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Loop fusion has been studied extensively, but in a manner isolated from other transformations. This was mainly due to the lack of a powerful intermediate representation for application of compositions of high-level transformations. Fusion presents strong interactions with parallelism and locality. Currently, there exist no models to determine good fusion structures integrated with all components of...
In our previous work we have presented an approach to learn interpretable classification rules using a Boolean compressed sensing formulation. Our approach uses a linear programming (LP) relaxation and allows us to find interpretable (sparse) classification rules that achieve good generalization accuracy. However, the resulting LP representation for problems with either a large number of samples or...
Convex relaxations for sparse representation problems, which aim to find sparse solutions to systems of equations, have enabled a variety of exciting applications in high-dimensional settings. Yet, with dimensions large enough, even these convex formulations become prohibitively expensive. Screening methods attempt to use duality theory to dramatically reduce the size of the optimization problem through...
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