Venue: ICLR 2017 workshop

Reviews:

final_score: rating: 7: Good paper, accept
review_content: This paper describes how to use Sum- and Maximum product networks for unsupervised feature learning and decoding and evaluate it within three different learning scenarios by either directly classifying a binary label set based on the original feature space, or by classifying the labels from generated feature encodings or decoding labels from their embedding. The authors further propose a full pipeline that produces feature embeddings and decodes them into the label space.

The paper alone is quite hard to comprehend and as a reader without prior knowledge in SPN/MPNs I had to consult a lot of literature, which however was provided sufficiently in the paper.  The authors compare their method to state of the art approaches like RBMs and auto-encoders and show promising results in their framework. Unfortunately the tasks were not described properly and again required to consult further literature. I would recommend putting the evaluations partly into the appendix and to elaborate a little bit on that. 

Minor remarks:

- Typo in first sentence of section 3: usupervisedly
- The change in font size and face on emphasized words makes the general look of the text inconsistent and is quite uncommon
review_title: A promising application of SPNs for learning representations and mapping them back into the input space

final_score: rating: 7: Good paper, accept
review_content: * Summary: the paper proposes to use sum-product networks (SPN) for feature extraction. The embedding of an input is the activations of all the nodes in the network or of only the inner nodes. To learn features in an unsupervised manner like in Autoencoder, a decoding method is introduced using the corresponding max-product network. The experimental results on MNIST show that the proposed method outperforms RBM, CAE, DAE in terms of the quality of embeddings for classification. 

* Discussion: 
The idea of the paper is neat, interesting, and innovative. The paper is well written yet quite brief (but understandable given the page limit). The authors should explain the third paragraph of Section 2 more clearly. In the 4th paragraph also of Section 2, it is unclear what \phi_n(u) is. 

The experiment results are quite strong and convincing. However,
1. can the proposed models also outperform the alternatives after fine-grain training (i.e. jointly train the feature extractors with the classifier)? 
2. I understood from the paper that for the other networks (RBM, MADE, CAE, DAE) only the activations of the top layer is used. However, because the SPN's embeddings are from all the nodes (or all the inner nodes), have the authors tried using the activations of all the hidden nodes for the other networks? 

* pros: 
- the idea is neat, interesting, and innovative
- experimental results are good and convincing 

* cons: 
- the paper is quite brief and unclear at some points (but this shouldn't be considered as a significantly negative point)
- the experiments can be done better 

review_title: good paper, should be accepted

Decision: Accept