From: NIPS.cc/2018/Workshop/CDNNRIA
title: Acceptance Decision
decision: accept

From: NIPS.cc/2018/Workshop/CDNNRIA/Paper9/Authors
comment: Thanks for your positive comment.
    -Thanks for raising the question on the hyperparameter selection. Our experience is that when the input size is huge, it is better to set more nodes in the tensor ring, since more nodes usually mean stronger representation power.
    - If we understand this question correctly, the pretrained CNN is used to extract local spatial features, while the following TR-LSTM was used to explore the temporal correlations.  Compared to the vanilla LSTM, TR-LSTM performs better in exploring the correlations probably due to the compact ring structure and the less number of parameters.
    - Thanks for the good suggestion. Yes, it is meaningful to explore other applications and limitations.

From: NIPS.cc/2018/Workshop/CDNNRIA/Paper9/Authors
comment: Thanks for the valuable comment. The computation time of TR-LSTM depends on the number of nodes in the tensor ring structure. As in the experiment on the UCF-11 dataset, when the number of nodes was set to 8,  the overall training time is slightly larger than the vanilla LSTM due to the serial computation of the tensor ring weights. When the number of nodes was set to 4, the computation time was smaller than LSTM.
    
    Thanks for raising the point of compressing hidden states. In our paper, we consider the setting that the input dimension is very big, such that compressing the input-to-hidden layer is more important, although it is also possible to compress the hidden gates. We will conduct experiments to show the results of compressing the hidden gates as well.

From: NIPS.cc/2018/Workshop/CDNNRIA/Paper9/Authors
comment:     We thank the reviewer for the positive comments and the valuable advice. Due to the space limitation, we did not include the computational time comparison. We will report the comparison in the full version.
    
    It is interesting to explore how to use GPU parallelism to conduct asynchronous updating on the weights.

From: NIPS.cc/2018/Workshop/CDNNRIA/Paper9/AnonReviewer2
title: A good work for compressing the input-to-hidden weights in LSTM
review: 
This paper propose to use the tensor ring decomposition to extract low rank structure and reformulate the input-to-hidden transformation weights in RNN or its variants. Experiments on action recognition task demonstrate the effectiveness of the proposed methods. The model is clearly presented and easy to follow. The performance is impressive. 

But it will be better to show some comparison of the computational overhead or running time, because I am not so sure about the compatibility of your proposed technique, tensor ring decomposition, with the GPU computation parallelism. 

rating: 3: Marginally above acceptance threshold
confidence: 2: The reviewer is fairly confident that the evaluation is correct

From: NIPS.cc/2018/Workshop/CDNNRIA/Paper9/AnonReviewer3
title: A good paper but more evaluation results are needed
review: This paper aims to enhance the efficiency of LSTM RNN when dealing with large-size input (e.g., videos). In particular, it focuses on the input gate of LSTM unit. By applying tensor ring decomposition, the tensor size for the multiplication weight in the input gate of LSTM can be reduced without sacrificing the performance too much. 

+ This paper targets at an interesting and important problem.
+ Using tensor ring decomposition is straight-forward, easy to implement and effective. 
+ Experiment results on video classification datasets demonstrate effectiveness of the proposed method. 

However, I have following concerns:
- smaller number of parameters does not always mean less time/computation cost. could the authors evaluate and compare FLOPs and time of their compressed model and other compression technique baselines?

- In some applications, the size of the hidden states may also be large. The  proposed compression method seems applicable to the hidden gates as well. Could the authors add experiments showing how the proposed TR method works for compressing hidden units? including the efficiency improvement and performance drop.
rating: 3: Marginally above acceptance threshold
confidence: 3: The reviewer is absolutely certain that the evaluation is correct and very familiar with the relevant literature

From: NIPS.cc/2018/Workshop/CDNNRIA/Paper9/AnonReviewer1
title:  
review: Pros:
- Solid experiments
- Clearly presented
- Results significantly better than previous methods

Cons: 
- Rationales behind practical choices of TR decomposition (structure) and hyperparameters seem lacking.
- It was mentioned that more efficient network frontends such as convolutional ones have a complementary effect, but no results exploring this tradeoff.
- Method seems more general and more widely applicable than the example presented, lack of discussion on extensions and ultimate limitations.
rating: 4: Top 50% of accepted papers, clear accept
confidence: 2: The reviewer is fairly confident that the evaluation is correct

From: ~Lixin_Fan1
comment: Authors please send author information to workshop chairs (lixin.fan@nokia.com), so that you submission can be assigned to reviewers without conflict of interests.
title: Authors please send author information to workshop chairs (lixin.fan@nokia.com).

From: ~Lixin_Fan1
comment: Authors please send author information to workshop chairs (lixin.fan@nokia.com), so that you submission can be assigned to reviewers without conflict of interests.
title: Authors please send author information to workshop chairs (lixin.fan@nokia.com).

From: ~Lixin_Fan1
comment: Authors please send author information to workshop chairs (lixin.fan@nokia.com), so that you submission can be assigned to reviewers without conflict of interests.
title: Authors please send author information to workshop chairs (lixin.fan@nokia.com).