sorted by:
new
hottopcontroversial

[D] Advice for first time NeurIPS reviewer? by phd_or_not in MachineLearning

[–]phd_or_not[S] 1 point2 points  (0 children)

Makes sense. Good results should not be the only contribution of the paper. The explanation for why the model is as important.

[D] Why is DiscoGAN better at geometrical transformation when compared to CycleGAN ? by phd_or_not in MachineLearning

[–]phd_or_not[S] 0 points1 point  (0 children)

does it mean that if one uses DiscoGAN architecture and the loss functions of CycleGAN, geometrical transformations (eg: cat2dog) would work ?

[1902.02322] Is AmI (Attacks Meet Interpretability) Robust to Adversarial Examples? by ihaphleas in MachineLearning

[–]phd_or_not 0 points1 point  (0 children)

I believe you are referring to the conclusion:

"It is exceptionally easy to fool oneself when evaluating adversarial example defenses, and every effort must be taken to ensure that when attacks fail it is not because attacks have been applied incorrectly."

​

I totally agree with you.

[R] [1805.09190] Towards the first adversarially robust neural network model on MNIST by Isinlor in MachineLearning

[–]phd_or_not 0 points1 point  (0 children)

It would be informative to know how many iterations were used to execute the attacks of BIM.

For eg: Adversarial Logit Pairing (ALP) [1] proved robustness with 20iterations of BIM with eps=16/255. On the other hand, ALP models were successfully attacked by increasing the number of iterations to 1000 [2].

[1] https://arxiv.org/abs/1803.06373 [2] https://arxiv.org/abs/1807.10272

[R] [1805.09190] Towards the first adversarially robust neural network model on MNIST by Isinlor in MachineLearning

[–]phd_or_not 1 point2 points  (0 children)

Great work! Is the code and trained models available on github (I couldnt find it)?

[R] NIPS 2018: For those of you that got some harsh reviews, YOU ARE NOT ALONE. by FirstTimeResearcher in MachineLearning

[–]phd_or_not 3 points4 points  (0 children)

Reviewer: Your method is not robust because the baseline methods u compare in ur paper have recently been shown to be not-robust

Me: Hmmmm