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CVPR
2006
IEEE
14 years 7 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 8 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 7 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 7 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 14 days 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