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Related Questions
- How do regularization techniques such as L1 and L2 regularization help prevent overfitting in multi-task learning?
- Can you explain the concept of task interference in multi-task learning and how regularization techniques mitigate this issue?
- What is the role of dropout regularization in reducing task interference and improving overall model performance?
- How do different types of regularization techniques, such as weight decay and early stopping, impact task interference in multi-task learning?
- In what scenarios is L1 regularization more effective than L2 regularization in minimizing task interference, and vice versa?
- Can you discuss the relationship between task interference and model capacity, and how regularization techniques can help manage this trade-off?
- How do techniques such as task clustering and task decomposition help reduce task interference, and what are their limitations in multi-task learning?
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