From: NeurIPS.cc/2020/Workshop/DL-IG/Paper7/Authors
title: NeurIPS2020 Workshop Content
comment: The link to the 5 minute presentation is:
https://youtu.be/B1F5Ujtb8zQ

The link to the poster is:
https://github.com/sjmluo/PosetDL/blob/master/PosetDL_NeurIPS2020_Poster.pdf

From: NeurIPS.cc/2020/Workshop/DL-IG/Program_Chairs
title: Paper Decision
decision: Accept (Poster)

From: NeurIPS.cc/2020/Workshop/DL-IG/Paper7/AnonReviewer1
title: Review
review: 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.
rating: 7: Good paper, accept
confidence: 4: The reviewer is confident but not absolutely certain that the evaluation is correct