Typical amortized inference in variational autoencoders is specialized for a single probabilistic query. Here we propose an inference network architecture that generalizes to unseen probabilistic queries. Instead of an encoder-decoder pair, we can train a single inference network directly from data, using a cost function that is stochastic not only over samples, but also over queries. We can use this network to perform the same inference tasks as we would in an undirected graphical model with hidden variables, without having to deal with the intractable partition function. The results can be mapped to the learning of an actual undirected model, which is a notoriously hard problem. Our network also marginalizes nuisance variables as required. We show that our approach generalizes to unseen probabilistic queries on also unseen test data, providing fast and flexible inference. Experiments show that this approach outperforms or matches PCD and AdVIL on 9 benchmark datasets.

翻译：在变异自动编码器中,典型的摊销式推论是专门用于单一概率查询的。 我们在此提议一个推论网络结构, 将其概括为不可见概率查询。 我们的网络可以直接从数据中训练单一推论网络, 而不是一个编码器- 解码器对配对, 我们可以直接从数据中训练单一推论网络, 使用成本函数, 不仅在样本中, 而且在查询中都是随机的。 我们可以使用这个网络来执行与在隐藏变量的无方向图形模型中的推论任务一样的推论任务, 而不必处理棘手的分区函数。 其结果可以映射到学习一个实际的非定向模型, 这是一种臭名昭著的难题。 我们的网络还可以按照需要, 将破坏变量边缘化。 我们显示我们的方法一般化了对看不见的测试数据进行隐形概率查询, 提供快速和灵活的推论。 实验显示, 这个方法在9个基准数据集上超越或匹配 PCD 和 AdVIL 。