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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2,800 | Efficient Unsupervised Learning for Localization and Detection in Object Categories Nicolas Loeff, Himanshu Arora ECE Department University of Illinois at Urbana-Champaign {loeff,harora1}@uiuc.edu Alexander Sorokin, David Forsyth Computer Science Department University of Illinois at Urbana-Champaign ... | 2005 | 178 |
2,801 | Beyond Gaussian Processes: On the Distributions of Infinite Networks Ricky Der Department of Mathematics University of Pennsylvania Philadelphia, PA 19104 rickyder@math.upenn.edu Daniel Lee Department of Electrical Engineering University of Pennsylvania Philadelphia, PA 19104 ddlee@seas.upenn.edu ... | 2005 | 179 |
2,802 | Learning Depth from Single Monocular Images Ashutosh Saxena, Sung H. Chung, and Andrew Y. Ng Computer Science Department Stanford University Stanford, CA 94305 asaxena@stanford.edu, {codedeft,ang}@cs.stanford.edu Abstract We consider the task of depth estimation from a single monocular image. We take a ... | 2005 | 18 |
2,803 | Preconditioner Approximations for Probabilistic Graphical Models Pradeep Ravikumar John Lafferty School of Computer Science Carnegie Mellon University Abstract We present a family of approximation techniques for probabilistic graphical models, based on the use of graphical preconditioners developed in t... | 2005 | 180 |
2,804 | Structured Prediction via the Extragradient Method Ben Taskar Computer Science UC Berkeley, Berkeley, CA 94720 taskar@cs.berkeley.edu Simon Lacoste-Julien Computer Science UC Berkeley, Berkeley, CA 94720 slacoste@cs.berkeley.edu Michael I. Jordan Computer Science and Statistics UC Berkeley, Berk... | 2005 | 181 |
2,805 | Noise and the two-thirds power law Uri Maoz1,2,3, Elon Portugaly3, Tamar Flash2 and Yair Weiss3,1 1 Interdisciplinary Center for Neural Computation, The Hebrew University of Jerusalem, Edmond Safra Campus, Givat Ram Jerusalem 91904, Israel; 2 Department of Computer Science and Applied Mathematics, The Weizmann ... | 2005 | 182 |
2,806 | From Lasso regression to Feature vector machine Fan Li1, Yiming Yang1 and Eric P. Xing1,2 1 LTI and 2CALD, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA USA 15213 {hustlf,yiming,epxing}@cs.cmu.edu Abstract Lasso regression tends to assign zero weights to most irrelevant or redunda... | 2005 | 183 |
2,807 | Maximum Margin Semi-Supervised Learning for Structured Variables Y. Altun, D. McAllester TTI at Chicago Chicago, IL 60637 altun,mcallester@tti-c.org M. Belkin Department of Computer Science University of Chicago Chicago, IL 60637 misha@cs.uchicago.edu Abstract Many real-world classification probl... | 2005 | 184 |
2,808 | Generalization in Clustering with Unobserved Features Eyal Krupka and Naftali Tishby School of Computer Science and Engineering, Interdisciplinary Center for Neural Computation The Hebrew University Jerusalem, 91904, Israel {eyalkr,tishby}@cs.huji.ac.il Abstract We argue that when objects are characte... | 2005 | 185 |
2,809 | Variable KD-Tree Algorithms for Spatial Pattern Search and Discovery Jeremy Kubica Robotics Institute Carnegie Mellon University Pittsburgh, PA 15213 jkubica@ri.cmu.edu Joseph Masiero Institute for Astronomy University of Hawaii Honolulu, HI 96822 masiero@ifa.hawaii.edu Andrew Moore Robotics I... | 2005 | 186 |
2,810 | Neural mechanisms of contrast dependent receptive field size in V1 Jim Wielaard and Paul Sajda Department of Biomedical Engineering Columbia University New York, NY 10027 (djw21, ps629)@columbia.edu Abstract Based on a large scale spiking neuron model of the input layers 4Cα and β of macaque, we identi... | 2005 | 187 |
2,811 | Benchmarking Non-Parametric Statistical Tests Mikaela Keller∗ IDIAP Research Institute 1920 Martigny Switzerland mkeller@idiap.ch Samy Bengio IDIAP Research Institute 1920 Martigny Switzerland bengio@idiap.ch Siew Yeung Wong IDIAP Research Institute 1920 Martigny Switzerland sywong@idiap.c... | 2005 | 188 |
2,812 | Convergence and Consistency of Regularized Boosting Algorithms with Stationary β-Mixing Observations Aur´elie C. Lozano Department of Electrical Engineering Princeton University Princeton, NJ 08544 alozano@princeton.edu Sanjeev R. Kulkarni Department of Electrical Engineering Princeton University ... | 2005 | 189 |
2,813 | Non-Local Manifold Parzen Windows Yoshua Bengio, Hugo Larochelle and Pascal Vincent Dept. IRO, Universit´e de Montr´eal P.O. Box 6128, Downtown Branch, Montreal, H3C 3J7, Qc, Canada {bengioy,larocheh,vincentp}@iro.umontreal.ca Abstract To escape from the curse of dimensionality, we claim that one can learn ... | 2005 | 19 |
2,814 | A Cortically-Plausible Inverse Problem Solving Method Applied to Recognizing Static and Kinematic 3D Objects David W. Arathorn Center for Computational Biology, Montana State University Bozeman, MT 59717 dwa@cns . montana . edu General Intelligence Corporation dwa@giclab . com Abstract ... | 2005 | 190 |
2,815 | Dynamic Social Network Analysis using Latent Space Models Purnamrita Sarkar, Andrew W. Moore Center for Automated Learning and Discovery Carnegie Mellon University Pittsburgh, PA 15213 (psarkar,awm)@cs.cmu.edu Abstract This paper explores two aspects of social network modeling. First, we generalize ... | 2005 | 191 |
2,816 | Principles of real-time computing with feedback applied to cortical microcircuit models Wolfgang Maass, Prashant Joshi Institute for Theoretical Computer Science Technische Universitaet Graz A-8010 Graz, Austria maass,joshi@igi.tugraz.at Eduardo D. Sontag Department of Mathematics Rutgers, The State U... | 2005 | 192 |
2,817 | Location-based Activity Recognition Lin Liao, Dieter Fox, and Henry Kautz Computer Science & Engineering University of Washington Seattle, WA 98195 Abstract Learning patterns of human behavior from sensor data is extremely important for high-level activity inference. We show how to extract and label a per... | 2005 | 193 |
2,818 | Modeling Memory Transfer and Savings in Cerebellar Motor Learning Naoki Masuda RIKEN Brain Science Institute Wako, Saitama 351-0198, Japan masuda@brain.riken.jp Shun-ichi Amari RIKEN Brain Science Institute Wako, Saitama 351-0198, Japan amari@brain.riken.jp Abstract There is a long-standing contro... | 2005 | 194 |
2,819 | TD(0) Leads to Better Policies than Approximate Value Iteration Benjamin Van Roy Management Science and Engineering and Electrical Engineering Stanford University Stanford, CA 94305 bvr@stanford.edu Abstract We consider approximate value iteration with a parameterized approximator in which the state spa... | 2005 | 195 |
2,820 | Gradient Flow Independent Component Analysis in Micropower VLSI Abdullah Celik, Milutin Stanacevic and Gert Cauwenberghs Johns Hopkins University, Baltimore, MD 21218 {acelik,miki,gert}@jhu.edu Abstract We present micropower mixed-signal VLSI hardware for real-time blind separation and localization of aco... | 2005 | 196 |
2,821 | An Alternative Infinite Mixture Of Gaussian Process Experts Edward Meeds and Simon Osindero Department of Computer Science University of Toronto Toronto, M5S 3G4 {ewm,osindero}@cs.toronto.edu Abstract We present an infinite mixture model in which each component comprises a multivariate Gaussian distributi... | 2005 | 197 |
2,822 | Silicon Growth Cones Map Silicon Retina Brian Taba and Kwabena Boahen∗ Department of Bioengineering University of Pennsylvania Philadelphia, PA 19104 {btaba,boahen}@seas.upenn.edu Abstract We demonstrate the first fully hardware implementation of retinotopic self-organization, from photon transduction to... | 2005 | 198 |
2,823 | Bayesian models of human action understanding Chris L. Baker, Joshua B. Tenenbaum & Rebecca R. Saxe {clbaker,jbt,saxe}@mit.edu Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Abstract We present a Bayesian framework for explaining how people reason about and predict the ac... | 2005 | 199 |
2,824 | On Local Rewards and Scaling Distributed Reinforcement Learning J. Andrew Bagnell Robotics Institute Carnegie Mellon University Pittsburgh, PA 15213 dbagnell@ri.cmu.edu Andrew Y. Ng Computer Science Department Stanford University Stanford, CA 94305 ang@cs.stanford.edu Abstract We consider the ... | 2005 | 2 |
2,825 | Bayesian Sets Zoubin Ghahramani∗and Katherine A. Heller Gatsby Computational Neuroscience Unit University College London London WC1N 3AR, U.K. {zoubin,heller}@gatsby.ucl.ac.uk Abstract Inspired by “Google™Sets”, we consider the problem of retrieving items from a concept or cluster, given a query consist... | 2005 | 20 |
2,826 | Learning Influence among Interacting Markov Chains Dong Zhang IDIAP Research Institute CH-1920 Martigny, Switzerland zhang@idiap.ch Daniel Gatica-Perez IDIAP Research Institute CH-1920 Martigny, Switzerland gatica@idiap.ch Samy Bengio IDIAP Research Institute CH-1920 Martigny, Switzerland bengi... | 2005 | 200 |
2,827 | Off-policy Learning with Options and Recognizers Doina Precup McGill University Montreal, QC, Canada Richard S. Sutton University of Alberta Edmonton, AB, Canada Cosmin Paduraru University of Alberta Edmonton, AB, Canada Anna Koop University of Alberta Edmonton, AB, Canada Satinder Singh U... | 2005 | 201 |
2,828 | A Probabilistic Interpretation of SVMs with an Application to Unbalanced Classification Yves Grandvalet ∗ Heudiasyc, CNRS/UTC 60205 Compi`egne cedex, France grandval@utc.fr Johnny Mari´ethoz Samy Bengio IDIAP Research Institute 1920 Martigny, Switzerland {marietho,bengio}@idiap.ch Abstract In thi... | 2005 | 202 |
2,829 | Nonparametric inference of prior probabilities from Bayes-optimal behavior Liam Paninski∗ Department of Statistics, Columbia University liam@stat.columbia.edu; http://www.stat.columbia.edu/∼liam Abstract We discuss a method for obtaining a subject’s a priori beliefs from his/her behavior in a psychophysic... | 2005 | 203 |
2,830 | Oblivious Equilibrium: A Mean Field Approximation for Large-Scale Dynamic Games Gabriel Y. Weintraub, Lanier Benkard, and Benjamin Van Roy Stanford University {gweintra,lanierb,bvr}@stanford.edu Abstract We propose a mean-field approximation that dramatically reduces the computational complexity of solving... | 2005 | 204 |
2,831 | Dynamical Synapses Give Rise to a Power-Law Distribution of Neuronal Avalanches Anna Levina3,4, J. Michael Herrmann1,2, Theo Geisel1,2,4 1 Bernstein Center for Computational Neuroscience G¨ottingen 2 Georg-August University G¨ottingen, Institute for Nonlinear Dynamics 3 Graduate School Identification in Mathem... | 2005 | 205 |
2,832 | From Weighted Classification to Policy Search D. Blatt Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor, MI 48109-2122 dblatt@eecs.umich.edu A. O. Hero Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor, MI 48109-2122... | 2005 | 206 |
2,833 | Off-Road Obstacle Avoidance through End-to-End Learning Yann LeCun Courant Institute of Mathematical Sciences New York University, New York, NY 10004, USA http://yann.lecun.com Urs Muller Net-Scale Technologies Morganville, NJ 07751, USA urs@net-scale.com Jan Ben Net-Scale Technologies Morganv... | 2005 | 207 |
2,834 | Modeling Neuronal Interactivity using Dynamic Bayesian Networks Lei Zhang†,‡, Dimitris Samaras†, Nelly Alia-Klein‡, Nora Volkow‡, Rita Goldstein‡ † Computer Science Department, SUNY at Stony Brook, Stony Brook, NY ‡ Medical Department, Brookhaven National Laboratory, Upton, NY Abstract Functional Magnetic R... | 2005 | 21 |
2,835 | A Theoretical Analysis of Robust Coding over Noisy Overcomplete Channels Eizaburo Doi1, Doru C. Balcan2, & Michael S. Lewicki1,2 1Center for the Neural Basis of Cognition, 2Computer Science Department, Carnegie Mellon University, Pittsburgh, PA 15213 {edoi,dbalcan,lewicki}@cnbc.cmu.edu Abstract Biologic... | 2005 | 22 |
2,836 | A Probabilistic Approach for Optimizing Spectral Clustering Rong Jin∗, Chris Ding†, Feng Kang∗ ∗Lawrence Berkeley National Laboratory, Berkeley, CA 94720 †Michigan State University, East Lansing , MI 48824 Abstract Spectral clustering enjoys its success in both data clustering and semisupervised learnin... | 2005 | 23 |
2,837 | Learning Multiple Related Tasks using Latent Independent Component Analysis Jian Zhang†, Zoubin Ghahramani†‡, Yiming Yang† † School of Computer Science ‡ Gatsby Computational Neuroscience Unit Cargenie Mellon University University College London Pittsburgh, PA 15213 London WC1N 3AR, UK {jian.zhang... | 2005 | 24 |
2,838 | Context as Filtering Daichi Mochihashi ATR, Spoken Language Communication Research Laboratories Hikaridai 2-2-2, Keihanna Science City Kyoto, Japan daichi.mochihashi@atr.jp Yuji Matsumoto Graduate School of Information Science Nara Institute of Science and Technology Takayama 8916-5, Ikoma City Na... | 2005 | 25 |
2,839 | A Matching Pursuit Approach to Sparse Gaussian Process Regression S. Sathiya Keerthi Yahoo! Research Labs 210 S. DeLacey Avenue Pasadena, CA 91105 selvarak@yahoo-inc.com Wei Chu Gatsby Computational Neuroscience Unit University College London London, WC1N 3AR, UK chuwei@gatsby.ucl.ac.uk Abstract... | 2005 | 26 |
2,840 | Phase Synchrony Rate for the Recognition of Motor Imagery in Brain-Computer Interface Le Song Nation ICT Australia School of Information Technologies The University of Sydney NSW 2006, Australia lesong@it.usyd.edu.au Evian Gordon Brain Resource Company Scientific Chair, Brain Dynamics Center Westme... | 2005 | 27 |
2,841 | Inference with Minimal Communication: a Decision-Theoretic Variational Approach O. Patrick Kreidl and Alan S. Willsky Department of Electrical Engineering and Computer Science MIT Laboratory for Information and Decision Systems Cambridge, MA 02139 {opk,willsky}@mit.edu Abstract Given a directed graphica... | 2005 | 28 |
2,842 | Variational EM Algorithms for Non-Gaussian Latent Variable Models J. A. Palmer, D. P. Wipf, K. Kreutz-Delgado, and B. D. Rao Department of Electrical and Computer Engineering University of California San Diego, La Jolla, CA 92093 {japalmer,dwipf,kreutz,brao}@ece.ucsd.edu Abstract We consider criteria for ... | 2005 | 29 |
2,843 | Learning Shared Latent Structure for Image Synthesis and Robotic Imitation Aaron P. Shon † Keith Grochow † Aaron Hertzmann ‡ Rajesh P. N. Rao † †Department of Computer Science and Engineering University of Washington Seattle, WA 98195 USA ‡Department of Computer Science University of Toronto Toron... | 2005 | 3 |
2,844 | Fast Information Value for Graphical Models Brigham S. Anderson School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 brigham@cmu.edu Andrew W. Moore School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 awm@cs.cmu.edu Abstract Calculations that quanti... | 2005 | 30 |
2,845 | Value Function Approximation with Diffusion Wavelets and Laplacian Eigenfunctions Sridhar Mahadevan Department of Computer Science University of Massachusetts Amherst, MA 01003 mahadeva@cs.umass.edu Mauro Maggioni Program in Applied Mathematics Department of Mathematics Yale University New Haven, ... | 2005 | 31 |
2,846 | A Bayesian Spatial Scan Statistic Daniel B. Neill Andrew W. Moore School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 {neill,awm}@cs.cmu.edu Gregory F. Cooper Center for Biomedical Informatics University of Pittsburgh Pittsburgh, PA 15213 gfc@cbmi.pitt.edu Abstract We pr... | 2005 | 32 |
2,847 | Diffusion Maps, Spectral Clustering and Eigenfunctions of Fokker-Planck Operators Boaz Nadler∗ St´ephane Lafon Ronald R. Coifman Department of Mathematics, Yale University, New Haven, CT 06520. {boaz.nadler,stephane.lafon,ronald.coifman}@yale.edu Ioannis G. Kevrekidis Department of Chemical Engineering ... | 2005 | 33 |
2,848 | Scaling Laws in Natural Scenes and the Inference of 3D Shape Brian Potetz Department of Computer Science Center for the Neural Basis of Cognition Carnegie Mellon University Pittsburgh, PA 15213 bpotetz@cs.cmu.edu Tai Sing Lee Department of Computer Science Center for the Neural Basis of Cognition ... | 2005 | 34 |
2,849 | On the Convergence of Eigenspaces in Kernel Principal Component Analysis Laurent Zwald D´epartement de Math´ematiques, Universit´e Paris-Sud, Bˆat. 425, F-91405 Orsay, France Laurent.Zwald@math.u-psud.fr Gilles Blanchard Fraunhofer First (IDA), K´ekul´estr. 7, D-12489 Berlin, Germany blanchar@first.... | 2005 | 35 |
2,850 | Layered Dynamic Textures Antoni B. Chan Nuno Vasconcelos Department of Electrical and Computer Engineering University of California, San Diego abchan@ucsd.edu, nuno@ece.ucsd.edu Abstract A dynamic texture is a video model that treats a video as a sample from a spatio-temporal stochastic process, specific... | 2005 | 36 |
2,851 | Subsequence Kernels for Relation Extraction Razvan C. Bunescu Department of Computer Sciences University of Texas at Austin 1 University Station C0500 Austin, TX 78712 razvan@cs.utexas.edu Raymond J. Mooney Department of Computer Sciences University of Texas at Austin 1 University Station C0500 Au... | 2005 | 37 |
2,852 | Optimal cue selection strategy Vidhya Navalpakkam Department of Computer Science USC, Los Angeles navalpak@usc.edu Laurent Itti Department of Computer Science USC, Los Angeles itti@usc.edu Abstract Survival in the natural world demands the selection of relevant visual cues to rapidly and reliably ... | 2005 | 38 |
2,853 | Infinite Latent Feature Models and the Indian Buffet Process Thomas L. Griffiths Zoubin Ghahramani Cognitive and Linguistic Sciences Gatsby Computational Neuroscience Unit Brown University, Providence RI University College London, London tom griffiths@brown.edu zoubin@gatsby.ucl.ac.uk Abstract We de... | 2005 | 39 |
2,854 | Recovery of Jointly Sparse Signals from Few Random Projections Michael B. Wakin ECE Department Rice University wakin@rice.edu Marco F. Duarte ECE Department Rice University duarte@rice.edu Shriram Sarvotham ECE Department Rice University shri@rice.edu Dror Baron ECE Department Rice Unive... | 2005 | 4 |
2,855 | Non-Gaussian Component Analysis: a Semi-parametric Framework for Linear Dimension Reduction G. Blanchard1, M. Sugiyama1,2, M. Kawanabe1, V. Spokoiny3, K.-R. M¨uller1,4 1 Fraunhofer FIRST.IDA, Kekul´estr. 7, 12489 Berlin, Germany 2 Dept. of CS, Tokyo Inst. of Tech., 2-12-1, O-okayama, Meguro-ku, Tokyo, 152-855... | 2005 | 40 |
2,856 | Divergences, surrogate loss functions and experimental design XuanLong Nguyen University of California Berkeley, CA 94720 xuanlong@cs.berkeley.edu Martin J. Wainwright University of California Berkeley, CA 94720 wainwrig@eecs.berkeley.edu Michael I. Jordan University of California Berkeley, CA 9... | 2005 | 41 |
2,857 | Mixture Modeling by Affinity Propagation Brendan J. Frey and Delbert Dueck University of Toronto Software and demonstrations available at www.psi.toronto.edu Abstract Clustering is a fundamental problem in machine learning and has been approached in many ways. Two general and quite different approaches inc... | 2005 | 42 |
2,858 | Tensor Subspace Analysis Xiaofei He1 Deng Cai2 Partha Niyogi1 1 Department of Computer Science, University of Chicago {xiaofei, niyogi}@cs.uchicago.edu 2 Department of Computer Science, University of Illinois at Urbana-Champaign dengcai2@uiuc.edu Abstract Previous work has demonstrated that the image ... | 2005 | 43 |
2,859 | Query By Committee Made Real Ran Gilad-Bachrach†♦ Amir Navot‡ Naftali Tishby†‡ † School of Computer Science and Engineering ‡ Interdisciplinary Center for Neural Computation The Hebrew University, Jerusalem, Israel. ♦Intel Research Abstract Training a learning algorithm is a costly task. A major goal ... | 2005 | 44 |
2,860 | An Application of Markov Random Fields to Range Sensing James Diebel and Sebastian Thrun Stanford AI Lab Stanford University, Stanford, CA 94305 Abstract This paper describes a highly successful application of MRFs to the problem of generating high-resolution range images. A new generation of range sensor... | 2005 | 45 |
2,861 | Analysis of Spectral Kernel Design based Semi-supervised Learning Tong Zhang Yahoo! Inc. New York City, NY 10011 Rie Kubota Ando IBM T. J. Watson Research Center Yorktown Heights, NY 10598 Abstract We consider a framework for semi-supervised learning using spectral decomposition based un-supervised ... | 2005 | 46 |
2,862 | Representing Part-Whole Relationships in Recurrent Neural Networks Viren Jain2, Valentin Zhigulin1,2, and H. Sebastian Seung1,2 1Howard Hughes Medical Institute and 2Brain & Cog. Sci. Dept., MIT viren@mit.edu, valentin@mit.edu, seung@mit.edu Abstract There is little consensus about the computational funct... | 2005 | 47 |
2,863 | Size Regularized Cut for Data Clustering Yixin Chen Department of CS Univ. of New Orleans yixin@cs.uno.edu Ya Zhang Department of EECS Uinv. of Kansas yazhang@ittc.ku.edu Xiang Ji NEC-Labs America, Inc. xji@sv.nec-labs.com Abstract We present a novel spectral clustering method that enables use... | 2005 | 48 |
2,864 | How fast to work: Response vigor, motivation and tonic dopamine Yael Niv1,2 Nathaniel D. Daw2 Peter Dayan2 1ICNC, Hebrew University, Jerusalem 2Gatsby Computational Neuroscience Unit, UCL yaelniv@alice.nc.huji.ac.il {daw,dayan}@gatsby.ucl.ac.uk Abstract Reinforcement learning models have long promis... | 2005 | 49 |
2,865 | Fixing two weaknesses of the Spectral Method Kevin J. Lang Yahoo Research 3333 Empire Ave, Burbank, CA 91504 langk@yahoo-inc.com Abstract We discuss two intrinsic weaknesses of the spectral graph partitioning method, both of which have practical consequences. The first is that spectral embeddings tend to... | 2005 | 5 |
2,866 | Robust design of biological experiments Patrick Flaherty EECS Department University of California Berkeley, CA 94720 flaherty@berkeley.edu Michael I. Jordan Computer Science and Statistics University of California Berkeley, CA 94720 jordan@cs.berkeley.edu Adam P. Arkin Bioengineering Department, ... | 2005 | 50 |
2,867 | On the Accuracy of Bounded Rationality: How Far from Optimal Is Fast and Frugal? Michael Schmitt Ludwig-Marum-Gymnasium Schlossgartenstraße 11 76327 Pfinztal, Germany mschmittm@googlemail.com Laura Martignon Institut f¨ur Mathematik und Informatik P¨adagogische Hochschule Ludwigsburg Reuteallee 46, 7... | 2005 | 51 |
2,868 | Assessing Approximations for Gaussian Process Classification Malte Kuss and Carl Edward Rasmussen Max Planck Institute for Biological Cybernetics Spemannstraße 38, 72076 T¨ubingen, Germany {kuss,carl}@tuebingen.mpg.de Abstract Gaussian processes are attractive models for probabilistic classification but u... | 2005 | 52 |
2,869 | Fast Online Policy Gradient Learning with SMD Gain Vector Adaptation Nicol N. Schraudolph Jin Yu Douglas Aberdeen Statistical Machine Learning, National ICT Australia, Canberra {nic.schraudolph,douglas.aberdeen}@nicta.com.au Abstract Reinforcement learning by direct policy gradient estimation is attract... | 2005 | 53 |
2,870 | A Bayes Rule for Density Matrices Manfred K. Warmuth∗ Computer Science Department University of California at Santa Cruz manfred@cse.ucsc.edu Abstract The classical Bayes rule computes the posterior model probability from the prior probability and the data likelihood. We generalize this rule to the case... | 2005 | 54 |
2,871 | Combining Graph Laplacians for Semi–Supervised Learning Andreas Argyriou, Mark Herbster, Massimiliano Pontil Department of Computer Science University College London Gower Street, London WC1E 6BT, England, UK {a.argyriou, m.herbster, m.pontil}@cs.ucl.ac.uk Abstract A foundational problem in semi-sup... | 2005 | 55 |
2,872 | Integrate-and-Fire models with adaptation are good enough: predicting spike times under random current injection Renaud Jolivet∗ Brain Mind Institute, EPFL CH-1015 Lausanne, Switzerland renaud.jolivet@epfl.ch Alexander Rauch MPI for Biological Cybernetics D-72012 T¨ubingen, Germany alexander.rauch@tu... | 2005 | 56 |
2,873 | Large-Scale Multiclass Transduction Thomas G¨artner Fraunhofer AIS.KD, 53754 Sankt Augustin, Thomas.Gaertner@ais.fraunhofer.de Quoc V. Le, Simon Burton, Alex J. Smola, Vishy Vishwanathan Statistical Machine Learning Program, NICTA and ANU, Canberra, ACT {Quoc.Le, Simon.Burton, Alex.Smola, SVN.Vishwanathan}@ni... | 2005 | 57 |
2,874 | Sparse Gaussian Processes using Pseudo-inputs Edward Snelson Zoubin Ghahramani Gatsby Computational Neuroscience Unit University College London 17 Queen Square, London WC1N 3AR, UK {snelson,zoubin}@gatsby.ucl.ac.uk Abstract We present a new Gaussian process (GP) regression model whose covariance is para... | 2005 | 58 |
2,875 | Fast biped walking with a reflexive controller and real-time policy searching Tao Geng1, Bernd Porr2 and Florentin W¨org¨otter1,3 1 Dept. Psychology, University of Stirling, UK. runbot05@gmail.com 2 Dept. Electronics & Electrical Eng., University of Glasgow, UK. b.porr@elec.gla.ac.uk 3 Bernstein Centre for... | 2005 | 59 |
2,876 | Saliency Based on Information Maximization Neil D.B. Bruce and John K. Tsotsos Department of Computer Science and Centre for Vision Research York University, Toronto, ON, M2N 5X8 {neil,tsotsos}@cs . yorku. c a Abstract A model of bottom-up overt attention is proposed based on the principle of maxim... | 2005 | 6 |
2,877 | Learning from Data of Variable Quality Koby Crammer, Michael Kearns, Jennifer Wortman Computer and Information Science University of Pennsylvania Philadelphia, PA 19103 {crammer,mkearns,wortmanj}@cis.upenn.edu Abstract We initiate the study of learning from multiple sources of limited data, each of whic... | 2005 | 60 |
2,878 | Two view learning: SVM-2K, Theory and Practice Jason D.R. Farquhar jdrf99r@ecs.soton.ac.uk David R. Hardoon drh@ecs.soton.ac.uk Hongying Meng hongying@cs.york.ac.uk John Shawe-Taylor jst@ecs.soton.ac.uk Sandor Szedmak ss03v@ecs.soton.ac.uk School of Electronics and Computer Science, University... | 2005 | 61 |
2,879 | Extracting Dynamical Structure Embedded in Neural Activity Byron M. Yu1, Afsheen Afshar1,2, Gopal Santhanam1, Stephen I. Ryu1,3, Krishna V. Shenoy1,4 1Department of Electrical Engineering, 2School of Medicine, 3Department of Neurosurgery, 4Neurosciences Program, Stanford University, Stanford, CA 94305 {byro... | 2005 | 62 |
2,880 | Large-scale biophysical parameter estimation in single neurons via constrained linear regression Misha B. Ahrens∗, Quentin J.M. Huys∗, Liam Paninski Gatsby Computational Neuroscience Unit University College London {ahrens, qhuys, liam}@gatsby.ucl.ac.uk Abstract Our understanding of the input-output functi... | 2005 | 63 |
2,881 | Data-Driven Online to Batch Conversions Ofer Dekel and Yoram Singer School of Computer Science and Engineering The Hebrew University, Jerusalem 91904, Israel {oferd,singer}@cs.huji.ac.il Abstract Online learning algorithms are typically fast, memory efficient, and simple to implement. However, many common le... | 2005 | 64 |
2,882 | Learning Rankings via Convex Hull Separation Glenn Fung, R´omer Rosales, Balaji Krishnapuram Computer Aided Diagnosis, Siemens Medical Solutions USA, Malvern, PA 19355 {glenn.fung, romer.rosales, balaji.krishnapuram}@siemens.com Abstract We propose efficient algorithms for learning ranking functions from order... | 2005 | 65 |
2,883 | Interpolating Between Types and Tokens by Estimating Power-Law Generators ∗ Sharon Goldwater Thomas L. Griffiths Mark Johnson Department of Cognitive and Linguistic Sciences Brown University, Providence RI 02912, USA {sharon goldwater,tom griffiths,mark johnson}@brown.edu Abstract Standard statistical ... | 2005 | 66 |
2,884 | Response Analysis of Neuronal Population with Synaptic Depression Wentao Huang Institute of Intelligent Information Processing, Xidian University, Xi'an 710071, China wthuang@mail.xidian.edu.cn Licheng Jiao Institute of Intelligent Information Processing, Xidian University, Xi'an 710071, China lch... | 2005 | 67 |
2,885 | Sequence and Tree Kernels with Statistical Feature Mining Jun Suzuki and Hideki Isozaki NTT Communication Science Laboratories, NTT Corp. 2-4 Hikaridai, Seika-cho, Soraku-gun, Kyoto,619-0237 Japan {jun, isozaki}@cslab.kecl.ntt.co.jp Abstract This paper proposes a new approach to feature selection based on... | 2005 | 68 |
2,886 | Faster Rates in Regression via Active Learning Rui Castro Rice University Houston, TX 77005 rcastro@rice.edu Rebecca Willett University of Wisconsin Madison, WI 53706 willett@cae.wisc.edu Robert Nowak University of Wisconsin Madison, WI 53706 nowak@engr.wisc.edu Abstract This paper presents ... | 2005 | 69 |
2,887 | Learning vehicular dynamics, with application to modeling helicopters Pieter Abbeel Computer Science Dept. Stanford University Stanford, CA 94305 Varun Ganapathi Computer Science Dept. Stanford University Stanford, CA 94305 Andrew Y. Ng Computer Science Dept. Stanford University Stanford, CA 9... | 2005 | 7 |
2,888 | Top-Down Control of Visual Attention: A Rational Account Michael C. Mozer Michael Shettel Shaun Vecera Dept. of Comp. Science & Dept. of Comp. Science & Dept. of Psychology Institute of Cog. Science Institute of Cog. Science University of Iowa University of Colorado University of Colorado Iow... | 2005 | 70 |
2,889 | The Role of Top-down and Bottom-up Processes in Guiding Eye Movements during Visual Search Gregory J. Zelinsky†‡, Wei Zhang‡, Bing Yu‡, Xin Chen†∗, Dimitris Samaras‡ Dept. of Psychology†, Dept. of Computer Science‡ State University of New York at Stony Brook Stony Brook, NY 11794 Gregory.Zelinsky@stonybrook... | 2005 | 71 |
2,890 | A Bayesian Framework for Tilt Perception and Confidence Odelia Schwartz HHMI and Salk Institute La Jolla, CA 92014 odelia@salk.edu Terrence J. Sejnowski HHMI and Salk Institute La Jolla, CA 92014 terry@salk.edu Peter Dayan Gatsby, UCL 17 Queen Square, London dayan@gatsby.ucl.ac.uk Abstract ... | 2005 | 72 |
2,891 | Multiple Instance Boosting for Object Detection Paul Viola, John C. Platt, and Cha Zhang Microsoft Research 1 Microsoft Way Redmond, WA 98052 {viola,jplatt}@microsoft.com Abstract A good image object detection algorithm is accurate, fast, and does not require exact locations of objects in a training set... | 2005 | 73 |
2,892 | Generalization to Unseen Cases Teemu Roos Helsinki Institute for Information Technology P.O.Box 68, 00014 Univ. of Helsinki, Finland teemu.roos@cs.helsinki.fi Peter Gr¨unwald CWI, P.O.Box 94079, 1090 GB, Amsterdam, The Netherlands pdg@cwi.nl Petri Myllym¨aki Helsinki Institute for Information Techno... | 2005 | 74 |
2,893 | Nearest Neighbor Based Feature Selection for Regression and its Application to Neural Activity Amir Navot12 Lavi Shpigelman12 Naftali Tishby12 Eilon Vaadia23 1School of computer Science and Engineering 2Interdisciplinary Center for Neural Computation 3Dept. of Physiology, Hadassah Medical School The H... | 2005 | 75 |
2,894 | Visual Encoding with Jittering Eyes Michele Rucci∗ Department of Cognitive and Neural Systems Boston University Boston, MA 02215 rucci@cns.bu.edu Abstract Under natural viewing conditions, small movements of the eye and body prevent the maintenance of a steady direction of gaze. It is known that stimu... | 2005 | 76 |
2,895 | Learning to Control an Octopus Arm with Gaussian Process Temporal Difference Methods Yaakov Engel∗ AICML, Dept. of Computing Science University of Alberta Edmonton, Canada yaki@cs.ualberta.ca Peter Szabo and Dmitry Volkinshtein Dept. of Electrical Engineering Technion Institute of Technology Haifa, ... | 2005 | 77 |
2,896 | Improved Risk Tail Bounds for On-Line Algorithms * Nicolo Cesa-Bianchi DSI, Universita di Milano via Comelico 39 20135 Milano, Italy cesa-bianchi@dsi.unimi.it Abstract Claudio Gentile DICOM, Universita dell'Insubria via Mazzini 5 21100 Varese, Italy gentile@dsi.unimi.it We prove t... | 2005 | 78 |
2,897 | Kernelized Infomax Clustering Felix V. Agakov Edinburgh University Edinburgh EH1 2QL, U.K. felixa@inf.ed.ac.uk David Barber IDIAP Research Institute CH-1920 Martigny Switzerland david.barber@idiap.ch Abstract We propose a simple information-theoretic approach to soft clustering based on maximizing t... | 2005 | 79 |
2,898 | Active Learning for Misspecified Models Masashi Sugiyama Department of Computer Science, Tokyo Institute of Technology 2-12-1, O-okayama, Meguro-ku, Tokyo, 152-8552, Japan sugi@cs.titech.ac.jp Abstract Active learning is the problem in supervised learning to design the locations of training input points so t... | 2005 | 8 |
2,899 | Temporally changing synaptic plasticity Minija Tamosiunaite1,2, Bernd Porr3, and Florentin W¨org¨otter1,4 1 Department of Psychology, University of Stirling Stirling FK9 4LA, Scotland 2 Department of Informatics, Vytautas Magnus University Kaunas, Lithuania 3 Department of Electronics & Electrical Engineeri... | 2005 | 80 |
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