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Related Questions
- How does meta-learning balance exploration and exploitation in few-shot learning tasks?
- What are the key strategies employed by meta-learning algorithms to handle the exploration-exploitation trade-off?
- Can you explain the role of exploration in meta-learning and how it affects model performance?
- How does the exploitation component of meta-learning impact the model's ability to generalize to new tasks?
- What are the implications of the exploration-exploitation trade-off on the overall performance of meta-learning models?
- In what ways can meta-learning algorithms adapt to changing task distributions and balance exploration and exploitation?
- How does the trade-off between exploration and exploitation affect the sample efficiency of meta-learning models?
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