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Related Questions
- What are some examples of model-agnostic interpretability techniques that can provide a more nuanced understanding of model behavior?
- How do techniques like feature importance and SHAP values address the limitations of context-independent metrics in model interpretability?
- What are some approaches to interpretability that focus on understanding the decision-making process of the model, rather than just the final output?
- Can you explain how techniques like saliency maps and visualizations can help to identify areas of the input data that are most influential in the model's predictions?
- How do techniques like model distillation and knowledge distillation provide insights into the model's decision-making process?
- What are some examples of techniques that use attention mechanisms to highlight the most relevant parts of the input data that contribute to the model's predictions?
- How do techniques like model interpretability through explanation and model interpretability through visualization address the limitations of context-independent metrics?
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