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Related Questions
- In multi-task learning, can the similarity of tasks in terms of input data structure or problem type enhance or hinder the sharing of knowledge between tasks?
- How does the semantic similarity between tasks impact the transfer of knowledge in models trained on multiple tasks simultaneously?
- When tasks share similar underlying distributional shifts, does that facilitate or impede knowledge sharing between tasks in joint models?
- Can the similarity of tasks regarding their data modalities influence the extent of knowledge exchange between tasks in multi-task learning models?
- What are the challenges and limitations of knowledge sharing in multi-task learning settings where tasks exhibit high variability in terms of data source or format?
- In what ways do the task similarity metrics like cosine similarity or Jensen-Shannon divergence affect the discovery of transferable knowledge and its utilization in multi-task learning models?
- How does the interaction between task similarity and gradient sharing in multi-task models impact the overall performance on individual tasks and the network as a whole?
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