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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.03885 |
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| _version_ | 1866918157750370304 |
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| 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 |