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What is Explaining Adversarial Examples in ML about?
This paper discusses the vulnerability of machine learning models, particularly neural networks, to adversarial examples, which are inputs that have been slightly altered to cause misclassification. The authors argue that the primary cause of this vulnerability is the linear nature of neural networks rather than nonlinearity or overfitting. They propose a fast method for generating adversarial examples that can be used for adversarial training, which reduces test set error and highlights the trade-off betwe
- Author
- erica jayasundera
- Language
- EN