Venue: ICLR 2017 workshop

Reviews:

final_score: rating: 3: Clear rejection
review_content: I can honestly say that despite several readings, I have no idea what this paper is actually about.  I believe the problem is relating two objects, despite not having a label that classifies the two objects as being of the same class.  From there, my comprehension goes downhill: EM algorithm mixed with pseudo-classes and a weighting scheme. Networks using the output from another network as the targets of other networks.  Target uniform statistical distributions.  Why a weighting scheme?  What's going on? 

I acknowledge that perhaps the workshop format is too small, and therefor limits too severely the required space to explain an idea.  Perhaps.  But I can safely say that almost nobody will glean any insight from this manuscript in the time that a reasonable person is willing to give a manuscript.  I would say that if the authors are confident of this work, they should write up a longer manuscript (or return to a longer one) that takes the time and space necessary to more effectively motivate the problem, and introduce the parts of the architecture, again with motivation, so that the reader has a chance of understanding the manuscript.

review_title: Classless association using neural networks

final_score: rating: 4: Ok but not good enough - rejection
review_content: I agree with R1, the workshop format is too small to efficiently describe an idea. 

This is what I understand: let's assume a young child is playing with toys from 2 different brands. The toys include several pieces of different  types (10 MNIST classes). The aim is to learn to put the same brand types into same buckets. We want a bucket to have the same time of toy of the same brand (purity, all block type t of brand j is in bucket b) also the object types are the same for 2 brands in the bucket (association, all block type t of both brands is in bucket b). The ultimate goal (future work) is to learn association between diffferent streams (e.g. what parents say when the child holds a lego).

This work models this problem with a MLP to first induce a feature vector z^{1,2} for 2 streams. A pseudo-class \hat{z^{1,2}} is predicted using these feature vectors. In the M-step the parameters are updated so that the distribution defined by  \hat{z^{1,2}} matches the target distribution \phi.

two issues I observed:
1) they do not provide any information about how they evaluated other clustering algorithms. If they are fed with raw pixels, I don't think the comparison would be fair because there is no featurization of raw fixels where the proposed model have this power. Comparison on a single layer MLP autoencoder's hidden features or output of PCA would be more fair.
2) The experiments are almost oracle type. The model knows the number of classes and the target distribution. I am not sure if other clustering algorithms make use of target distribution information. In a real life scenario, none of these assumptions are true. An early attempt in that direction would make this work acceptable for workshop publication.
review_title: classless association using neural networks

Decision: Reject