A parallel implementation of the specialized interior-point algorithm for multicommodity network flows introduced in [5] is presented. In this algorithm, the positive definite syst...
We consider efficient strategies for the parallel and distributed computation of large sets of multivariate integrals. These arise in many applications such as computational chem...
Bayesian forecasting models provide distributional estimates for random parameters, and relative to classical schemes, have the advantage that they can rapidly capture changes in ...
We present an application of the analytical inductive programming system Igor to learning sets of recursive rules from positive experience. We propose that this approach can be us...
We cast some new insights into solving the digital matting
problem by treating it as a semi-supervised learning
task in machine learning. A local learning based approach
and a g...