The problem of transfer learning, where information gained in one learning task is used to improve performance in another related task, is an important new area of research. While...
The problem of computing a maximum a posteriori (MAP) configuration is a central computational challenge associated with Markov random fields. There has been some focus on “tr...
Pradeep Ravikumar, Alekh Agarwal, Martin J. Wainwr...
In this paper, a novel discrete differential evolution (DDE) algorithm is presented to solve the permutation flowhop scheduling problem with the makespan criterion. The DDE algori...
Quan-Qe Pan, Mehmet Fatih Tasgetiren, Yun-Chia Lia...
Maximum a posteriori (MAP) filtering using the HuberMarkov random field (HMRF) image model has been shown in the past to be an effective method of reducing compression artifacts i...
This paper proposes a novel method for computing linear basis images from tensor-valued image data. As a generalization of the nonnegative matrix factorization, the proposed method...