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ICPR
2008
IEEE
16 years 8 months ago
Supervised learning of a generative model for edge-weighted graphs
This paper addresses the problem of learning archetypal structural models from examples. To this end we define a generative model for graphs where the distribution of observed nod...
Andrea Torsello, David L. Dowe
ICPR
2004
IEEE
16 years 8 months ago
To FRAME or not to FRAME in Probabilistic Texture Modelling?
The maximum entropy principle is a cornerstone of FRAME (Filters, RAndom fields, and Maximum Entropy) model considered at times as a first-ever step towards a universal theory of ...
Georgy L. Gimel'farb, Luc J. Van Gool, Alexey Zale...
ICML
2009
IEEE
16 years 8 months ago
A Bayesian approach to protein model quality assessment
Given multiple possible models b1, b2, . . . bn for a protein structure, a common sub-task in in-silico Protein Structure Prediction is ranking these models according to their qua...
Hetunandan Kamisetty, Christopher James Langmead
CSE
2009
IEEE
16 years 2 months ago
Probabilistic Generative Models of the Social Annotation Process
—With the growth in the past few years of social tagging services like Delicious and CiteULike, there is growing interest in modeling and mining these social systems for deriving...
Said Kashoob, James Caverlee, Elham Khabiri
IPPS
2009
IEEE
16 years 2 months ago
Performance modeling in action: Performance prediction of a Cray XT4 system during upgrade
We present predictive performance models of two of the petascale applications, S3D and GTC, from the DOE Office of Science workload. We outline the development of these models and...
Kevin J. Barker, Kei Davis, Darren J. Kerbyson