Paper:
GibbsNet: Iterative Adversarial Inference for Deep Graphical Models
Directed latent variable models that formulate the joint distribution as p(x, z)

Review:
The authors proposed an extension over the Adverserially Learnt Inference(ALI) GAN that cycles between the latent and visible space for a few steps. The model iteratively refines the latent distribution by alternating the generator and approximate inference model in a chain computation. A joint distribution of both latent and visible data is then learnt by backpropagating through the iterative refinement process. The authors empirically demonstrated their model on a few imprint tasks. 
        Strength:
           - The paper is well-organized and is easy to follow.
           - The experimental results on semi-supervised learning are encouraging. (more on that see the comment below. )
        Weakness:
           - The main objection I have with the paper is that the authors did not put in any effort to quantitatively evaluate their newly proposed GAN training method. Comparing the inception score on CIFAR-10 with ALI and other benchmark GAN methods should be a must. The authors should also consider estimating the actual log likelihood of their GAN model by running the evaluation method proposed:"On the Quantitative Analysis of Decoder-Based Generative Models", Wu et al. The bottom line is that without appropriate quantitative analysis, it is hard to evaluate how well the proposed method does in general. What should help is to see a plot where an x-axis is the number of Gibbs steps and y-axis as one of the quantitative measures. 
           - The improvement of the proposed method seems to be very marginal compared to ALI. The appropriate baseline comparison should be a deeper ALI model that has 2N number of layers. The "Gibbs chain" used throughout this paper is almost like a structured recurrent neural network with some of the intermediate hidden layers actually represents x and z. So, it is unfair to compare a 3-step GibbsNet with a 2-layer feedforward ALI model.
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After I have read the rebuttal from the author, I have increased my score to reflect the new experiments conducted by the authors. The inception score results and architecture comparisons have addressed my previous concerns on evaluation.  
I am still concerned regarding the experimental protocols. The exact experimental setup for the semi-supervised learning results was not explained in detail. I suspect the GibbsNet uses a very different experimental protocol for SVHN and MNIST than the original ALI paper. It is hard to evaluate the relative improvement over ALI if the protocols are totally different. It is necessary to include all the experimental details in a future revision for reproducibility.