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CVPR
2007
IEEE
14 years 9 months ago
Hybrid learning of large jigsaws
A jigsaw is a recently proposed generative model that describes an image as a composition of non-overlapping patches of varying shape, extracted from a latent image. By learning t...
Julia A. Lasserre, Anitha Kannan, John M. Winn
ECCV
2008
Springer
14 years 9 months ago
Improving Shape Retrieval by Learning Graph Transduction
Abstract. Shape retrieval/matching is a very important topic in computer vision. The recent progress in this domain has been mostly driven by designing smart features for providing...
Xingwei Yang, Xiang Bai, Longin Jan Latecki, Zhuow...
ICML
2005
IEEE
14 years 8 months ago
Compact approximations to Bayesian predictive distributions
We provide a general framework for learning precise, compact, and fast representations of the Bayesian predictive distribution for a model. This framework is based on minimizing t...
Edward Snelson, Zoubin Ghahramani
CVPR
2010
IEEE
14 years 3 months ago
Efficient Piecewise Learning for Conditional Random Fields
Conditional Random Field models have proved effective for several low-level computer vision problems. Inference in these models involves solving a combinatorial optimization probl...
Karteek Alahari, Phil Torr
DAC
2006
ACM
14 years 8 months ago
Predicate learning and selective theory deduction for a difference logic solver
Design and verification of systems at the Register-Transfer (RT) or behavioral level require the ability to reason at higher levels of abstraction. Difference logic consists of an...
Chao Wang, Aarti Gupta, Malay K. Ganai