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ICML
2004
IEEE
15 years 10 months ago
Generative modeling for continuous non-linearly embedded visual inference
Many difficult visual perception problems, like 3D human motion estimation, can be formulated in terms of inference using complex generative models, defined over high-dimensional ...
Cristian Sminchisescu, Allan D. Jepson
ICDM
2008
IEEE
172views Data Mining» more  ICDM 2008»
15 years 4 months ago
Active Learning of Equivalence Relations by Minimizing the Expected Loss Using Constraint Inference
Selecting promising queries is the key to effective active learning. In this paper, we investigate selection techniques for the task of learning an equivalence relation where the ...
Steffen Rendle, Lars Schmidt-Thieme
CVPR
2006
IEEE
15 years 4 months ago
Learning Joint Top-Down and Bottom-up Processes for 3D Visual Inference
We present an algorithm for jointly learning a consistent bidirectional generative-recognition model that combines top-down and bottom-up processing for monocular 3d human motion ...
Cristian Sminchisescu, Atul Kanaujia, Dimitris N. ...
TCBB
2008
122views more  TCBB 2008»
14 years 9 months ago
Mixed Integer Linear Programming for Maximum-Parsimony Phylogeny Inference
Reconstruction of phylogenetic trees is a fundamental problem in computational biology. While excellent heuristic methods are available for many variants of this problem, new adva...
Srinath Sridhar, Fumei Lam, Guy E. Blelloch, R. Ra...
PE
2007
Springer
112views Optimization» more  PE 2007»
14 years 9 months ago
Multicast inference of temporal loss characteristics
Multicast-based inference has been proposed as a method of estimating average loss rates of internal network links, using end-to-end loss measurements of probes sent over a multic...
Vijay Arya, Nick G. Duffield, Darryl Veitch