Summary of Exadam: the Power Of Adaptive Cross-moments, by Ahmed M. Adly
EXAdam: The Power of Adaptive Cross-Moments
by Ahmed M. Adly
First submitted to arxiv on: 29 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 A novel optimization algorithm called EXAdam is introduced in this paper, building upon the widely-used Adam optimizer. The algorithm incorporates three key enhancements: new debiasing terms for improved moment estimation, a gradient-based acceleration mechanism for increased responsiveness to the current loss landscape, and a dynamic step size formula that allows for continuous growth of the learning rate throughout training. These innovations work synergistically to address limitations of the original Adam algorithm, potentially offering improved convergence properties, enhanced ability to escape saddle points, and greater robustness to hyperparameter choices. Theoretical analysis of EXAdam’s components and their interactions highlights the algorithm’s potential advantages in navigating complex optimization landscapes. Empirical evaluations demonstrate EXAdam’s superiority over Adam, achieving 48.07% faster convergence and yielding improvements of 4.6%, 4.13%, and 2.39% in training, validation, and testing accuracies, respectively, when applied to a CNN trained on the CIFAR-10 dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary EXAdam is a new way to make machine learning algorithms work better. The old Adam algorithm was popular but had some problems, like getting stuck or not working well with certain settings. EXAdam fixes these issues by adding new ideas that help it learn more efficiently and accurately. This means it can be used for all sorts of machine learning tasks, from recognizing images to understanding language. |
Keywords
» Artificial intelligence » Cnn » Hyperparameter » Machine learning » Optimization
