Summary of Adadagrad: Adaptive Batch Size Schemes For Adaptive Gradient Methods, by Tim Tsz-kit Lau et al.
AdAdaGrad: Adaptive Batch Size Schemes for Adaptive Gradient Methods
by Tim Tsz-Kit Lau, Han Liu, Mladen Kolar
First submitted to arxiv on: 17 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers investigate the impact of batch sizes on deep learning model optimization and generalization performance. They explore adaptive batch size strategies to mitigate the “generalization gap” phenomenon, where larger batch sizes lead to decreased performance. The authors introduce AdAdaGrad and its variant, which adaptively increase batch sizes during training using AdaGrad and AdaGradNorm. They theoretically prove that these schemes converge at a rate of O(1/K) for smooth nonconvex functions within K iterations. Experimental results demonstrate the efficiency and generalization capabilities of the proposed methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, scientists are trying to find a way to make deep learning models work better. Right now, if you use really big batches of data to train these models, they don’t perform as well as they would with smaller batches. The researchers came up with new ways to adjust the batch sizes during training to help the models learn and generalize better. They tested their ideas and showed that they can make the models work more efficiently and accurately. |
Keywords
* Artificial intelligence * Deep learning * Generalization * Optimization
