Summary of Conjugate-gradient-like Based Adaptive Moment Estimation Optimization Algorithm For Deep Learning, by Jiawu Tian et al.
Conjugate-Gradient-like Based Adaptive Moment Estimation Optimization Algorithm for Deep Learning
by Jiawu Tian, Liwei Xu, Xiaowei Zhang, Yongqi Li
First submitted to arxiv on: 2 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Optimization and Control (math.OC)
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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 This paper proposes a new optimization algorithm, CG-like-Adam, for training deep neural networks. By rectifying the vanilla conjugate gradient and incorporating it into the generic Adam algorithm, CG-like-Adam aims to speed up training and enhance performance. The proposed algorithm replaces first-order and second-order moment estimation with conjugate-gradient-like methods. Convergence analysis is conducted for cases where exponential moving average coefficients are constant and first-order moment estimations are unbiased. Experimental results on CIFAR10/100 datasets demonstrate the superiority of CG-like-Adam. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to train deep neural networks, called CG-like-Adam. It’s an improvement over previous methods that helps training go faster and better. They replaced some parts of the old algorithm with new ones that work like the conjugate gradient method. The researchers studied how well this new algorithm works and found it performs well on certain datasets. |
Keywords
* Artificial intelligence * Optimization
