Summary of Iteration and Stochastic First-order Oracle Complexities Of Stochastic Gradient Descent Using Constant and Decaying Learning Rates, by Kento Imaizumi et al.
Iteration and Stochastic First-order Oracle Complexities of Stochastic Gradient Descent using Constant and Decaying Learning Rates
by Kento Imaizumi, Hideaki Iiduka
First submitted to arxiv on: 23 Feb 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 The paper investigates the relationship between batch size and optimization performance in deep learning using stochastic gradient descent (SGD). The authors demonstrate that both learning rate and batch size impact the number of iterations required for training, as well as the stochastic first-order oracle complexity. They show that SGD with a constant learning rate achieves optimal SFO complexity at a specific batch size, which increases when the batch size exceeds this critical value. Additionally, they compare SGD to existing optimizers and highlight its effectiveness. The findings have implications for optimizing deep neural networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper is about finding the best way to train deep learning models using a popular algorithm called stochastic gradient descent (SGD). The authors show that two important factors – the learning rate and batch size – affect how well SGD works. They discover that when the batch size is just right, SGD becomes more efficient, and they compare it to other methods. This research can help improve the performance of deep learning models. |
Keywords
* Artificial intelligence * Deep learning * Optimization * Stochastic gradient descent
