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Related Questions
- What are the different types of perturbations that can be used to test a model's robustness, such as adversarial attacks, noise, or data augmentation?
- How can you use gradient-based methods to measure the sensitivity of a model's predictions to small changes in the input data?
- What is the concept of Jacobian-based methods and how can they be used to analyze the model's behavior under different input perturbations?
- Can you explain the difference between gradient-based and Jacobian-based methods for assessing model robustness, and when would you use each?
- How can you use gradient-based methods to identify the most sensitive features of a model to input perturbations?
- What are some common challenges associated with using gradient-based methods for assessing model robustness, such as vanishing gradients or exploding gradients?
- How can you use gradient-based methods to evaluate the robustness of a model to different types of input perturbations, such as spatial or temporal perturbations?
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