From: NIPS.cc/2018/Workshop/IRASL
title: Acceptance Decision
decision: Reject

From: NIPS.cc/2018/Workshop/IRASL/Paper35/AnonReviewer2
title: A nice visualization tool for a specific neural network, but I don't see the novelty 
review: The authors of this papers implemented an interactive application to visualize the spectrogram representations of the hidden layers of a convolutional neural network for spoken digit utterance classification. 

Judging from the submitted youtube video, the interface seems clear and easy to use. 

My criticism however is that, I don't really see what novel visualization concept is being proposed here. I see that the authors implemented a feature where the users can manipulate the input (repeat, cross fade and so on) to check whether the extracted neural-net features change too much or not. This can be useful, but I don't think we have a novel and useful visualization idea in this paper. 

Also, the authors only tackle one specific neural network for one very specific (toy) application. I would perhaps suggest to investigate a more involved application. (such as speaker verification maybe)

I think the goal of this type of research should be to come up with ideas to extract useful insights from data. I fail to see this in this paper. 
rating: 2: Marginally below acceptance threshold
confidence: 2: The reviewer is fairly confident that the evaluation is correct

From: NIPS.cc/2018/Workshop/IRASL/Paper35/Authors
title: Dear Reviewer, Thanks a lot for the constructive comments. However, I want to address some of your concerns.
comment: 1. The main goal of this project is to develop an application which can help to give insights into the multi-modal learning model, especially, illustrate the internal reasoning of learning model which is trained on the audio sequence data. This application helps to extract the audio features which is learned by the internal layers. Most importantly, to the best of our knowledge, we first propose an approach which helps to illustrate the internal reasoning of an audio sequence learning model.

2. Due to the page limitation, we did not describe the learned features. However, in the web application, you can play the audio feature which is extracted by the internal layers. Even though we describe a couple of user study of our proposed approach, in the future work, we have a plan to include a detailed study of the extracted features in addition to the user studies. You can also see the demo video to know more: http://bit.ly/ddevis-demo

3.  Additionally, we are collecting user feedback, which may also help to evaluate our proposed approach. We have a plan to publish the user study in the future work.

4. Regarding your minor comment: LSTMVis [14] ” => “LSTMV [14] is 
You may misunderstand, actually LSTMVis is the proposed approach name which is described in the following work: LSTMVis: A tool for visual analysis of hidden state dynamics in recurrent neural networks.

5. “adversarial audio example data”- here the adversarial example means that user can create some input data in order to debug the learning model.

Finally, we will try our best to address your minor comments. Thanks again.

From: NIPS.cc/2018/Workshop/IRASL/Paper35/AnonReviewer3
title: A Gray Box Interpretable Visual Debugging Approach for Deep Sequence Learning Model
review: Summary: the paper proposes a software called d-DeVIS.  The toolkit implements some features that allow users to visualize their model, possibly helping them to gain some useful intuitions on what is learned in the intermediate hidden representations. 

Pros:
The toolkit could be rather useful for the community.

Cons:
Visualizing filters is clearly very useful and is a good practice in deep learning. However, I don’t see any novelty in this paper. 
The authors use almost all the manuscript to describe the functionality of the toolkit, but they don’t clearly describe the insights that have gained using it. I think this should have been the most interesting part of the work. For instance, the authors show some filters in Fig. 2.  What have you learned from that? Can you gain some insights that help one design a better ASR system?
The paper is not well written. The English, for instance, is not fluent enough for a good publication (see some suggestions in minor comments).
 

Minor comments:
“In effect, the interpretability and transparency of DL models have been reduced significantly.” => I might agree on it, but this statement should be better justified and clarified. In particular,  reduced respect to what? to standard machine learning techniques? Which technique in particular?

“Although DL approaches have been studied widely, a few works address the interpretability issue of deep learning models in the literature”. => Please, add here some references.

“non-linear systems, such as Deep Neural Network(DNN), is complex to understand and visualize” => non-linear systems, such as Deep Neural Networks(DNNs), are complex to understand and visualize.

“the learners are interested to visualize the algorithms to develop the basic intuition of the algorithm” => It would be better to avoid repeating the word algorithm two times.

“Nonetheless, it is rather black-box type visualization approach to reveal the model behavior, as such it can not interpret the internal reasoning” => Nonetheless, it is a rather “black-box” type of visualization approach to reveal the model behavior, as it can not interpret the internal reasoning”

“that focuses on gray box concept, where outcome” => “that focuses on the gray box concept, where the outcome”

“multi modal” => “multi-modal”

“The main contributions of the paper are as follows” => “The main contributions of the paper are the following”

“which demonstrates layer-wise features’ representation and model hyper parameter” => “which shows layer-wise feature representations and model hyper-parameters”

“in various artificial intelligence task ” => “in various artificial intelligence tasks”

“hidden layers weights, weights’ volatility,” => “hidden layer weights, weight volatility,”
“, LSTMVis [14] ” => “LSTMV [14] is”

“The prime goal of ” => “The primary goal”
“Table 1 lists a number of major design goals for designing ” => Please, avoid repetition of “design”

“adversarial audio example data” => what do you mean?

“allows user to not only” => “allows user not only to”
“but also it enables” => “but it also enables”
" tunned filters" => “tuned filters”

“the users to not only examine” => “the users not only to examine”

“let’s users ” =>?

“allowed the users to visualize” => allows

rating: 1: Strong rejection
confidence: 2: The reviewer is fairly confident that the evaluation is correct