CVPR
2012
IEEE
11 years 6 months ago
2012
IEEE
We describe a novel max-margin parameter learning approach for structured prediction problems under certain non-decomposable performance measures. Structured prediction is a commo...
CVPR
2012
IEEE
11 years 6 months ago
2012
IEEE
We present a novel framework for multiple object tracking in which the problems of object detection and data association are expressed by a single objective function. The framewor...
EMNLP
2011
12 years 4 months ago
2011
Dual decomposition has been recently proposed as a way of combining complementary models, with a boost in predictive power. However, in cases where lightweight decompositions are ...
AAAI
2011
12 years 4 months ago
2011
We present a dual decomposition approach to the treereweighted belief propagation objective. Each tree in the tree-reweighted bound yields one subproblem, which can be solved with...
ICCV
2011
IEEE
12 years 4 months ago
2011
IEEE
This paper proposes a very general max-margin learning framework for distance-based clustering. To this end, it formulates clustering as a high order energy minimization problem w...
ACL
2011
12 years 8 months ago
2011
Via an oracle experiment, we show that the upper bound on accuracy of a CCG parser is significantly lowered when its search space is pruned using a supertagger, though the supert...
EMNLP
2010
13 years 2 months ago
2010
This paper introduces algorithms for nonprojective parsing based on dual decomposition. We focus on parsing algorithms for nonprojective head automata, a generalization of head-au...
CDC
2009
IEEE
13 years 9 months ago
2009
IEEE
Abstract— Many problems associated with networked systems can be formulated as network utility maximization (NUM) problems. NUM problems maximize a global separable measure of ne...
CDC
2009
IEEE
13 years 9 months ago
2009
IEEE
— Most existing work uses dual decomposition and subgradient methods to solve network optimization problems in a distributed manner, which suffer from slow convergence rate prope...
CVPR
2010
IEEE
14 years 23 days ago
2010
IEEE
Graph cuts methods are at the core of many state-of-theart algorithms in computer vision due to their efficiency in computing globally optimal solutions. In this paper, we solve t...