Variational Bayesian inference and (collapsed) Gibbs sampling are the two important classes of inference algorithms for Bayesian networks. Both have their advantages and disadvant...
Parallel scalability allows an application to efficiently utilize an increasing number of processing elements. In this paper we explore a design space for parallel scalability for...
Increasing the efficiency of meander line antennas is an important real-world problem within radio frequency identification (RFID). Meta-heuristic search algorithms, such as ant c...
Marcus Randall, Andrew Lewis, Amir Galehdar, David...
This paper describes a parameter estimation method for multi-label classification that does not rely on approximate inference. It is known that multi-label classification involvin...
We study the problem of combining the outcomes of several different classifiers in a way that provides a coherent inference that satisfies some constraints. In particular, we deve...