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...
We present a new class of randomized approximation algorithms for unrelated parallel machine scheduling problems with the average weighted completion time objective. The key idea i...
We propose a general framework to index very large datasets of spatial data in a distributed system. Our proposal is built on the recently proposed Scalable Distributed Rtree (SD-...
The problems of dense stereo reconstruction and object class segmentation can both be formulated as Conditional Random Field based labelling problems, in which every pixel in the ...
Lubor Ladicky, Paul Sturgess, Christopher Russell,...
In this paper, we propose a novel framework for fast multilevel routing considering crosstalk and performance optimization. To handle the crosstalk minimization problem, we incorp...
Tsung-Yi Ho, Yao-Wen Chang, Sao-Jie Chen, D. T. Le...