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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3,000 | Learning Motion Style Synthesis from Perceptual Observations Lorenzo Torresani Riya, Inc. lorenzo@riya.com Peggy Hackney Integrated Movement Studies pjhackney@aol.com Christoph Bregler New York University chris.bregler@nyu.edu Abstract This paper presents an algorithm for synthesis of human moti... | 2006 | 171 |
3,001 | Linearly-solvable Markov decision problems Emanuel Todorov Department of Cognitive Science University of California San Diego todorov@cogsci.ucsd.edu Abstract We introduce a class of MPDs which greatly simplify Reinforcement Learning. They have discrete state spaces and continuous control spaces. The cont... | 2006 | 172 |
3,002 | Learning on Graph with Laplacian Regularization Rie Kubota Ando IBM T.J. Watson Research Center Hawthorne, NY 10532, U.S.A. rie1@us.ibm.com Tong Zhang Yahoo! Inc. New York City, NY 10011, U.S.A. tzhang@yahoo-inc.com Abstract We consider a general form of transductive learning on graphs with Laplacia... | 2006 | 173 |
3,003 | An Oracle Inequality for Clipped Regularized Risk Minimizers Ingo Steinwart, Don Hush, and Clint Scovel Modelling, Algorithms and Informatics Group, CCS-3 Los Alamos National Laboratory Los Alamos, NM 87545 {ingo,dhush,jcs}@lanl.gov Abstract We establish a general oracle inequality for clipped approxima... | 2006 | 174 |
3,004 | Clustering appearance and shape by learning jigsaws Anitha Kannan, John Winn, Carsten Rother Microsoft Research Cambridge [ankannan, jwinn, carrot]@microsoft.com Abstract Patch-based appearance models are used in a wide range of computer vision applications. To learn such models it has previously been necessa... | 2006 | 175 |
3,005 | Large Margin Component Analysis Lorenzo Torresani Riya, Inc. lorenzo@riya.com Kuang-chih Lee Riya, Inc. kclee@riya.com Abstract Metric learning has been shown to significantly improve the accuracy of k-nearest neighbor (kNN) classification. In problems involving thousands of features, distance learning ... | 2006 | 176 |
3,006 | Kernels on Structured Objects Through Nested Histograms Marco Cuturi Institute of Statistical Mathematics Minami-azabu 4-6-7, Minato ku, Tokyo, Japan. Kenji Fukumizu Institute of Statistical Mathematics Minami-azabu 4-6-7, Minato ku, Tokyo, Japan. Abstract We propose a family of kernels for struct... | 2006 | 177 |
3,007 | Learning with Hypergraphs: Clustering, Classification, and Embedding Dengyong Zhou†, Jiayuan Huang‡, and Bernhard Sch¨olkopf§ †NEC Laboratories America, Inc. 4 Independence Way, Suite 200, Princeton, NJ 08540, USA ‡School of Computer Science, University of Waterloo Waterloo ON, N2L3G1, Canada §Max Planck I... | 2006 | 178 |
3,008 | Learning Dense 3D Correspondence Florian Steinke∗, Bernhard Sch¨olkopf∗, Volker Blanz+ ∗Max Planck Institute for Biological Cybernetics, 72076 T¨ubingen, Germany {steinke, bs}@tuebingen.mpg.de +Universit¨at Siegen, 57068 Siegen, Germany blanz@mpi-sb.mpg.de Abstract Establishing correspondence between dist... | 2006 | 179 |
3,009 | Detecting Humans via Their Pose Alessandro Bissacco Computer Science Department University of California, Los Angeles Los Angeles, CA 90095 bissacco@cs.ucla.edu Ming-Hsuan Yang Honda Research Institute 800 California Street Mountain View, CA 94041 mhyang@ieee.org Stefano Soatto Computer Science ... | 2006 | 18 |
3,010 | Fast Computation of Graph Kernels S.V. N. Vishwanathan svn.vishwanathan@nicta.com.au Statistical Machine Learning, National ICT Australia, Locked Bag 8001, Canberra ACT 2601, Australia Research School of Information Sciences & Engineering Australian National University, Canberra ACT 0200, Australia Karste... | 2006 | 180 |
3,011 | Multi-dynamic Bayesian Networks Karim Filali and Jeff A. Bilmes Departments of Computer Science & Engineering and Electrical Engineering University of Washington Seattle, WA 98195 {karim@cs,bilmes@ee}.washington.edu Abstract We present a generalization of dynamic Bayesian networks to concisely describe ... | 2006 | 181 |
3,012 | Max-margin classification of incomplete data Gal Chechik1, Geremy Heitz2, Gal Elidan1, Pieter Abbeel 1, Daphne Koller 1 1 Department of Computer Science, Stanford University, Stanford CA, 94305 2 Department of Electrical Engineering, Stanford University, Stanford CA, 94305 Email for correspondence: gal@ai.stan... | 2006 | 182 |
3,013 | Scalable Discriminative Learning for Natural Language Parsing and Translation Joseph Turian, Benjamin Wellington, and I. Dan Melamed {lastname}@cs.nyu.edu Computer Science Department New York University New York, New York 10003 Abstract Parsing and translating natural languages can be viewed as problems... | 2006 | 183 |
3,014 | On the Relation Between Low Density Separation, Spectral Clustering and Graph Cuts Hariharan Narayanan Department of Computer Science University of Chicago Chicago IL 60637 hari@cs.uchicago.edu Mikhail Belkin Department of Computer Science and Engineering The Ohio State University Columbus, OH 43210... | 2006 | 184 |
3,015 | A Kernel Method for the Two-Sample-Problem Arthur Gretton MPI for Biological Cybernetics T¨ubingen, Germany arthur@tuebingen.mpg.de Karsten M. Borgwardt Ludwig-Maximilians-Univ. Munich, Germany kb@dbs.ifi.lmu.de Malte Rasch Graz Univ. of Technology, Graz, Austria malte.rasch@igi.tu-graz.ac.at B... | 2006 | 185 |
3,016 | Modeling General and Specific Aspects of Documents with a Probabilistic Topic Model Chaitanya Chemudugunta, Padhraic Smyth Department of Computer Science University of California, Irvine Irvine, CA 92697-3435, USA {chandra,smyth}@ics.uci.edu Mark Steyvers Department of Cognitive Sciences University of ... | 2006 | 186 |
3,017 | Emergence of conjunctive visual features by quadratic independent component analysis J.T. Lindgren Department of Computer Science University of Helsinki Finland jtlindgr@cs.helsinki.fi Aapo Hyv¨arinen HIIT Basic Research Unit University of Helsinki Finland aapo.hyvarinen@cs.helsinki.fi Abstract ... | 2006 | 187 |
3,018 | Nonlinear physically-based models for decoding motor-cortical population activity Gregory Shakhnarovich Sung-Phil Kim Michael J. Black Department of Computer Science Brown University Providence, RI 02912 {gregory,spkim,black}@cs.brown.edu Abstract Neural motor prostheses (NMPs) require the accurate ... | 2006 | 188 |
3,019 | Conditional mean field Peter Carbonetto Department of Computer Science University of British Columbia Vancouver, BC, Canada V6T 1Z4 pcarbo@cs.ubc.ca Nando de Freitas Department of Computer Science University of British Columbia Vancouver, BC, Canada V6T 1Z4 nando@cs.ubc.ca Abstract Despite all th... | 2006 | 189 |
3,020 | Hidden Markov Dirichlet Process: Modeling Genetic Recombination in Open Ancestral Space Kyung-Ah Sohn School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 ksohn@cs.cmu.edu Eric P. Xing School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 epxing@cs.cmu.ed... | 2006 | 19 |
3,021 | Unsupervised Regression with Applications to Nonlinear System Identification Ali Rahimi Intel Research Seattle Seattle, WA 98105 ali.rahimi@intel.com Ben Recht California Institute of Technology Pasadena, CA 91125 brecht@ist.caltech.edu Abstract We derive a cost functional for estimating the relati... | 2006 | 190 |
3,022 | Large Margin Hidden Markov Models for Automatic Speech Recognition Fei Sha Computer Science Division University of California Berkeley, CA 94720-1776 feisha@cs.berkeley.edu Lawrence K. Saul Department of Computer Science and Engineering University of California (San Diego) La Jolla, CA 92093-0404 ... | 2006 | 191 |
3,023 | No-regret Algorithms for Online Convex Programs Geoffrey J. Gordon Department of Machine Learning Carnegie Mellon University Pittsburgh, PA 15213 ggordon@cs.cmu.edu Abstract Online convex programming has recently emerged as a powerful primitive for designing machine learning algorithms. For example, OCP... | 2006 | 192 |
3,024 | A PAC-Bayes Risk Bound for General Loss Functions Pascal Germain D´epartement IFT-GLO Universit´e Laval Qu´ebec, Canada Pascal.Germain.1@ulaval.ca Alexandre Lacasse D´epartement IFT-GLO Universit´e Laval Qu´ebec, Canada Alexandre.Lacasse@ift.ulaval.ca Franc¸ois Laviolette D´epartement IFT-GLO ... | 2006 | 193 |
3,025 | Distributed Inference in Dynamical Systems Stanislav Funiak Carlos Guestrin Carnegie Mellon University Mark Paskin Google Rahul Sukthankar Intel Research Abstract We present a robust distributed algorithm for approximate probabilistic inference in dynamical systems, such as sensor networks and teams... | 2006 | 194 |
3,026 | Modelling transcriptional regulation using Gaussian processes Neil D. Lawrence School of Computer Science University of Manchester, U.K. neill@cs.man.ac.uk Guido Sanguinetti Department of Computer Science University of Sheffield, U.K. guido@dcs.shef.ac.uk Magnus Rattray School of Computer Science ... | 2006 | 195 |
3,027 | T rueSkill TM : A Ba y esian Skill Rating System Ralf Herbric h Microsoft Researc h Ltd. Cam bridge, UK rherb@micr osoft.c om T om Mink a Microsoft Researc h Ltd. Cam bridge, UK minka@micr osoft.c om Thore Graep el Microsoft Researc h... | 2006 | 196 |
3,028 | Learnability and the Doubling Dimension Yi Li Genome Institute of Singapore liy3@gis.a-star.edu.sg Philip M. Long Google plong@google.com Abstract Given a set F of classifiers and a probability distribution over their domain, one can define a metric by taking the distance between a pair of classifiers ... | 2006 | 197 |
3,029 | Sample complexity of policy search with known dynamics Peter L. Bartlett Divison of Computer Science and Department of Statistics University of California, Berkeley Berkeley, CA 94720-1776 bartlett@cs.berkeley.edu Ambuj Tewari Division of Computer Science University of California, Berkeley Berkeley,... | 2006 | 198 |
3,030 | Large Scale Hidden Semi-Markov SVMs Gunnar R¨atsch∗ Friedrich Miescher Laboratoy, Max Planck Society Spemannstr. 39, 72070 T¨ubingen, Germany Gunnar.Raetsch@tuebingen.mpg.de S¨oren Sonnenburg Fraunhofer FIRST.IDA Kekul´estr. 7, 12489 Berlin, Germany sonne@first.fhg.de Abstract We describe Hidden Sem... | 2006 | 199 |
3,031 | Learning to Traverse Image Manifolds Piotr Doll´ar, Vincent Rabaud and Serge Belongie University of California, San Diego {pdollar,vrabaud,sjb}@cs.ucsd.edu Abstract We present a new algorithm, Locally Smooth Manifold Learning (LSML), that learns a warping function from a point on an manifold to its neighbor... | 2006 | 2 |
3,032 | Online Classification for Complex Problems Using Simultaneous Projections Yonatan Amit1 Shai Shalev-Shwartz1 Yoram Singer1,2 1 School of Computer Sci. & Eng., The Hebrew University, Jerusalem 91904, Israel 2 Google Inc. 1600 Amphitheatre Pkwy, Mountain View, CA 94043, USA {mitmit,shais,singer}@cs.huji.ac.i... | 2006 | 20 |
3,033 | MLLE: Modified Locally Linear Embedding Using Multiple Weights Zhenyue Zhang Department of Mathematics Zhejiang University, Yuquan Campus, Hangzhou, 310027, P. R. China zyzhang@zju.edu.cn Jing Wang College of Information Science and Engineering Huaqiao University Quanzhou, 362021, P. R. China Dep. ... | 2006 | 200 |
3,034 | Hyperparameter Learning for Graph Based Semi-supervised Learning Algorithms Xinhua Zhang∗ Statistical Machine Learning Program National ICT Australia, Canberra, Australia and CSL, RSISE, ANU, Canberra, Australia xinhua.zhang@nicta.com.au Wee Sun Lee Department of Computer Science National University o... | 2006 | 201 |
3,035 | Boosting Structured Prediction for Imitation Learning Nathan Ratliff, David Bradley, J. Andrew Bagnell, Joel Chestnutt Robotics Institute Carnegie Mellon University Pittsburgh, PA 15213 {ndr, dbradley, dbagnell, joel.chestnutt}@ri.cmu.edu Abstract The Maximum Margin Planning (MMP) (Ratliff et al., 2006)... | 2006 | 202 |
3,036 | Denoising and Dimension Reduction in Feature Space Mikio L. Braun Fraunhofer Institute1 FIRST.IDA Kekul´estr. 7, 12489 Berlin mikio@first.fhg.de Joachim Buhmann Inst. of Computational Science ETH Zurich CH-8092 Z¨urich jbuhmann@inf.ethz.ch Klaus-Robert M¨uller2,1 Technical University of Berlin2 ... | 2006 | 203 |
3,037 | Relational Learning with Gaussian Processes Wei Chu CCLS Columbia Univ. New York, NY 10115 Vikas Sindhwani Dept. of Comp. Sci. Univ. of Chicago Chicago, IL 60637 Zoubin Ghahramani Dept. of Engineering Univ. of Cambridge Cambridge, UK S. Sathiya Keerthi Yahoo! Research Media Studios North ... | 2006 | 204 |
3,038 | Convex Repeated Games and Fenchel Duality Shai Shalev-Shwartz1 and Yoram Singer1,2 1 School of Computer Sci. & Eng., The Hebrew University, Jerusalem 91904, Israel 2 Google Inc. 1600 Amphitheater Parkway, Mountain View, CA 94043, USA Abstract We describe an algorithmic framework for an abstract game which we ... | 2006 | 21 |
3,039 | Towards a general independent subspace analysis Fabian J. Theis Max Planck Institute for Dynamics and Self-Organisation & Bernstein Center for Computational Neuroscience Bunsenstr. 10, 37073 G¨ottingen, Germany fabian@theis.name Abstract The increasingly popular independent component analysis (ICA) may on... | 2006 | 22 |
3,040 | In-Network PCA and Anomaly Detection Ling Huang University of California Berkeley, CA 94720 hling@cs.berkeley.edu XuanLong Nguyen University of California Berkeley, CA 94720 xuanlong@cs.berkeley.edu Minos Garofalakis Intel Research Berkeley, CA 94704 minos.garofalakis@intel.com Michael I. Jord... | 2006 | 23 |
3,041 | Robotic Grasping of Novel Objects Ashutosh Saxena, Justin Driemeyer, Justin Kearns, Andrew Y. Ng Computer Science Department Stanford University, Stanford, CA 94305 {asaxena,jdriemeyer,jkearns,ang}@cs.stanford.edu Abstract We consider the problem of grasping novel objects, specifically ones that are being se... | 2006 | 24 |
3,042 | Bayesian Detection of Infrequent Differences in Sets of Time Series with Shared Structure Jennifer Listgarten†, Radford M. Neal†, Sam T. Roweis† Rachel Puckrin‡ and Sean Cutler‡ † Department of Computer Science, ‡ Department of Botany, University of Toronto, Toronto, Ontario, M5S 3G4 {jenn,radford,roweis}@cs.... | 2006 | 25 |
3,043 | Training Conditional Random Fields for Maximum Labelwise Accuracy Samuel S. Gross Computer Science Department Stanford University Stanford, CA, USA ssgross@cs.stanford.edu Olga Russakovsky Computer Science Department Stanford University Stanford, CA, USA olga@cs.stanford.edu Chuong B. Do Compu... | 2006 | 26 |
3,044 | Modeling Dyadic Data with Binary Latent Factors Edward Meeds Department of Computer Science University of Toronto ewm@cs.toronto.edu Zoubin Ghahramani Department of Engineering Cambridge University zoubin@eng.cam.ac.uk Radford Neal Department of Computer Science University of Toronto radford@cs.... | 2006 | 27 |
3,045 | Reducing Calibration Time For Brain-Computer Interfaces: A Clustering Approach Matthias Krauledat1,2, Michael Schröder2, Benjamin Blankertz2, Klaus-Robert Müller1,2 1Technical University Berlin, Str. des 17. Juni 135, 10 623 Berlin, Germany 2 Fraunhofer FIRST.IDA, Kekuléstr. 7, 12 489 Berlin, Germany {kraulem... | 2006 | 28 |
3,046 | Theory and Dynamics of Perceptual Bistability Paul R. Schrater∗ Departments of Psychology and Computer Sci. & Eng. University of Minnesota Minneapolis, MN 55455 schrater@umn.edu Rashmi Sundareswara Department of Computer Sci. & Eng. University of Minnesota sundares@cs.umn.edu Abstract Perceptual B... | 2006 | 29 |
3,047 | Game theoretic algorithms for Protein-DNA binding Luis P´erez-Breva CSAIL-MIT lpbreva@csail.mit.edu Luis E. Ortiz CSAIL - MIT leortiz@csail.mit.edu Chen-Hsiang Yeang UCSC chyeang@soe.ucsc.edu Tommi Jaakkola CSAIL - MIT tommi@csail.mit.edu Abstract We develop and analyze game-theoretic algori... | 2006 | 3 |
3,048 | Aggregating Classification Accuracy across Time: Application to Single Trial EEG Steven Lemm ∗ Intelligent Data Analysis Group, Fraunhofer Institute FIRST, Kekulestr. 7 12489 Berlin, Germany Christin Sch¨afer Intelligent Data Analysis Group, Fraunhofer Institute FIRST, Kekulestr. 7 12489 Berlin, ... | 2006 | 30 |
3,049 | Blind Motion Deblurring Using Image Statistics Anat Levin∗ School of Computer Science and Engineering The Hebrew University of Jerusalem Abstract We address the problem of blind motion deblurring from a single image, caused by a few moving objects. In such situations only part of the image may be blurred, ... | 2006 | 31 |
3,050 | Accelerated Variational Dirichlet Process Mixtures Kenichi Kurihara Dept. of Computer Science Tokyo Institute of Technology Tokyo, Japan kurihara@mi.cs.titech.ac.jp Max Welling Bren School of Information and Computer Science UC Irvine Irvine, CA 92697-3425 welling@ics.uci.edu Nikos Vlassis Infor... | 2006 | 32 |
3,051 | Online Clustering of Moving Hyperplanes Ren´e Vidal Center for Imaging Science, Department of Biomedical Engineering, Johns Hopkins University 308B Clark Hall, 3400 N. Charles St., Baltimore, MD 21218, USA rvidal@cis.jhu.edu Abstract We propose a recursive algorithm for clustering trajectories lying in mult... | 2006 | 33 |
3,052 | Efficient sparse coding algorithms Honglak Lee Alexis Battle Rajat Raina Andrew Y. Ng Computer Science Department Stanford University Stanford, CA 94305 Abstract Sparse coding provides a class of algorithms for finding succinct representations of stimuli; given only unlabeled input data, it discovers ... | 2006 | 34 |
3,053 | Approximate Correspondences in High Dimensions Kristen Grauman Department of Computer Sciences University of Texas at Austin grauman@cs.utexas.edu Trevor Darrell CS and AI Laboratory Massachusetts Institute of Technology trevor@csail.mit.edu Abstract Pyramid intersection is an efficient method for co... | 2006 | 35 |
3,054 | Temporal Coding using the Response Properties of Spiking Neurons Thomas Voegtlin INRIA - Campus Scientifique, B.P. 239 F-54506 Vandoeuvre-Les-Nancy Cedex, FRANCE voegtlin@loria.fr Abstract In biological neurons, the timing of a spike depends on the timing of synaptic currents, in a way that is classicall... | 2006 | 36 |
3,055 | A Nonparametric Bayesian Method for Inferring Features From Similarity Judgments Daniel J. Navarro Thomas L. Griffiths School of Psychology Department of Psychology University of Adelaide UC Berkeley Adelaide, SA 5005, Australia Berkeley, CA 94720, USA daniel.navarro@adelaide.edu.au tom griffiths@b... | 2006 | 37 |
3,056 | Hierarchical Dirichlet Processes with Random Effects Seyoung Kim Department of Computer Science University of California, Irvine Irvine, CA 92697-3435 sykim@ics.uci.edu Padhraic Smyth Department of Computer Science University of California, Irvine Irvine, CA 92697-3435 smyth@ics.uci.edu Abstract ... | 2006 | 38 |
3,057 | Bayesian Model Scoring in Markov Random Fields Sridevi Parise Bren School of Information and Computer Science UC Irvine Irvine, CA 92697-3425 sparise@ics.uci.edu Max Welling Bren School of Information and Computer Science UC Irvine Irvine, CA 92697-3425 welling@ics.uci.edu Abstract Scoring struc... | 2006 | 39 |
3,058 | Kernel Maximum Entropy Data Transformation and an Enhanced Spectral Clustering Algorithm Robert Jenssen1∗, Torbjørn Eltoft1, Mark Girolami2 and Deniz Erdogmus3 1 Department of Physics and Technology, University of Tromsø, Norway 2 Department of Computing Science, University of Glasgow, Scotland 3Department of... | 2006 | 4 |
3,059 | Isotonic Conditional Random Fields and Local Sentiment Flow Yi Mao School of Elec. and Computer Engineering Purdue University - West Lafayette, IN ymao@ecn.purdue.edu Guy Lebanon Department of Statistics, and School of Elec. and Computer Engineering Purdue University - West Lafayette, IN lebanon@sta... | 2006 | 40 |
3,060 | Logistic Regression for Single Trial EEG Classification Ryota Tomioka∗ Kazuyuki Aihara† Dept. of Mathematical Informatics, IST, The University of Tokyo, 113-8656 Tokyo, Japan. ryotat@first.fhg.de aihara@sat.t.u-tokyo.ac.jp Klaus-Robert M¨uller∗ Dept. of Computer Science, Technical University of Ber... | 2006 | 41 |
3,061 | A Local Learning Approach for Clustering Mingrui Wu, Bernhard Sch¨olkopf Max Planck Institute for Biological Cybernetics 72076 T¨ubingen, Germany {mingrui.wu, bernhard.schoelkopf}@tuebingen.mpg.de Abstract We present a local learning approach for clustering. The basic idea is that a good clustering result... | 2006 | 42 |
3,062 | Temporal and Cross-Subject Probabilistic Models for fMRI Prediction Tasks Alexis Battle Gal Chechik Daphne Koller Department of Computer Science Stanford University Stanford, CA 94305-9010 {ajbattle,gal,koller}@cs.stanford.edu Abstract We present a probabilistic model applied to the fMRI video ratin... | 2006 | 43 |
3,063 | Particle Filtering for Nonparametric Bayesian Matrix Factorization Frank Wood Department of Computer Science Brown University Providence, RI 02912 fwood@cs.brown.edu Thomas L. Griffiths Department of Psychology University of California, Berkeley Berkeley, CA 94720 tom griffiths@berkeley.edu Abstr... | 2006 | 44 |
3,064 | Large-Scale Sparsified Manifold Regularization Ivor W. Tsang James T. Kwok Department of Computer Science and Engineering The Hong Kong University of Science and Technology Clear Water Bay, Kowloon, Hong Kong {ivor,jamesk}@cse.ust.hk Abstract Semi-supervised learning is more powerful than supervised lear... | 2006 | 45 |
3,065 | Non-rigid point set registration: Coherent Point Drift Andriy Myronenko Xubo Song Miguel ´A. Carreira-Perpi˜n´an Department of Computer Science and Electrical Engineering OGI School of Science and Engineering Oregon Health and Science University Beaverton, OR, USA, 97006 {myron, xubosong, miguel}@csee.o... | 2006 | 46 |
3,066 | Learning Nonparametric Models for Probabilistic Imitation David B. Grimes Daniel R. Rashid Rajesh P.N. Rao Department of Computer Science University of Washington Seattle, WA 98195 grimes,rashid8,rao@cs.washington.edu Abstract Learning by imitation represents an important mechanism for rapid acquisi... | 2006 | 47 |
3,067 | Active learning for misspecified generalized linear models Francis R. Bach Centre de Morphologie Math´ematique Ecole des Mines de Paris Fontainebleau, France francis.bach@mines.org Abstract Active learning refers to algorithmic frameworks aimed at selecting training data points in order to reduce the n... | 2006 | 48 |
3,068 | Tighter PAC-Bayes Bounds Amiran Ambroladze Dep. of Mathematics Lund University/LTH Box 118, S-221 00 Lund, SWEDEN amiran.ambroladze@math.lth.se Emilio Parrado-Hern´andez Dep. of Signal Processing and Communications University Carlos III of Madrid Legan´es, 28911, SPAIN emipar@tsc.uc3m.es John Shaw... | 2006 | 49 |
3,069 | Unified Inference for Variational Bayesian Linear Gaussian State-Space Models David Barber IDIAP Research Institute rue du Simplon 4, Martigny, Switzerland david.barber@idiap.ch Silvia Chiappa IDIAP Research Institute rue du Simplon 4, Martigny, Switzerland silvia.chiappa@idiap.ch Abstract Linear G... | 2006 | 5 |
3,070 | Statistical Modeling of Images with Fields of Gaussian Scale Mixtures Siwei Lyu Eero. P. Simoncelli Howard Hughes Medical Institute Center for Neural Science, and Courant Institute of Mathematical Sciences New York University, New York, NY 10003 Abstract The local statistical properties of photographi... | 2006 | 50 |
3,071 | Near-Uniform Sampling of Combinatorial Spaces Using XOR Constraints Carla P. Gomes Ashish Sabharwal Bart Selman Department of Computer Science Cornell University, Ithaca NY 14853-7501, USA {gomes,sabhar,selman}@cs.cornell.edu ∗ Abstract We propose a new technique for sampling the solutions of combinat... | 2006 | 51 |
3,072 | Recursive Attribute Factoring David Cohn Google Inc., 1600 Amphitheatre Parkway Mountain View, CA 94043 cohn@google.com Deepak Verma Dept. of CSE, Univ. of Washington, Seattle WA- 98195-2350 deepak@cs.washington.edu Karl Pfleger Google Inc., 1600 Amphitheatre Parkway Mountain View, CA 94043 k... | 2006 | 52 |
3,073 | Information Bottleneck for Non Co-Occurrence Data Yevgeny Seldin† Noam Slonim∗ Naftali Tishby†‡ †School of Computer Science and Engineering ‡Interdisciplinary Center for Neural Computation The Hebrew University of Jerusalem ∗The Lewis-Sigler Institute for Integrative Genomics Princeton University {sel... | 2006 | 53 |
3,074 | A Probabilistic Algorithm Integrating Source Localization and Noise Suppression of MEG and EEG Data Johanna M. Zumer Biomagnetic Imaging Lab Department of Radiology Joint Graduate Group in Bioengineering University of California, San Francisco San Francisco, CA 94143-0628 johannaz@mrsc.ucsf.edu Haga... | 2006 | 54 |
3,075 | Attentional Processing on a Spike-Based VLSI Neural Network Yingxue Wang, Rodney Douglas, and Shih-Chii Liu Institute of Neuroinformatics University of Zurich and ETH Zurich Winterthurerstrasse 190 CH-8057 Zurich, Switzerland yingxue,rjd,shih@ini.phys.ethz.ch Abstract The neurons of the neocortex comm... | 2006 | 55 |
3,076 | A Bayesian Approach to Diffusion Models of Decision-Making and Response Time Michael D. Lee∗ Department of Cognitive Sciences University of California, Irvine Irvine, CA, 92697-5100. mdlee@uci.edu Ian G. Fuss Defence Science and Technology Organisation PO Box 1500, Edinburgh, SA 5111, Australia ian.... | 2006 | 56 |
3,077 | Graph-Based Visual Saliency Jonathan Harel, Christof Koch , Pietro Perona California Institute of Technology Pasadena, CA 91125 {harel,koch}@klab.caltech.edu, perona@vision.caltech.edu Abstract A new bottom-up visual saliency model, Graph-Based Visual Saliency (GBVS), is proposed. It consists of two s... | 2006 | 57 |
3,078 | Doubly Stochastic Normalization for Spectral Clustering Ron Zass and Amnon Shashua ∗ Abstract In this paper we focus on the issue of normalization of the affinity matrix in spectral clustering. We show that the difference between N-cuts and Ratio-cuts is in the error measure being used (relative-entropy vers... | 2006 | 58 |
3,079 | A Scalable Machine Learning Approach to Go Lin Wu and Pierre Baldi School of Information and Computer Sciences University of California, Irvine Irvine, CA 92697-3435 lwu,pfbaldi@ics.uci.edu Abstract Go is an ancient board game that poses unique opportunities and challenges for AI and machine learning. H... | 2006 | 59 |
3,080 | Graph Laplacian Regularization for Large-Scale Semidefinite Programming Kilian Q. Weinberger Dept of Computer and Information Science U of Pennsylvania, Philadelphia, PA 19104 kilianw@seas.upenn.edu Fei Sha Computer Science Division UC Berkeley, CA 94720 feisha@cs.berkeley.edu Qihui Zhu Dept of Com... | 2006 | 6 |
3,081 | Stratification Learning: Detecting Mixed Density and Dimensionality in High Dimensional Point Clouds Gloria Haro, Gregory Randall, and Guillermo Sapiro IMA and Electrical and Computer Engineering University of Minnesota, Minneapolis, MN 55455 haro@ima.umn.edu,randall@fing.edu.uy,guille@umn.edu Abstract The... | 2006 | 60 |
3,082 | A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation Yee Whye Teh Gatsby Computational Neuroscience Unit University College London 17 Queen Square, London WC1N 3AR, UK ywteh@gatsby.ucl.ac.uk David Newman and Max Welling Bren School of Information and Computer Science Un... | 2006 | 61 |
3,083 | Dynamic Foreground/Background Extraction from Images and Videos using Random Patches Le Lu∗ Integrated Data Systems Department Siemens Corporate Research Princeton, NJ 08540 le-lu@siemens.com Gregory Hager Department of Computer Science Johns Hopkins University Baltimore, MD 21218 hager@cs.jhu.edu... | 2006 | 62 |
3,084 | Subordinate class recognition using relational object models Aharon Bar Hillel Department of Computer Science The Hebrew university of Jerusalem aharonbh@cs.huji.ac.il Daphna Weinshall Department of Computer Science The Hebrew university of Jerusalem daphna@cs.huji.ac.il Abstract We address the pr... | 2006 | 63 |
3,085 | Dirichlet-Enhanced Spam Filtering based on Biased Samples Steffen Bickel and Tobias Scheffer Max-Planck-Institut f¨ur Informatik, Saarbr¨ucken, Germany {bickel, scheffer}@mpi-inf.mpg.de Abstract We study a setting that is motivated by the problem of filtering spam messages for many users. Each user receive... | 2006 | 64 |
3,086 | Stability of K-Means Clustering Alexander Rakhlin Department of Computer Science UC Berkeley Berkeley, CA 94720 rakhlin@cs.berkeley.edu Andrea Caponnetto Department of Computer Science University of Chicago Chicago, IL 60637 and D.I.S.I., Universit`a di Genova, Italy caponnet@uchicago.edu Abst... | 2006 | 65 |
3,087 | Convergence of Laplacian Eigenmaps Mikhail Belkin Department of Computer Science Ohio State University Columbus, OH 43210 mbelkin@cse.ohio-state.edu Partha Niyogi Department of Computer Science The University of Chicago Hyde Park, Chicago, IL 60637. niyogi@cs.uchicago.edu Abstract Geometrically ... | 2006 | 66 |
3,088 | Bayesian Policy Gradient Algorithms Mohammad Ghavamzadeh Yaakov Engel Department of Computing Science, University of Alberta Edmonton, Alberta, Canada T6E 4Y8 {mgh,yaki}@cs.ualberta.ca Abstract Policy gradient methods are reinforcement learning algorithms that adapt a parameterized policy by following a p... | 2006 | 67 |
3,089 | Handling Advertisements of Unknown Quality in Search Advertising Sandeep Pandey Christopher Olston Carnegie Mellon University Yahoo! Research spandey@cs.cmu.edu olston@yahoo-inc.com Abstract We consider how a search engine should select advertisements to display with search results, in order to maxi... | 2006 | 68 |
3,090 | Part-based Probabilistic Point Matching using Equivalence Constraints Graham McNeill, Sethu Vijayakumar Institute of Perception, Action and Behavior School of Informatics, University of Edinburgh, Edinburgh, UK. EH9 3JZ [graham.mcneill, sethu.vijayakumar]@ed.ac.uk Abstract Correspondence algorithms typica... | 2006 | 69 |
3,091 | Multi-Task Feature Learning Andreas Argyriou Department of Computer Science University College London Gower Street, London WC1E 6BT, UK a.argyriou@cs.ucl.ac.uk Theodoros Evgeniou Technology Management and Decision Sciences, INSEAD, Bd de Constance, Fontainebleau 77300, France theodoros.evgeniou@inse... | 2006 | 7 |
3,092 | Greedy Layer-Wise Training of Deep Networks Yoshua Bengio, Pascal Lamblin, Dan Popovici, Hugo Larochelle Universit´e de Montr´eal Montr´eal, Qu´ebec {bengioy,lamblinp,popovicd,larocheh}@iro.umontreal.ca Abstract Complexity theory of circuits strongly suggests that deep architectures can be much more effici... | 2006 | 70 |
3,093 | Optimal Change-Detection and Spiking Neurons Angela J. Yu CSBMB, Princeton University Princeton, NJ 08540 ajyu@princeton.edu Abstract Survival in a non-stationary, potentially adversarial environment requires animals to detect sensory changes rapidly yet accurately, two oft competing desiderata. Neurons... | 2006 | 71 |
3,094 | Temporal dynamics of information content carried by neurons in the primary visual cortex Danko NikoliC* Department of Neurophysiology Max-Planck-Institute for Brain Research, Frankfurt (Main), Germany danko@mpih-frankfurt.mpg.de Wolf Singer Department of Neurophysiology Max-Planck-Institute f... | 2006 | 72 |
3,095 | A Switched Gaussian Process for Estimating Disparity and Segmentation in Binocular Stereo Oliver Williams Microsoft Research Ltd. Cambridge, UK omcw2@cam.ac.uk Abstract This paper describes a Gaussian process framework for inferring pixel-wise disparity and bi-layer segmentation of a scene given a stere... | 2006 | 73 |
3,096 | Automated Hierarchy Discovery for Planning in Partially Observable Environments Laurent Charlin & Pascal Poupart David R. Cheriton School of Computer Science Faculty of Mathematics University of Waterloo Waterloo, Ontario {lcharlin,ppoupart}@cs.uwaterloo.ca Romy Shioda Dept of Combinatorics and Optimi... | 2006 | 74 |
3,097 | Adaptor Grammars: A Framework for Specifying Compositional Nonparametric Bayesian Models Mark Johnson Microsoft Research / Brown University Mark Johnson@Brown.edu Thomas L. Griffiths University of California, Berkeley Tom Griffiths@Berkeley.edu Sharon Goldwater Stanford University sgwater@gmail.com ... | 2006 | 75 |
3,098 | On Transductive Regression Corinna Cortes Google Research 76 Ninth Avenue New York, NY 10011 corinna@google.com Mehryar Mohri Courant Institute of Mathematical Sciences and Google Research 251 Mercer Street New York, NY 10012 mohri@cs.nyu.edu Abstract In many modern large-scale learning applic... | 2006 | 76 |
3,099 | Large Margin Multi-channel Analog-to-Digital Conversion with Applications to Neural Prosthesis Amit Gore and Shantanu Chakrabartty Department of Electrical and Computer Engineering Michigan State University East Lansing, MI 48823 {goreamit,shantanu}@egr.msu.edu Abstract A key challenge in designing anal... | 2006 | 77 |
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