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CVPR
2006
IEEE
14 years 9 months ago
Solving Markov Random Fields using Second Order Cone Programming Relaxations
This paper presents a generic method for solving Markov random fields (MRF) by formulating the problem of MAP estimation as 0-1 quadratic programming (QP). Though in general solvi...
M. Pawan Kumar, Philip H. S. Torr, Andrew Zisserma...
JMLR
2012
11 years 10 months ago
Message-Passing Algorithms for MAP Estimation Using DC Programming
We address the problem of finding the most likely assignment or MAP estimation in a Markov random field. We analyze the linear programming formulation of MAP through the lens of...
Akshat Kumar, Shlomo Zilberstein, Marc Toussaint
NIPS
2008
13 years 9 months ago
Improved Moves for Truncated Convex Models
We consider the problem of obtaining the approximate maximum a posteriori estimate of a discrete random field characterized by pairwise potentials that form a truncated convex mod...
M. Pawan Kumar, Philip H. S. Torr
NIPS
2007
13 years 9 months ago
An Analysis of Convex Relaxations for MAP Estimation
The problem of obtaining the maximum a posteriori estimate of a general discrete random field (i.e. a random field defined using a finite and discrete set of labels) is known ...
Pawan Mudigonda, Vladimir Kolmogorov, Philip H. S....
TIT
2010
130views Education» more  TIT 2010»
13 years 2 months ago
The power of convex relaxation: near-optimal matrix completion
This paper is concerned with the problem of recovering an unknown matrix from a small fraction of its entries. This is known as the matrix completion problem, and comes up in a gr...
Emmanuel J. Candès, Terence Tao