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AI
2011
Springer
14 years 9 months ago
Parallelizing a Convergent Approximate Inference Method
Probabilistic inference in graphical models is a prevalent task in statistics and artificial intelligence. The ability to perform this inference task efficiently is critical in l...
Ming Su, Elizabeth Thompson
EMNLP
2011
14 years 5 months ago
Training a Log-Linear Parser with Loss Functions via Softmax-Margin
Log-linear parsing models are often trained by optimizing likelihood, but we would prefer to optimise for a task-specific metric like Fmeasure. Softmax-margin is a convex objecti...
Michael Auli, Adam Lopez
CVPR
2012
IEEE
13 years 8 months ago
From Pictorial Structures to deformable structures
Pictorial Structures (PS) define a probabilistic model of 2D articulated objects in images. Typical PS models assume an object can be represented by a set of rigid parts connecte...
Silvia Zuffi, Oren Freifeld, Michael J. Black
ICPR
2008
IEEE
16 years 7 months ago
Asymmetric post-processing for stereo correspondence
This paper presents a novel approach that performs post-processing for stereo correspondence. We improve the performance of stereo correspondence by performing consistency check a...
Dong Bo Min, Juhyun Oh, Kwanghoon Sohn
ICML
2003
IEEE
16 years 6 months ago
Link-based Classification
Over the past few years, a number of approximate inference algorithms for networked data have been put forth. We empirically compare the performance of three of the popular algori...
Qing Lu, Lise Getoor