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ICML
2008
IEEE
14 years 9 months ago
On the quantitative analysis of deep belief networks
Deep Belief Networks (DBN's) are generative models that contain many layers of hidden variables. Efficient greedy algorithms for learning and approximate inference have allow...
Ruslan Salakhutdinov, Iain Murray
MICAI
2005
Springer
14 years 2 months ago
Directed Cycles in Bayesian Belief Networks: Probabilistic Semantics and Consistency Checking Complexity
Although undirected cycles in directed graphs of Bayesian belief networks have been thoroughly studied, little attention has so far been given to a systematic analysis of directed ...
Alexander L. Tulupyev, Sergey I. Nikolenko
IJAR
2000
140views more  IJAR 2000»
13 years 8 months ago
Belief updating in multiply sectioned Bayesian networks without repeated local propagations
Multiply sectioned Bayesian networks (MSBNs) provide a coherent and flexible formalism for representing uncertain knowledge in large domains. Global consistency among subnets in a...
Yang Xiang
HCI
2009
13 years 6 months ago
A Visualization Approach for Group Behaviors, Beliefs and Intentions to Support Critical Decisions
During persistent surveillance of a given population in a conflict situation, data management can quickly become unwieldy due to the inundation of low-level information from many, ...
Colleen L. Phillips, Norman D. Geddes, Justin T. S...
CVPR
2005
IEEE
14 years 11 months ago
Learning to Estimate Human Pose with Data Driven Belief Propagation
We propose a statistical formulation for 2-D human pose estimation from single images. The human body configuration is modeled by a Markov network and the estimation problem is to...
Gang Hua, Ming-Hsuan Yang, Ying Wu