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

From: NIPS.cc/2018/Workshop/IRASL/Paper14/Authors
title: Thanks
comment: Thanks, we agree that indeed the paper doesn't address the 2 main themes (interpretability and robustness) of this workshop.

Thanks for the suggestions of transfer learning with kernel SVMs! We will look into that as we carry on with this work.

From: NIPS.cc/2018/Workshop/IRASL/Paper14/Authors
title: Thanks
comment: Thanks, we agree that indeed the paper doesn't address the 2 main themes (interpretability and robustness) of this workshop.

From: NIPS.cc/2018/Workshop/IRASL/Paper14/Authors
title: Response
comment: Hi Sunil, yes that's correct. It is a binary classification problem.

First, we avoided reporting the accuracy metric, since it is a relatively uninformative measure for imbalanced datasets. Instead, we report sensitivity (accuracy in positive class) and specificity (accuracy in negative class) separately in order to keep track of performance in both classes, and monitor for skewed predictions.

Indeed data imbalance is always problematic and we applied specific technique to address it in this work - balanced sampling of the mini-batches used to train the network. In other words, we ensured that each batch contained the same number of normal or asphyxia samples. This implies that the minority class (asphyxia) was oversampled for every epoch. This approach doesn't balance the dataset literally, but does help to balance the training and steer the gradients in a direction that would not lead to too much skew. And we found that it helped immensely in our work.

Overall, would we get better performance if the data was more balanced? Hard to say, but having more asphyxia samples would definitely help us build a models that generalise better to future data.

From: ~Sunil_Kumar_Kopparapu1
title: Imbalance Data Effect
comment: Interesting piece of work.

I understand this to be a 2 class problem? Normal or asphyxia.

My only comment is how much of the classification accuracy depends on the data being imbalanced? Would you get better accuracies if the data was more balanced?

From: NIPS.cc/2018/Workshop/IRASL/Paper14/AnonReviewer2
title: The authors showed that essentially pre-training of convnets helps if the target data is limited.
review: The article is very brief and focuses on a very specific task: infant cry classification for medical diagnoses. The MFCC+SVM baseline is pretty pointless (see for example COMPARE challenge for reasonable baselines), and the main finding of the article is that pre-training the net helps since the target data is very limited. I'm sorry to say this, but this is no surprise or new insight.
rating: 1: Strong rejection
confidence: 3: The reviewer is absolutely certain that the evaluation is correct and very familiar with the relevant literature

From: NIPS.cc/2018/Workshop/IRASL/Paper14/Authors
title: Thanks!
comment: Dorothea, thanks for taking the time to share your feedback. It is very much appreciated!

From: NIPS.cc/2018/Workshop/IRASL/Paper14/AnonReviewer3
title: Well written paper, nice application but not very relevant to IRASL workshop topics.
review: Clarity:
Very clear paper. Well written. The only comments I have:
- Abstract starts with a question which I found hard to interpret.
- I think the NIPS template is not used, at least for citations, it uses
"()" instead of "[]".

Originality:
Not original from machine learning perspective.

Novelty:
Not novel. The main message of paper is using neural network penultimate
layer features instead of raw MFCC. This is not new at all.

Suggestion: authors might consider applying kernel SVMs (maybe also transfer
kernels from another pre-trained model). This might performs better given limited
amount data in your task, and it might requires less tuning of hyper parameters.
rating: 1: Strong rejection
confidence: 2: The reviewer is fairly confident that the evaluation is correct

From: NIPS.cc/2018/Workshop/IRASL/Paper14/AnonReviewer1
title: Nice work, clearly written, but irrelevant to IRASL
review: The conclusion is that pretraining and transfer learning from a task of adult speech to a task on speech by new-borns is successful. A priori not clear it would be the case indeed, but it is totally unclear how this accuracy study provides any insight into interpretability of ANNs or how it contributes to their robustness.
rating: 1: Strong rejection
confidence: 3: The reviewer is absolutely certain that the evaluation is correct and very familiar with the relevant literature

From: ~Dorothea_Kolossa1
title: nice and interesting work
comment: The author(s) propose a transfer-learning approach to increase the recognition rate of a pathology detection setup for infants. 

The abstract states the research issue of this work well and motivates this 
work nicely while introducing the problem solution (transfer-learning in deep 
neural networks). They also give a short summary of their results and conclusion. 

The importance of this work is made clear in the introduction. The paper is addressing the early detection of medical problems - specifically of asphyxia - in infants. The problem of compiling a  fitting corpus of training data is made clear. 
Relevant previous work is discussed by the author(s), but could be more detailed ([2,3,4] are only a few sources for this research field). Transfer-learning seems to be new in this field (not explicitly stated, though).

The methods are introduced nicely and the system is explained well.
MFCCs are commonly used, so this choice seems convenient, as well as the 
selection of a convolutional model on "visual" 2d input, even if it is a 
representation of sound.
However, there is no statistical test of the research hypothesis (see 
Discussion first sentence). The authors might also want to perform a 
cross-validation or run further statistical relevance tests on their results. 
No error bound is given on the sensitivity, specificity, precision or F1-score.

All in all, this is a good and well-written work, except for the few stated problems.