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Related Questions
- What are some key architectural differences between transformer and recurrent neural network models that impact generalizability?
- How do the size and complexity of training datasets influence a model's ability to generalize to new tasks?
- Can you explain how pre-training on large, diverse datasets affects a model's ability to adapt to novel tasks?
- What role does transfer learning play in improving a model's generalizability to new tasks?
- How do the choices of hyperparameters, such as learning rate and batch size, impact a model's ability to generalize?
- What are some common techniques used to induce transfer learning in models, and how do they affect generalizability?
- Can you discuss the trade-offs between model performance on in-distribution tasks and out-of-distribution tasks, and how they relate to generalizability?
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