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Related Questions
- How does the dot-product attention mechanism improve the transformer model's ability to handle long-range dependencies in input sequences?
- Can you explain the difference between dot-product attention and other types of attention mechanisms, such as additive attention?
- In what scenarios does the dot-product attention mechanism excel, and where does it struggle?
- How does the dot-product attention mechanism interact with the multi-head attention mechanism in the transformer model?
- What are some potential pitfalls or limitations of using dot-product attention in transformer models?
- How can the dot-product attention mechanism be modified or extended to better handle specific tasks or domains?
- Can you provide an example or illustration of how the dot-product attention mechanism works, using a simple example input sequence?
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