Unraveling the Black Box of Neural Networks: A Dynamic Extremum Mapper

Shengjian Chen

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arXiv · 2507.03885

Unraveling the Black Box of Neural Networks: A Dynamic Extremum Mapper

Shengjian Chen

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Abstract

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.

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Shengjian Chen. 2025-07-05. Unraveling the Black Box of Neural Networks: A Dynamic Extremum Mapper. https://arxiv.org/abs/2507.03885

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