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Related Questions
- How does self-attention enable parallelization of the model, and what impact does this have on computational complexity?
- What are the memory requirements for each attention head, and how does this contribute to overall memory usage?
- How do multiple attention heads improve the model's ability to capture long-range dependencies, and what trade-offs are involved in using multiple heads?
- Can you explain the effect of increasing the number of attention heads on model capacity and training time?
- How do the attention heads interact with each other, and are there any potential pitfalls or issues with using multiple heads?
- Can you discuss the impact of using multiple attention heads on model interpretability and feature learning?
- Are there any strategies or techniques for optimizing the number and configuration of attention heads for a given model or task?
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