Gradient boosting is a flexible machine learning technique that produces accurate predictions by combining many weak learners. In this work, we investigate its use in two applica...
Bin Zhang, Abhinav Sethy, Tara N. Sainath, Bhuvana...
There is a growing concern about the increasing vulnerability of future computing systems to errors in the underlying hardware. Traditional redundancy techniques are expensive for...
Larkhoon Leem, Hyungmin Cho, Jason Bau, Quinn A. J...
In the online linear optimization problem, a learner must choose, in each round, a decision from a set D ⊂ Rn in order to minimize an (unknown and changing) linear cost function...
— This paper investigates the use of physical layer symbol error rate (SER) optimization to minimize wireless sensor network (WSN) energy consumption. Increasing the SER maintain...
Jennifer Hartwell, Geoffrey G. Messier, Robert J. ...
Nanometer circuits are highly susceptible to soft errors generated by alpha-particle or atmospheric neutron strikes to circuit nodes. The reasons for the high susceptibility are t...