We address failure location and restoration in both optical and wireless ad hoc networks. First, we show how Maximum Likelihood inference can improve failure location algorithms in...
Frederick Ducatelle, Luca Maria Gambardella, Macie...
The task of learning models for many real-world problems requires incorporating domain knowledge into learning algorithms, to enable accurate learning from a realistic volume of t...
Radu Stefan Niculescu, Tom M. Mitchell, R. Bharat ...
We consider the problem of learning Bayesian network models in a non-informative setting, where the only available information is a set of observational data, and no background kn...
— Wireless ad hoc networks often require a method for estimating their nodes’ locations. Typically this is achieved by the use of pair-wise measurements between nodes and their...
Jon Arnold, Nigel Bean, Miro Kraetzl, Matthew Roug...
We consider the problem of inferring the most likely social network given connectivity constraints imposed by observations of outbreaks within the network. Given a set of vertices ...