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» Learning from Highly Structured Data by Decomposition
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84
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CVPR
2008
IEEE
16 years 7 days ago
Globally optimal bilinear programming for computer vision applications
We present a practical algorithm that provably achieves the global optimum for a class of bilinear programs commonly arising in computer vision applications. Our approach relies o...
Manmohan Krishna Chandraker, David J. Kriegman
TIP
2002
102views more  TIP 2002»
14 years 10 months ago
Multilayered image representation: application to image compression
Abstract--The main contribution of this work is a new paradigm for image representation and image compression. We describe a new multilayered representation technique for images. A...
François G. Meyer, Amir Averbuch, Ronald R....
90
Voted
VC
2008
143views more  VC 2008»
14 years 10 months ago
A single-pass GPU ray casting framework for interactive out-of-core rendering of massive volumetric datasets
We present an adaptive out-of-core technique for rendering massive scalar volumes employing single pass GPU raycasting. The method is based on the decomposition of a volumetric dat...
Enrico Gobbetti, Fabio Marton, José Antonio...
82
Voted
BMCBI
2006
115views more  BMCBI 2006»
14 years 10 months ago
Multivariate curve resolution of time course microarray data
Background: Modeling of gene expression data from time course experiments often involves the use of linear models such as those obtained from principal component analysis (PCA), i...
Peter D. Wentzell, Tobias K. Karakach, Sushmita Ro...
95
Voted
ACL
2012
13 years 19 days ago
Automatic Event Extraction with Structured Preference Modeling
This paper presents a novel sequence labeling model based on the latent-variable semiMarkov conditional random fields for jointly extracting argument roles of events from texts. ...
Wei Lu, Dan Roth