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Related Questions
- How does self-attention mechanism process multiple positions in a sequence simultaneously, unlike RNNs which process one position at a time?
- Can you explain the concept of keys, queries, and values in self-attention and how they contribute to the final output?
- What are the limitations of traditional RNNs in handling long-term dependencies and how does self-attention address these limitations?
- How does the parallelization of self-attention mechanism affect the computational efficiency and training speed compared to RNNs?
- Can you provide examples of tasks where self-attention outperforms RNNs, such as machine translation or question answering?
- How does self-attention mechanism handle out-of-vocabulary words or unknown tokens in a sequence?
- What are some common applications of self-attention in natural language processing and computer vision, and how do they leverage its strengths?
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