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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2,900 | Modeling Neural Population Spiking Activity with Gibbs Distributions Frank Wood, Stefan Roth, and Michael J. Black Department of Computer Science Brown University Providence, RI 02912 {fwood,roth,black}@cs.brown.edu Abstract Probabilistic modeling of correlated neural population firing activity is cent... | 2005 | 81 |
2,901 | Searching for Character Models Jaety Edwards Department of Computer Science UC Berkeley Berkeley, CA 94720 jaety@cs.berkeley.edu David Forsyth Department of Computer Science UC Berkeley Berkeley, CA 94720 daf@cs.berkeley.edu Abstract We introduce a method to automatically improve character model... | 2005 | 82 |
2,902 | *School of Electrical and Information Engineering, +Institute of Perception, Action and Behaviour. An aVLSI cricket ear model André van Schaik* Richard Reeve+ The University of Sydney University of Edinburgh NSW 2006, AUSTRALIA Edinburgh, UK andre@ee.usyd.edu.au ri... | 2005 | 83 |
2,903 | An Analog Visual Pre-Processing Processor Employing Cyclic Line Access in Only-Nearest-Neighbor-Interconnects Architecture Yusuke Nakashita Department of Frontier Informatics School of Frontier Sciences The University of Tokyo 5-1-5 Kashiwanoha, Kashiwa-shi, Chiba 277-8561, Japan yusuke@else.k.u-tok... | 2005 | 84 |
2,904 | The Curse of Highly Variable Functions for Local Kernel Machines Yoshua Bengio, Olivier Delalleau, Nicolas Le Roux Dept. IRO, Universit´e de Montr´eal P.O. Box 6128, Downtown Branch, Montreal, H3C 3J7, Qc, Canada {bengioy,delallea,lerouxni}@iro.umontreal.ca Abstract We present a series of theoretical argu... | 2005 | 85 |
2,905 | Fast Gaussian Process Regression using KD-Trees Yirong Shen Electrical Engineering Dept. Stanford University Stanford, CA 94305 Andrew Y. Ng Computer Science Dept. Stanford University Stanford, CA 94305 Matthias Seeger Computer Science Div. UC Berkeley Berkeley, CA 94720 Abstract The compu... | 2005 | 86 |
2,906 | Using “epitomes” to model genetic diversity: Rational design of HIV vaccine cocktails Nebojsa Jojic, Vladimir Jojic, Brendan Frey, Chris Meek and David Heckerman Microsoft Research Abstract We introduce a new model of genetic diversity which summarizes a large input dataset into an epitome, a short sequence... | 2005 | 87 |
2,907 | Learning Cue-Invariant Visual Responses Jarmo Hurri HIIT Basic Research Unit, University of Helsinki P.O.Box 68, FIN-00014 University of Helsinki, Finland Abstract Multiple visual cues are used by the visual system to analyze a scene; achromatic cues include luminance, texture, contrast and motion. Singlece... | 2005 | 88 |
2,908 | Learning Topology with the Generative Gaussian Graph and the EM Algorithm Micha¨el Aupetit CEA - DASE BP 12 - 91680 Bruy`eres-le-Chˆatel, France aupetit@dase.bruyeres.cea.fr Abstract Given a set of points and a set of prototypes representing them, how to create a graph of the prototypes whose topology... | 2005 | 89 |
2,909 | Identifying Distributed Object Representations in Human Extrastriate Visual Cortex Rory Sayres David Ress Department of Neuroscience Department of Neuroscience Stanford University Brown University Stanford, CA 94305 Providence, RI 02912 sayres@stanford.edu ress@brown.edu Kalanit Grill-Spect... | 2005 | 9 |
2,910 | Optimizing spatio-temporal filters for improving Brain-Computer Interfacing Guido Dornhege1, Benjamin Blankertz1, Matthias Krauledat1,3, Florian Losch2, Gabriel Curio2 and Klaus-Robert Müller1,3 1Fraunhofer FIRST.IDA, Kekuléstr. 7, 12 489 Berlin, Germany 2Campus Benjamin Franklin, Charité University Medicine B... | 2005 | 90 |
2,911 | Unbiased Estimator of Shape Parameter for Spiking Irregularities under Changing Environments Keiji Miura Kyoto University JST PRESTO Masato Okada University of Tokyo JST PRESTO RIKEN BSI Shun-ichi Amari RIKEN BSI Abstract We considered a gamma distribution of interspike intervals as a statisti... | 2005 | 91 |
2,912 | Coarse sample complexity bounds for active learning Sanjoy Dasgupta UC San Diego dasgupta@cs.ucsd.edu Abstract We characterize the sample complexity of active learning problems in terms of a parameter which takes into account the distribution over the input space, the specific target hypothesis, and the ... | 2005 | 92 |
2,913 | A PAC-Bayes approach to the Set Covering Machine Fran¸cois Laviolette, Mario Marchand IFT-GLO, Universit´e Laval Sainte-Foy (QC) Canada, G1K-7P4 given name.surname@ift.ulaval.ca Mohak Shah SITE, University of Ottawa Ottawa, Ont. Canada,K1N-6N5 mshah@site.uottawa.ca Abstract We design a new learnin... | 2005 | 93 |
2,914 | Logic and MRF Circuitry for Labeling Occluding and Thinline Visual Contours Eric Saund Palo Alto Research Center 3333 Coyote Hill Rd. Palo Alto, CA 94304 saund@parc.com Abstract This paper presents representation and logic for labeling contrast edges and ridges in visual scenes in terms of both surfac... | 2005 | 94 |
2,915 | Non-iterative Estimation with Perturbed Gaussian Markov Processes Yunsong Huang B. Keith Jenkins Signal and Image Processing Institute Department of Electrical Engineering-Systems University of Southern California Los Angeles, CA 90089-2564 {yunsongh,jenkins}@sipi.usc.edu Abstract We develop an appr... | 2005 | 95 |
2,916 | Products of “Edge-perts” Peter Gehler Max Planck Institute for Biological Cybernetics Spemannstraße 38, 72076 T¨ubingen, Germany pgehler@tuebingen.mpg.de Max Welling Department of Computer Science University of California Irvine welling@ics.uci.edu Abstract Images represent an important and abundant... | 2005 | 96 |
2,917 | Ideal Observers for Detecting Motion: Correspondence Noise Hongjing Lu Department of Psychology, UCLA Los Angeles, CA 90095 hongjing@psych.ucla.edu Alan Yuille Department of Statistics, UCLA Los Angeles, CA 90095 yuille@stat.ucla.edu Abstract We derive a Bayesian Ideal Observer (BIO) for detecting... | 2005 | 97 |
2,918 | Selecting Landmark Points for Sparse Manifold Learning J. G. Silva ISEL/ISR R. Conselheiro Emidio Navarro 1950.062 Lisbon, Portugal jgs@isel.ipl.pt J. S. Marques IST/ISR Av. Rovisco Pais 1949-001 Lisbon, Portugal jsm@isr.ist.utl.pt J. M. Lemos INESC-ID/IST R. Alves Redol, 9 1000-029 Lisbon... | 2005 | 98 |
2,919 | Statistical Convergence of Kernel CCA Kenji Fukumizu Institute of Statistical Mathematics Tokyo 106-8569 Japan fukumizu@ism.ac.jp Francis R. Bach Centre de Morphologie Mathematique Ecole des Mines de Paris, France francis.bach@mines.org Arthur Gretton Max Planck Institute for Biological Cybernetics ... | 2005 | 99 |
2,920 | Ordinal Regression by Extended Binary Classification Ling Li Learning Systems Group California Institute of Technology ling@caltech.edu Hsuan-Tien Lin Learning Systems Group California Institute of Technology htlin@caltech.edu Abstract We present a reduction framework from ordinal regression to binar... | 2006 | 1 |
2,921 | Multiple timescales and uncertainty in motor adaptation Konrad P. K¨ording Rehabilitation Institute of Chicago Northwestern University, Dept. PM&R Chicago, IL 60611 konrad@koerding.com Joshua B. Tenenbaum Massachusetts Institute of Technology Cambridge, MA 02139 jbt@mit.edu Reza Shadmehr Johns H... | 2006 | 10 |
2,922 | Efficient Learning of Sparse Representations with an Energy-Based Model Marc’Aurelio Ranzato Christopher Poultney Sumit Chopra Yann LeCun Courant Institute of Mathematical Sciences New York University, New York, NY 10003 {ranzato,crispy,sumit,yann}@cs.nyu.edu Abstract We describe a novel unsupervised... | 2006 | 100 |
2,923 | Learning Time-Intensity Profiles of Human Activity using Non-Parametric Bayesian Models Alexander T. Ihler Padhraic Smyth Donald Bren School of Information and Computer Science U.C. Irvine ihler@ics.uci.edu smyth@ics.uci.edu Abstract Data sets that characterize human activity over time through collecti... | 2006 | 101 |
2,924 | Using Combinatorial Optimization within Max-Product Belief Propagation John Duchi Daniel Tarlow Gal Elidan Daphne Koller Department of Computer Science Stanford University Stanford, CA 94305-9010 {jduchi,dtarlow,galel,koller}@cs.stanford.edu Abstract In general, the problem of computing a maximum ... | 2006 | 102 |
2,925 | Branch and Bound for Semi-Supervised Support Vector Machines Olivier Chapelle1 Max Planck Institute T¨ubingen, Germany chapelle@tuebingen.mpg.de Vikas Sindhwani University of Chicago Chicago, USA vikass@cs.uchicago.edu S. Sathiya Keerthi Yahoo! Research Santa Clara, USA selvarak@yahoo-inc.com ... | 2006 | 103 |
2,926 | Differential Entropic Clustering of Multivariate Gaussians Jason V. Davis Inderjit Dhillon Dept. of Computer Science University of Texas at Austin Austin, TX 78712 {jdavis,inderjit}@cs.utexas.edu Abstract Gaussian data is pervasive and many learning algorithms (e.g., k-means) model their inputs as a... | 2006 | 104 |
2,927 | Multi-Instance Multi-Label Learning with Application to Scene Classification Zhi-Hua Zhou Min-Ling Zhang National Laboratory for Novel Software Technology Nanjing University, Nanjing 210093, China {zhouzh,zhangml}@lamda.nju.edu.cn Abstract In this paper, we formalize multi-instance multi-label learning, ... | 2006 | 105 |
2,928 | Analysis of Contour Motions Ce Liu William T. Freeman Edward H. Adelson Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge, MA 02139, USA {celiu,billf,adelson}@csail.mit.edu Abstract A reliable motion estimation algorithm must function under a wide r... | 2006 | 106 |
2,929 | Learning to be Bayesian without Supervision Martin Raphan Courant Inst. of Mathematical Sciences New York University raphan@cims.nyu.edu Eero P. Simoncelli Center for Neural Science, and Courant Inst. of Mathematical Sciences New York University eero.simoncelli@nyu.edu Bayesian estimators are defined... | 2006 | 107 |
2,930 | Causal inference in sensorimotor integration Konrad P. K¨ording Department of Physiology and PM&R Northwestern University Chicago, IL 60611 konrad@koerding.com Joshua B. Tenenbaum Massachusetts Institute of Technology Cambridge, MA 02139 jbt@mit.edu Abstract Many recent studies analyze how data fr... | 2006 | 108 |
2,931 | A Kernel Subspace Method by Stochastic Realization for Learning Nonlinear Dynamical Systems Yoshinobu Kawahara∗ Takehisa Yairi Kazuo Machida Dept. of Aeronautics & Astronautics Research Center for Advanced Science and Technology The University of Tokyo The University of Tokyo Komaba 4-6-1, Meguro-ku, ... | 2006 | 109 |
2,932 | Learning from Multiple Sources Koby Crammer, Michael Kearns, Jennifer Wortman Department of Computer and Information Science University of Pennsylvania Philadelphia, PA 19104 Abstract We consider the problem of learning accurate models from multiple sources of “nearby” data. Given distinct samples from mu... | 2006 | 11 |
2,933 | Approximate inference using planar graph decomposition Amir Globerson Tommi Jaakkola Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge, MA 02139 gamir,tommi@csail.mit.edu Abstract A number of exact and approximate methods are available for inference... | 2006 | 110 |
2,934 | Context Effects in Category Learning: An Investigation of Four Probabilistic Models Michael C. Mozer+⋄, Michael Jones⋄†, Michael Shettel+ +Dept. of Computer Science, †Dept. of Psychology, and ⋄Institute of Cognitive Science University of Colorado, Boulder, CO 80309-0430 {mozer,mike.jones,shettel}@colorado.edu... | 2006 | 111 |
2,935 | An Application of Reinforcement Learning to Aerobatic Helicopter Flight Pieter Abbeel, Adam Coates, Morgan Quigley, Andrew Y. Ng Computer Science Dept. Stanford University Stanford, CA 94305 Abstract Autonomous helicopter flight is widely regarded to be a highly challenging control problem. This paper pr... | 2006 | 112 |
2,936 | Parameter Expanded Variational Bayesian Methods Yuan (Alan) Qi MIT CSAIL 32 Vassar street Cambridge, MA 02139 alanqi@csail.mit.edu Tommi S. Jaakkola MIT CSAIL 32 Vassar street Cambridge, MA 02139 tommi@csail.mit.edu Abstract Bayesian inference has become increasingly important in statistical mac... | 2006 | 113 |
2,937 | An EM Algorithm for Localizing Multiple Sound Sources in Reverberant Environments Michael I. Mandel, Daniel P. W. Ellis LabROSA, Dept. of Electrical Engineering Columbia University New York, NY {mim,dpwe}@ee.columbia.edu Tony Jebara Dept. of Computer Science Columbia University New York, NY jebara... | 2006 | 114 |
2,938 | Recursive ICA Honghao Shan, Lingyun Zhang, Garrison W. Cottrell Department of Computer Science and Engineering University of California, San Diego La Jolla, CA 92093-0404 {hshan,lingyun,gary}@cs.ucsd.edu Abstract Independent Component Analysis (ICA) is a popular method for extracting independent featu... | 2006 | 115 |
2,939 | The Neurodynamics of Belief Propagation on Binary Markov Random Fields Thomas Ott Institute of Neuroinformatics ETH/UNIZH Zurich Switzerland tott@ini.phys.ethz.ch Ruedi Stoop Institute of Neuroinformatics ETH/UNIZH Zurich Switzerland ruedi@ini.phys.ethz.ch Abstract We rigorously establish a cl... | 2006 | 116 |
2,940 | Unsupervised Learning of a Probabilistic Grammar for Object Detection and Parsing Long (Leo) Zhu Department of Statistics University of California at Los Angeles Los Angeles, CA 90095 lzhu@stat.ucla.edu Yuanhao Chen Department of Automation University of Science and Technology of China Hefei, Anhui ... | 2006 | 117 |
2,941 | A Humanlike Predictor of Facial Attractiveness Amit Kagian*1, Gideon Dror‡2, Tommer Leyvand*3, Daniel Cohen-Or*4, Eytan Ruppin*5 * School of Computer Sciences, Tel-Aviv University, Tel-Aviv, 69978, Israel. ‡ The Academic College of Tel-Aviv-Yaffo, Tel-Aviv, 64044, Israel. Email: {1kagianam, 3tommer, 4dcor, ... | 2006 | 118 |
2,942 | Mutagenetic tree Fisher kernel improves prediction of HIV drug resistance from viral genotype Tobias Sing Department of Computational Biology Max Planck Institute for Informatics Saarbr¨ucken, Germany tobias.sing@mpi-sb.mpg.de Niko Beerenwinkel∗ Department of Mathematics University of California Ber... | 2006 | 119 |
2,943 | Modeling Human Motion Using Binary Latent Variables Graham W. Taylor, Geoffrey E. Hinton and Sam Roweis Dept. of Computer Science University of Toronto Toronto, M5S 2Z9 Canada {gwtaylor,hinton,roweis}@cs.toronto.edu Abstract We propose a non-linear generative model for human motion data that uses an u... | 2006 | 12 |
2,944 | Image Retrieval and Classification Using Local Distance Functions Andrea Frome Department of Computer Science UC Berkeley Berkeley, CA 94720 andrea.frome@gmail.com Yoram Singer Google, Inc. Mountain View, CA 94043 singer@google.com Jitendra Malik Department of Computer Science UC Berkeley mal... | 2006 | 120 |
2,945 | Chained Boosting Christian R. Shelton University of California Riverside CA 92521 cshelton@cs.ucr.edu Wesley Huie University of California Riverside CA 92521 whuie@cs.ucr.edu Kin Fai Kan University of California Riverside CA 92521 kkan@cs.ucr.edu Abstract We describe a method to learn to mak... | 2006 | 121 |
2,946 | Support Vector Machines on a Budget Ofer Dekel and Yoram Singer School of Computer Science and Engineering The Hebrew University Jerusalem 91904, Israel {oferd,singer}@cs.huji.ac.il Abstract The standard Support Vector Machine formulation does not provide its user with the ability to explicitly control ... | 2006 | 122 |
2,947 | Manifold Denoising Matthias Hein Markus Maier Max Planck Institute for Biological Cybernetics T¨ubingen, Germany {first.last}@tuebingen.mpg.de Abstract We consider the problem of denoising a noisily sampled submanifold M in Rd, where the submanifold M is a priori unknown and we are only given a noisy po... | 2006 | 123 |
2,948 | Learning to Model Spatial Dependency: Semi-Supervised Discriminative Random Fields Chi-Hoon Lee Department of Computing Science University of Alberta chihoon@cs.ualberta.ca Shaojun Wang ∗ Department of Computer Science and Engineering Wright State University shaojun.wang@wright.edu Feng Jiao Depar... | 2006 | 124 |
2,949 | Shifting, One-Inclusion Mistake Bounds and Tight Multiclass Expected Risk Bounds Benjamin I. P. Rubinstein Computer Science Division University of California, Berkeley Berkeley, CA 94720-1776, U.S.A. benr@cs.berkeley.edu Peter L. Bartlett Computer Science Division and Department of Statistics Univer... | 2006 | 125 |
2,950 | Learning to parse images of articulated bodies Deva Ramanan Toyota Technological Institute at Chicago Chicago, IL 60637 ramanan@tti-c.org Abstract We consider the machine vision task of pose estimation from static images, specifically for the case of articulated objects. This problem is hard because of the ... | 2006 | 126 |
2,951 | Correcting Sample Selection Bias by Unlabeled Data Jiayuan Huang School of Computer Science Univ. of Waterloo, Canada j9huang@cs.uwaterloo.ca Alexander J. Smola NICTA, ANU Canberra, Australia Alex.Smola@anu.edu.au Arthur Gretton MPI for Biological Cybernetics T¨ubingen, Germany arthur@tuebingen.... | 2006 | 127 |
2,952 | A Nonparametric Approach to Bottom-Up Visual Saliency Wolf Kienzle, Felix A. Wichmann, Bernhard Sch¨olkopf, and Matthias O. Franz Max Planck Institute for Biological Cybernetics, Spemannstr. 38, 72076 T¨ubingen, Germany {kienzle,felix,bs,mof}@tuebingen.mpg.de Abstract This paper addresses the bottom-up in... | 2006 | 128 |
2,953 | Computation of Similarity Measures for Sequential Data using Generalized Suffix Trees Konrad Rieck Fraunhofer FIRST.IDA Kekul´estr. 7 12489 Berlin, Germany rieck@first.fhg.de Pavel Laskov Fraunhofer FIRST.IDA Kekul´estr. 7 12489 Berlin, Germany laskov@first.fhg.de S¨oren Sonnenburg Fraunhofer F... | 2006 | 129 |
2,954 | Bayesian Image Super-resolution, Continued Lyndsey C. Pickup, David P. Capel†, Stephen J. Roberts Andrew Zisserman Information Engineering Building, Dept. of Eng. Science, Parks Road, Oxford, OX1 3PJ, UK {elle,sjrob,az}@robots.ox.ac.uk † 2D3, d.capel@2d3.com Abstract This paper develops a multi-frame image ... | 2006 | 13 |
2,955 | Randomized PCA Algorithms with Regret Bounds that are Logarithmic in the Dimension Manfred K. Warmuth Computer Science Department University of California - Santa Cruz manfred@cse.ucsc.edu Dima Kuzmin Computer Science Department University of California - Santa Cruz dima@cse.ucsc.edu Abstract We d... | 2006 | 130 |
2,956 | Efficient Structure Learning of Markov Networks using L1-Regularization Su-In Lee Varun Ganapathi Daphne Koller Department of Computer Science Stanford University Stanford, CA 94305-9010 {silee,varung,koller}@cs.stanford.edu Abstract Markov networks are commonly used in a wide variety of applications... | 2006 | 131 |
2,957 | Attribute-efficient learning of decision lists and linear threshold functions under unconcentrated distributions Philip M. Long Google Mountain View, CA plong@google.com Rocco A. Servedio Department of Computer Science Columbia University New York, NY rocco@cs.columbia.edu Abstract We consider ... | 2006 | 132 |
2,958 | Mixture Regression for Covariate Shift Amos J Storkey Institute of Adaptive and Neural Computation School of Informatics, University of Edinburgh a.storkey@ed.ac.uk Masashi Sugiyama Department of Computer Science Tokyo Institute of Technology sugi@cs.titech.ac.jp Abstract In supervised learning ther... | 2006 | 133 |
2,959 | Implicit Online Learning with Kernels Li Cheng S.V. N. Vishwanathan National ICT Australia li.cheng@nicta.com.au SVN.Vishwanathan@nicta.com.au Dale Schuurmans Department of Computing Science University of Alberta, Canada dale@cs.ualberta.ca Shaojun Wang Department of Computer Science and Engineeri... | 2006 | 134 |
2,960 | PG-means: learning the number of clusters in data Yu Feng Greg Hamerly Computer Science Department Baylor University Waco, Texas 76798 {yu feng, greg hamerly}@baylor.edu Abstract We present a novel algorithm called PG-means which is able to learn the number of clusters in a classical Gaussian mixture ... | 2006 | 135 |
2,961 | Conditional Random Sampling: A Sketch-based Sampling Technique for Sparse Data Ping Li Department of Statistics Stanford University Stanford, CA 94305 pingli@stat.stanford.edu Kenneth W. Church Microsoft Research One Microsoft Way Redmond, WA 98052 church@microsoft.com Trevor J. Hastie Departm... | 2006 | 136 |
2,962 | Multi-Robot Negotiation: Approximating the Set of Subgame Perfect Equilibria in General-Sum Stochastic Games Chris Murray Carnegie Mellon University 5000 Forbes Avenue Pittsburgh, PA 15213 Geoffrey J. Gordon Carnegie Mellon University 5000 Forbes Avenue Pittsburgh, PA 15213 Abstract In real-world... | 2006 | 137 |
2,963 | Optimal Single-Class Classification Strategies Ran El-Yaniv Department of Computer Science Technion- Israel Institute of Technology Technion, Israel 32000 rani@cs.technion.ac.il Mordechai Nisenson Department of Computer Science Technion - Israel Institute of Technology Technion, Israel 32000 motin@cs... | 2006 | 138 |
2,964 | Learning to Rank with Nonsmooth Cost Functions Christopher J.C. Burges Microsoft Research One Microsoft Way Redmond, WA 98052, USA cburges@microsoft.com Robert Ragno Microsoft Research One Microsoft Way Redmond, WA 98052, USA rragno@microsoft.com Quoc Viet Le Statistical Machine Learning Progr... | 2006 | 139 |
2,965 | Geometric entropy minimization (GEM) for anomaly detection and localization Alfred O Hero, III University of Michigan Ann Arbor, MI 48109-2122 hero@umich.edu Abstract We introduce a novel adaptive non-parametric anomaly detection approach, called GEM, that is based on the minimal covering properties of ... | 2006 | 14 |
2,966 | Analysis of Representations for Domain Adaptation Shai Ben-David School of Computer Science University of Waterloo shai@cs.uwaterloo.ca John Blitzer, Koby Crammer, and Fernando Pereira Department of Computer and Information Science University of Pennsylvania {blitzer, crammer, pereira}@cis.upenn.edu A... | 2006 | 140 |
2,967 | Neurophysiological Evidence of Cooperative Mechanisms for Stereo Computation Jason M. Samonds Brian R. Potetz Tai Sing Lee Center for the Neural Basis CNBC and Computer CNBC and Computer of Cognition (CNBC) Science Depart... | 2006 | 141 |
2,968 | Sparse Multinomial Logistic Regression via Bayesian L1 Regularisation Gavin C. Cawley School of Computing Sciences University of East Anglia Norwich, Norfolk, NR4 7TJ, U.K. gcc@cmp.uea.ac.uk Nicola L. C. Talbot School of Computing Sciences University of East Anglia Norwich, Norfolk, NR4 7TJ, U.K. ... | 2006 | 142 |
2,969 | Gaussian and Wishart Hyperkernels Risi Kondor, Tony Jebara Computer Science Department, Columbia University 1214 Amsterdam Avenue, New York, NY 10027, U.S.A. {risi,jebara}@cs.columbia.edu Abstract We propose a new method for constructing hyperkenels and define two promising special cases that can be comput... | 2006 | 143 |
2,970 | A Complexity-Distortion Approach to Joint Pattern Alignment Andrea Vedaldi Stefano Soatto Department of Computer Science University of California at Los Angeles Los Angeles, CA 90035 {vedaldi,soatto}@cs.ucla.edu Abstract Image Congealing (IC) is a non-parametric method for the joint alignment of a col... | 2006 | 144 |
2,971 | AdaBoost is Consistent Peter L. Bartlett Department of Statistics and Computer Science Division University of California, Berkeley bartlett@stat.berkeley.edu Mikhail Traskin Department of Statistics University of California, Berkeley mtraskin@stat.berkeley.edu Abstract The risk, or probability of er... | 2006 | 145 |
2,972 | Cross-Validation Optimization for Large Scale Hierarchical Classification Kernel Methods Matthias W. Seeger Max Planck Institute for Biological Cybernetics P.O. Box 2169, 72012 T¨ubingen, Germany seeger@tuebingen.mpg.de Abstract We propose a highly efficient framework for kernel multi-class models with a ... | 2006 | 146 |
2,973 | Effects of Stress and Genotype on Meta-parameter Dynamics in Reinforcement Learning Gediminas Lukˇsys1,2 gediminas.luksys@epfl.ch J´er´emie Kn¨usel1 jeremie.knuesel@epfl.ch Denis Sheynikhovich1 denis.sheynikhovich@epfl.ch Carmen Sandi2 carmen.sandi@epfl.ch Wulfram Gerstner1 wulfram.gerstner@epfl.c... | 2006 | 147 |
2,974 | A Novel Gaussian Sum Smoother for Approximate Inference in Switching Linear Dynamical Systems David Barber and Bertrand Mesot IDIAP Research Institute Martigny 1920, Switzerland david.barber/bertrand.mesot@idiap.ch Abstract We introduce a method for approximate smoothed inference in a class of switching ... | 2006 | 148 |
2,975 | Information Bottleneck Optimization and Independent Component Extraction with Spiking Neurons Stefan Klampfl, Robert Legenstein, Wolfgang Maass Institute for Theoretical Computer Science Graz University of Technology A-8010 Graz, Austria {klampfl,legi,maass}@igi.tugraz.at Abstract The extraction of sta... | 2006 | 149 |
2,976 | Bayesian Ensemble Learning Hugh A. Chipman Department of Mathematics and Statistics Acadia University Wolfville, NS, Canada Edward I. George Department of Statistics The Wharton School University of Pennsylvania Philadelphia, PA 19104-6302 Robert E. McCulloch Graduate School of Business Universi... | 2006 | 15 |
2,977 | A Theory of Retinal Population Coding Eizaburo Doi Center for the Neural Basis of Cognition Carnegie Mellon University Pittsburgh, PA 15213 edoi@cnbc.cmu.edu Michael S. Lewicki Center for the Neural Basis of Cognition Carnegie Mellon University Pittsburgh, PA 15213 lewicki@cnbc.cmu.edu Abstract ... | 2006 | 150 |
2,978 | An Approach to Bounded Rationality Eli Ben-Sasson Department of Computer Science Technion — Israel Institute of Technology Adam Tauman Kalai Department of Computer Science College of Computing Georgia Tech Ehud Kalai MEDS Department Kellogg Graduate School of Management Northwestern University ... | 2006 | 151 |
2,979 | Fundamental Limitations of Spectral Clustering Boaz Nadler∗, Meirav Galun Department of Applied Mathematics and Computer Science Weizmann Institute of Science, Rehovot, Israel 76100 boaz.nadler,meirav.galun@weizmann.ac.il Abstract Spectral clustering methods are common graph-based approaches to clustering o... | 2006 | 152 |
2,980 | Generalized Maximum Margin Clustering and Unsupervised Kernel Learning Hamed Valizadegan Computer Science and Engineering Michigan State University East Lansing, MI 48824 valizade@msu.edu Rong Jin Computer Science and Engineering Michigan State University East Lansing, MI 48824 rongjin@cse.msu.edu... | 2006 | 153 |
2,981 | Clustering Under Prior Knowledge with Application to Image Segmentation M´ario A. T. Figueiredo Instituto de Telecomunicac¸˜oes Instituto Superior T´ecnico Technical University of Lisbon Portugal mario.figueiredo@lx.it.pt Dong Seon Cheng, Vittorio Murino Vision, Image Processing, and Sound Laboratory... | 2006 | 154 |
2,982 | Inferring Network Structure from Co-Occurrences Michael G. Rabbat Electrical and Computer Eng. University of Wisconsin Madison, WI 53706 rabbat@cae.wisc.edu M´ario A.T. Figueiredo Instituto de Telecomunicac¸˜oes Instituto Superior T´ecnico Lisboa, Portugal mtf@lx.it.pt Robert D. Nowak Electrical... | 2006 | 155 |
2,983 | Logarithmic Online Regret Bounds for Undiscounted Reinforcement Learning Peter Auer Ronald Ortner University of Leoben, Franz-Josef-Strasse 18, 8700 Leoben, Austria {auer,rortner}@unileoben.ac.at Abstract We present a learning algorithm for undiscounted reinforcement learning. Our interest lies in bou... | 2006 | 156 |
2,984 | Fast Iterative Kernel PCA Nicol N. Schraudolph Simon G¨unter S.V. N. Vishwanathan {nic.schraudolph,simon.guenter,svn.vishwanathan}@nicta.com.au Statistical Machine Learning, National ICT Australia Locked Bag 8001, Canberra ACT 2601, Australia Research School of Information Sciences & Engineering Austral... | 2006 | 157 |
2,985 | Uncertainty, phase and oscillatory hippocampal recall M´at´e Lengyel and Peter Dayan Gatsby Computational Neuroscience Unit University College London 17 Queen Square, London WC1N 3AR, United Kingdom {lmate,dayan}@gatsby.ucl.ac.uk Abstract Many neural areas, notably, the hippocampus, show structured, dynam... | 2006 | 158 |
2,986 | Speakers optimize information density through syntactic reduction Roger Levy Department of Linguistics UC San Diego 9500 Gilman Drive La Jolla, CA 92093-0108, USA rlevy@ling.ucsd.edu T. Florian Jaeger Department of Linguistics & Department of Psychology Stanford University & UC San Diego 9500 Gilm... | 2006 | 159 |
2,987 | The Robustness-Performance Tradeoff in Markov Decision Processes Huan Xu, Shie Mannor Department of Electrical and Computer Engineering McGill University Montreal, Quebec, Canada, H3A2A7 xuhuan@cim.mcgill.ca shie@ece.mcgill.ca Abstract Computation of a satisfactory control policy for a Markov decision... | 2006 | 16 |
2,988 | Learning Structural Equation Models for fMRI Amos J. Storkey School of Informatics University of Edinburgh Enrico Simonotto Division of Psychiatry University of Edinburgh Heather Whalley Division of Psychiatry University of Edinburgh Stephen Lawrie Division of Psychiatry University of Edinburgh ... | 2006 | 160 |
2,989 | Simplifying Mixture Models through Function Approximation Kai Zhang James T. Kwok Department of Computer Science and Engineering The Hong Kong University of Science and Technology Clear Water Bay, Kowloon, Hong Kong {twinsen, jamesk}@cse.ust.hk Abstract Finite mixture model is a powerful tool in many ... | 2006 | 161 |
2,990 | Sparse Representation for Signal Classification Ke Huang and Selin Aviyente Department of Electrical and Computer Engineering Michigan State University, East Lansing, MI 48824 {kehuang, aviyente}@egr.msu.edu Abstract In this paper, application of sparse representation (factorization) of signals over an ove... | 2006 | 162 |
2,991 | Similarity by Composition Oren Boiman Michal Irani Dept. of Computer Science and Applied Math The Weizmann Institute of Science 76100 Rehovot, Israel Abstract We propose a new approach for measuring similarity between two signals, which is applicable to many machine learning tasks, and to many signal ty... | 2006 | 163 |
2,992 | An Information Theoretic Framework for Eukaryotic Gradient Sensing Joseph M. Kimmel∗and Richard M. Salter† joekimmel@uchicago.edu, rms@cs.oberlin.edu Computer Science Program Oberlin College Oberlin, Ohio 44074 Peter J. Thomas‡ peter.j.thomas@case.edu Departments of Mathematics, Biology and Cognitiv... | 2006 | 164 |
2,993 | Prediction on a Graph with a Perceptron Mark Herbster, Massimiliano Pontil Department of Computer Science University College London Gower Street, London WC1E 6BT, England, UK {m.herbster, m.pontil}@cs.ucl.ac.uk Abstract We study the problem of online prediction of a noisy labeling of a graph with the ... | 2006 | 165 |
2,994 | An Efficient Method for Gradient-Based Adaptation of Hyperparameters in SVM Models S. Sathiya Keerthi Yahoo! Research 3333 Empire Avenue Burbank, CA 91504 selvarak@yahoo-inc.com Vikas Sindhwani Department of Computer Science University of Chicago Chicago, IL 60637 vikass@cs.uchicago.edu Olivier C... | 2006 | 166 |
2,995 | Analysis of Empirical Bayesian Methods for Neuroelectromagnetic Source Localization David Wipf1, Rey Ram´ırez2, Jason Palmer1,2, Scott Makeig2, & Bhaskar Rao1 ∗ 1Signal Processing and Intelligent Systems Lab 2Swartz Center for Computational Neuroscience University of California, San Diego 92093 {dwipf,japal... | 2006 | 167 |
2,996 | Efficient Methods for Privacy Preserving Face Detection Shai Avidan Mitsubishi Electric Research Labs 201 Broadway Cambridge, MA 02139 avidan@merl.com Moshe Butman Department of Computer Science Bar Ilan University Ramat-Gan, Israel butmanm@cs.biu.edu Abstract Bob offers a face-detection web se... | 2006 | 168 |
2,997 | Balanced Graph Matching Timothee Cour, Praveen Srinivasan and Jianbo Shi Department of Computer and Information Science University of Pennsylvania Philadelphia, PA 19104 {timothee,psrin,jshi}@seas.upenn.edu Abstract Graph matching is a fundamental problem in Computer Vision and Machine Learning. We ... | 2006 | 169 |
2,998 | Combining causal and similarity-based reasoning Charles Kemp, Patrick Shafto, Allison Berke & Joshua B. Tenenbaum Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139 {ckemp,shafto,berke,jbt}@mit.edu Abstract Everyday inductive reasoning draws on many kinds of knowledge, including knowledge ... | 2006 | 17 |
2,999 | Fast Discriminative Visual Codebooks using Randomized Clustering Forests Frank Moosmann∗, Bill Triggs and Frederic Jurie GRAVIR-CNRS-INRIA, 655 avenue de l’Europe, Montbonnot 38330, France firstname.lastname@inrialpes.fr Abstract Some of the most effective recent methods for content-based image classificatio... | 2006 | 170 |
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