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Related Questions
- What is the significance of attention mechanisms in models for capturing long-range dependencies?
- How do transformer architectures improve a model's ability to handle sequential data and dependencies?
- What is the impact of increasing the model's depth and number of layers on its ability to capture long-range dependencies?
- How do the choice of activation functions, such as ReLU and Swish, affect the model's ability to learn long-range dependencies?
- What is the effect of batch normalization and residual connections on the model's ability to capture long-range dependencies?
- In what ways do pre-trained language models, such as BERT and RoBERTa, improve a model's ability to capture long-range dependencies?
- What is the role of hyperparameters, such as learning rate and dropout rate, in determining a model's ability to capture long-range dependencies?
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