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» Complexity of Inference in Graphical Models
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CVPR
2012
IEEE
13 years 4 months ago
Bridging the past, present and future: Modeling scene activities from event relationships and global rules
This paper addresses the discovery of activities and learns the underlying processes that govern their occurrences over time in complex surveillance scenes. To this end, we propos...
Jagannadan Varadarajan, Rémi Emonet, Jean-M...
138
Voted
ICML
2004
IEEE
16 years 3 months ago
Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data
In sequence modeling, we often wish to represent complex interaction between labels, such as when performing multiple, cascaded labeling tasks on the same sequence, or when longra...
Charles A. Sutton, Khashayar Rohanimanesh, Andrew ...
134
Voted
PAMI
2008
145views more  PAMI 2008»
15 years 2 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
134
Voted
CIA
2007
Springer
15 years 8 months ago
A Probabilistic Framework for Decentralized Management of Trust and Quality
In this paper, we propose a probabilistic framework targeting three important issues in the computation of quality and trust in decentralized systems. Specifically, our approach a...
Le-Hung Vu, Karl Aberer
123
Voted
ECML
2006
Springer
15 years 6 months ago
Combinatorial Markov Random Fields
Abstract. A combinatorial random variable is a discrete random variable defined over a combinatorial set (e.g., a power set of a given set). In this paper we introduce combinatoria...
Ron Bekkerman, Mehran Sahami, Erik G. Learned-Mill...