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» Graph model selection using maximum likelihood
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ICSE
2001
IEEE-ACM
13 years 12 months ago
Encoding Program Executions
Dynamic analysis is based on collecting data as the program runs. However, raw traces tend to be too voluminous and too unstructured to be used directly for visualization and unde...
Steven P. Reiss, Manos Renieris
ISLPED
2007
ACM
109views Hardware» more  ISLPED 2007»
13 years 9 months ago
A multi-model power estimation engine for accuracy optimization
RTL power macromodeling is a mature research topic with a variety of equation and table-based approaches. Despite its maturity, macromodeling is not yet widely accepted as an indu...
Felipe Klein, Guido Araujo, Rodolfo Azevedo, Rober...
KDD
2009
ACM
191views Data Mining» more  KDD 2009»
14 years 8 months ago
Scalable pseudo-likelihood estimation in hybrid random fields
Learning probabilistic graphical models from high-dimensional datasets is a computationally challenging task. In many interesting applications, the domain dimensionality is such a...
Antonino Freno, Edmondo Trentin, Marco Gori
BMCBI
2010
149views more  BMCBI 2010»
13 years 7 months ago
Identifying common prognostic factors in genomic cancer studies: A novel index for censored outcomes
Background: With the growing number of public repositories for high-throughput genomic data, it is of great interest to combine the results produced by independent research groups...
Sigrid Rouam, Thierry Moreau, Philippe Broët
NECO
2011
13 years 2 months ago
Least Squares Estimation Without Priors or Supervision
Selection of an optimal estimator typically relies on either supervised training samples (pairs of measurements and their associated true values), or a prior probability model for...
Martin Raphan, Eero P. Simoncelli