Recently, instead of selecting a single kernel, multiple kernel learning (MKL) has been proposed which uses a convex combination of kernels, where the weight of each kernel is opt...
The asymptotic behavior of stochastic gradient algorithms is studied. Relying on some results of differential geometry (Lojasiewicz gradient inequality), the almost sure pointconve...
Background: Designing appropriate machine learning methods for identifying genes that have a significant discriminating power for disease outcomes has become more and more importa...
We present physically based algorithms for interactive deformable shape and motion modeling. We coarsely sample the objects with simulation nodes, and apply a meshless finite elem...
Bart Adams, Martin Wicke, Maks Ovsjanikov, Michael...
In this paper we present an algorithm for scheduling parallel applications that consist of a divisible workload. Our algorithm uses multiple rounds to overlap communication and co...