# Evidence, Definitions and Algorithms regarding the Existence of Cohesive-Convergence Groups in Neural Network Optimization

arXiv: https://arxiv.org/abs/2403.05610 · HTML: https://mlsanity.com/paper/2403.05610/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 5 references that are in the map.
- First posted on arXiv 8 Mar 2024.

## Builds on (most cited first, 2 of 2)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)

## Abstract (arXiv)

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.