Unraveling the Black Box of Neural Networks: A Dynamic Extremum Mapper
Fuente:
arXiv
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866918157750370304 |
|---|---|
| author | Chen, Shengjian |
| author_facet | Chen, Shengjian |
| contents | We point out that neural networks are not black boxes, and their generalization stems from the ability to dynamically map a dataset to the extrema of the model function. We further prove that the number of extrema in a neural network is positively correlated with the number of its parameters. We then propose a new algorithm that is significantly different from back-propagation algorithm, which mainly obtains the values of parameters by solving a system of linear equations. Some difficult situations, such as gradient vanishing and overfitting, can be simply explained and dealt with in this framework. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_03885 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unraveling the Black Box of Neural Networks: A Dynamic Extremum Mapper Chen, Shengjian Machine Learning We point out that neural networks are not black boxes, and their generalization stems from the ability to dynamically map a dataset to the extrema of the model function. We further prove that the number of extrema in a neural network is positively correlated with the number of its parameters. We then propose a new algorithm that is significantly different from back-propagation algorithm, which mainly obtains the values of parameters by solving a system of linear equations. Some difficult situations, such as gradient vanishing and overfitting, can be simply explained and dealt with in this framework. |
| title | Unraveling the Black Box of Neural Networks: A Dynamic Extremum Mapper |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2507.03885 |