CLEVER score
 | The topic of this article may not meet Wikipedia's general notability guideline. (September 2018) |
The CLEVER (Cross Lipschitz Extreme Value for nEtwork Robustness) score is a way of measuring the robustness of an artificial neural network towards adversarial attacks.[1] It was developed by a team at the MIT-IBM Watson AI Lab in IBM Research and first presented at the 2018 International Conference on Learning Representations.[2] It was mentioned and reviewed by Ian Goodfellow[3] as well. It was adopted into an educational game Fool The Bank[4] by Narendra Nath Joshi,[5] Abhishek Bhandwaldar and Casey Dugan
References
[edit]- ^ Weng, Tsui-Wei (2018). "Evaluating the robustness of neural networks: An extreme value theory approach". arXiv:1801.10578 [stat.ML].
- ^ "A CLEVER Way to Resist Adversarial Attack". IBM. May 2, 2018. Retrieved September 12, 2018.
- ^ "Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach". 10 February 2022.
- ^ "Fool the Bank - IBM Research".
- ^ "Narendra Nath Joshi".
 | This computer science article is a stub. You can help Wikipedia by expanding it. |