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JMLR
2010
140views more  JMLR 2010»
14 years 10 months ago
Learning Non-Stationary Dynamic Bayesian Networks
Learning dynamic Bayesian network structures provides a principled mechanism for identifying conditional dependencies in time-series data. An important assumption of traditional D...
Joshua W. Robinson, Alexander J. Hartemink
121
Voted
ICASSP
2011
IEEE
14 years 7 months ago
Variational methods for spectral unmixing of hyperspectral images
This paper studies a variational Bayesian unmixing algorithm for hyperspectral images based on the standard linear mixing model. Each pixel of the image is modeled as a linear com...
Olivier Eches, Nicolas Dobigeon, Jean-Yves Tourner...
SENSYS
2003
ACM
15 years 9 months ago
GEM: graph eMbedding for routing and data-centric storage in sensor networks without geographic information
The widespread deployment of sensor networks is on the horizon. One of the main challenges in sensor networks is to process and aggregate data in the network rather than wasting e...
James Newsome, Dawn Xiaodong Song
122
Voted
MICCAI
2006
Springer
16 years 5 months ago
Logarithm Odds Maps for Shape Representation
The concept of the Logarithm of the Odds (LogOdds) is frequently used in areas such as artificial neural networks, economics, and biology. Here, we utilize LogOdds for a shape repr...
Kilian M. Pohl, John W. Fisher III, Martha Elizabe...
136
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DATE
2009
IEEE
126views Hardware» more  DATE 2009»
15 years 10 months ago
On hierarchical statistical static timing analysis
— Statistical static timing analysis deals with the increasing variations in manufacturing processes to reduce the pessimism in the worst case timing analysis. Because of the cor...
Bing Li, Ning Chen, Manuel Schmidt, Walter Schneid...