BriefGPT.xyz
Mar, 2017
梯度贝叶斯优化
Bayesian Optimization with Gradients
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Jian Wu, Matthias Poloczek, Andrew Gordon Wilson, Peter I. Frazier
TL;DR
本文提出了一种新的贝叶斯优化算法,它利用导数信息来降低目标函数评估的数量,可以适用于包括逐步和批量形式在内的嘈杂和不完整的导数信息,并且可以自动选择保留方向导数以减少推论的计算成本,该算法在众多优化过程中表现出最新的状态性能。
Abstract
In recent years,
bayesian optimization
has proven successful for global optimization of expensive-to-evaluate
multimodal objective functions
. However, unlike most optimization methods,
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