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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3,500 | Multiscale Random Fields with Application to Contour Grouping Longin Jan Latecki Dept. of Computer and Info. Sciences Temple University, Philadelphia, USA latecki@temple.edu ChengEn Lu Dept. of Electronics and Info. Eng. Huazhong Univ. of Sci. and Tech., China luchengen@gmail.com Marc Sobel Statis... | 2008 | 242 |
3,501 | Modeling Short-term Noise Dependence of Spike Counts in Macaque Prefrontal Cortex Arno Onken Technische Universit¨at Berlin / BCCN Berlin aonken@cs.tu-berlin.de Steffen Gr¨unew¨alder Technische Universit¨at Berlin Franklinstr. 28/29, 10587 Berlin, Germany gruenew@cs.tu-berlin.de Matthias Munk MPI ... | 2008 | 243 |
3,502 | Predictive Indexing for Fast Search Sharad Goel Yahoo! Research New York, NY 10018 goel@yahoo-inc.com John Langford Yahoo! Research New York, NY 10018 jl@yahoo-inc.com Alex Strehl Yahoo! Research New York, NY 10018 strehl@yahoo-inc.com Abstract We tackle the computational problem of query-co... | 2008 | 244 |
3,503 | Human Active Learning Rui Castro1, Charles Kalish2, Robert Nowak3, Ruichen Qian4, Timothy Rogers2, Xiaojin Zhu4∗ 1Department of Electrical Engineering Columbia University. New York, NY 10027 Department of {2Psychology, 3Electrical and Computer Engineering, 4Computer Sciences} University of Wisconsin-Madison. ... | 2008 | 245 |
3,504 | Clustered Multi-Task Learning: a Convex Formulation Laurent Jacob Mines ParisTech – CBIO INSERM U900, Institut Curie 35, rue Saint Honor´e, 77300 Fontainebleau, France laurent.jacob@mines-paristech.fr Francis Bach INRIA – Willow Project Ecole Normale Sup´erieure, 45, rue d’Ulm, 75230 Paris, France ... | 2008 | 246 |
3,505 | Temporal Difference Based Actor Critic Learning Convergence and Neural Implementation Dotan Di Castro, Dmitry Volkinshtein and Ron Meir Department of Electrical Engineering Technion, Haifa 32000, Israel {dot@tx},{dmitryv@tx},{rmeir@ee}.technion.ac.il Abstract Actor-critic algorithms for reinforcement learni... | 2008 | 247 |
3,506 | One Sketch For All: Theory and Application of Conditional Random Sampling Ping Li Dept. of Statistical Science Cornell University pingli@cornell.edu Kenneth W. Church Microsoft Research Microsoft Corporation church@microsoft.com Trevor J. Hastie Dept. of Statistics Stanford University hastie@s... | 2008 | 248 |
3,507 | The Mondrian Process Daniel M. Roy Massachusetts Institute of Technology droy@mit.edu Yee Whye Teh Gatsby Unit, University College London ywteh@gatsby.ucl.ac.uk Abstract We describe a novel class of distributions, called Mondrian processes, which can be interpreted as probability distributions over kd... | 2008 | 249 |
3,508 | Semi-supervised Learning with Weakly-Related Unlabeled Data: Towards Better Text Categorization Liu Yang Machine Learning Dept. Carnegie Mellon University 5000 Forbes Avenue Pittsburgh, PA 15213 liuy@cs.cmu.edu Rong Jin Dept. of Computer Sci. and Eng. 3115 Engineering Building Michigan State Unive... | 2008 | 25 |
3,509 | Scalable Algorithms for String Kernels with Inexact Matching Pavel P. Kuksa, Pai-Hsi Huang, Vladimir Pavlovic Department of Computer Science, Rutgers University, Piscataway, NJ 08854 {pkuksa,paihuang,vladimir}@cs.rutgers.edu Abstract We present a new family of linear time algorithms for string comparison ... | 2008 | 250 |
3,510 | Evaluating probabilities under high-dimensional latent variable models Iain Murray and Ruslan Salakhutdinov Department of Computer Science University of Toronto Toronto, ON. M5S 3G4. Canada. {murray,rsalakhu}@cs.toronto.edu Abstract We present a simple new Monte Carlo algorithm for evaluating probabilit... | 2008 | 26 |
3,511 | Counting Solution Clusters in Graph Coloring Problems Using Belief Propagation Lukas Kroc Ashish Sabharwal Bart Selman Department of Computer Science Cornell University, Ithaca NY 14853-7501, U.S.A. {kroc,sabhar,selman}@cs.cornell.edu ∗ Abstract We show that an important and computationally challengin... | 2008 | 27 |
3,512 | Supervised Bipartite Graph Inference Yoshihiro Yamanishi Mines ParisTech CBIO Institut Curie, INSERM U900, 35 rue Saint-Honore, Fontainebleau, F-77300 France yoshihiro.yamanishi@ensmp.fr Abstract We formulate the problem of bipartite graph inference as a supervised learning problem, and propose a new me... | 2008 | 28 |
3,513 | Domain Adaptation with Multiple Sources Yishay Mansour Google Research and Tel Aviv Univ. mansour@tau.ac.il Mehryar Mohri Courant Institute and Google Research mohri@cims.nyu.edu Afshin Rostamizadeh Courant Institute New York University rostami@cs.nyu.edu Abstract This paper presents a theor... | 2008 | 29 |
3,514 | Learning Bounded Treewidth Bayesian Networks Gal Elidan Department of Statistics Hebrew University Jerusalem, 91905, Israel galel@huji.ac.il Stephen Gould Department of Electrical Engineering Stanford University Stanford, CA 94305, USA sgould@stanford.edu Abstract With the increased availability... | 2008 | 3 |
3,515 | Weighted Sums of Random Kitchen Sinks: Replacing minimization with randomization in learning Paper #858 Abstract Randomized neural networks are immortalized in this AI Koan: In the days when Sussman was a novice, Minsky once came to him as he sat hacking at the PDP-6. “What are you doing?” asked Minsky. “... | 2008 | 30 |
3,516 | Regularized Learning with Networks of Features Ted Sandler, Partha Pratim Talukdar, and Lyle H. Ungar Department of Computer & Information Science, University of Pennsylvania {tsandler,partha,ungar}@cis.upenn.edu John Blitzer Department of Computer Science, U.C. Berkeley blitzer@cs.berkeley.edu Abstract ... | 2008 | 31 |
3,517 | Breaking Audio CAPTCHAs Jennifer Tam Computer Science Department Carnegie Mellon University 5000 Forbes Ave, Pittsburgh 15217 jdtam@cs.cmu.edu Sean Hyde Electrical and Computer Engineering Carnegie Mellon University 5000 Forbes Ave, Pittsburgh 15217 sean.a.hyde@gmail.com ... | 2008 | 32 |
3,518 | Efficient Direct Density Ratio Estimation for Non-stationarity Adaptation and Outlier Detection Takafumi Kanamori Nagoya University Nagoya, Japan kanamori@is.nagoya-u.ac.jp Shohei Hido IBM Research Kanagawa, Japan hido@jp.ibm.com Masashi Sugiyama Tokyo Institute of Technology Tokyo, Japan sugi@... | 2008 | 33 |
3,519 | Differentiable Sparse Coding David M. Bradley Robotics Institute Carnegie Mellon University Pittsburgh, PA 15213 dbradley@cs.cmu.edu J. Andrew Bagnell Robotics Institute Carnegie Mellon University Pittsburgh, PA 15213 dbagnell@ri.cmu.edu Abstract Prior work has shown that features which appear t... | 2008 | 34 |
3,520 | Inferring rankings under constrained sensing Srikanth Jagabathula Devavrat Shah Laboratory of Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, MA 02139. {jskanth, devavrat}@mit.edu Abstract Motivated by applications like elections, web-page ranking, revenue maximizatio... | 2008 | 35 |
3,521 | Sparse Convolved Gaussian Processes for Multi-output Regression Mauricio Alvarez School of Computer Science University of Manchester, U.K. alvarezm@cs.man.ac.uk Neil D. Lawrence School of Computer Science University of Manchester, U.K. neill@cs.man.ac.uk Abstract We present a sparse approximation ... | 2008 | 36 |
3,522 | A “Shape Aware” Model for semi-supervised Learning of Objects and its Context Abhinav Gupta1, Jianbo Shi2 and Larry S. Davis1 1 Dept. of Computer Science, Univ. of Maryland, College Park 2 Dept. of Computer and Information Sciences, Univ. of Pennsylvania agupta@cs.umd.edu, jshi@cis.upenn.edu, lsd@cs.umd.edu ... | 2008 | 37 |
3,523 | Multi-task Gaussian Process Learning of Robot Inverse Dynamics Kian Ming A. Chai Christopher K. I. Williams Stefan Klanke Sethu Vijayakumar School of Informatics, University of Edinburgh, 10 Crichton Street, Edinburgh EH8 9AB, UK {k.m.a.chai, c.k.i.williams, s.klanke, sethu.vijayakumar}@ed.ac.uk Abstrac... | 2008 | 38 |
3,524 | Efficient Inference in Phylogenetic InDel Trees Alexandre Bouchard-Cˆot´e† Michael I. Jordan†‡ Dan Klein† Computer Science Division†, Department of Statistics‡ University of California at Berkeley Berkeley, CA 94720 {bouchard,jordan,klein}@cs.berkeley.edu Abstract Accurate and efficient inference in evo... | 2008 | 39 |
3,525 | Local Gaussian Process Regression for Real Time Online Model Learning and Control Duy Nguyen-Tuong Jan Peters Matthias Seeger Max Planck Institute for Biological Cybernetics Spemannstraße 38, 72076 T¨ubingen, Germany {duy,jan.peters,matthias.seeger}@tuebingen.mpg.de Abstract Learning in real-time appl... | 2008 | 4 |
3,526 | Estimating vector fields using sparse basis field expansions Stefan Haufe1, 2, * Vadim V. Nikulin3, 4 Andreas Ziehe1, 2 Klaus-Robert M¨uller1, 2, 4 Guido Nolte2 1TU Berlin, Dept. of Computer Science, Machine Learning Laboratory, Berlin, Germany 2Fraunhofer Institute FIRST (IDA), Berlin, Germany 3Charit´... | 2008 | 40 |
3,527 | Probabilistic detection of short events, with application to critical care monitoring Norm Aleks U.C. Berkeley norm@cs.berkeley.edu Stuart Russell U.C. Berkeley russell@cs.berkeley.edu Michael G. Madden National U. of Ireland, Galway michael.madden@nuigalway.ie Diane Morabito U.C. San Francisco ... | 2008 | 41 |
3,528 | Spectral Clustering with Perturbed Data Ling Huang Intel Research ling.huang@intel.com Donghui Yan UC Berkeley dhyan@stat.berkeley.edu Michael I. Jordan UC Berkeley jordan@cs.berkeley.edu Nina Taft Intel Research nina.taft@intel.com Abstract Spectral clustering is useful for a wide-ranging s... | 2008 | 42 |
3,529 | Estimating the Location and Orientation of Complex, Correlated Neural Activity using MEG D.P. Wipf, J.P. Owen, H.T. Attias, K. Sekihara, and S.S. Nagarajan Biomagnetic Imaging Laboratory University of California, San Francisco Abstract The synchronous brain activity measured via MEG (or EEG) can be interpre... | 2008 | 43 |
3,530 | Multi-stage Convex Relaxation for Learning with Sparse Regularization Tong Zhang Statistics Department Rutgers University, NJ tzhang@stat.rutgers.edu Abstract We study learning formulations with non-convex regularizaton that are natural for sparse linear models. There are two approaches to this problem:... | 2008 | 44 |
3,531 | Nonparametric sparse hierarchical models describe V1 fMRI responses to natural images Pradeep Ravikumar, Vincent Q. Vu and Bin Yu Department of Statistics University of California, Berkeley Berkeley, CA 94720-3860 Thomas Naselaris, Kendrick N. Kay and Jack L. Gallant Department of Psychology University ... | 2008 | 45 |
3,532 | A Scalable Hierarchical Distributed Language Model Andriy Mnih Department of Computer Science University of Toronto amnih@cs.toronto.edu Geoffrey Hinton Department of Computer Science University of Toronto hinton@cs.toronto.edu Abstract Neural probabilistic language models (NPLMs) have been shown to... | 2008 | 46 |
3,533 | Supervised Exponential Family Principal Component Analysis via Convex Optimization Yuhong Guo Computer Sciences Laboratory Australian National University yuhongguo.cs@gmail.com Abstract Recently, supervised dimensionality reduction has been gaining attention, owing to the realization that data labels ar... | 2008 | 47 |
3,534 | Stochastic Relational Models for Large-scale Dyadic Data using MCMC Shenghuo Zhu Kai Yu Yihong Gong NEC Laboratories America, Cupertino, CA 95014, USA {zsh, kyu, ygong}@sv.nec-labs.com Abstract Stochastic relational models (SRMs) [15] provide a rich family of choices for learning and predicting dyadic... | 2008 | 48 |
3,535 | Online Models for Content Optimization Deepak Agarwal, Bee-Chung Chen, Pradheep Elango, Nitin Motgi, Seung-Taek Park, Raghu Ramakrishnan, Scott Roy, Joe Zachariah Yahoo! Inc. 701 First Avenue Sunnyvale, CA 94089 Abstract We describe a new content publishing system that selects articles to serve to a user,... | 2008 | 49 |
3,536 | Grouping Contours Via a Related Image Praveen Srinivasan GRASP Laboratory University of Pennsylvania Philadelphia, PA 19104 psrin@seas.upenn.edu Liming Wang Fudan University Shanghai, PRC 200433 wanglm@fudan.edu.cn Jianbo Shi GRASP Laboratory University of Pennsylvania Philadelphia, PA 19104 ... | 2008 | 5 |
3,537 | Robust Regression and Lasso Huan Xu Department of Electrical and Computer Engineering McGill University Montreal, QC Canada xuhuan@cim.mcgill.ca Constantine Caramanis Department of Electrical and Computer Engineering The University of Texas at Austin Austin, Texas cmcaram@ece.utexas.edu Shie Manno... | 2008 | 50 |
3,538 | Hierarchical Fisher Kernels for Longitudinal Data Zhengdong Lu Todd K. Leen Dept. of Computer Science & Engineering Oregon Health & Science University Beaverton, OR 97006 luz@cs.utexas.edu,tleen@csee.ogi.edu Jeffrey Kaye Layton Aging & Alzheimer’s Disease Center Oregon Health & Science University Po... | 2008 | 51 |
3,539 | Correlated Bigram LSA for Unsupervised Language Model Adaptation Yik-Cheung Tam∗ InterACT, Language Technologies Institute Carnegie Mellon University Pittsburgh, PA 15213 yct@cs.cmu.edu Tanja Schultz InterACT, Language Technologies Institute Carnegie Mellon University Pittsburgh, PA 15213 tanja@cs... | 2008 | 52 |
3,540 | The Infinite Factorial Hidden Markov Model Jurgen Van Gael∗ Department of Engineering University of Cambridge, UK jv279@cam.ac.uk Yee Whye Teh Gatsby Unit University College London, UK ywteh@gatsby.ucl.ac.uk Zoubin Ghahramani Department of Engineering University of Cambridge, UK zoubin@eng.cam.ac... | 2008 | 53 |
3,541 | Sparse Signal Recovery Using Markov Random Fields Volkan Cevher Rice University volkan@rice.edu Marco F. Duarte Rice University duarte@rice.edu Chinmay Hegde Rice University chinmay@rice.edu Richard G. Baraniuk Rice University richb@rice.edu Abstract Compressive Sensing (CS) combines samplin... | 2008 | 54 |
3,542 | Clustering via LP-based Stabilities Nikos Komodakis University of Crete komod@csd.uoc.gr Nikos Paragios Ecole Centrale de Paris INRIA Saclay Ile-de-France nikos.paragios@ecp.fr Georgios Tziritas University of Crete tziritas@csd.uoc.gr Abstract A novel center-based clustering algorithm is propose... | 2008 | 55 |
3,543 | Spike Feature Extraction Using Informative Samples Zhi Yang, Qi Zhao and Wentai Liu School of Engineering University of California at Santa Cruz 1156 High Street, Santa Cruz, CA 95064 {yangzhi, zhaoqi, wentai}@soe.ucsc.edu Abstract This paper presents a spike feature extraction algorithm that targets real... | 2008 | 56 |
3,544 | Fast Computation of Posterior Mode in Multi-Level Hierarchical Models Liang Zhang Department of Statistical Science Duke University Durham, NC 27708 lz9@stat.duke.edu Deepak Agarwal Yahoo! Research 2821 Mission College Blvd. Santa Clara, CA 95054 dagarwal@yahoo-inc.com Abstract Multi-level hie... | 2008 | 57 |
3,545 | An improved estimator of Variance Explained in the presence of noise Ralf. M. Haefner∗ Laboratory for Sensorimotor Research National Eye Institute, NIH Bethesda, MD 20892 ralf.haefner@gmail.com Bruce. G. Cumming Laboratory for Sensorimotor Research National Eye Institute, NIH Bethesda, MD 20892 bg... | 2008 | 58 |
3,546 | The Infinite Hierarchical Factor Regression Model Piyush Rai and Hal Daum´e III School of Computing, University of Utah {piyush,hal}@cs.utah.edu Abstract We propose a nonparametric Bayesian factor regression model that accounts for uncertainty in the number of factors, and the relationship between factors. T... | 2008 | 59 |
3,547 | Designing neurophysiology experiments to optimally constrain receptive field models along parametric submanifolds. Jeremy Lewi ∗ School of Bioengineering Georgia Institute of Technology jeremy@lewi.us Robert Butera School of Electrical and Computer Engineering Georgia Institute of Technology rbutera@... | 2008 | 6 |
3,548 | Deep Learning with Kernel Regularization for Visual Recognition Kai Yu Wei Xu Yihong Gong NEC Laboratories America, Cupertino, CA 95014, USA {kyu, wx, ygong}@sv.nec-labs.com Abstract In this paper we aim to train deep neural networks for rapid visual recognition. The task is highly challenging, largel... | 2008 | 60 |
3,549 | Learning Transformational Invariants from Natural Movies Charles F. Cadieu & Bruno A. Olshausen Helen Wills Neuroscience Institute University of California, Berkeley Berkeley, CA 94720 {cadieu, baolshausen}@berkeley.edu Abstract We describe a hierarchical, probabilistic model that learns to extract comp... | 2008 | 61 |
3,550 | On Bootstrapping the ROC Curve Patrice Bertail CREST (INSEE) & MODAL’X - Universit´e Paris 10 pbertail@u-paris10.fr St´ephan Cl´emenc¸on Telecom Paristech (TSI) - LTCI UMR Institut Telecom/CNRS 5141 stephan.clemencon@telecom-paristech.fr Nicolas Vayatis ENS Cachan & UniverSud - CMLA UMR CNRS 8536 vaya... | 2008 | 62 |
3,551 | QUIC-SVD: Fast SVD Using Cosine Trees Michael P. Holmes, Alexander G. Gray and Charles Lee Isbell, Jr. College of Computing Georgia Tech Atlanta, GA 30327 {mph, agray, isbell}@cc.gatech.edu Abstract The Singular Value Decomposition is a key operation in many machine learning methods. Its computational c... | 2008 | 63 |
3,552 | A Massively Parallel Digital Learning Processor Hans Peter Graf Srihari Cadambi Igor Durdanovic hpg@nec-labs.com cadambi@nec-labs.com igord@nec-labs.com Venkata Jakkula Murugan Sankardadass Eric Cosatto Srimat Chakra... | 2008 | 64 |
3,553 | Characterizing response behavior in multi-sensory perception with conflicting cues Rama Natarajan1 Iain Murray1 Ladan Shams2 Richard S. Zemel1 1Department of Computer Science, University of Toronto, Canada {rama,murray,zemel}@cs.toronto.edu 2Department of Psychology, University of California Los Angeles,... | 2008 | 65 |
3,554 | The Gaussian Process Density Sampler Ryan Prescott Adams∗ Cavendish Laboratory University of Cambridge Cambridge CB3 0HE, UK rpa23@cam.ac.uk Iain Murray Dept. of Computer Science University of Toronto Toronto, Ontario. M5S 3G4 murray@cs.toronto.edu David J.C. MacKay Cavendish Laboratory Univer... | 2008 | 66 |
3,555 | Cyclizing Clusters via Zeta Function of a Graph Deli Zhao and Xiaoou Tang Department of Information Engineering, Chinese University of Hong Kong Hong Kong, China {dlzhao,xtang}@ie.cuhk.edu.hk Abstract Detecting underlying clusters from large-scale data plays a central role in machine learning research. In... | 2008 | 67 |
3,556 | Integrating locally learned causal structures with overlapping variables Robert E. Tillman Carnegie Mellon University Pittsburgh, PA 15213 rtillman@andrew.cmu.edu David Danks, Clark Glymour Carnegie Mellon University & Institute for Human & Machine Cognition Pittsburgh, PA 15213 {ddanks,cg09}@andrew... | 2008 | 68 |
3,557 | A mixture model for the evolution of gene expression in non-homogeneous datasets Gerald Quon1, Yee Whye Teh2, Esther Chan3, Timothy Hughes3, Michael Brudno1,3, Quaid Morris3 1Department of Computer Science, and 3Banting and Best Department of Medical Research, University of Toronto, Canada, 2Gatsby Computat... | 2008 | 69 |
3,558 | Analyzing human feature learning as nonparametric Bayesian inference Joseph L. Austerweil Department of Psychology University of California, Berkeley Berkeley, CA 94720 Joseph.Austerweil@gmail.com Thomas L. Griffiths Department of Psychology University of California, Berkeley Berkeley, CA 94720 Tom... | 2008 | 7 |
3,559 | An Homotopy Algorithm for the Lasso with Online Observations Pierre J. Garrigues Department of EECS Redwood Center for Theoretical Neuroscience University of California Berkeley, CA 94720 garrigue@eecs.berkeley.edu Laurent El Ghaoui Department of EECS University of California Berkeley, CA 94720 ... | 2008 | 70 |
3,560 | Adaptive Martingale Boosting Philip M. Long Google plong@google.com Rocco A. Servedio Columbia University rocco@cs.columbia.edu Abstract In recent work Long and Servedio [LS05] presented a “martingale boosting” algorithm that works by constructing a branching program over weak classifiers and has a sim... | 2008 | 71 |
3,561 | Fast High-dimensional Kernel Summations Using the Monte Carlo Multipole Method Dongryeol Lee Computational Science and Engineering Georgia Institute of Technology Atlanta, GA 30332 dongryel@cc.gatech.edu Alexander Gray Computational Science and Engineering Georgia Institute of Technology Atlanta, GA... | 2008 | 72 |
3,562 | ICA based on a Smooth Estimation of the Differential Entropy Lev Faivishevsky School of Engineering, Bar-Ilan University levtemp@gmail.com Jacob Goldberger School of Engineering, Bar-Ilan University goldbej@eng.biu.ac.il Abstract In this paper we introduce the MeanNN approach for estimation of main in... | 2008 | 73 |
3,563 | Support Vector Machines with a Reject Option Yves Grandvalet 1, 2, Alain Rakotomamonjy 3, Joseph Keshet 2 and St´ephane Canu 3 1 Heudiasyc, UMR CNRS 6599 2 Idiap Research Institute Universit´e de Technologie de Compi`egne Centre du Parc BP 20529, 60205 Compi`egne Cedex, France CP 592, CH-1920 Martigny Swi... | 2008 | 74 |
3,564 | Generative versus discriminative training of RBMs for classification of fMRI images Tanya Schmah Department of Computer Science University of Toronto Toronto, Canada schmah@cs.toronto.edu Geoffrey E. Hinton Department of Computer Science University of Toronto Toronto, Canada hinton@cs.toronto.edu ... | 2008 | 75 |
3,565 | Particle Filter-based Policy Gradient in POMDPs Pierre-Arnaud Coquelin CMAP, Ecole Polytechnique coquelin@cmapx.polytechnique.fr Romain Deguest∗ CMAP, Ecole Polytechnique deguest@cmapx.polytechnique.fr R´emi Munos INRIA Lille - Nord Europe, SequeL project, remi.munos@inria.fr Abstract Our setting ... | 2008 | 76 |
3,566 | Algorithms for Infinitely Many-Armed Bandits Yizao Wang∗ Department of Statistics - University of Michigan 437 West Hall, 1085 South University, Ann Arbor, MI, 48109-1107, USA yizwang@umich.edu Jean-Yves Audibert Université Paris Est, Ecole des Ponts, ParisTech, Certis & Willow - ENS / INRIA, Paris, France... | 2008 | 77 |
3,567 | On the asymptotic equivalence between differential Hebbian and temporal difference learning using a local third factor Christoph Kolodziejski1,2, Bernd Porr3, Minija Tamosiunaite1,2,4, Florentin Wörgötter1,2 1 Bernstein Center for Computational Neuroscience Göttingen 2 Georg-August University Göttingen, Depar... | 2008 | 78 |
3,568 | Gates Tom Minka Microsoft Research Ltd. Cambridge, UK John Winn Microsoft Research Ltd. Cambridge, UK Abstract Gates are a new notation for representing mixture models and context-sensitive independence in factor graphs. Factor graphs provide a natural representation for message-passing algorithms, ... | 2008 | 79 |
3,569 | From Online to Batch Learning with Cutoff-Averaging Anonymous Author(s) Affiliation Address email Abstract We present cutoff averaging, a technique for converting any conservative online learning algorithm into a batch learning algorithm. Most online-to-batch conversion techniques work well with certain ... | 2008 | 8 |
3,570 | Multi-Level Active Prediction of Useful Image Annotations for Recognition Sudheendra Vijayanarasimhan and Kristen Grauman Department of Computer Sciences University of Texas at Austin {svnaras,grauman}@cs.utexas.edu Abstract We introduce a framework for actively learning visual categories from a mixture o... | 2008 | 80 |
3,571 | MAS: a multiplicative approximation scheme for probabilistic inference Ydo Wexler Microsoft Research Redmond, WA 98052 ydow@microsoft.com Christopher Meek Microsoft Research Redmond, WA 98052 meek@microsoft.com Abstract We propose a multiplicative approximation scheme (MAS) for inference problems ... | 2008 | 81 |
3,572 | Learning Hybrid Models for Image Annotation with Partially Labeled Data Xuming He Department of Statistics UCLA hexm@stat.ucla.edu Richard S. Zemel Department of Computer Science University of Toronto zemel@cs.toronto.edu Abstract Extensive labeled data for image annotation systems, which learn to... | 2008 | 82 |
3,573 | Efficient Sampling for Gaussian Process Inference using Control Variables Michalis K. Titsias, Neil D. Lawrence and Magnus Rattray School of Computer Science, University of Manchester Manchester M13 9PL, UK Abstract Sampling functions in Gaussian process (GP) models is challenging because of the highly cor... | 2008 | 83 |
3,574 | An Online Algorithm for Maximizing Submodular Functions Matthew Streeter Google, Inc. Pittsburgh, PA 15213 mstreeter@google.com Daniel Golovin Carnegie Mellon University Pittsburgh, PA 15213 dgolovin@cs.cmu.edu Abstract We present an algorithm for solving a broad class of online resource allocatio... | 2008 | 84 |
3,575 | On the Complexity of Linear Prediction: Risk Bounds, Margin Bounds, and Regularization Sham M. Kakade TTI Chicago Chicago, IL 60637 sham@tti-c.org Karthik Sridharan TTI Chicago Chicago, IL 60637 karthik@tti-c.org Ambuj Tewari TTI Chicago Chicago, IL 60637 tewari@tti-c.org Abstract This wor... | 2008 | 85 |
3,576 | Bayesian Exponential Family PCA Shakir Mohamed Katherine Heller Zoubin Ghahramani Department of Engineering, University of Cambridge Cambridge, CB2 1PZ, UK {sm694,kah60,zoubin}@eng.cam.ac.uk Abstract Principal Components Analysis (PCA) has become established as one of the key tools for dimensionality ... | 2008 | 86 |
3,577 | Sequential effects: Superstition or rational behavior? Angela J. Yu Department of Cognitive Science University of California, San Diego ajyu@ucsd.edu Jonathan D. Cohen Department of Psychology Princeton University jdc@princeton.edu Abstract In a variety of behavioral tasks, subjects exhibit an autom... | 2008 | 87 |
3,578 | PSDBoost: Matrix-Generation Linear Programming for Positive Semidefinite Matrices Learning Chunhua Shen†‡, Alan Welsh‡, Lei Wang‡ †NICTA Canberra Research Lab, Canberra, ACT 2601, Australia∗ ‡Australian National University, Canberra, ACT 0200, Australia Abstract In this work, we consider the problem of learn... | 2008 | 88 |
3,579 | Kernel-ARMA for Hand Tracking and Brain-Machine Interfacing During 3D Motor Control Lavi Shpigelman1 , Hagai Lalazar 2 and Eilon Vaadia 3 Interdisciplinary Center for Neural Computation The Hebrew University of Jerusalem, Israel 1shpigi@gmail.com, 2hagai@alice.nc.huji.ac.il, 3eilonv@ekmd.huji.ac.il Abst... | 2008 | 89 |
3,580 | Structured Ranking Learning using Cumulative Distribution Networks Jim C. Huang Probabilistic and Statistical Inference Group University of Toronto Toronto, ON, Canada M5S 3G4 jim@psi.toronto.edu Brendan J. Frey Probabilistic and Statistical Inference Group University of Toronto Toronto, ON, Canada ... | 2008 | 9 |
3,581 | Model Selection in Gaussian Graphical Models: High-Dimensional Consistency of ℓ1-regularized MLE Pradeep Ravikumar†, Garvesh Raskutti†, Martin J. Wainwright†∗and Bin Yu†∗ Department of Statistics†, Department of EECS∗, University of California, Berkeley {pradeepr,garveshr,wainwright,binyu}@stat.berkeley.edu ... | 2008 | 90 |
3,582 | Stress, noradrenaline, and realistic prediction of mouse behaviour using reinforcement learning Gediminas Lukˇsys1,2, Carmen Sandi2, Wulfram Gerstner1 1Laboratory of Computational Neuroscience 2Laboratory of Behavioural Genetics Ecole Polytechnique F´ed´erale de Lausanne (EPFL) Lausanne, CH-1015, Switzerlan... | 2008 | 91 |
3,583 | How memory biases affect information transmission: A rational analysis of serial reproduction Jing Xu Thomas L. Griffiths Department of Psychology University of California, Berkeley Berkeley, CA 94720-1650 {jing.xu,tom griffiths}@berkeley.edu Abstract Many human interactions involve pieces of information... | 2008 | 92 |
3,584 | Diffeomorphic Dimensionality Reduction Christian Walder and Bernhard Sch¨olkopf Max Planck Institute for Biological Cybernetics 72076 T¨ubingen, Germany first.last@tuebingen.mpg.de Abstract This paper introduces a new approach to constructing meaningful lower dimensional representations of sets of data poin... | 2008 | 93 |
3,585 | Using Bayesian Dynamical Systems for Motion Template Libraries Silvia Chiappa, Jens Kober, Jan Peters Max-Planck Institute for Biological Cybernetics Spemannstraße 38, 72076 Tübingen, Germany {silvia.chiappa,jens.kober,jan.peters}@tuebingen.mpg.de Abstract Motor primitives or motion templates have become ... | 2008 | 94 |
3,586 | An Efficient Sequential Monte Carlo Algorithm for Coalescent Clustering Dilan G¨or¨ur Gatsby Unit University College London dilan@gatsby.ucl.ac.uk Yee Whye Teh Gatsby Unit University College London ywteh@gatsby.ucl.ac.uk Abstract We propose an efficient sequential Monte Carlo inference scheme for th... | 2008 | 95 |
3,587 | Bounding Performance Loss in Approximate MDP Homomorphisms Jonathan J. Taylor Dept. of Computer Science University of Toronto Toronto, Canada, M5S 3G4 jonathan.taylor@utoronto.ca Doina Precup School of Computer Science McGill University Montreal, Canada, H3A 2A7 dprecup@cs.mcgill.ca Prakash Pana... | 2008 | 96 |
3,588 | Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks Alex Graves TU Munich, Germany graves@in.tum.de J¨urgen Schmidhuber IDSIA, Switzerland and TU Munich, Germany juergen@idsia.ch Abstract Offline handwriting recognition—the automatic transcription of images of handwritten tex... | 2008 | 97 |
3,589 | Robust Near-Isometric Matching via Structured Learning of Graphical Models Julian J. McAuley NICTA/ANU julian.mcauley @nicta.com.au Tib´erio S. Caetano NICTA/ANU tiberio.caetano @nicta.com.au Alexander J. Smola Yahoo! Research∗ alex@smola.org Abstract Models for near-rigid shape matching are... | 2008 | 98 |
3,590 | Fast Prediction on a Tree Mark Herbster, Massimiliano Pontil, Sergio Rojas-Galeano Department of Computer Science University College London Gower Street, London WC1E 6BT, England, UK {m.herbster, m.pontil,s.rojas}@cs.ucl.ac.uk Abstract Given an n-vertex weighted tree with structural diameter S and a s... | 2008 | 99 |
3,591 | Monte Carlo Sampling for Regret Minimization in Extensive Games Marc Lanctot Department of Computing Science University of Alberta Edmonton, Alberta, Canada T6G 2E8 lanctot@ualberta.ca Kevin Waugh School of Computer Science Carnegie Mellon University Pittsburgh PA 15213-3891 waugh@cs.cmu.edu Mar... | 2009 | 1 |
3,592 | Spatial Normalized Gamma Processes Vinayak Rao Gatsby Computational Neuroscience Unit University College London vrao@gatsby.ucl.ac.uk Yee Whye Teh Gatsby Computational Neuroscience Unit University College London ywteh@gatsby.ucl.ac.uk Abstract Dependent Dirichlet processes (DPs) are dependent sets o... | 2009 | 10 |
3,593 | Learning to Hash with Binary Reconstructive Embeddings Brian Kulis and Trevor Darrell UC Berkeley EECS and ICSI Berkeley, CA {kulis,trevor}@eecs.berkeley.edu Abstract Fast retrieval methods are increasingly critical for many large-scale analysis tasks, and there have been several recent methods that att... | 2009 | 100 |
3,594 | Multi-Label Prediction via Compressed Sensing Daniel Hsu UC San Diego djhsu@cs.ucsd.edu Sham M. Kakade TTI-Chicago sham@tti-c.org John Langford Yahoo! Research jl@hunch.net Tong Zhang Rutgers University tongz@rci.rutgers.edu Abstract We consider multi-label prediction problems with large out... | 2009 | 101 |
3,595 | Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions Bharath K. Sriperumbudur Department of ECE UC San Diego, La Jolla, USA bharathsv@ucsd.edu Kenji Fukumizu The Institute of Statistical Mathematics Tokyo, Japan fukumizu@ism.ac.jp Arthur Gretton Carnegie Mellon Univers... | 2009 | 102 |
3,596 | Semi-Supervised Learning with the Graph Laplacian: The Limit of Infinite Unlabelled Data Boaz Nadler Dept. of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot, Israel 76100 boaz.nadler@weizmann.ac.il Nathan Srebro Toyota Technological Institute Chicago, IL 60637 nati@uch... | 2009 | 103 |
3,597 | Maximin affinity learning of image segmentation Srinivas C. Turaga ∗ MIT Kevin L. Briggman Max-Planck Insitute for Medical Research Moritz Helmstaedter Max-Planck Insitute for Medical Research Winfried Denk Max-Planck Insitute for Medical Research H. Sebastian Seung MIT, HHMI Abstract Images can ... | 2009 | 104 |
3,598 | Perceptual Multistability as Markov Chain Monte Carlo Inference Samuel J. Gershman Department of Psychology and Neuroscience Institute Princeton University Princeton, NJ 08540 sjgershm@princeton.edu Edward Vul & Joshua B. Tenenbaum Department of Brain and Cognitive Sciences Massachusetts Institute of ... | 2009 | 105 |
3,599 | Probabilistic Relational PCA Wu-Jun Li Dit-Yan Yeung Dept. of Comp. Sci. and Eng. Hong Kong University of Science and Technology Hong Kong, China {liwujun,dyyeung}@cse.ust.hk Zhihua Zhang School of Comp. Sci. and Tech. Zhejiang University Zhejiang 310027, China zhzhang@cs.zju.edu.cn Abstract O... | 2009 | 106 |
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