In this paper, we study the optimal way of distributing sensors in a random field to minimize the estimation distortion. We show that this problem is equivalent to certain proble...
A frequent problem in density level-set estimation is the choice of the right features that give rise to compact and concise representations of the observed data. We present an e...
This paper describes the new localisation algorithms under implementation for the mail distributing mobile robot, MOPS, of the Institute of Robotics, Swiss Federal Institute of Te...
In this paper, we present the Gauss-Newton method as a unified approach to optimizing non-linear noise compensation models, such as vector Taylor series (VTS), data-driven parall...
Learning data representations is a fundamental challenge in modeling neural processes and plays an important role in applications such as object recognition. In multi-stage Optima...