BriefGPT.xyz
Oct, 2018
使用导数扩展高斯过程回归模型
Scaling Gaussian Process Regression with Derivatives
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David Eriksson, Kun Dong, Eric Hans Lee, David Bindel, Andrew Gordon Wilson
TL;DR
本文提出了一种使用快速矩阵向量乘法的迭代求解方法,结合基于设阵的Cholesky预处理,使得高斯过程能够处理函数值与导数,从而实现了基于贝叶斯优化的核学习以及维度约减,该方法能够适用于高维度和较大评估预算。
Abstract
gaussian processes
(GPs) with
derivatives
are useful in many applications, including
bayesian optimization
, implicit surface reconstructio
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