This paper introduces a novel algorithm to approximate the matrix with minimum nuclear norm among all matrices obeying a set of convex constraints. This problem may be understood a...
Reinforcement learning (RL) can be impractical for many high dimensional problems because of the computational cost of doing stochastic search in large state spaces. We propose a ...
In nature, systems with enormous numbers of components (i.e. cells) are evolved from a relatively small genotype. It has not yet been demonstrated that artificial evolution is su...
Simon Harding, Julian Francis Miller, Wolfgang Ban...
A novel energy reduction strategy to maximally exploit the dynamic workload variation is proposed for the offline voltage scheduling of preemptive systems. The idea is to construc...
Most pattern discovery algorithms easily generate very large numbers of patterns, making the results impossible to understand and hard to use. Recently, the problem of instead sel...
Hannes Heikinheimo, Jilles Vreeken, Arno Siebes, H...