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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4,400 | Two is better than one: distinct roles for familiarity and recollection in retrieving palimpsest memories Cristina Savin1 cs664@cam.ac.uk Peter Dayan2 dayan@gatsby.ucl.ac.uk M´at´e Lengyel1 m.lengyel@eng.cam.ac.uk 1Computational & Biological Learning Lab, Dept. of Engineering, University of Cambridge, U... | 2011 | 53 |
4,401 | Automated Refinement of Bayes Networks’ Parameters based on Test Ordering Constraints Omar Zia Khan & Pascal Poupart David R. Cheriton School of Computer Science University of Waterloo Waterloo, ON Canada {ozkhan,ppoupart}@cs.uwaterloo.ca John Mark Agosta∗ Intel Labs Santa Clara, CA, USA johnmark.ago... | 2011 | 54 |
4,402 | Structure Learning for Optimization Shulin (Lynn) Yang Department of Computer Science University of Washington Seattle, WA 98195 yang@cs.washington.edu Ali Rahimi Red Bow Labs Berkeley, CA 94704 ali@redbowlabs.com Abstract We describe a family of global optimization procedures that automatically d... | 2011 | 55 |
4,403 | Multiclass Boosting: Theory and Algorithms Mohammad J. Saberian Statistical Visual Computing Laboratory, University of California, San Diego saberian@ucsd.edu Nuno Vasconcelos Statistical Visual Computing Laboratory, University of California, San Diego nuno@ucsd.edu Abstract The problem of multi-cla... | 2011 | 56 |
4,404 | Composite Multiclass Losses Elodie Vernet ENS Cachan evernet@ens-cachan.fr Robert C. Williamson ANU and NICTA Bob.Williamson@anu.edu.au Mark D. Reid ANU and NICTA Mark.Reid@anu.edu.au Abstract We consider loss functions for multiclass prediction problems. We show when a multiclass loss can be ex... | 2011 | 57 |
4,405 | Scalable Training of Mixture Models via Coresets Dan Feldman MIT Matthew Faulkner Caltech Andreas Krause ETH Zurich Abstract How can we train a statistical mixture model on a massive data set? In this paper, we show how to construct coresets for mixtures of Gaussians and natural generalizations. A c... | 2011 | 58 |
4,406 | Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance Peter Vincent Gehler Max Planck Institut for Informatics pgehler@mpii.de Carsten Rother Microsoft Research Cambridge carrot@microsoft.com Martin Kiefel, Lumin Zhang, Bernhard Sch¨olkopf Max Planck Institute for Intelligent Systems ... | 2011 | 59 |
4,407 | The Kernel Beta Process Lu Ren∗ Electrical & Computer Engineering Dept. Duke University Durham, NC 27708 lr22@duke.edu Yingjian Wang∗ Electrical & Computer Engineering Dept. Duke University Durham, NC 27708 yw65@duke.edu David Dunson Department of Statistical Science Duke University Durham, ... | 2011 | 6 |
4,408 | Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation Cho-Jui Hsieh, M´aty´as A. Sustik, Inderjit S. Dhillon, and Pradeep Ravikumar Department of Computer Science University of Texas at Austin Austin, TX 78712 USA {cjhsieh,sustik,inderjit,pradeepr}@cs.utexas.edu Abstract The ℓ1 regul... | 2011 | 60 |
4,409 | Testing a Bayesian Measure of Representativeness Using a Large Image Database Joshua T. Abbott Department of Psychology University of California, Berkeley Berkeley, CA 94720 joshua.abbott@berkeley.edu Katherine A. Heller Department of Brain and Cognitive Sciences Massachusetts Institute of Technology ... | 2011 | 61 |
4,410 | Dynamical segmentation of single trials from population neural data Biljana Petreska Gatsby Computational Neuroscience Unit University College London biljana@gatsby.ucl.ac.uk Byron M. Yu ECE and BME Carnegie Mellon University byronyu@cmu.edu John P. Cunningham Dept of Engineering University of C... | 2011 | 62 |
4,411 | Approximating Semidefinite Programs in Sublinear Time Dan Garber Technion - Israel Institute of Technology Haifa 32000 Israel dangar@cs.technion.ac.il Elad Hazan Technion - Israel Institute of Technology Haifa 32000 Israel ehazan@ie.technion.ac.il Abstract In recent years semidefinite optimization h... | 2011 | 63 |
4,412 | Active Classification based on Value of Classifier Tianshi Gao Department of Electrical Engineering Stanford University Stanford, CA 94305 tianshig@stanford.edu Daphne Koller Department of Computer Science Stanford University Stanford, CA 94305 koller@cs.stanford.edu Abstract Modern classification ... | 2011 | 64 |
4,413 | Efficient Learning of Generalized Linear and Single Index Models with Isotonic Regression Sham M. Kakade Microsoft Research and Wharton, U Penn skakade@microsoft.com Adam Tauman Kalai Microsoft Research adum@microsoft.com Varun Kanade SEAS, Harvard University vkanade@fas.harvard.edu Ohad Shamir M... | 2011 | 65 |
4,414 | Co-regularized Multi-view Spectral Clustering Abhishek Kumar∗ Dept. of Computer Science University of Maryland, College Park, MD abhishek@cs.umd.edu Piyush Rai∗ Dept. of Computer Science University of Utah, Salt Lake City, UT piyush@cs.utah.edu Hal Daum´e III Dept. of Computer Science Universi... | 2011 | 66 |
4,415 | A concave regularization technique for sparse mixture models Martin Larsson School of Operations Research and Information Engineering Cornell University mol23@cornell.edu Johan Ugander Center for Applied Mathematics Cornell University jhu5@cornell.edu Abstract Latent variable mixture models are a ... | 2011 | 67 |
4,416 | Image Parsing via Stochastic Scene Grammar Yibiao Zhao∗ Department of Statistics University of California, Los Angeles Los Angeles, CA 90095 ybzhao@ucla.edu Song-Chun Zhu Department of Statistics and Computer Science University of California, Los Angeles Los Angeles, CA 90095 sczhu@stat.ucla.edu A... | 2011 | 68 |
4,417 | Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection Richard Socher, Eric H. Huang, Jeffrey Pennington∗, Andrew Y. Ng, Christopher D. Manning Computer Science Department, Stanford University, Stanford, CA 94305, USA ∗SLAC National Accelerator Laboratory, Stanford University, Stanford, C... | 2011 | 69 |
4,418 | Lower Bounds for Passive and Active Learning Maxim Raginsky∗ Coordinated Science Laboratory University of Illinois at Urbana-Champaign Alexander Rakhlin Department of Statistics University of Pennsylvania Abstract We develop unified information-theoretic machinery for deriving lower bounds for passive ... | 2011 | 7 |
4,419 | Trace Lasso: a trace norm regularization for correlated designs ´Edouard Grave INRIA, Sierra Project-team ´Ecole Normale Sup´erieure, Paris edouard.grave@inria.fr Guillaume Obozinski INRIA, Sierra Project-team ´Ecole Normale Sup´erieure, Paris guillaume.obozinski@inria.fr Francis Bach INRIA, Sierr... | 2011 | 70 |
4,420 | Linear Submodular Bandits and their Application to Diversified Retrieval Yisong Yue iLab, Heinz College Carnegie Mellon University yisongyue@cmu.edu Carlos Guestrin Machine Learning Department Carnegie Mellon University guestrin@cs.cmu.edu Abstract Diversified retrieval and online learning are two c... | 2011 | 71 |
4,421 | Learning Eigenvectors for Free Wouter M. Koolen Royal Holloway and CWI wouter@cs.rhul.ac.uk Wojtek Kotłowski Centrum Wiskunde & Informatica kotlowsk@cwi.nl Manfred K. Warmuth UC Santa Cruz manfred@cse.ucsc.edu Abstract We extend the classical problem of predicting a sequence of outcomes from a fini... | 2011 | 72 |
4,422 | Neural Reconstruction with Approximate Message Passing (NeuRAMP) Alyson K. Fletcher University of California, Berkeley alyson@eecs.berkeley.edu Sundeep Rangan Polytechnic Institute of New York University srangan@poly.edu Lav R. Varshney IBM Thomas J. Watson Research Center lrvarshn@us.ibm.com Anir... | 2011 | 73 |
4,423 | Bayesian Partitioning of Large-Scale Distance Data David Adametz Volker Roth Department of Computer Science & Mathematics University of Basel Basel, Switzerland {david.adametz,volker.roth}@unibas.ch Abstract A Bayesian approach to partitioning distance matrices is presented. It is inspired by the Tran... | 2011 | 74 |
4,424 | Dimensionality Reduction Using the Sparse Linear Model Ioannis Gkioulekas Harvard SEAS Cambridge, MA 02138 igkiou@seas.harvard.edu Todd Zickler Harvard SEAS Cambridge, MA 02138 zickler@seas.harvard.edu Abstract We propose an approach for linear unsupervised dimensionality reduction, based on the... | 2011 | 75 |
4,425 | RTRMC: A Riemannian trust-region method for low-rank matrix completion Nicolas Boumal∗ ICTEAM Institute Universit´e catholique de Louvain B-1348 Louvain-la-Neuve nicolas.boumal@uclouvain.be P.-A. Absil ICTEAM Institute Universit´e catholique de Louvain B-1348 Louvain-la-Neuve absil@inma.ucl.ac.be ... | 2011 | 76 |
4,426 | Complexity of Inference in Latent Dirichlet Allocation David Sontag New York University⇤ Daniel M. Roy University of Cambridge Abstract We consider the computational complexity of probabilistic inference in Latent Dirichlet Allocation (LDA). First, we study the problem of finding the maximum a posteriori... | 2011 | 77 |
4,427 | A Denoising View of Matrix Completion Weiran Wang Miguel ´A. Carreira-Perpi˜n´an EECS, University of California, Merced http://eecs.ucmerced.edu Zhengdong Lu Microsoft Research Asia, Beijing zhengdol@microsoft.com Abstract In matrix completion, we are given a matrix where the values of only some of th... | 2011 | 78 |
4,428 | Generalization Bounds and Consistency for Latent Structural Probit and Ramp Loss David McAllester TTI-Chicago mcallester@ttic.edu Joseph Keshet TTI-Chicago jkeshet@ttic.edu Abstract We consider latent structural versions of probit loss and ramp loss. We show that these surrogate loss functions are c... | 2011 | 79 |
4,429 | k-NN Regression Adapts to Local Intrinsic Dimension Samory Kpotufe Max Planck Institute for Intelligent Systems samory@tuebingen.mpg.de Abstract Many nonparametric regressors were recently shown to converge at rates that depend only on the intrinsic dimension of data. These regressors thus escape the curse ... | 2011 | 8 |
4,430 | Budgeted Optimization with Concurrent Stochastic-Duration Experiments Javad Azimi, Alan Fern, Xiaoli Z. Fern School of EECS, Oregon State University {azimi, afern, xfern}@eecs.oregonstate.edu Abstract Budgeted optimization involves optimizing an unknown function that is costly to evaluate by requesting a li... | 2011 | 80 |
4,431 | Data Skeletonization via Reeb Graphs Xiaoyin Ge Issam Safa Mikhail Belkin Yusu Wang Computer Science and Engineering Department The Ohio State University gex,safa,mbelkin,yusu@cse.ohio-state.edu Abstract Recovering hidden structure from complex and noisy non-linear data is one of the most fundamenta... | 2011 | 81 |
4,432 | MAP Inference for Bayesian Inverse Reinforcement Learning Jaedeug Choi and Kee-Eung Kim bDepartment of Computer Science Korea Advanced Institute of Science and Technology Daejeon 305-701, Korea jdchoi@ai.kaist.ac.kr, kekim@cs.kaist.ac.kr Abstract The difficulty in inverse reinforcement learning (IRL) ari... | 2011 | 82 |
4,433 | Learning Sparse Representations of High Dimensional Data on Large Scale Dictionaries Zhen James Xiang Hao Xu Peter J. Ramadge Department of Electrical Engineering, Princeton University Princeton, NJ 08544, USA {zxiang,haoxu,ramadge}@princeton.edu Abstract Learning sparse representations on data adapti... | 2011 | 83 |
4,434 | Efficient Online Learning via Randomized Rounding Nicol`o Cesa-Bianchi DSI, Universit`a degli Studi di Milano Italy nicolo.cesa-bianchi@unimi.it Ohad Shamir Microsoft Research New England USA ohadsh@microsoft.com Abstract Most online algorithms used in machine learning today are based on variants of... | 2011 | 84 |
4,435 | Spatial distance dependent Chinese restaurant processes for image segmentation Soumya Ghosh1, Andrei B. Ungureanu2, Erik B. Sudderth1, and David M. Blei3 1Department of Computer Science, Brown University, {sghosh,sudderth}@cs.brown.edu 2Morgan Stanley, andrei.b.ungureanu@gmail.com 3Department of Computer Scie... | 2011 | 85 |
4,436 | History distribution matching method for predicting effectiveness of HIV combination therapies Jasmina Bogojeska Max-Planck Institute for Computer Science Campus E1 4 66123 Saarbr¨ucken, Germany jasmina@mpi-inf.mpg.de Abstract This paper presents an approach that predicts the effectiveness of HIV combin... | 2011 | 86 |
4,437 | Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Machine Learning Francis Bach INRIA - Sierra Project-team Ecole Normale Sup´erieure, Paris, France francis.bach@ens.fr Eric Moulines LTCI Telecom ParisTech, Paris, France eric.moulines@enst.fr Abstract We consider the minimization ... | 2011 | 87 |
4,438 | A Non-Parametric Approach to Dynamic Programming Oliver B. Kroemer1,2 Jan Peters1,2 1Intelligent Autonomous Systems, Technische Universität Darmstadt 2Robot Learning Lab, Max Planck Institute for Intelligent Systems {kroemer,peters}@ias.tu-darmstadt.de Abstract In this paper, we consider the problem of ... | 2011 | 88 |
4,439 | Extracting Speaker-Specific Information with a Regularized Siamese Deep Network Ke Chen and Ahmad Salman School of Computer Science, The University of Manchester Manchester M13 9PL, United Kingdom {chen,salmana}@cs.manchester.ac.uk Abstract Speech conveys different yet mixed information ranging from lingui... | 2011 | 89 |
4,440 | Variational Learning for Recurrent Spiking Networks Danilo Jimenez Rezende Brain Mind Institute ´Ecole Polytechnique F´ed´erale de Lausanne 1015 Lausanne EPFL, Switzerland danilo.rezende@epfl.ch Daan Wierstra School of Computer and Communication Sciences, Brain Mind Institute ´Ecole Polytechnique F´ed´e... | 2011 | 9 |
4,441 | Variance Penalizing AdaBoost Pannagadatta K. Shivaswamy Department of Computer Science Cornell University, Ithaca NY pannaga@cs.cornell.edu Tony Jebara Department of Compter Science Columbia University, New York NY jebara@cs.columbia.edu Abstract This paper proposes a novel boosting algorithm called... | 2011 | 90 |
4,442 | Autonomous Learning of Action Models for Planning Neville Mehta Prasad Tadepalli Alan Fern School of Electrical Engineering and Computer Science Oregon State University, Corvallis, OR 97331, USA. {mehtane,tadepall,afern}@eecs.oregonstate.edu Abstract This paper introduces two new frameworks for learning... | 2011 | 91 |
4,443 | Hierarchically Supervised Latent Dirichlet Allocation Adler Perotte Nicholas Bartlett No´emie Elhadad Frank Wood Columbia University, New York, NY 10027, USA {ajp9009@dbmi,bartlett@stat,noemie@dbmi,fwood@stat}.columbia.edu Abstract We introduce hierarchically supervised latent Dirichlet allocation (HSLD... | 2011 | 92 |
4,444 | Agnostic Selective Classification Ran El-Yaniv and Yair Wiener Computer Science Department Technion – Israel Institute of Technology {rani,wyair}@{cs,tx}.technion.ac.il Abstract For a learning problem whose associated excess loss class is (β, B)-Bernstein, we show that it is theoretically possible to track... | 2011 | 93 |
4,445 | Additive Gaussian Processes David Duvenaud Department of Engineering Cambridge University dkd23@cam.ac.uk Hannes Nickisch MPI for Intelligent Systems T¨ubingen, Germany hn@tue.mpg.de Carl Edward Rasmussen Department of Engineering Cambridge University cer54@cam.ac.uk Abstract We introduce a ... | 2011 | 94 |
4,446 | A Collaborative Mechanism for Crowdsourcing Prediction Problems Jacob Abernethy Division of Computer Science University of California at Berkeley jake@cs.berkeley.edu Rafael M. Frongillo Division of Computer Science University of California at Berkeley raf@cs.berkeley.edu Abstract Machine Learning... | 2011 | 95 |
4,447 | Sparse Bayesian Multi-Task Learning C´edric Archambeau, Shengbo Guo, Onno Zoeter Xerox Research Centre Europe {Cedric.Archambeau, Shengbo.Guo, Onno.Zoeter}@xrce.xerox.com Abstract We propose a new sparse Bayesian model for multi-task regression and classification. The model is able to capture correlations betw... | 2011 | 96 |
4,448 | Orthogonal Matching Pursuit with Replacement Prateek Jain Microsoft Research India Bangalore, INDIA prajain@microsoft.com AmbujTewari The University of Texas at Austin Austin, TX ambuj@cs.utexas.edu Inderjit S. Dhillon The University of Texas at Austin Austin, TX inderjit@cs.utexas.e... | 2011 | 97 |
4,449 | High-Dimensional Graphical Model Selection: Tractable Graph Families and Necessary Conditions Anima Anandkumar Dept. of EECS, Univ. of California Irvine, CA, 92697 a.anandkumar@uci.edu Vincent Y.F. Tan Dept. of ECE, Univ. of Wisconsin Madison, WI, 53706. vtan@wisc.edu Alan S. Willsky Dept. of ... | 2011 | 98 |
4,450 | Optimal learning rates for least squares SVMs using Gaussian kernels M. Eberts, I. Steinwart Institute for Stochastics and Applications University of Stuttgart D-70569 Stuttgart {eberts,ingo.steinwart}@mathematik.uni-stuttgart.de Abstract We prove a new oracle inequality for support vector machines with... | 2011 | 99 |
4,451 | Topology Constraints in Graphical Models Marcelo Fiori Universidad de la Rep´ublica, Uruguay mfiori@fing.edu.uy Pablo Mus´e Universidad de la Rep´ublica, Uruguay pmuse@fing.edu.uy Guillermo Sapiro Duke University Durham, NC 27708 guillermo.sapiro@duke.edu Abstract Graphical models are a very... | 2012 | 1 |
4,452 | Bayesian nonparametric models for bipartite graphs Franc¸ois Caron INRIA IMB - University of Bordeaux Talence, France Francois.Caron@inria.fr Abstract We develop a novel Bayesian nonparametric model for random bipartite graphs. The model is based on the theory of completely random measures and is able ... | 2012 | 10 |
4,453 | Meta-Gaussian Information Bottleneck M´elanie Rey Department of Mathematics and Computer Science University of Basel melanie.rey@unibas.ch Volker Roth Department of Mathematics and Computer Science University of Basel volker.roth@unibas.ch Abstract We present a reformulation of the information bottl... | 2012 | 100 |
4,454 | Efficient Monte Carlo Counterfactual Regret Minimization in Games with Many Player Actions Richard Gibson, Neil Burch, Marc Lanctot, and Duane Szafron Department of Computing Science, University of Alberta Edmonton, Alberta, T6G 2E8, Canada {rggibson | nburch | lanctot | dszafron}@ualberta.ca Abstract Coun... | 2012 | 101 |
4,455 | Fusion with Diffusion for Robust Visual Tracking Yu Zhou1∗, Xiang Bai1, Wenyu Liu1, Longin Jan Latecki2 1 Dept. of Electronics and Information Engineering, Huazhong Univ. of Science and Technology, P. R. China 2 Dept. of Computer and Information Sciences, Temple Univ., Philadelphia, USA {zhouyu.hust,xiang.bai}@... | 2012 | 102 |
4,456 | Convolutional-Recursive Deep Learning for 3D Object Classification Richard Socher, Brody Huval, Bharath Bhat, Christopher D. Manning, Andrew Y. Ng Computer Science Department, Stanford University, Stanford, CA 94305, USA richard@socher.org, {brodyh,bbhat,manning}@stanford.edu, ang@cs.stanford.edu Abstract Re... | 2012 | 103 |
4,457 | Transferring Expectations in Model-based Reinforcement Learning Trung Thanh Nguyen, Tomi Silander, Tze-Yun Leong School of Computing National University of Singapore Singapore, 117417 {nttrung, silander, leongty}@comp.nus.edu.sg Abstract We study how to automatically select and adapt multiple abstractio... | 2012 | 104 |
4,458 | Modelling Reciprocating Relationships with Hawkes Processes Charles Blundell Gatsby Computational Neuroscience Unit University College London London, United Kingdom c.blundell@gatsby.ucl.ac.uk Katherine A. Heller Duke University Durham, NC, USA kheller@stat.duke.edu Jeffrey M. Beck University of... | 2012 | 105 |
4,459 | Ancestor Sampling for Particle Gibbs Fredrik Lindsten Div. of Automatic Control Link¨oping University lindsten@isy.liu.se Michael I. Jordan Dept. of EECS and Statistics University of California, Berkeley jordan@cs.berkeley.edu Thomas B. Sch¨on Div. of Automatic Control Link¨oping University scho... | 2012 | 106 |
4,460 | Interpreting prediction markets: a stochastic approach Rafael M. Frongillo Computer Science Divison University of California, Berkeley raf@cs.berkeley.edu Nicol´as Della Penna Research School of Computer Science The Australian National University me@nikete.com Mark D. Reid Research School of Compu... | 2012 | 107 |
4,461 | Nonparanormal Belief Propagation (NPNBP) Gal Elidan Department of Statistics Hebrew University galel@huji.ac.il Cobi Cario School of Computer Science and Engineering Hebrew University cobi.cario@mail.huji.ac.il Abstract The empirical success of the belief propagation approximate inference algorithm ... | 2012 | 108 |
4,462 | Adaptive Stratified Sampling for Monte-Carlo integration of Differentiable functions Alexandra Carpentier Statistical Laboratory, CMS Wilberforce Road, Cambridge CB3 0WB UK a.carpentier@statslab.cam.ac.uk R´emi Munos INRIA Lille - Nord Europe 40, avenue Halley 59000 Villeneuve d’ascq, France remi.m... | 2012 | 109 |
4,463 | Learning Label Trees for Probabilistic Modelling of Implicit Feedback Andriy Mnih amnih@gatsby.ucl.ac.uk Gatsby Computational Neuroscience Unit University College London Yee Whye Teh ywteh@gatsby.ucl.ac.uk Gatsby Computational Neuroscience Unit University College London Abstract User preferences f... | 2012 | 11 |
4,464 | Bayesian Nonparametric Modeling of Suicide Attempts Francisco J. R. Ruiz Department of Signal Processing and Communications University Carlos III in Madrid franrruiz@tsc.uc3m.es Isabel Valera Department of Signal Processing and Communications University Carlos III in Madrid ivalera@tsc.uc3m.es C... | 2012 | 110 |
4,465 | Non-linear Metric Learning Dor Kedem, Stephen Tyree, Kilian Q. Weinberger Dept. of Comp. Sci. & Engi. Washington U. St. Louis, MO 63130 kedem.dor,swtyree,kilian@wustl.edu Fei Sha Dept. of Comp. Sci. U. of Southern California Los Angeles, CA 90089 feisha@usc.edu Gert Lanckriet Dept. of Elec. & Co... | 2012 | 111 |
4,466 | Putting Bayes to sleep Wouter M. Koolen∗ Dmitry Adamskiy† Manfred K. Warmuth‡ Abstract We consider sequential prediction algorithms that are given the predictions from a set of models as inputs. If the nature of the data is changing over time in that different models predict well on different segments of ... | 2012 | 112 |
4,467 | Sparse Approximate Manifolds for Differential Geometric MCMC Ben Calderhead∗ CoMPLEX University College London London, WC1E 6BT, UK b.calderhead@ucl.ac.uk Mátyás A. Sustik Department of Computer Sciences University of Texas at Austin Austin, TX 78712, USA sustik@cs.utexas.edu Abstract One of t... | 2012 | 113 |
4,468 | Bayesian active learning with localized priors for fast receptive field characterization Mijung Park Electrical and Computer Engineering The University of Texas at Austin mjpark@mail.utexas.edu Jonathan W. Pillow Center For Perceptual Systems The University of Texas at Austin pillow@mail.utexas.edu A... | 2012 | 114 |
4,469 | Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images Dan C. Cires¸an∗ IDSIA USI-SUPSI Lugano 6900 dan@idsia.ch Alessandro Giusti IDSIA USI-SUPSI Lugano 6900 alessandrog@idsia.ch Luca M. Gambardella IDSIA USI-SUPSI Lugano 6900 luca@idsia.ch J¨urgen Schmidhuber ... | 2012 | 115 |
4,470 | Learning from the Wisdom of Crowds by Minimax Entropy Dengyong Zhou, John C. Platt, Sumit Basu, and Yi Mao Microsoft Research 1 Microsoft Way, Redmond, WA 98052 {denzho,jplatt,sumitb,yimao}@microsoft.com Abstract An important way to make large training sets is to gather noisy labels from crowds of nonex... | 2012 | 116 |
4,471 | Parametric Local Metric Learning for Nearest Neighbor Classification Jun Wang Department of Computer Science University of Geneva Switzerland Jun.Wang@unige.ch Adam Woznica Department of Computer Science University of Geneva Switzerland Adam.Woznica@unige.ch Alexandros Kalousis Department of Bu... | 2012 | 117 |
4,472 | The topographic unsupervised learning of natural sounds in the auditory cortex Hiroki Terashima The University of Tokyo / JSPS Tokyo, Japan teratti@teratti.jp Masato Okada The University of Tokyo / RIKEN BSI Tokyo, Japan okada@k.u-tokyo.ac.jp Abstract The computational modelling of the primary aud... | 2012 | 118 |
4,473 | Efficient Sampling for Bipartite Matching Problems Maksims N. Volkovs University of Toronto mvolkovs@cs.toronto.edu Richard S. Zemel University of Toronto zemel@cs.toronto.edu Abstract Bipartite matching problems characterize many situations, ranging from ranking in information retrieval to corresponde... | 2012 | 119 |
4,474 | Factoring nonnegative matrices with linear programs Victor Bittorf bittorf@cs.wisc.edu Benjamin Recht brecht@cs.wisc.edu Computer Sciences University of Wisconsin Christopher R´e chrisre@cs.wisc.edu Joel A. Tropp Computing and Mathematical Sciences California Institute of Technology tropp@cms.ca... | 2012 | 12 |
4,475 | Volume Regularization for Binary Classification Koby Crammer Department of Electrical Enginering The Technion - Israel Institute of Technology Haifa, 32000 Israel koby@ee.technion.ac.il Tal Wagner∗ Faculty of Mathematics and Computer Science Weizmann Institute of Science Rehovot, 76100, Israel tal.wa... | 2012 | 120 |
4,476 | Causal discovery with scale-mixture model for spatiotemporal variance dependencies Zhitang Chen*, Kun Zhang†, and Laiwan Chan* *Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong {ztchen,lwchan}@cse.cuhk.edu.hk †Max Planck Institute for Intelligent Systems, T¨ubingen, G... | 2012 | 121 |
4,477 | Neurally Plausible Reinforcement Learning of Working Memory Tasks Jaldert O. Rombouts, Sander M. Bohte CWI, Life Sciences Amsterdam, The Netherlands {j.o.rombouts, s.m.bohte}@cwi.nl Pieter R. Roelfsema Netherlands Institute for Neuroscience Amsterdam, The Netherlands p.r.roelfsema@nin.knaw.nl Abstra... | 2012 | 122 |
4,478 | Simultaneously Leveraging Output and Task Structures for Multiple-Output Regression Piyush Rai† Dept. of Computer Science University of Texas at Austin Austin, TX piyush@cs.utexas.edu Abhishek Kumar† Dept. of Computer Science University of Maryland College Park, MD abhishek@cs.umd.edu Hal Daum´e... | 2012 | 123 |
4,479 | Feature Clustering for Accelerating Parallel Coordinate Descent Chad Scherrer Independent Consultant Yakima, WA chad.scherrer@gmail.com Ambuj Tewari Department of Statistics University of Michigan Ann Arbor, MI tewaria@umich.edu Mahantesh Halappanavar Pacific Northwest National Laboratory Richl... | 2012 | 124 |
4,480 | Phoneme Classification using Constrained Variational Gaussian Process Dynamical System Hyunsin Park Department of EE, KAIST Daejeon, South Korea hs.park@kaist.ac.kr Sungrack Yun Qualcomm Korea Seoul, South Korea sungrack@qualcomm.com Sanghyuk Park Department of EE, KAIST Daejeon, South Korea sh... | 2012 | 125 |
4,481 | Fast Variational Inference in the Conjugate Exponential Family James Hensman∗ Department of Computer Science The University of Sheffield james.hensman@sheffield.ac.uk Magnus Rattray Faculty of Life Science The University of Manchester magnus.rattray@manchester.ac.uk Neil D. Lawrence∗ Department of ... | 2012 | 126 |
4,482 | Identifiability and Unmixing of Latent Parse Trees Daniel Hsu Microsoft Research Sham M. Kakade Microsoft Research Percy Liang Stanford University Abstract This paper explores unsupervised learning of parsing models along two directions. First, which models are identifiable from infinite data? We use a g... | 2012 | 127 |
4,483 | On the (Non-)existence of Convex, Calibrated Surrogate Losses for Ranking Cl´ement Calauz`enes, Nicolas Usunier, Patrick Gallinari LIP6 - UPMC 4 place Jussieu, 75005 Paris, France firstname.lastname@lip6.fr Abstract We study surrogate losses for learning to rank, in a framework where the rankings are in... | 2012 | 128 |
4,484 | Learning with Partially Absorbing Random Walks Xiao-Ming Wu1, Zhenguo Li1, Anthony Man-Cho So3, John Wright1 and Shih-Fu Chang1,2 1Department of Electrical Engineering, Columbia University 2Department of Computer Science, Columbia University 3Department of SEEM, The Chinese University of Hong Kong {xmwu, zgli... | 2012 | 129 |
4,485 | Privacy Aware Learning John C. Duchi1 Michael I. Jordan1,2 Martin J. Wainwright1,2 1Department of Electrical Engineering and Computer Science, 2Department of Statistics University of California, Berkeley Berkeley, CA USA 94720 {jduchi,jordan,wainwrig}@eecs.berkeley.edu Abstract We study statistical ri... | 2012 | 13 |
4,486 | Relax and Randomize: From Value to Algorithms Alexander Rakhlin University of Pennsylvania Ohad Shamir Microsoft Research Karthik Sridharan University of Pennsylvania Abstract We show a principled way of deriving online learning algorithms from a minimax analysis. Various upper bounds on the minimax v... | 2012 | 130 |
4,487 | Inverse Reinforcement Learning through Structured Classification Edouard Klein1,2 1LORIA – team ABC Nancy, France edouard.klein@supelec.fr Matthieu Geist2 2Supélec – IMS-MaLIS Research Group Metz, France matthieu.geist@supelec.fr Bilal Piot2,3, Olivier Pietquin2,3 3 UMI 2958 (GeorgiaTech-CNRS) Me... | 2012 | 131 |
4,488 | To appear in: Neural Information Processing Systems (NIPS), Lake Tahoe, Nevada. December 3-6, 2012. Efficient and direct estimation of a neural subunit model for sensory coding Brett Vintch Andrew D. Zaharia J. Anthony Movshon Eero P. Simoncelli † Center for Neural Science, and †Howard Hughes Medical I... | 2012 | 132 |
4,489 | Discriminative Learning of Sum-Product Networks Robert Gens Pedro Domingos Department of Computer Science and Engineering University of Washington Seattle, WA 98195-2350, U.S.A. {rcg,pedrod}@cs.washington.edu Abstract Sum-product networks are a new deep architecture that can perform fast, exact inferenc... | 2012 | 133 |
4,490 | Stochastic optimization and sparse statistical recovery: Optimal algorithms for high dimensions Alekh Agarwal Microsoft Research New York NY alekha@microsoft.com Sahand N. Negahban Dept. of EECS MIT sahandn@mit.edu Martin J. Wainwright Dept. of EECS and Statistics UC Berkeley wainwrig@stat.ber... | 2012 | 134 |
4,491 | Bandit Algorithms boost motor-task selection for Brain Computer Interfaces Joan Fruitet INRIA, Sophia Antipolis 2004 Route des Lucioles 06560 Sophia Antipolis, France joan.fruitet@inria.fr Alexandra Carpentier Statistical Laboratory, CMS Wilberforce Road, Cambridge CB3 0WB UK a.carpentier@statslab... | 2012 | 135 |
4,492 | Localizing 3D cuboids in single-view images Jianxiong Xiao Bryan C. Russell∗ Antonio Torralba Massachusetts Institute of Technology ∗University of Washington Abstract In this paper we seek to detect rectangular cuboids and localize their corners in uncalibrated single-view images depicting everyday scen... | 2012 | 136 |
4,493 | A Convex Formulation for Learning Scale-Free Networks via Submodular Relaxation Aaron J. Defazio NICTA/Australian National University Canberra, ACT, Australia aaron.defazio@anu.edu.au Tiberio S. Caetano NICTA/ANU/University of Sydney Canberra and Sydney, Australia tiberio.caetano@nicta.com.au Abstra... | 2012 | 137 |
4,494 | Hamming Distance Metric Learning Mohammad Norouzi† David J. Fleet† Ruslan Salakhutdinov†,‡ Departments of Computer Science† and Statistics‡ University of Toronto [norouzi,fleet,rsalakhu]@cs.toronto.edu Abstract Motivated by large-scale multimedia applications we propose to learn mappings from high-dim... | 2012 | 138 |
4,495 | Co-Regularized Hashing for Multimodal Data Yi Zhen and Dit-Yan Yeung Department of Computer Science and Engineering Hong Kong University of Science and Technology Clear Water Bay, Kowloon, Hong Kong {yzhen,dyyeung}@cse.ust.hk Abstract Hashing-based methods provide a very promising approach to large-scale ... | 2012 | 139 |
4,496 | Truncation-free Stochastic Variational Inference for Bayesian Nonparametric Models Chong Wang∗ Machine Learning Department Carnegie Mellon University chongw@cs.cmu.edu David M. Blei Computer Science Department Princeton Univeristy blei@cs.princeton.edu Abstract We present a truncation-free stochas... | 2012 | 14 |
4,497 | The Coloured Noise Expansion and Parameter Estimation of Diffusion Processes Simon M.J. Lyons School of Informatics University of Edinburgh 10 Crichton Street, Edinburgh, EH8 9AB S.Lyons-4@sms.ed.ac.uk Simo S¨arkk¨a Aalto University Department of Biomedical Engineering and Computational Science Ra... | 2012 | 140 |
4,498 | How Prior Probability Influences Decision Making: A Unifying Probabilistic Model Yanping Huang University of Washington huangyp@cs.washington.edu Abram L. Friesen University of Washington afriesen@cs.washington.edu Timothy D. Hanks Princeton University thanks@princeton.edu Michael N. Shadlen Colu... | 2012 | 141 |
4,499 | Structured Learning of Gaussian Graphical Models Karthik Mohan∗, Michael Jae-Yoon Chung†, Seungyeop Han†, Daniela Witten‡, Su-In Lee§, Maryam Fazel∗ Abstract We consider estimation of multiple high-dimensional Gaussian graphical models corresponding to a single set of nodes under several distinct conditions. We... | 2012 | 142 |
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