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Bayesian optimization has been demonstrated as an effective methodology for the global optimization. However, it suffers from a computational bottleneck that the inference time grows cubically with the number of observations. In this paper, a Bayesian optimization based on the data-parallel approach is proposed to alleviate this problem. Firstly, an improved geometry motivated clustering algorithm...
Distribution network reconfiguration is a multi- objective and discrete nonlinear combinatory optimization problem. In this paper, a novel multi-object distribution network reconfiguration method is proposed based on system homogeneity. Firstly, the relationship between power loss and reliability of systems with different homogeneity is analyzed. Then, to enhance system reliability and homogeneity,...
Multiple-Instance learning (MIL), which relaxes training annotation granularity from instance level to instance collection (bag) level by applying bag concept, obtains increasing attentions from computer vision community. Due to its flexible annotation mechanism, MIL has been naturally utilized on a variety of computer vision problems. And numerous models have been proposed, each of which is ingeniously...
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