Deep generative models often perform poorly in real-world applications due to the heterogeneity of natural data sets. Heterogeneity arises from data containing different types of features (categorical, ordinal, continuous, etc.) and features of the same type having different marginal distributions. We propose an extension of variational autoencoders (VAEs) called VAEM to handle such heterogeneous data. VAEM is a deep generative model that is trained in a two stage manner such that the first stage provides a more uniform representation of the data to the second stage, thereby sidestepping the problems caused by heterogeneous data. We provide extensions of VAEM to handle partially observed data, and demonstrate its performance in data generation, missing data prediction and sequential feature selection tasks. Our results show that VAEM broadens the range of real-world applications where deep generative models can be successfully deployed.

VAEM是VAE的扩展，可以处理包含不同类型特征和相同类型特征不同边缘分布的异质数据。VAEM不仅可以用于数据生成，还可以处理缺失数据预测和序列特征选择等任务，扩展了深度生成模型的实际应用领域。

VAEM：用于异构混合类型数据的深度生成模型