Deep belief networks are a powerful way to model complex probability distributions. However, it is difficult to learn the structure of a belief network, particularly one with hidd...
Ryan Prescott Adams, Hanna M. Wallach, Zoubin Ghah...
Constructing tractable dependent probability distributions over structured continuous random vectors is a central problem in statistics and machine learning. It has proven diffic...
In this paper, we study the dynamic RWA problem in WDM networks with sparse wavelength conversion and propose a novel hybrid algorithm for it based on the combination of mobile ag...
Vinh Trong Le, Xiaohong Jiang, Son-Hong Ngo, Susum...
In this paper we investigate the utilization of nondedicated, opportunistic resources in a desktop environment to provide statistical assurances to a class of QoS sensitive, soft ...
The problem of optimal transportation between a set of sources and a set of wells has become recently the object of new mathematical models generalizing the Monge-Kantorovich prob...