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115
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IJAR
2010
152views more  IJAR 2010»
14 years 11 months ago
Structural-EM for learning PDG models from incomplete data
Probabilistic Decision Graphs (PDGs) are a class of graphical models that can naturally encode some context specific independencies that cannot always be efficiently captured by...
Jens D. Nielsen, Rafael Rumí, Antonio Salme...
110
Voted
CDC
2010
IEEE
186views Control Systems» more  CDC 2010»
14 years 7 months ago
Approximation and limiting behavior of random models
In this paper, we investigate limiting behavior of linear dynamic systems driven by random stochastic matrices. We introduce and study the new concepts of partial ergodicity and 1-...
Behrouz Touri, Angelia Nedic
97
Voted
ICASSP
2011
IEEE
14 years 4 months ago
Heterogeneous multiprocessor mapping for real-time streaming systems
Real-time streaming signal processing systems typically desire high throughput and low latency. Many such systems can be modeled as synchronous data flow graphs. In this paper, w...
Jing Lin, Akshaya Srivatsa, Andreas Gerstlauer, Br...
107
Voted
CVPR
1998
IEEE
16 years 2 months ago
Markov Random Fields with Efficient Approximations
Markov Random Fields (MRF's) can be used for a wide variety of vision problems. In this paper we focus on MRF's with two-valued clique potentials, which form a generaliz...
Yuri Boykov, Olga Veksler, Ramin Zabih
ICML
2006
IEEE
16 years 1 months ago
Quadratic programming relaxations for metric labeling and Markov random field MAP estimation
Quadratic program relaxations are proposed as an alternative to linear program relaxations and tree reweighted belief propagation for the metric labeling or MAP estimation problem...
Pradeep D. Ravikumar, John D. Lafferty