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Related Questions
- What are the limitations of traditional recurrent neural networks in capturing long-range dependencies in sequential data?
- How does the attention mechanism allow the model to focus on specific parts of the input sequence when processing long-range dependencies?
- Can you explain the difference between additive and scalar attention in the context of capturing long-range dependencies?
- How does the use of attention impact the training and inference speed of a model that needs to capture long-range dependencies?
- What are some common techniques used to improve the performance of attention-based models on sequential data with long-range dependencies?
- Can you provide an example of how attention is used in a real-world application to capture long-range dependencies in sequential data?
- How does the attention mechanism interact with other components of a neural network, such as the encoder and decoder, when processing sequential data with long-range dependencies?
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