The paper investigates parameterized approximate message-passing schemes that are based on bounded inference and are inspired by Pearl’s belief propagation algorithm (BP). We st...
Robert Mateescu, Kalev Kask, Vibhav Gogate, Rina D...
The use of Mercer kernel methods in statistical learning theory provides for strong learning capabilities, as seen in kernel principal component analysis and support vector machin...
Exploiting prior knowledge, we use Bayesian estimation to localize a source heard by a fixed sensor network. The method has two main aspects: Firstly, the probability density fun...
The approximation of surfaces to scattered data is an important problem encountered in a variety of scientific applications, such as reverse engineering, computer vision, computer...
— Grasping is a central issue of various robot applications, especially when unknown objects have to be manipulated by the system. In earlier work, we have shown the efficiency ...