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Related Questions
- What are the scenarios where the inverse square root learning rate schedule might not be effective?
- Can you explain why the inverse square root learning rate schedule may not be suitable for certain neural network architectures?
- Under what conditions does the inverse square root learning rate schedule tend to converge slowly or not at all?
- How does the inverse square root learning rate schedule perform on tasks with varying levels of difficulty and complexity?
- Are there any specific types of neural networks or datasets where the inverse square root learning rate schedule is known to struggle?
- Can you provide examples of situations where a more adaptive learning rate schedule, such as the cosine annealing schedule, might be preferred over the inverse square root schedule?
- What are the theoretical limitations of the inverse square root learning rate schedule that might make it less effective in certain situations?
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