7 search results - page 1 / 2 » Learning Efficiently with Approximate Inference via Dual Los... |
ICML
2010
IEEE
13 years 5 months ago
2010
IEEE
Many structured prediction tasks involve complex models where inference is computationally intractable, but where it can be well approximated using a linear programming relaxation...
COLT
2006
Springer
13 years 8 months ago
2006
Springer
Abstract. In this paper we unify divergence minimization and statistical inference by means of convex duality. In the process of doing so, we prove that the dual of approximate max...
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...
ICML
2010
IEEE
13 years 5 months ago
2010
IEEE
Approximate MAP inference in graphical models is an important and challenging problem for many domains including computer vision, computational biology and natural language unders...
ECCV
2008
Springer
14 years 6 months ago
2008
Springer
As richer models for stereo vision are constructed, there is a growing interest in learning model parameters. To estimate parameters in Markov Random Field (MRF) based stereo formu...