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Related Questions
- What are the key differences in architecture between transformer models and traditional recurrent neural networks?
- How do transformer models handle out-of-vocabulary words or tokens with unknown meanings?
- Can transformer models learn to capture long-range dependencies in sequential data as effectively as recurrent neural networks?
- What are some common techniques used to improve the performance of transformer models on sequential data with long-range dependencies?
- How do transformer models handle the vanishing gradient problem associated with long-range dependencies in sequential data?
- Are there any specific types of sequential data that transformer models are particularly well-suited for handling long-range dependencies?
- How do transformer models compare to other types of neural networks, such as LSTM or GRU networks, in terms of handling long-range dependencies?
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