arXiv:2605.29273v1 Announce Type: new Abstract: A crucial component of machine learning algorithms is minimizing loss functions with less computational cost and less oscillations. While adaptive learning rate-based optimizers have been widely used for real-world tasks, they do not guarantee convergence, which is why AMSGrad was later introduced to investigate the non-convergence behaviour of Adam. In this paper, popular adaptive optimization methods like Adam and AMSGrad are critically reviewed with an emphasis on their fundamental design concepts. To address limitations of the above mentioned
The continuous evolution of AI algorithms necessitates constant refinement in optimization techniques to improve performance and efficiency, pushing toward more robust solutions.
This research provides a deeper theoretical and experimental understanding of adaptive learning algorithms, which are fundamental to the performance and reliability of many machine learning applications.
The explicit re-evaluation of widely used optimizers like Adam and AMSGrad, coupled with the introduction of a novel adaptive learning algorithm, could lead to more stable and computationally efficient AI model training.
- · AI researchers and practitioners
- · Machine learning platform providers
- · Companies with large-scale AI deployments
- · Developers relying on suboptimal or unproven optimization methods
Improved stability and speed of AI model training due to better optimization algorithms.
Faster innovation cycles in AI research and development as computational bottlenecks are reduced.
Enhanced performance and reliability of AI-powered products and services across various sectors.
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