Venue: ICLR 2018 Workshop

Reviews:

final_score: rating: 6: Marginally above acceptance threshold
review_content: This work provides an analytical expression for the effect of label noise on the performance of deep neural networks.

The studied problem is interesting. But the proposed method can be better motivated. The validation is relatively weak.
review_title: An interesting work with relatively weak validation

final_score: rating: 4: Ok but not good enough - rejection
review_content: This work proposes an analytical expression for the effect of label noise on the performance of deep neural networks. The main issue is that it does not provide such an expression. On a single example, the work illustrates that with random label noise the model's classification accuracy degrades more gracefully than with concentrated noise. This is obvious and falls short of the promise.
review_title: Filtering Adversarial Examples

Meta-review: Based on the reviews, this paper has not been accepted for presentation at the ICLR workshop. However, the conversation and updates can continue to appear here on OpenReview.
Decision: Reject