Variational Flow Graphical Model
Shaogang Ren, Belhal Karimi, Dingcheng Li, Ping Li · Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining · 2022
This paper introduces a novel approach embedding flow-based models in hierarchical structures. The proposed model learns the representation of high-dimensional data via a message-passing scheme by integrating flow-based functions through variational inference. Meanwhile, our model produces a representation of the data using a lower dimension, thus overcoming the drawbacks of many flow-based models, usually requiring a high dimensional latent space involving many trivial variables. With the proposed aggregation nodes, our model provides a new approach for distribution modeling and numerical inference on datasets. Multiple experiments on synthetic and real-world datasets show the benefits of our~proposed~method and potentially broad applications.