Reviews: Cooperative neural networks (CoNN): Exploiting prior independence structure for improved classification

Summary: The authors propose a new method that combines a latent Dirichlet allocation (LDA) model with a neural network architecture for the application of supervised text classification –– a model that can be trained end-to-end. In particular, they use a network structure to approximate the intractable inference equations that solve the KL-divergence between the LDA posterior and its approximation which is based on marginal distributions. The authors show that an embedding in a Hilbert space can allow for the approximation of the inference equations, and they choose neural networks to parametrize the functional mapping. Finally, based on two applications, the authors demonstrate an incremental advancement over previous models. Clarity: The overall writing is good, especially as it is a very technical paper with many mathematical details.