Evidence, Definitions and Algorithms regarding the Existence of Cohesive-Convergence Groups in Neural Network Optimization
<|reference_start|>Evidence, Definitions and Algorithms regarding the Existence of Cohesive-Convergence Groups in Neural Network Optimization: Understanding the convergence process of neural networks is one of the most complex and crucial issues in the field of machine learning. Despite the close association of notable successes in this domain with the convergence of artificial neural networks, this concept remains predominantly theoretical. In reality, due to the non-convex nature of the optimization problems that artificial neural networks tackle, very few trained networks actually achieve convergence. To expand recent research efforts on artificial-neural-network convergence, this paper will discuss a different approach based on observations of cohesive-convergence groups emerging during the optimization process of an artificial neural network.<|reference_end|>
@article{nguyen2024evidence,,
    title={Evidence, Definitions and Algorithms regarding the Existence of
  Cohesive-Convergence Groups in Neural Network Optimization},
    author={Thien An L. Nguyen},
    journal={arXiv preprint arXiv:2403.05610},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.05610},
    primaryClass={cs.LG cs.CV}
}