In this paper, we propose a probabilistic kernel approach to preference learning based on Gaussian processes. A new likelihood function is proposed to capture the preference relat...
The run-time binding of web services has been recently put forward in order to support rapid and dynamic web service compositions. With the growing number of alternative web servi...
Many image and signal processing kernels can be optimized for performance consuming a reasonable area by doing loops parallelization with extensive use of pipelining. This paper p...
Zubair Nawaz, Thomas Marconi, Koen Bertels, Todor ...
This paper reconsiders the problems of discovering symmetries in constraint satisfaction problems (CSPs). It proposes a compositional approach which derives symmetries of the appli...
Pascal Van Hentenryck, Pierre Flener, Justin Pears...
This article presents a global optimization approach to reconstruct surfaces from a single document image. Instead of assuming globally developable in previous works which restric...
Yuanlong Shao, Xinguo Liu, Xueying Qin, Yi Xu, Huj...