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NIPS
1998
15 years 7 months ago
Approximate Learning of Dynamic Models
Inference is a key component in learning probabilistic models from partially observable data. When learning temporal models, each of the many inference phases requires a complete ...
Xavier Boyen, Daphne Koller
JASIS
2007
122views more  JASIS 2007»
15 years 6 months ago
Exploiting parallelism to support scalable hierarchical clustering
A distributed memory parallel version of the group average Hierarchical Agglomerative Clustering algorithm is proposed to enable scaling the document clustering problem to large c...
Rebecca Cathey, Eric C. Jensen, Steven M. Beitzel,...
PAMI
1998
128views more  PAMI 1998»
15 years 6 months ago
A Hierarchical Latent Variable Model for Data Visualization
—Visualization has proven to be a powerful and widely-applicable tool for the analysis and interpretation of multivariate data. Most visualization algorithms aim to find a projec...
Christopher M. Bishop, Michael E. Tipping
ICCV
2007
IEEE
16 years 8 months ago
Fitting a Morphable Model to 3D Scans of Faces
This paper presents a top-down approach to 3D data analysis by fitting a Morphable Model to scans of faces. In a unified framework, the algorithm optimizes shape, texture, pose an...
Volker Blanz, Kristina Scherbaum, Hans-Peter Seide...
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MICCAI
2006
Springer
16 years 7 months ago
An Energy Minimization Approach to the Data Driven Editing of Presegmented Images/Volumes
Fully automatic, completely reliable segmentation in medical images is an unrealistic expectation with today's technology. However, many automatic segmentation algorithms may ...
Leo Grady, Gareth Funka-Lea