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Related Questions
- How does incorporating negative examples into a model's training data affect its capacity to handle unseen data points?
- Can you explain the role of negative examples in improving a model's robustness to out-of-distribution inputs?
- What are the potential drawbacks of relying heavily on negative examples during model training for generalization?
- In what ways can negative examples help a model to avoid overfitting on in-distribution data?
- How does the balance between positive and negative examples impact the model's ability to generalize?
- What are some strategies for augmenting a model's training data with negative examples from various distributions?
- Can you describe the relationship between the diversity of negative examples and the model's ability to generalize to new environments?
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