论文标题
自然梯度下降的多元高斯变异推断
Multivariate Gaussian Variational Inference by Natural Gradient Descent
论文作者
论文摘要
此简短说明回顾了多元高斯人所谓的自然梯度下降（NGD）。 Fisher Information矩阵（FIM）是用于高斯人的几种不同参数化的。计算衍生物时，请仔细注意协方差矩阵的对称性质。我们表明，选择一个包括均值和反向协方差矩阵的参数化有一些优势，并提供了一个简单的NGD更新，该更新是逆协方差矩阵的对称（且稀疏）性质的简单更新。
This short note reviews so-called Natural Gradient Descent (NGD) for multivariate Gaussians. The Fisher Information Matrix (FIM) is derived for several different parameterizations of Gaussians. Careful attention is paid to the symmetric nature of the covariance matrix when calculating derivatives. We show that there are some advantages to choosing a parameterization comprising the mean and inverse covariance matrix and provide a simple NGD update that accounts for the symmetric (and sparse) nature of the inverse covariance matrix.
