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JMLR
2010
202views more  JMLR 2010»
14 years 11 months ago
Learning the Structure of Deep Sparse Graphical Models
Deep belief networks are a powerful way to model complex probability distributions. However, it is difficult to learn the structure of a belief network, particularly one with hidd...
Ryan Prescott Adams, Hanna M. Wallach, Zoubin Ghah...
AAAI
2011
14 years 4 months ago
Sparse Matrix-Variate t Process Blockmodels
We consider the problem of modeling network interactions and identifying latent groups of network nodes. This problem is challenging due to the facts i) that the network nodes are...
Zenglin Xu, Feng Yan, Yuan Qi
AI
2007
Springer
15 years 10 months ago
Learning Network Topology from Simple Sensor Data
In this paper, we present an approach for recovering a topological map of the environment using only detection events from a deployed sensor network. Unlike other solutions to this...
Dimitri Marinakis, Philippe Giguère, Gregor...
OTM
2007
Springer
15 years 10 months ago
Adaptive Semantic Interoperability Strategies for Knowledge Based Networking
A Knowledge Based Network is a type of ontological content based network. As Knowledge Based Networks scale, semantic interoperability becomes an important issue since larger popul...
Song Guo, John Keeney, Declan O'Sullivan, David Le...
IMR
2003
Springer
15 years 9 months ago
An Adaptable Surface Parameterization Method
Parameterizations of triangulated surfaces are used in an increasing number of mesh processing applications for various purposes. Although demands vary, they are often required to...
Patrick Degener, Jan Meseth, Reinhard Klein