The generalized Gauss-Newton (GGN) approximation is often used to make
practical Bayesian deep learning approaches scalable by replacing a second
order derivative with a product of first order derivatives. In this paper we
argue that the GGN approximation should be understood as a local linearization
of the underlying Bayesian neural network (BNN), which turns the BNN into a
generalized linear model (GLM). Because we use this linearized model for
posterior inference, we should also predict using this modified model instead
of the original one. We refer to this modified predictive as "GLM predictive"
and show that it effectively resolves common underfitting problems of the
Laplace approximation. It extends previous results in this vein to general
likelihoods and has an equivalent Gaussian process formulation, which enables
alternative inference schemes for BNNs in function space. We demonstrate the
effectiveness of our approach on several standard classification datasets as
well as on out-of-distribution detection. We provide an implementation at
this https URL

本文提出了一种基于广义高斯牛顿近似方法的贝叶斯神经网络预测方法，将原始预测模型线性化为广义线性模型（GLM）后，用于后验推理和预测中，解决了拉普拉斯近似方法下的欠拟合问题。在多个标准分类数据集上以及外部分布检测中得到了验证。