— This paper presents an important outcome of a research programme which focuses on the development of a method for synthesizing, under controlled conditions in the laboratory, t...
We study a sparse coding learning algorithm that allows for a simultaneous learning of the data sparseness and the basis functions. The algorithm is derived based on a generative m...
Relational datasets, i.e., datasets in which individuals are described both by their own features and by their relations to other individuals, arise from various sources such as d...
Background: Current methods for the automated generation of genome-scale metabolic networks focus on genome annotation and preliminary biochemical reaction network assembly, but d...
Matthew DeJongh, Kevin Formsma, Paul Boillot, John...
Distributed estimation of an unknown signal is a common task in sensor networks. The scenario usually envisioned consists of several nodes, each making an observation correlated wi...