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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3,800 | No evidence for active sparsification in the visual cortex Pietro Berkes, Benjamin L. White, and J´ozsef Fiser Volen Center for Complex Systems Brandeis University, Waltham, MA 02454 Abstract The proposal that cortical activity in the visual cortex is optimized for sparse neural activity is one of the most e... | 2009 | 51 |
3,801 | Distribution Matching for Transduction Novi Quadrianto RSISE, ANU & SML, NICTA Canberra, ACT, Australia novi.quad@gmail.com James Petterson RSISE, ANU & SML, NICTA Canberra, ACT, Australia james.petterson@nicta.com.au Alex J. Smola Yahoo! Research Santa Clara, CA, USA alex@smola.org Abstract ... | 2009 | 52 |
3,802 | Canonical Time Warping for Alignment of Human Behavior Feng Zhou Robotics Institute Carnegie Mellon University www.f-zhou.com Fernando de la Torre Robotics Institute Carnegie Mellon University ftorre@cs.cmu.edu Abstract Alignment of time series is an important problem to solve in many scientific di... | 2009 | 53 |
3,803 | A Parameter-free Hedging Algorithm Kamalika Chaudhuri ITA, UC San Diego kamalika@soe.ucsd.edu Yoav Freund CSE, UC San Diego yfreund@ucsd.edu Daniel Hsu CSE, UC San Diego djhsu@cs.ucsd.edu Abstract We study the problem of decision-theoretic online learning (DTOL). Motivated by practical applicati... | 2009 | 54 |
3,804 | Learning Bregman Distance Functions and Its Application for Semi-Supervised Clustering Lei Wu†♯, Rong Jin‡, Steven C.H. Hoi†, Jianke Zhu♮, and Nenghai Yu♯ †School of Computer Engineering, Nanyang Technological University, Singapore ‡Department of Computer Science & Engineering, Michigan State University ♮Comp... | 2009 | 55 |
3,805 | Ranking Measures and Loss Functions in Learning to Rank Wei Chen∗ Chinese Academy of sciences chenwei@amss.ac.cn Tie-Yan Liu Microsoft Research Asia tyliu@micorsoft.com Yanyan Lan Chinese Academy of sciences lanyanyan@amss.ac.cn Zhiming Ma Chinese Academy of sciences mazm@amt.ac.cn Hang Li ... | 2009 | 56 |
3,806 | Constructing Topological Maps using Markov Random Fields and Loop-Closure Detection Roy Anati Kostas Daniilidis GRASP Laboratory Department of Computer and Information Science University of Pennsylvania Philadelphia, PA 19104 {royanati,kostas}@cis.upenn.edu Abstract We present a system which constru... | 2009 | 57 |
3,807 | Replicated Softmax: an Undirected Topic Model Ruslan Salakhutdinov Brain and Cognitive Sciences and CSAIL Massachusetts Institute of Technology rsalakhu@mit.edu Geoffrey Hinton Department of Computer Science University of Toronto hinton@cs.toronto.edu Abstract We introduce a two-layer undirected gra... | 2009 | 58 |
3,808 | Fast, smooth and adaptive regression in metric spaces Samory Kpotufe UCSD CSE Abstract It was recently shown that certain nonparametric regressors can escape the curse of dimensionality when the intrinsic dimension of data is low ([1, 2]). We prove some stronger results in more general settings. In particul... | 2009 | 59 |
3,809 | Neurometric function analysis of population codes Philipp Berens, Sebastian Gerwinn, Alexander S. Ecker and Matthias Bethge Max Planck Institute for Biological Cybernetics Center for Integrative Neuroscience, University of T¨ubingen Computational Vision and Neuroscience Group Spemannstrasse 41, 72076, T¨ubing... | 2009 | 6 |
3,810 | Bayesian Belief Polarization Alan Jern Department of Psychology Carnegie Mellon University ajern@cmu.edu Kai-min K. Chang Language Technologies Institute Carnegie Mellon University kkchang@cs.cmu.edu Charles Kemp Department of Psychology Carnegie Mellon University ckemp@cmu.edu Abstract Empi... | 2009 | 60 |
3,811 | Linearly constrained Bayesian matrix factorization for blind source separation Mikkel N. Schmidt Department of Engineering University of Cambridge mns@imm.dtu.dk Abstract We present a general Bayesian approach to probabilistic matrix factorization subject to linear constraints. The approach is based on a ... | 2009 | 61 |
3,812 | Sparse and Locally Constant Gaussian Graphical Models Jean Honorio, Luis Ortiz, Dimitris Samaras Department of Computer Science Stony Brook University Stony Brook, NY 11794 {jhonorio,leortiz,samaras}@cs.sunysb.edu Nikos Paragios Laboratoire MAS Ecole Centrale Paris Chatenay-Malabry, France nik... | 2009 | 62 |
3,813 | Sparse Metric Learning via Smooth Optimization Yiming Ying†, Kaizhu Huang‡, and Colin Campbell† †Department of Engineering Mathematics, University of Bristol, Bristol BS8 1TR, United Kingdom ‡National Laboratory of Pattern Recognition, Institute of Automation, The Chinese Academy of Sciences, 100190 Beijing, ... | 2009 | 63 |
3,814 | The "tree-dependent components" of natural scenes are edge filters Daniel Zoran Interdisciplinary Center for Neural Computation Hebrew University of Jerusalem daniez@cs.huji.ac.il Yair Weiss School of Computer Science Hebrew University of Jerusalem yweiss@cs.huji.ac.il Abstract We propose a new mod... | 2009 | 64 |
3,815 | Bootstrapping from Game Tree Search Joel Veness University of NSW and NICTA Sydney, NSW, Australia 2052 joelv@cse.unsw.edu.au David Silver University of Alberta Edmonton, AB Canada T6G2E8 silver@cs.ualberta.ca William Uther NICTA and the University of NSW Sydney, NSW, Australia 2052 William.Uthe... | 2009 | 65 |
3,816 | Object discovery and identification Charles Kemp & Alan Jern Department of Psychology Carnegie Mellon University {ckemp,ajern}@cmu.edu Fei Xu Department of Psychology University of California, Berkeley fei xu@berkeley.edu Abstract Humans are typically able to infer how many objects their environment ... | 2009 | 66 |
3,817 | Modeling Social Annotation Data with Content Relevance using a Topic Model Tomoharu Iwata Takeshi Yamada Naonori Ueda NTT Communication Science Laboratories 2-4 Hikaridai, Seika-cho, Soraku-gun, Kyoto, Japan {iwata,yamada,ueda}@cslab.kecl.ntt.co.jp Abstract We propose a probabilistic topic model for a... | 2009 | 67 |
3,818 | Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation Hamid R. Maei University of Alberta Edmonton, AB, Canada Csaba Szepesv´ari∗ University of Alberta Edmonton, AB, Canada Shalabh Bhatnagar Indian Institute of Science Bangalore, India Doina Precup McGill Universit... | 2009 | 68 |
3,819 | Group Sparse Coding Samy Bengio Google Mountain View, CA bengio@google.com Fernando Pereira Google Mountain View, CA pereira@google.com Yoram Singer Google Mountain View, CA singer@google.com Dennis Strelow Google Mountain View, CA strelow@google.com Abstract Bag-of-words document re... | 2009 | 69 |
3,820 | Slow, Decorrelated Features for Pretraining Complex Cell-like Networks James Bergstra University of Montreal james.bergstra@umontreal.ca Yoshua Bengio University of Montreal yoshua.bengio@umontreal.ca Abstract We introduce a new type of neural network activation function based on recent physiologica... | 2009 | 7 |
3,821 | Decoupling Sparsity and Smoothness in the Discrete Hierarchical Dirichlet Process Chong Wang Computer Science Department Princeton University chongw@cs.princeton.edu David M. Blei Computer Science Department Princeton University blei@cs.princeton.edu Abstract We present a nonparametric hierarchica... | 2009 | 70 |
3,822 | Fast Image Deconvolution using Hyper-Laplacian Priors Dilip Krishnan, Dept. of Computer Science, Courant Institute, New York University dilip@cs.nyu.edu Rob Fergus, Dept. of Computer Science, Courant Institute, New York University fergus@cs.nyu.edu Abstract The heavy-tailed distribution of gra... | 2009 | 71 |
3,823 | Compositionality of optimal control laws Emanuel Todorov Applied Mathematics and Computer Science & Engineering University of Washington todorov@cs.washington.edu Abstract We present a theory of compositionality in stochastic optimal control, showing how task-optimal controllers can be constructed from ce... | 2009 | 72 |
3,824 | AUC optimization and the two-sample problem St´ephan Cl´emenc¸on Telecom Paristech (TSI) - LTCI UMR Institut Telecom/CNRS 5141 stephan.clemencon@telecom-paristech.fr Marine Depecker Telecom Paristech (TSI) - LTCI UMR Institut Telecom/CNRS 5141 marine.depecker@telecom-paristech.fr Nicolas Vayatis ENS Cac... | 2009 | 73 |
3,825 | Measuring Invariances in Deep Networks Ian J. Goodfellow, Quoc V. Le, Andrew M. Saxe, Honglak Lee, Andrew Y. Ng Computer Science Department Stanford University Stanford, CA 94305 {ia3n,quocle,asaxe,hllee,ang}@cs.stanford.edu Abstract For many pattern recognition tasks, the ideal input feature would be inv... | 2009 | 74 |
3,826 | Nonparametric Latent Feature Models for Link Prediction Kurt T. Miller EECS University of California Berkeley, CA 94720 tadayuki@cs.berkeley.edu Thomas L. Griffiths Psychology and Cognitive Science University of California Berkeley, CA 94720 tom griffiths@berkeley.edu Michael I. Jordan EECS and... | 2009 | 75 |
3,827 | Asymptotically Optimal Regularization in Smooth Parametric Models Percy Liang University of California, Berkeley pliang@cs.berkeley.edu Francis Bach INRIA - ´Ecole Normale Sup´erieure, France francis.bach@ens.fr Guillaume Bouchard Xerox Research Centre Europe, France Guillaume.Bouchard@xrce.xerox.co... | 2009 | 76 |
3,828 | Variational Gaussian-process factor analysis for modeling spatio-temporal data Jaakko Luttinen Adaptive Informatics Research Center Helsinki University of Technology, Finland Jaakko.Luttinen@tkk.fi Alexander Ilin Adaptive Informatics Research Center Helsinki University of Technology, Finland Alexander... | 2009 | 77 |
3,829 | Lattice Regression Eric K. Garcia Department of Electrical Engineering University of Washington Seattle, WA 98195 garciaer@ee.washington.edu Maya R. Gupta Department of Electrical Engineering University of Washington Seattle, WA 98195 gupta@ee.washington.edu Abstract We present a new empirical r... | 2009 | 78 |
3,830 | Sharing Features among Dynamical Systems with Beta Processes Emily B. Fox Electrical Engineering & Computer Science, Massachusetts Institute of Technology ebfox@mit.edu Erik B. Sudderth Computer Science, Brown University sudderth@cs.brown.edu Michael I. Jordan Electrical Engineering & Computer Science... | 2009 | 79 |
3,831 | Code-specific policy gradient rules for spiking neurons Henning Sprekeler∗ Guillaume Hennequin Wulfram Gerstner Laboratory for Computational Neuroscience ´Ecole Polytechnique F´ed´erale de Lausanne 1015 Lausanne Abstract Although it is widely believed that reinforcement learning is a suitable tool for ... | 2009 | 8 |
3,832 | The Wisdom of Crowds in the Recollection of Order Information Mark Steyvers, Michael Lee, Brent Miller, Pernille Hemmer Department of Cognitive Sciences University of California Irvine mark.steyvers@uci.edu Abstract When individuals independently recollect events or retrieve facts from ... | 2009 | 80 |
3,833 | Efficient Recovery of Jointly Sparse Vectors Liang Sun, Jun Liu, Jianhui Chen, Jieping Ye School of Computing, Informatics, and Decision Systems Engineering Arizona State University Tempe, AZ 85287 {sun.liang,j.liu,jianhui.chen,jieping.ye}asu.edu Abstract We consider the reconstruction of sparse signals in... | 2009 | 81 |
3,834 | Efficient Match Kernels between Sets of Features for Visual Recognition Liefeng Bo Toyota Technological Institute at Chicago blf0218@tti-c.org Cristian Sminchisescu University of Bonn sminchisescu.ins.uni-bonn.de Abstract In visual recognition, the images are frequently modeled as unordered collections... | 2009 | 82 |
3,835 | Clustering Sequence Sets for Motif Discovery Jong Kyoung Kim and Seungjin Choi Department of Computer Science Pohang University of Science and Technology San 31 Hyoja-dong, Nam-gu Pohang 790-784, Korea {blkimjk,seungjin}@postech.ac.kr Abstract Most of existing methods for DNA motif discovery consider on... | 2009 | 83 |
3,836 | Bayesian Nonparametric Models on Decomposable Graphs Franc¸ois Caron INRIA Bordeaux Sud–Ouest Institut de Math´ematiques de Bordeaux University of Bordeaux, France francois.caron@inria.fr Arnaud Doucet Departments of Computer Science & Statistics University of British Columbia, Vancouver, Canada and... | 2009 | 84 |
3,837 | Correlation Coefficients Are Insufficient for Analyzing Spike Count Dependencies Arno Onken Technische Universit¨at Berlin / BCCN Berlin Franklinstr. 28/29, 10587 Berlin, Germany aonken@cs.tu-berlin.de Steffen Gr¨unew¨alder University College London Gower Street, London WC1E 6BT, UK steffen@cs.ucl.ac.uk... | 2009 | 85 |
3,838 | Streaming k-means approximation Nir Ailon Google Research nailon@google.com Ragesh Jaiswal∗ Columbia University rjaiswal@gmail.com Claire Monteleoni† Columbia University cmontel@ccls.columbia.edu Abstract We provide a clustering algorithm that approximately optimizes the k-means objective, in the ... | 2009 | 86 |
3,839 | Help or Hinder: Bayesian Models of Social Goal Inference Tomer D. Ullman, Chris L. Baker, Owen Macindoe, Owain Evans, Noah D. Goodman and Joshua B. Tenenbaum {tomeru, clbaker, owenm, owain, ndg, jbt}@mit.edu Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Abstract Everyd... | 2009 | 87 |
3,840 | Sequential effects reflect parallel learning of multiple environmental regularities Matthew H. Wilder⋆, Matt Jones†, & Michael C. Mozer⋆ ⋆Dept. of Computer Science †Dept. of Psychology University of Colorado Boulder, CO 80309 <wildermh@colorado.edu, mcj@colorado.edu, mozer@colorado.edu> Abstract Across... | 2009 | 88 |
3,841 | Noisy Generalized Binary Search Robert Nowak University of Wisconsin-Madison 1415 Engineering Drive, Madison WI 53706 nowak@ece.wisc.edu Abstract This paper addresses the problem of noisy Generalized Binary Search (GBS). GBS is a well-known greedy algorithm for determining a binary-valued hypothesis throu... | 2009 | 89 |
3,842 | Positive Semidefinite Metric Learning with Boosting Chunhua Shen†‡, Junae Kim†‡, Lei Wang‡, Anton van den Hengel¶ † NICTA Canberra Research Lab, Canberra, ACT 2601, Australia∗ ‡ Australian National University, Canberra, ACT 0200, Australia ¶ The University of Adelaide, Adelaide, SA 5005, Australia Abstract T... | 2009 | 9 |
3,843 | Solving Stochastic Games Liam Mac Dermed College of Computing Georgia Tech 801 Atlantic Drive Atlanta, GA 30332-0280 liam@cc.gatech.edu Charles Isbell College of Computing Georgia Tech 801 Atlantic Drive Atlanta, GA 30332-0280 isbell@cc.gatech.edu Abstract Solving multi-agent reinforcement l... | 2009 | 90 |
3,844 | Modelling Relational Data using Bayesian Clustered Tensor Factorization Ilya Sutskever University of Toronto ilya@cs.utoronto.ca Ruslan Salakhutdinov MIT rsalakhu@mit.edu Joshua B. Tenenbaum MIT jbt@mit.edu Abstract We consider the problem of learning probabilistic models for complex relational ... | 2009 | 91 |
3,845 | Kernel Methods for Deep Learning Youngmin Cho and Lawrence K. Saul Department of Computer Science and Engineering University of California, San Diego 9500 Gilman Drive, Mail Code 0404 La Jolla, CA 92093-0404 {yoc002,saul}@cs.ucsd.edu Abstract We introduce a new family of positive-definite kernel function... | 2009 | 92 |
3,846 | Optimal context separation of spiking haptic signals by second-order somatosensory neurons Romain Brasselet CNRS - UPMC Univ Paris 6, UMR 7102 F 75005, Paris, France romain.brasselet@upmc.fr Roland S. Johansson UMEA Univ, Dept Integr Medical Biology SE-901 87 Umea, Sweden roland.s.johansson@physiol.um... | 2009 | 93 |
3,847 | Heterogeneous Multitask Learning with Joint Sparsity Constraints Xiaolin Yang Department of Statistics Carnegie Mellon University Pittsburgh, PA 15213 xyang@stat.cmu.edu Seyoung Kim Machine Learning Department Carnegie Mellon University Pittsburgh, PA 15213 sssykim@cs.cmu.edu Eric P. Xing Mach... | 2009 | 94 |
3,848 | Learning with Compressible Priors Volkan Cevher Rice University volkan@rice.edu Abstract We describe a set of probability distributions, dubbed compressible priors, whose independent and identically distributed (iid) realizations result in p-compressible signals. A signal x ∈RN is called p-compressible wi... | 2009 | 95 |
3,849 | Inter-domain Gaussian Processes for Sparse Inference using Inducing Features Miguel L´azaro-Gredilla and An´ıbal R. Figueiras-Vidal Dep. Signal Processing & Communications Universidad Carlos III de Madrid, SPAIN {miguel,arfv}@tsc.uc3m.es Abstract We present a general inference framework for inter-domain G... | 2009 | 96 |
3,850 | Particle-based Variational Inference for Continuous Systems Alexander T. Ihler Dept. of Computer Science Univ. of California, Irvine ihler@ics.uci.edu Andrew J. Frank Dept. of Computer Science Univ. of California, Irvine ajfrank@ics.uci.edu Padhraic Smyth Dept. of Computer Science Univ. of Calif... | 2009 | 97 |
3,851 | Efficient Learning using Forward-Backward Splitting John Duchi University of California Berkeley jduchi@cs.berkeley.edu Yoram Singer Google singer@google.com Abstract We describe, analyze, and experiment with a new framework for empirical loss minimization with regularization. Our algorithmic framework... | 2009 | 98 |
3,852 | L1-Penalized Robust Estimation for a Class of Inverse Problems Arising in Multiview Geometry Arnak S. Dalalyan and Renaud Keriven IMAGINE/LabIGM, Universit´e Paris Est - Ecole des Ponts ParisTech, Marne-la-Vall´ee, France dalalyan,keriven@imagine.enpc.fr Abstract We propose a new approach to the problem... | 2009 | 99 |
3,853 | Agnostic Active Learning Without Constraints Alina Beygelzimer IBM Research Hawthorne, NY beygel@us.ibm.com Daniel Hsu Rutgers University & University of Pennsylvania djhsu@rci.rutgers.edu John Langford Yahoo! Research New York, NY jl@yahoo-inc.com Tong Zhang Rutgers University Piscataway,... | 2010 | 1 |
3,854 | Label Embedding Trees for Large Multi-Class Tasks Samy Bengio(1) Jason Weston(1) David Grangier(2) (1) Google Research, New York, NY {bengio, jweston}@google.com (2)NEC Labs America, Princeton, NJ {dgrangier}@nec-labs.com Abstract Multi-class classification becomes challenging at test time when the num... | 2010 | 10 |
3,855 | Optimal Web-scale Tiering as a Flow Problem Gilbert Leung eBay, Inc. San Jose, CA, USA gleung@alum.mit.edu Novi Quadrianto SML-NICTA & RSISE-ANU Canberra, ACT, Australia novi.quad@gmail.com Alexander J. Smola Yahoo! Research Santa Clara, CA, USA alex@smola.org Kostas Tsioutsiouliklis Yahoo! ... | 2010 | 100 |
3,856 | Slice sampling covariance hyperparameters of latent Gaussian models Iain Murray School of Informatics University of Edinburgh Ryan Prescott Adams Dept. Computer Science University of Toronto Abstract The Gaussian process (GP) is a popular way to specify dependencies between random variables in a proba... | 2010 | 101 |
3,857 | Efficient Optimization for Discriminative Latent Class Models Armand Joulin∗ INRIA 23, avenue d’Italie, 75214 Paris, France. armand.joulin@inria.fr Francis Bach∗ INRIA 23, avenue d’Italie, 75214 Paris, France. francis.bach@inria.fr Jean Ponce∗ Ecole Normale Sup´erieure 45, rue d’Ulm 75005 P... | 2010 | 102 |
3,858 | Universal Kernels on Non-Standard Input Spaces Andreas Christmann University of Bayreuth Department of Mathematics D-95440 Bayreuth andreas.christmann@uni-bayreuth.de Ingo Steinwart University of Stuttgart Department of Mathematics D-70569 Stuttgart ingo.steinwart@mathematik.uni-stuttgart.de Abstr... | 2010 | 103 |
3,859 | Worst-Case Linear Discriminant Analysis Yu Zhang and Dit-Yan Yeung Department of Computer Science and Engineering Hong Kong University of Science and Technology {zhangyu,dyyeung}@cse.ust.hk Abstract Dimensionality reduction is often needed in many applications due to the high dimensionality of the data in... | 2010 | 104 |
3,860 | Learning Multiple Tasks with a Sparse Matrix-Normal Penalty Yi Zhang Machine Learning Department Carnegie Mellon University yizhang1@cs.cmu.edu Jeff Schneider The Robotics Institute Carnegie Mellon University schneide@cs.cmu.edu Abstract In this paper, we propose a matrix-variate normal penalty wi... | 2010 | 105 |
3,861 | Network Flow Algorithms for Structured Sparsity Julien Mairal∗ INRIA - Willow Project-Team† julien.mairal@inria.fr Rodolphe Jenatton∗ INRIA - Willow Project-Team† rodolphe.jenatton@inria.fr Guillaume Obozinski INRIA - Willow Project-Team† guillaume.obozinski@inria.fr Francis Bach INRIA - Willow Pr... | 2010 | 106 |
3,862 | Active Learning by Querying Informative and Representative Examples Sheng-Jun Huang1 Rong Jin2 Zhi-Hua Zhou1 1National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, China 2Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 4882... | 2010 | 107 |
3,863 | Optimal Bayesian Recommendation Sets and Myopically Optimal Choice Query Sets Paolo Viappiani∗ Department of Computer Science University of Toronto paolo.viappiani@gmail.com Craig Boutilier Department of Computer Science University of Toronto cebly@cs.toronto.edu Abstract Bayesian approaches to ut... | 2010 | 108 |
3,864 | Semi-Supervised Learning with Adversarially Missing Label Information Umar Syed Ben Taskar Department of Computer and Information Science University of Pennsylvania Philadelphia, PA 19104 {usyed,taskar}@cis.upenn.edu Abstract We address the problem of semi-supervised learning in an adversarial setting... | 2010 | 109 |
3,865 | Deep Coding Network Yuanqing Lin† Tong Zhang‡ Shenghuo Zhu† Kai Yu† †NEC Laboratories America, Cupertino, CA 95129 ‡Rutgers University, Piscataway, NJ 08854 Abstract This paper proposes a principled extension of the traditional single-layer flat sparse coding scheme, where a two-layer coding scheme is ... | 2010 | 11 |
3,866 | Gated Softmax Classification Roland Memisevic Department of Computer Science ETH Zurich Switzerland roland.memisevic@gmail.com Christopher Zach Department of Computer Science ETH Zurich Switzerland chzach@inf.ethz.ch Geoffrey Hinton Department of Computer Science University of Toronto Canada ... | 2010 | 110 |
3,867 | Estimation of R´enyi Entropy and Mutual Information Based on Generalized Nearest-Neighbor Graphs D´avid P´al Department of Computing Science University of Alberta Edmonton, AB, Canada dpal@cs.ualberta.ca Barnab´as P´oczos School of Computer Science Carnegie Mellon University Pittsburgh, PA, USA po... | 2010 | 111 |
3,868 | Causal discovery in multiple models from different experiments Tom Claassen Radboud University Nijmegen The Netherlands tomc@cs.ru.nl Tom Heskes Radboud University Nijmegen The Netherlands tomh@cs.ru.nl Abstract A long-standing open research problem is how to use information from different exper... | 2010 | 112 |
3,869 | Learning Bounds for Importance Weighting Corinna Cortes Google Research New York, NY 10011 corinna@google.com Yishay Mansour Tel-Aviv University Tel-Aviv 69978, Israel mansour@tau.ac.il Mehryar Mohri Courant Institute and Google New York, NY 10012 mohri@cims.nyu.edu Abstract This paper prese... | 2010 | 113 |
3,870 | A Reduction from Apprenticeship Learning to Classification Umar Syed∗ Department of Computer and Information Science University of Pennsylvania Philadelphia, PA 19104 usyed@cis.upenn.edu Robert E. Schapire Department of Computer Science Princeton University Princeton, NJ 08540 schapire@cs.princeton... | 2010 | 114 |
3,871 | Extensions of Generalized Binary Search to Group Identification and Exponential Costs Gowtham Bellala1, Suresh K. Bhavnani2,3,4, Clayton Scott1 1Department of EECS, University of Michigan, Ann Arbor, MI 48109 2Institute for Translational Sciences, 3Dept. of Preventative Medicine and Community Health, Universit... | 2010 | 115 |
3,872 | Feature Set Embedding for Incomplete Data David Grangier NEC Labs America Princeton, NJ dgrangier@nec-labs.com Iain Melvin NEC Labs America Princeton, NJ iain@nec-labs.com Abstract We present a new learning strategy for classification problems in which train and/or test data suffer from missing fea... | 2010 | 116 |
3,873 | Decontaminating Human Judgments by Removing Sequential Dependencies Michael C. Mozer,⋆Harold Pashler,† Matthew Wilder,⋆ Robert V. Lindsey,⋆Matt C. Jones,◦& Michael N. Jones‡ ⋆Dept. of Computer Science, University of Colorado †Dept. of Psychology, UCSD ◦Dept. of Psychology, University of Colorado ‡Dept. of... | 2010 | 117 |
3,874 | Scrambled Objects for Least-Squares Regression Odalric-Ambrym Maillard and R´emi Munos SequeL Project, INRIA Lille - Nord Europe, France {odalric.maillard, remi.munos}@inria.fr Abstract We consider least-squares regression using a randomly generated subspace GP ⊂ F of finite dimension P, where F is a functio... | 2010 | 118 |
3,875 | Subgraph Detection Using Eigenvector L1 Norms Benjamin A. Miller Lincoln Laboratory Massachusetts Institute of Technology Lexington, MA 02420 bamiller@ll.mit.edu Nadya T. Bliss Lincoln Laboratory Massachusetts Institute of Technology Lexington, MA 02420 nt@ll.mit.edu Patrick J. Wolfe Statistics ... | 2010 | 119 |
3,876 | Transduction with Matrix Completion: Three Birds with One Stone Andrew B. Goldberg1, Xiaojin Zhu1, Benjamin Recht1, Jun-Ming Xu1, Robert Nowak2 Department of {1Computer Sciences, 2Electrical and Computer Engineering} University of Wisconsin-Madison, Madison, WI 53706 {goldberg, jerryzhu, brecht, xujm}@cs.wisc... | 2010 | 12 |
3,877 | Error Propagation for Approximate Policy and Value Iteration Amir massoud Farahmand Department of Computing Science University of Alberta Edmonton, Canada, T6G 2E8 amirf@ualberta.ca R´emi Munos Sequel Project, INRIA Lille Lille, France remi.munos@inria.fr Csaba Szepesv´ari ∗ Department of Comput... | 2010 | 120 |
3,878 | PAC-Bayesian Model Selection for Reinforcement Learning Mahdi Milani Fard School of Computer Science McGill University Montreal, Canada mmilan1@cs.mcgill.ca Joelle Pineau School of Computer Science McGill University Montreal, Canada jpineau@cs.mcgill.ca Abstract This paper introduces the first ... | 2010 | 121 |
3,879 | Learning to combine foveal glimpses with a third-order Boltzmann machine Hugo Larochelle and Geoffrey Hinton Department of Computer Science, University of Toronto 6 King’s College Rd, Toronto, ON, Canada, M5S 3G4 {larocheh,hinton}@cs.toronto.edu Abstract We describe a model based on a Boltzmann machine wi... | 2010 | 122 |
3,880 | Collaborative Filtering in a Non-Uniform World: Learning with the Weighted Trace Norm Ruslan Salakhutdinov Brain and Cognitive Sciences and CSAIL, MIT Cambridge, MA 02139 rsalakhu@mit.edu Nathan Srebro Toyota Technological Institute at Chicago Chicago, Illinois 60637 nati@ttic.edu Abstract We show... | 2010 | 123 |
3,881 | Worst-case bounds on the quality of max-product fixed-points Meritxell Vinyals Artificial Intelligence Research Institute (IIIA) Spanish Scientific Research Council (CSIC) Campus UAB, Bellaterra, Spain meritxell@iiia.csic.es Jes´us Cerquides Artificial Intelligence Research Institute (IIIA) Spanish Scient... | 2010 | 124 |
3,882 | A POMDP Extension with Belief-dependent Rewards Mauricio Araya-L´opez Olivier Buffet Vincent Thomas Franc¸ois Charpillet Nancy Universit´e / INRIA LORIA – Campus Scientifique – BP 239 54506 Vandoeuvre-l`es-Nancy Cedex – France firstname.lastname@loria.fr Abstract Partially Observable Markov Decision ... | 2010 | 125 |
3,883 | Infinite Relational Modeling of Functional Connectivity in Resting State fMRI Morten Mørup Section for Cognitive Systems DTU Informatics Technical University of Denmark mm@imm.dtu.dk Kristoffer Hougaard Madsen Danish Research Centre for Magnetic Resonance Copenhagen University Hospital Hvidovre khm@d... | 2010 | 126 |
3,884 | An Alternative to Low-Level-Synchrony-Based Methods for Speech Detection Paul Ruvolo University of California, San Diego Machine Perception Laboratory Atkinson Hall (CALIT2), 6100 9500 Gilman Dr., Mail Code 0440 La Jolla, CA 92093-0440 paul@mplab.ucsd.edu Javier R. Movellan University of California,... | 2010 | 127 |
3,885 | A Rational Decision-Making Framework for Inhibitory Control Pradeep Shenoy Department of Cognitive Science University of California, San Diego pshenoy@ucsd.edu Rajesh P. N. Rao Department of Computer Science University of Washington rao@cs.washington.edu Angela J. Yu Department of Cognitive Science ... | 2010 | 128 |
3,886 | Policy gradients in linearly-solvable MDPs Emanuel Todorov Applied Mathematics and Computer Science & Engineering University of Washington todorov@cs.washington.edu Abstract We present policy gradient results within the framework of linearly-solvable MDPs. For the first time, compatible function approximat... | 2010 | 129 |
3,887 | Extended Bayesian Information Criteria for Gaussian Graphical Models Rina Foygel University of Chicago rina@uchicago.edu Mathias Drton University of Chicago drton@uchicago.edu Abstract Gaussian graphical models with sparsity in the inverse covariance matrix are of significant interest in many modern ... | 2010 | 13 |
3,888 | Sufficient Conditions for Generating Group Level Sparsity in a Robust Minimax Framework Hongbo Zhou and Qiang Cheng Computer Science department, Southern Illinois University Carbondale, IL, 62901 hongboz@siu.edu, qcheng@cs.siu.edu Abstract Regularization technique has become a principled tool for statistic... | 2010 | 130 |
3,889 | Learning Concept Graphs from Text with Stick-Breaking Priors America L. Chambers Department of Computer Science University of California, Irvine Irvine, CA 92697 ahollowa@ics.uci.edu Padhraic Smyth Department of Computer Science University of California, Irvine Irvine, CA 92607 smyth@ics.uci.edu ... | 2010 | 131 |
3,890 | Fast Large-scale Mixture Modeling with Component-specific Data Partitions Bo Thiesson∗ Microsoft Research Chong Wang∗† Princeton University Abstract Remarkably easy implementation and guaranteed convergence has made the EM algorithm one of the most used algorithms for mixture modeling. On the downside, ... | 2010 | 132 |
3,891 | Improvements to the Sequence Memoizer Jan Gasthaus Gatsby Computational Neuroscience Unit University College London London, WC1N 3AR, UK j.gasthaus@gatsby.ucl.ac.uk Yee Whye Teh Gatsby Computational Neuroscience Unit University College London London, WC1N 3AR, UK ywteh@gatsby.ucl.ac.uk Abstract ... | 2010 | 133 |
3,892 | Simultaneous Object Detection and Ranking with Weak Supervision Matthew B. Blaschko Andrea Vedaldi Department of Engineering Science University of Oxford United Kingdom Andrew Zisserman Abstract A standard approach to learning object category detectors is to provide strong supervision in the form of a... | 2010 | 134 |
3,893 | Generating more realistic images using gated MRF’s Marc’Aurelio Ranzato Volodymyr Mnih Geoffrey E. Hinton Department of Computer Science University of Toronto {ranzato,vmnih,hinton}@cs.toronto.edu Abstract Probabilistic models of natural images are usually evaluated by measuring performance on rather in... | 2010 | 135 |
3,894 | Efficient Minimization of Decomposable Submodular Functions Peter Stobbe California Institute of Technology Pasadena, CA 91125 stobbe@caltech.edu Andreas Krause California Institute of Technology Pasadena, CA 91125 krausea@caltech.edu Abstract Many combinatorial problems arising in machine learning... | 2010 | 136 |
3,895 | Implicit Differentiation by Perturbation Justin Domke Rochester Institute of Technology justin.domke@rit.edu Abstract This paper proposes a simple and efficient finite difference method for implicit differentiation of marginal inference results in discrete graphical models. Given an arbitrary loss function, defined ... | 2010 | 137 |
3,896 | The Maximal Causes of Natural Scenes are Edge Filters Gervasio Puertas∗ Frankfurt Institute for Advanced Studies Goethe-University Frankfurt, Germany puertas@fias.uni-frankfurt.de J¨org Bornschein∗ Frankfurt Institute for Advanced Studies Goethe-University Frankfurt, Germany bornschein@fias.uni-frankfur... | 2010 | 138 |
3,897 | Regularized estimation of image statistics by Score Matching Diederik P. Kingma Department of Information and Computing Sciences Universiteit Utrecht d.p.kingma@students.uu.nl Yann LeCun Courant Institute of Mathematical Sciences New York University yann@cs.nyu.edu Abstract Score Matching is a rec... | 2010 | 139 |
3,898 | Estimating Spatial Layout of Rooms using Volumetric Reasoning about Objects and Surfaces David C. Lee, Abhinav Gupta, Martial Hebert, Takeo Kanade Carnegie Mellon University {dclee,abhinavg,hebert,tk}@cs.cmu.edu Abstract There has been a recent push in extraction of 3D spatial layout of scenes. However, n... | 2010 | 14 |
3,899 | Identifying Patients at Risk of Major Adverse Cardiovascular Events Using Symbolic Mismatch Zeeshan Syed University of Michigan Ann Arbor, MI 48109 zhs@eecs.umich.edu John Guttag Massachusetts Institute of Technology Cambridge, MA 02139 guttag@csail.mit.edu Abstract Cardiovascular disease is the l... | 2010 | 140 |
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