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Approximate Clustering without the Approximation MariaFlorina Balcan Avrim Blum

Approximate Clustering without the Approximation MariaFlorina Balcan Avrim Blum

  • phoebe-click
  • 10 Slides

This quest for better approximation algorithms is...

Robust Face Recognition via Sparse Representation John Wright Student Member IEE

Robust Face Recognition via Sparse Representation John Wright Student Member IEE

  • cheryl-pisano
  • 18 Slides

Yang Member IEEE Arvind Ganesh Student Member IEE...

Sparse but not Grandmothercell coding in the medial temporal lobe R

Sparse but not Grandmothercell coding in the medial temporal lobe R

  • tatiana-dople
  • 5 Slides

Quian Quiroga G Kreiman C Koch and I Fried Depa...

The Diameter of Sparse Random Graphs Fan Chung y Linyuan Lu This paper is dedicated to the memory of Paul Erd os

The Diameter of Sparse Random Graphs Fan Chung y Linyuan Lu This paper is dedicated to the memory of Paul Erd os

  • ellena-manuel
  • 22 Slides

Abstract We consider the diameter of a random gra...

Image Signature Highlighting Sparse Salient Regions Xiaodi Hou Jonathan Harel and Christof Koch Member IEEE Abstract We introduce a simple image descriptor referred to as the image signature

Image Signature Highlighting Sparse Salient Regions Xiaodi Hou Jonathan Harel and Christof Koch Member IEEE Abstract We introduce a simple image descriptor referred to as the image signature

  • marina-yarberry
  • 8 Slides

We showwithin the theoretical framework of sparse ...

Sparse deep belief net model for visual area V Honglak Lee Chaitanya Ekanadham Andrew Y

Sparse deep belief net model for visual area V Honglak Lee Chaitanya Ekanadham Andrew Y

  • marina-yarberry
  • 8 Slides

Ng Computer Science Department Stanford Universit...

Efcient sparse coding algorithms Honglak Lee Alexis Battle Rajat Raina Andrew Y

Efcient sparse coding algorithms Honglak Lee Alexis Battle Rajat Raina Andrew Y

  • lindy-dunigan
  • 8 Slides

Ng Computer Science Department Stanford Universit...

Adjacency Queries in Dynamic Sparse Graphs Lukasz Kowalik Abstract We deal with the problem of maintaining a dynamic graph so tha t queries of the form is there an edge between and are processed fas

Adjacency Queries in Dynamic Sparse Graphs Lukasz Kowalik Abstract We deal with the problem of maintaining a dynamic graph so tha t queries of the form is there an edge between and are processed fas

  • trish-goza
  • 8 Slides

We consider graphs of bounded arboricity ie graph...

Using Dual Approximation Algorithms for Scheduling Problems Theoretical and Practical Results DORIT S

Using Dual Approximation Algorithms for Scheduling Problems Theoretical and Practical Results DORIT S

  • tatyana-admore
  • 19 Slides

HOCHBAUM University of California Berkeley Calijo...

Approximation Schemes for Covering and Packing Problems in Image Processing and VLSI DORIT S

Approximation Schemes for Covering and Packing Problems in Image Processing and VLSI DORIT S

  • briana-ranney
  • 7 Slides

HOCHBAUM AND WOLFGANG MAASS University of Califor...

CHAPTER Computing the Cholesky Factorization of Sparse Matrices In many support preconditioners the preconditioner is factored before the iterations begin

CHAPTER Computing the Cholesky Factorization of Sparse Matrices In many support preconditioners the preconditioner is factored before the iterations begin

  • alida-meadow
  • 25 Slides

The Cholesky factorization of allows us to e64259...

An Adaptive Accelerated Proximal Gradient Method and its Ho motopy Continuation for Sparse Optimization Qihang Lin QIHANG LIN UIOWA EDU The University of Iowa Iowa City IA USA Lin Xiao LIN XIAO MICR

An Adaptive Accelerated Proximal Gradient Method and its Ho motopy Continuation for Sparse Optimization Qihang Lin QIHANG LIN UIOWA EDU The University of Iowa Iowa City IA USA Lin Xiao LIN XIAO MICR

  • tatiana-dople
  • 9 Slides

This method in corporates a restarting scheme to ...

Predicting Execution Time of Computer Programs Using Sparse Polynomial Regression Ling Huang Intel Labs Berkeley ling

Predicting Execution Time of Computer Programs Using Sparse Polynomial Regression Ling Huang Intel Labs Berkeley ling

  • marina-yarberry
  • 9 Slides

huangintelcom Jinzhu Jia UC Berkeley jzjiastatberk...

Sparse Filtering Jiquan Ngiam Pang Wei Koh Zhenghao Chen Sonia Bhaskar Andrew Y

Sparse Filtering Jiquan Ngiam Pang Wei Koh Zhenghao Chen Sonia Bhaskar Andrew Y

  • trish-goza
  • 9 Slides

Ng Computer Science Department Stanford Universit...

Simple and Practical Algorithm for Sparse Fourier Transfor Haitham Hassanieh MIT Piotr Indyk MIT Dina Katabi MIT Eric Price MIT haithamhindykdkecprice mit

Simple and Practical Algorithm for Sparse Fourier Transfor Haitham Hassanieh MIT Piotr Indyk MIT Dina Katabi MIT Eric Price MIT haithamhindykdkecprice mit

  • calandra-battersby
  • 12 Slides

edu Abstract We consider the sparse Fourier transf...

Sparse Gaussian Processes using Pseudoinputs Edward Snelson Zoubin Ghahramani Gatsby Computational Neuroscience Unit University College London Queen Square London WCN AR UK snelsonzoubin gatsby

Sparse Gaussian Processes using Pseudoinputs Edward Snelson Zoubin Ghahramani Gatsby Computational Neuroscience Unit University College London Queen Square London WCN AR UK snelsonzoubin gatsby

  • natalia-silvester
  • 8 Slides

uclacuk Abstract We present a new Gaussian process...

SIGNAL APPROXIMATION USING THE BILINEAR TRANSFORM Archana Venkataraman Alan V

SIGNAL APPROXIMATION USING THE BILINEAR TRANSFORM Archana Venkataraman Alan V

  • tawny-fly
  • 4 Slides

Oppenheim MIT Digital Signal Processing Group 77 ...

Sparse classiers for Automated Heart Wall Motion Abnormal ity Detection Glenn Fung Maleeha Qazi Sriram Krishnan Jinbo Bi Bharat Rao Siemens Medical Solutions Valley Stream Parkway Malvern PA USA gle

Sparse classiers for Automated Heart Wall Motion Abnormal ity Detection Glenn Fung Maleeha Qazi Sriram Krishnan Jinbo Bi Bharat Rao Siemens Medical Solutions Valley Stream Parkway Malvern PA USA gle

  • tatyana-admore
  • 7 Slides

fungsiemenscom Alan Katz St Francis Hospital 100 P...

Linguistic Regularities in Sparse and Explicit Word Representations Omer Levy and Yoav Goldberg Computer Science Department BarIlan University RamatGan Israel omerlevyyoav

Linguistic Regularities in Sparse and Explicit Word Representations Omer Levy and Yoav Goldberg Computer Science Department BarIlan University RamatGan Israel omerlevyyoav

  • natalia-silvester
  • 10 Slides

goldberg gmailcom Abstract Recent work has shown t...

Noisy Interpolation of Sparse Polynomials and Applications Shubhangi Saraf MIT shibsmit

Noisy Interpolation of Sparse Polynomials and Applications Shubhangi Saraf MIT shibsmit

  • stefany-barnette
  • 11 Slides

edu Sergey Yekhanin Microsoft Research yekhaninmic...

A SPARSE APPROXIMATE INVERSE PRECONDITIONER FOR NONSYMMETRIC LINEAR SYSTEMS MICHELE BENZI AND MIROSLAV T UMA SIAM J

A SPARSE APPROXIMATE INVERSE PRECONDITIONER FOR NONSYMMETRIC LINEAR SYSTEMS MICHELE BENZI AND MIROSLAV T UMA SIAM J

  • luanne-stotts
  • 27 Slides

S CI C OMPUT 1998 Society for Industrial and Appl...

L Vandenberghe EEA Fall Lecture Piecewiselinear optimization piecewiselinear minimization and norm approximation examples modeling software Linear and ane functions linear function a function is

L Vandenberghe EEA Fall Lecture Piecewiselinear optimization piecewiselinear minimization and norm approximation examples modeling software Linear and ane functions linear function a function is

  • kittie-lecroy
  • 24 Slides

Vandenberghe EE236A Fall 201314 Lecture 2 Piecewi...

Communicated by Halbert White Universal Approximation Using RadialBasisFunction Networks J

Communicated by Halbert White Universal Approximation Using RadialBasisFunction Networks J

  • lindy-dunigan
  • 12 Slides

Park I W Sandberg Department of Electrical arid C...

Chapter APPROXIMATION BY RADIAL BASIS FUNCTION NETWORKS pplication to Option Pricin A

Chapter APPROXIMATION BY RADIAL BASIS FUNCTION NETWORKS pplication to Option Pricin A

  • lois-ondreau
  • 12 Slides

Lendasse J Lee E de Bodt V Wertz M Verleysen ...

Communicated by Halbert White Universal Approximation using RadialBasisFunction Networks J

Communicated by Halbert White Universal Approximation using RadialBasisFunction Networks J

  • giovanna-bartolotta
  • 12 Slides

Park I W Sandberg Department ot Electrical and Co...

ight Approximation Algorithms for Maximum General Assignment Problems Lisa Fleischer Michel X

ight Approximation Algorithms for Maximum General Assignment Problems Lisa Fleischer Michel X

  • lois-ondreau
  • 10 Slides

Goemans ahab S Mirrokni Maxim Sviridenk Abstract ...

The Risky SteadyState By Nicolas Coeurdacier H el ene Rey and Pablo Winant I

The Risky SteadyState By Nicolas Coeurdacier H el ene Rey and Pablo Winant I

  • lindy-dunigan
  • 4 Slides

Approximation around a risky steadystate A The ri...

Noisy Sparse Subspace Clustering YuXiang Wang yuxiangwangnus

Noisy Sparse Subspace Clustering YuXiang Wang yuxiangwangnus

  • phoebe-click
  • 9 Slides

edusg Department of Mechanical Engineering Nationa...

A Tight Linear Time Approximation for Unconstrained Submodular Maximization Niv Buchbinder Statistics and Operations Research Dept

A Tight Linear Time Approximation for Unconstrained Submodular Maximization Niv Buchbinder Statistics and Operations Research Dept

  • pamella-moone
  • 10 Slides

Tel Aviv University Tel Aviv Israel nivbuchbinder...

Efcient Learning of Sparse Representations with an EnergyBased Model MarcAurelio Ranzato Christopher Poultney Sumit Chopra Yan n LeCun Courant Institute of Mathematical Sciences New York University N

Efcient Learning of Sparse Representations with an EnergyBased Model MarcAurelio Ranzato Christopher Poultney Sumit Chopra Yan n LeCun Courant Institute of Mathematical Sciences New York University N

  • alexa-scheidler
  • 8 Slides

nyuedu Abstract We describe a novel unsupervised m...

Optimally Sparse Representation in General nonOrthogonal Dictionaries via Minimization David L

Optimally Sparse Representation in General nonOrthogonal Dictionaries via Minimization David L

  • natalia-silvester
  • 19 Slides

Donoho and Michael Elad Classi64257cation P ysica...

Sparse Recovery Using Sparse Matrices Anna Gilbert Piotr Indyk Abstract We survey algorithms for sparse recovery prob lems that are based on sparse random matrices

Sparse Recovery Using Sparse Matrices Anna Gilbert Piotr Indyk Abstract We survey algorithms for sparse recovery prob lems that are based on sparse random matrices

  • briana-ranney
  • 10 Slides

Such matrices has several attractive properties t...

Ecient Highly OverComplete Sparse Coding using a Mixture Model Jianchao Yang Kai Yu and Thomas Huang Beckman Institute University of Illinois at Urbana Champaign IL NEC Laboratories America Cuperti

Ecient Highly OverComplete Sparse Coding using a Mixture Model Jianchao Yang Kai Yu and Thomas Huang Beckman Institute University of Illinois at Urbana Champaign IL NEC Laboratories America Cuperti

  • kittie-lecroy
  • 14 Slides

illinoisedu kyusvneclabscom Abstract Sparse coding...

A PolynomialTime Approximation Algorithm for Joint Probabilistic Data Association Songhwai Oh and Shankar Sastry Abstract Joint probabilistic data association JPDA is a powerful tool for solving dat

A PolynomialTime Approximation Algorithm for Joint Probabilistic Data Association Songhwai Oh and Shankar Sastry Abstract Joint probabilistic data association JPDA is a powerful tool for solving dat

  • tatyana-admore
  • 6 Slides

However the exact compu tation of association pro...

Behavior Recognition via Sparse SpatioTemporal Features Piotr Doll ar Vincent Rabaud Garrison Cottrell Serge Belongie Department of Computer Science and Engineering University of California San Diego

Behavior Recognition via Sparse SpatioTemporal Features Piotr Doll ar Vincent Rabaud Garrison Cottrell Serge Belongie Department of Computer Science and Engineering University of California San Diego

  • kittie-lecroy
  • 8 Slides

ucsdedu Abstract A common trend in object recognit...

Chance constrained optimization chance constraints and percentile optimization chance constraints for logconcave distributions convex approximation of chance constraints sources Rockafellar Uryasev

Chance constrained optimization chance constraints and percentile optimization chance constraints for logconcave distributions convex approximation of chance constraints sources Rockafellar Uryasev

  • olivia-moreira
  • 22 Slides

e EE364A Chance Constrained Optimization brPage 7...

Storing a Sparse Table with O Worst Case Access Time MICHAEL L

Storing a Sparse Table with O Worst Case Access Time MICHAEL L

  • cheryl-pisano
  • 7 Slides

FREDMAN AND JNOS KOMLOS University of Califorma S...

Detecting High LogDensities an Approximation for Densest Subgraph Aditya Bhaskara Moses Charikar Eden Chlamtac Uriel Feige Aravindan Vijayaraghavan Abstract In the Densest Subgraph problem given a

Detecting High LogDensities an Approximation for Densest Subgraph Aditya Bhaskara Moses Charikar Eden Chlamtac Uriel Feige Aravindan Vijayaraghavan Abstract In the Densest Subgraph problem given a

  • phoebe-click
  • 24 Slides

There is a signi64257cant gap between the best kn...

Static Single Assignment SSA Form A sparse program representation for dataow

Static Single Assignment SSA Form A sparse program representation for dataow

  • olivia-moreira
  • 18 Slides

CSL862 SSA form brPage 2br Computing Static Singl...

A Fast Approximation of the Bilateral Filter using a Signal Processing Approach Sylvain Paris and Fr edo Durand Massachusetts Institute of Technology Computer Science and Articial Intelligence Labora

A Fast Approximation of the Bilateral Filter using a Signal Processing Approach Sylvain Paris and Fr edo Durand Massachusetts Institute of Technology Computer Science and Articial Intelligence Labora

  • luanne-stotts
  • 12 Slides

The bilateral 64257lter is a nonlinear 64257lter ...

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