Abstract. This work addresses a class of total-variation based multilabeling problems over a spatially continuous image domain, where the data fidelity term can be any bounded fun...
We consider the problem of learning incoherent sparse and lowrank patterns from multiple tasks. Our approach is based on a linear multi-task learning formulation, in which the spa...
: As cloud services grow to span more and more globally distributed datacenters, there is an increasingly urgent need for automated mechanisms to place application data across thes...
Sharad Agarwal, John Dunagan, Navendu Jain, Stefan...
Obtaining high-quality and up-to-date labeled data can be difficult in many real-world machine learning applications, especially for Internet classification tasks like review spam...
A key obstacle to large-scale network simulation over PC clusters is the memory balancing problem where a memory-overloaded machine can slow down an entire simulation due to disk ...