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Related Questions
- Can you explain how attention mechanisms adapt to varying input sequence lengths and their impact on modeling long-range dependencies?
- How do different types of attention weights (e.g., additive, scaling, and dot-product attention) influence the model's ability to capture dependencies at different distances?
- What are the effects of using different attention strategies, such as global and relative attention, on modeling long-range dependencies in the context of sequence-to-sequence tasks?
- In what ways do the learnable parameters of the attention mechanism, such as scaling factors and attention strengths, contribute to the model's ability to capture long-range dependencies?
- How do the attention weights update during training, and how do they relate to the model's ability to refine its understanding of long-range dependencies over time?
- Can you provide a mathematical explanation of how attention weights are computed, and how this computation influences the model's ability to model long-range dependencies?
- What are the advantages and limitations of using external knowledge or pre-training techniques to improve the model's ability to capture long-range dependencies in input data?
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