We show how to build hierarchical, reduced-rank representation for large stochastic matrices and use this representation to design an efficient algorithm for computing the largest...
Solving linear systems with a large number of variables is at the core of many scienti c problems. Parallel processing techniques for solving such systems have received much attent...
Arun Nagari, Itamar Elhanany, Ben Thompson, Fangxi...
In this paper, we present a novel approach to learning semantic localized patterns with binary projections in a supervised manner. The pursuit of these binary projections is refor...
Task graphs are used for scheduling tasks on parallel processors when the tasks have dependencies. If the execution of the program is known ahead of time, then the tasks can be st...
Modern applications such as Internet traffic, telecommunication records, and large-scale social networks generate massive amounts of data with multiple aspects and high dimensiona...