Abstract. Bayesian inference provides a powerful framework to optimally integrate statistically learned prior knowledge into numerous computer vision algorithms. While the Bayesian...
Many techniques in the social sciences and graph theory deal with the problem of examining and analyzing patterns found in the underlying structure and associations of a group of ...
Jeremy Kubica, Andrew W. Moore, David Cohn, Jeff G...
Abstract—This work considers the problem of reaching consensus in an unreliable linear consensus network. A solution to this problem is relevant for several tasks in multi-agent ...
Fabio Pasqualetti, Antonio Bicchi, Francesco Bullo
In this paper we analyze the problem of estimating a function from different noisy data sets collected by spatially distributed sensors and subject to unknown temporal shifts. We p...
This paper studies compressed sensing for the recovery of non-negative sparse vectors from a smaller number of measurements than the ambient dimension of the unknown vector. We fo...