The global inducing point variational approximation for BNNs is based on using a set of inducing inputs to construct a series of conditional distributions that accurately approximate the conditionals of the true posterior distribution. Our key insight is that these inducing inputs can be replaced by the actual data, such that the variational distribution consists of a set of approximate likelihoods for each datapoint. This structure lends itself to amortised inference, in which the parameters of each approximate likelihood are obtained by passing each datapoint through a meta-model known as the inference network. By training this inference network across related datasets, we can meta-learn Bayesian inference over task-specific BNNs.

翻译：贝叶斯神经网络的全局诱导点变分近似基于使用一组诱导输入构建一系列条件分布，这些分布能精确逼近真实后验分布的条件概率。我们的核心见解在于，这些诱导输入可被实际数据替代，从而使变分分布由每个数据点的近似似然集合构成。这种结构天然适用于摊销推断，其中每个近似似然的参数通过将每个数据点输入称为推断网络的元模型来获得。通过在相关数据集上训练此推断网络，我们可以实现针对任务特定贝叶斯神经网络的元学习式贝叶斯推断。