Venue: ICLR 2017 workshop

Reviews:

final_score: rating: 5: Marginally below acceptance threshold
review_content: This paper is well-motivated, which improve Jaderberg's by exploiting the structure of gradients. The experimental results are promising. However, the cons of the paper are:
1. The English of the paper is not good. 
2. The paper should provides more details of the experiments, such as the configuration of the proposed model and the baseline. Moreover, the paper should also provide the time used to train the model, since Jaderberg's model is more simple. 
review_title: More details should be described, 

final_score: rating: 7: Good paper, accept
review_content: Summary:

This work extends the work of Jaderberg et al (2016) on synthetic gradients,
by replacing their gradient regressors with a mini-neural networks (called
Virtual Forward Backward Networks or, VFBN), which predict the final label
(instead of the gradients), and are trained by minimizing the l2-loss between
gradients backpropagated to the layer from these mini-networks and the
actual gradients from the full model.

These VFBNS can be seen as implementing "auxiliary losses", but are notably not
trained by miniming the label-prediction loss.

Cons:

1. The overall presentation is clear, however there are a few typos.
2. The exact details of how the various components were updated are missing.
3. Only one layer was decoupled. More experiments on decoupling the whole 
   network would be insightful.
4. These "mini (or virtual) networks incur additional costs; experiments 
   which control for computational costs should be insightful.

Pros:

1. Potentially interesting insight into training with synthetic gradients, but
   requires further investigation.
2. Impressive performance on CIFAR-10.

The work aligns well with Workshop Track's objective to "stimulate discussion of new ideas and directions".

review_title: Review

Decision: Accept