We present a new, statistical approach to rule learning. Doing so, we address two of the problems inherent in traditional rule learning: The computational hardness of finding rule...
Global likelihood maximization is an important aspect of many statistical analyses. Often the likelihood function is highly multi-extremal. This presents a significant challenge t...
This paper presents a modular optimization framework for custom digital circuits in the power – performance space. The method uses a static timer and a nonlinear optimizer to max...
We discuss the advantages of lexicalized tree-adjoining grammar as an alternative to lexicalized PCFG for statistical parsing, describingthe induction of a probabilistic LTAG mode...
In this paper we present an optimization solution for power and performance management in a platform running multiple independent applications. Our approach assumes a virtualized ...
Vinicius Petrucci, Orlando Loques, Daniel Moss&eac...