Venue: ICLR 2017 workshop

Reviews:

final_score: rating: 7: Good paper, accept
review_content: This paper is basically pointer networks but with reinforcement learning. The reward is the negative tour length.

I think this approach is superior to the original pointer networks, which assumed a supervised signal. Since we're working with NP-hard problems, this is difficult to obtain.

Pros:
+ Good ablation study. I particularly liked the Active Learning ablation, which tries to train a network from scratch for one instance of the problem.
+ Results on toy TSP problems (lengths 25, 50, 100) are near optimal.
+ Authors recognize the limitations of their approach w.r.t. order of magnitude worse running time

Cons:
- Unclear how well this approach will scale up to non-toy scales
- Would like to have seen other NP-hard problems

This paper is a good fit for the workshop.
review_title: Neural networks for NP-hard problems

final_score: rating: 7: Good paper, accept
review_content: This is a well written and interesting paper.
To carry out the inference for TSP problem by REINFORCE algorithm make a lot more sense.
However, I feel to apply LSTM as the encoder is still not perfect.
As the extension of pointer network on this problem, the paper could have more explorations on the model design or combination of heuristics instead of simply reusing the ptrNet structure with REINFORCE algorithm. 
Though I would be conservative about using vanilla pointer network on combinatorial optimization problems, the paper gave enough insights and experiments showing the significance of applying neural network on the classical problems.
review_title: review

Decision: Accept