XuanLong Nguyen University of California Berkeley, CA 94720 xuanlong@cs.berkeley.edu Michael I. Jordan University of California Berkeley, CA 94720 jordan@cs.berkeley.edu Abstract We present an analysis of concentrati... | 2003 | 177 |
2,395 | Identifying Structure across Prepartitioned Data Zvika Marx Neural Computation Center The Hebrew University Jerusalem, Israel, 91904 Ido Dagan Department of CS Bar-Ilan University Ramat-Gan, Israel, 52900 Eli Shamir School for CS The Hebrew University Jerusalem, Israel, 91904... | 2003 | 178 |
2,396 | Eigenvoice Speaker Adaptation via Composite Kernel PCA James T. Kwok, Brian Mak and Simon Ho Department of Computer Science Hong Kong University of Science and Technology Clear Water Bay, Hong Kong [jamesk,mak,csho]@cs.ust.hk Abstract Eigenvoice speaker adaptation has been shown to be effective when onl... | 2003 | 179 |
2,397 | When Does Non-Negative Matrix Factorization Give a Correct Decomposition into Parts? David Donoho Department of Statistics Stanford University Stanford, CA 94305 donoho@stat.stanford.edu Victoria Stodden Department of Statistics Stanford University Stanford, CA 94305 vcs@stat.stanford.edu Abstra... | 2003 | 18 |
2,398 | A Summating, Exponentially-Decaying CMOS Synapse for Spiking Neural Systems Rock Z. Shi1,2 and Timothy Horiuchi1,2,3 1Electrical and Computer Engineering Department 2Institute for Systems Research 3Neuroscience and Cognitive Science Program University of Maryland, College Park, MD 20742 rshi@glue.umd.edu,... | 2003 | 180 |
2,399 | Extending Q-Learning to General Adaptive Multi-Agent Systems Gerald Tesauro IBM Thomas J. Watson Research Center 19 Skyline Drive, Hawthorne, NY 10532 USA tesauro@watson.ibm.com Abstract Recent multi-agent extensions of Q-Learning require knowledge of other agents’ payoffs and Q-functions, and assume ga... | 2003 | 181 |
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