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Related Questions
- Can a large batch size improve the model's convergence speed at the expense of the local minimum issue?
- How does the trade-off between gradient noise and exploration-exploitation occur in large-batch SGD?
- What role does the magnitude of batch size play in the distribution of gradient vectors and optimization landscapes?
- Are smaller batch sizes more robust to model complexities and harder to optimize when compared to larger batch sizes?
- What empirical evidence is there that optimal batch size for a certain model could lead to significant improvements in performance?
- Are larger batch sizes more vulnerable to exploding gradient issues or other instability, and can batch normalization and gradient clipping help?
- In terms of overall optimization and generalization capabilities, which approach: 'small-batch with SGD' vs. 'larger batch with ADAM' could provide a slight edge over each other and why?
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