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Related Questions
- What is the key difference between meta-learning and traditional machine learning?
- Can you explain the concept of few-shot learning and its importance in meta-learning?
- How do meta-learning algorithms, such as Model-Agnostic Meta-Learning (MAML), adapt to new tasks or domains?
- What role does the meta-learner play in the meta-learning process, and how does it interact with the task-specific learner?
- How do meta-learning algorithms handle the trade-off between exploration and exploitation in the context of rapid adaptation?
- Can you discuss the relationship between meta-learning and transfer learning, and how they differ in their approaches to adapting to new tasks or domains?
- What are some of the challenges and limitations of meta-learning algorithms, and how are researchers addressing these issues?
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