We introduce a method for learning Bayesian networks that handles the discretization of continuous variables as an integral part of the learning process. The main ingredient in th...
FPGA devices have witnessed popularity in their use for the rapid prototyping of biological Spiking Neural Network (SNNs) applications, as they offer the key requirement of reconf...
Jim Harkin, Fearghal Morgan, Steve Hall, Piotr Dud...
Abstract. Recent developments in computer and communication networks require scheduling decisions to be made under increasingly complex system dynamics. We model and analyze the pr...
We propose a robust estimation method of gene networks based on microarray gene expression data. It is well-known that microarray data contain a large amount of noise and some outl...
Seiya Imoto, Tomoyuki Higuchi, SunYong Kim, Euna J...
Scalability is the primary challenge to studying large complex network systems with network emulation. This paper studies topology partitioning, assigning disjoint pieces of the n...
Ken Yocum, Ethan Eade, Julius Degesys, David Becke...