Venue: ICLR 2017 workshop

Reviews:

final_score: rating: 6: Marginally above acceptance threshold
review_content: The authors propose a clustering method based on a mixture distribution of components which are parameterized by neural networks. The system finds a clustering using generalized EM updates. The parameters are trained by propagating the likelihood gradient through these EM updates. The authors also propose a less principled but potentially more flexible version of the model in which the EM updates are replaced with more conventional recurrent neural network state updates. 

To my knowledge, the proposed combination of models and trained inference method are new and an interesting approach to the grouping problem. The idea to backpropagate through inference updates is not new by itself and it would have been nice to see some references to earlier work in this area but obviously there was not much space available for that. 

I find it very unfortunate that there are not more details about the empirical work. It is not clear to me how many EM steps are used. While an extended abstract may not need to be detailed enough for an exact replication of the research, details like this are in my opinion necessary to at least get a rough idea about the complexity and practical value of the method. 

Pros:
- Nice new approach to the grouping problem.
- Results seem to compare favorably to prior work.

Cons:
- Lack of implementation details.
- Limited evaluation and analysis of the methods.

review_title: Nice idea but details are missing.

final_score: rating: 5: Marginally below acceptance threshold
review_content:    The paper concerns the task of perceptual grouping (essentially clustering pixels in an image; pixels in the same cluster are meant to form meaningful perceptual constructs, hopefully relevant shapes; a kind of unsupervised segmentation task). The paper proposes a generative model for images in which a neural network function F encodes the dependencies between pixel. Each cluster has a different latent variable theta, such that a mixture of F(theta) with Gaussian noise makes up the final image. The important part in this generative model (relevant to the task) is to be able to infer to which clusters the pixels should be affected and that the clusters be meaningful. The model is trained with a loose approximation of EM  which takes the form of backdrop through time in a recurrent neural network.

- An assessment of novelty, clarity, significance, and quality.
   - the method seems novel
   - the paper is not very clear and must be read many times to gather information about what the model is which is all over the place. Event then a lot remains unclear.
   - negative results on real dataset suggest the method is not significant in its current form

- A list of pros and cons (reasons to accept/reject).
   pros:
      - the method seems new
      - implementation of a loose EM approximation with a RNN seems new and somewhat interesting
   cons
       - not clear but maybe this cannot be helped due to the 3 page limit.
       - results seem encouraging on toy dataset (method seems to cluster pixels based on shape) but not on real dataset (pixels are clustered based on colour which is exactly what the authors did not want). This undermines the whole point of the method which is to capture dependency between pixels. This makes one doubt the applicability of the method to pixel grouping let alone applicability to anything else.

review_title: null

Decision: Accept