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119
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INEX
2005
Springer
15 years 8 months ago
Parameter Estimation for a Simple Hierarchical Generative Model for XML Retrieval
Abstract. This paper explores the possibility of using a modified Expectation-Maximization algorithm to estimate parameters for a simple hierarchical generative model for XML retr...
Paul Ogilvie, Jamie Callan
DATE
2002
IEEE
146views Hardware» more  DATE 2002»
15 years 7 months ago
Automatic Generation of Fast Timed Simulation Models for Operating Systems in SoC Design
To enable fast and accurate evaluation of HW/SW implementation choices of on-chip communication, we present a method to automatically generate timed OS simulation models. The meth...
Sungjoo Yoo, Gabriela Nicolescu, Lovic Gauthier, A...
112
Voted
EJC
2000
15 years 4 months ago
A Communicating Tree Model for Generating Network Applications
We present a new model for generating network applications. Our model is based on trees communicating with each other. Our model is simple to understand, but yet powerful enough to...
Tomohiro Matsuzaki, Takehiro Tokuda
105
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ECMDAFA
2009
Springer
109views Hardware» more  ECMDAFA 2009»
15 years 9 months ago
Uniform Random Generation of Huge Metamodel Instances
The size and the number of models is drastically increasing, preventing organizations from fully exploiting Model Driven Engineering benefits. Regarding this problem of scalabilit...
Alix Mougenot, Alexis Darrasse, Xavier Blanc, Mich...
NIPS
1997
15 years 4 months ago
Learning Generative Models with the Up-Propagation Algorithm
Up-propagation is an algorithm for inverting and learning neural network generative models. Sensory input is processed by inverting a model that generates patterns from hidden var...
Jong-Hoon Oh, H. Sebastian Seung