Venue: NeurIPS 2020 Workshop DL-IG

Reviews:

final_score: rating: 7: Good paper, accept
review_content: This paper investigates a partial-order on the variables in a neural network. The authors, motivated from log-linear models and a poset structure on its variables, construct a similar model for a deep neural network. The parameters of the model are optimized using the expectation-maximization (EM) algorithm. The paper derives the expressions for these updates. This approach is interesting and potentially beneficial because the EM steps do not require computing back-propagation gradients, both steps can be executed using closed-form formulae.

The paper will benefit from an empirical study, where this method can perhaps be compared to a Boltzmann machine.
review_title: Review

Decision: Accept (Poster)