Adversarial Examples Are Not Bugs, They Are Features with Aleksander Madry - #369
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
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Exploring Theoretical Foundations of Classification and Gradient Interpretability
This chapter investigates the theoretical underpinnings of classification tasks, specifically the maximum likelihood classification of Gaussian distributions. It contrasts traditional classification methods with robust approaches while examining gradient interpretability and the impact of adversarial perturbations on decision boundaries.
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