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Related Questions
- What are the key differences between multi-task learning and meta-learning, and how do they contribute to adapting to new tasks or domains?
- Can you provide examples of how multi-task learning can be used to adapt to new tasks, such as transfer learning from one domain to another?
- How do meta-learning algorithms, such as MAML and Reptile, leverage the concept of 'learning to learn' to adapt to new tasks or domains?
- What are some challenges and limitations of using multi-task learning and meta-learning for adapting to new tasks or domains?
- How can the combination of multi-task learning and meta-learning be used to improve the generalizability of models to new tasks or domains?
- Can you discuss the role of task similarity and task distance in multi-task learning and meta-learning, and how they impact adaptation to new tasks or domains?
- What are some applications of multi-task learning and meta-learning in real-world scenarios, such as in robotics, natural language processing, or computer vision?
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