论文标题
NOT-MIWAE：缺少随机数据的深层生成建模
not-MIWAE: Deep Generative Modelling with Missing not at Random Data
论文作者
论文摘要
当丢失的过程取决于丢失的值本身时，需要在进行基于可能性的推理时明确建模并考虑到它。在丢失过程取决于丢失的数据的情况下，我们提出了一种构建和拟合深层变量模型（DLVM）的方法。具体而言，深层神经网络使我们能够灵活地对鉴于数据的缺失模式的条件分布进行建模。这允许将有关丢失类型（例如自我审查）类型（例如自我审查）的先前信息纳入模型。我们的推理技术基于重要性加权变异推理，涉及最大化关节可能性的下限。通过使用潜在空间和数据空间中的重新聚集技巧来获得结合的随机梯度。我们在各种数据集和丢失模式上显示，明确建模丢失过程是无价的。
When a missing process depends on the missing values themselves, it needs to be explicitly modelled and taken into account while doing likelihood-based inference. We present an approach for building and fitting deep latent variable models (DLVMs) in cases where the missing process is dependent on the missing data. Specifically, a deep neural network enables us to flexibly model the conditional distribution of the missingness pattern given the data. This allows for incorporating prior information about the type of missingness (e.g. self-censoring) into the model. Our inference technique, based on importance-weighted variational inference, involves maximising a lower bound of the joint likelihood. Stochastic gradients of the bound are obtained by using the reparameterisation trick both in latent space and data space. We show on various kinds of data sets and missingness patterns that explicitly modelling the missing process can be invaluable.
