Summary of Reducing Bias in Deep Learning Optimization: the Rsgdm Approach, by Honglin Qin et al.
Reducing Bias in Deep Learning Optimization: The RSGDM Approach
by Honglin Qin, Hongye Zheng, Bingxing Wang, Zhizhong Wu, Bingyao Liu, Yuanfang Yang
First submitted to arxiv on: 5 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposes a new deep learning optimizer, RSGDM (Recursive Stochastic Gradient Descent with Momentum), which addresses the limitations of widely used first-order optimizers like SGDM and Adam. By analyzing the bias and lag introduced by exponential moving averages in SGDM, the authors develop a differential correction term to correct these issues. Experiments on the CIFAR datasets demonstrate that RSGDM outperforms SGDM in terms of convergence accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating a new way for computers to learn from mistakes, making it better at recognizing patterns and making decisions. The problem with current methods is that they can be slow and inaccurate. The authors have developed a new method called RSGDM that tries to fix these problems by using a different way of calculating the direction in which to move. They tested this method on pictures and showed that it works better than the old method. |
Keywords
» Artificial intelligence » Deep learning » Stochastic gradient descent
