We present a novel approach to dealing with overfitting in black-box models. It is based on the leverages of the samples, i.e. on the influence that each observation has on the pa...
Historically, compilers have operated by applying a fixed set of optimizations in a predetermined order. We call such an ordered list of optimizations a compilation sequence. This...
Keith D. Cooper, Devika Subramanian, Linda Torczon
In this paper we propose a framework for learning a regression function form a set of local features in an image. The regression is learned from an embedded representation that re...
We introduce the Linear Resource Temporal Network (LRTN), which consists of activities that consume or produce a resource, subject to absolute and relative metric temporal constra...
Over the past few years, several local search algorithms have been proposed for various problems related to multicast routing in the off-line mode. We describe a population-based ...
Mohammed S. Zahrani, Martin J. Loomes, James A. Ma...