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Related Questions
- Can task-specific weights in a multi-task learning model encourage or discourage learning across related tasks?
- Do task-specific weights have any impact on the balance of learning between tasks, especially when tasks have varying numbers of samples?
- How do task-specific weights influence the representation learning shared across tasks, and does it affect the overall generality of the model?
- What happens if task-specific weights are highly imbalanced, leading to significant differences in the magnitude of the task weights?
- Can task-specific weights be used to prioritize certain tasks over others, and how does this affect the convergence of the model?
- Are there any hyperparameter tuning strategies for adjusting task-specific weights to improve model convergence and performance?
- Can the relationship between task-specific weights and model convergence be further studied using techniques such as spectral analysis or sensitivity analysis?
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