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FTCGV
2011
122views more  FTCGV 2011»
14 years 10 months ago
Structured Learning and Prediction in Computer Vision
Powerful statistical models that can be learned efficiently from large amounts of data are currently revolutionizing computer vision. These models possess a rich internal structur...
Sebastian Nowozin, Christoph H. Lampert
ACL
2012
13 years 9 months ago
Learning to "Read Between the Lines" using Bayesian Logic Programs
Most information extraction (IE) systems identify facts that are explicitly stated in text. However, in natural language, some facts are implicit, and identifying them requires â€...
Sindhu Raghavan, Raymond J. Mooney, Hyeonseo Ku
CVPR
2004
IEEE
16 years 8 months ago
Collaborative Tracking of Multiple Targets
Coalescence, meaning the tracker associates more than one trajectories to some targets while loses track for others, is a challenging problem for visual tracking of multiple targe...
Ting Yu, Ying Wu
PAMI
2008
145views more  PAMI 2008»
15 years 6 months ago
Latent-Space Variational Bayes
Variational Bayesian Expectation-Maximization (VBEM), an approximate inference method for probabilistic models based on factorizing over latent variables and model parameters, has ...
JaeMo Sung, Zoubin Ghahramani, Sung Yang Bang
CORR
2012
Springer
170views Education» more  CORR 2012»
14 years 2 months ago
What Cannot be Learned with Bethe Approximations
We address the problem of learning the parameters in graphical models when inference is intractable. A common strategy in this case is to replace the partition function with its B...
Uri Heinemann, Amir Globerson