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Related Questions
- What are some key differences between self-attention and other contextual representation methods like recurrent neural networks and transformers?
- Can you explain how the use of multiple attention heads in self-attention improves the model's ability to capture long-range dependencies?
- How does the self-attention mechanism compare to other attention-based methods like graph attention and window-based attention in terms of capturing long-range dependencies?
- What are some challenges and limitations of using self-attention with multiple attention heads for capturing long-range dependencies?
- Can you discuss the role of positional encoding in self-attention and how it contributes to the model's ability to capture long-range dependencies?
- How does the use of self-attention with multiple attention heads affect the model's computational complexity and memory requirements?
- Can you compare the performance of self-attention with multiple attention heads to other contextual representation methods on tasks like language modeling and machine translation?
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