Probabilistic inference techniques can be used to estimate variable bias, or the proportion of solutions to a given SAT problem that fix a variable positively or negatively. Metho...
We point out several problems in scalingup statistical approaches to spoken dialogue systems to enable them to deal with complex but natural user goals, such as disjunctive and ne...
We develop a new algorithm, based on EM, for learning the Linear Dynamical System model. Called the method of Approximated Second-Order Statistics (ASOS) our approach achieves dra...
This paper introduces a method for regularization of HMM systems that avoids parameter overfitting caused by insufficient training data. Regularization is done by augmenting the E...
The design and analysis of today’s complex real-time systems requires advanced methods. Due to ever growing functionality, hardware complexity and component interaction, applyin...