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Understanding Deep Learning via Entropy Space Theory

2026-08-29 14:05:57
Li Li, Tong Zhang, Wentao Yu, Zuobin Wang
arXiv_AI
     
Abstract

Deep learning is often criticized for its theoretical research lagging behind practice. To make deep learning easier to understand, the entropy space theory is first introduced here. The entropy space can cover all the possibilities of any deep learning model by topological structure. It is independent of network parameters. Through the designed fundamental operations and norm, entropy space is proven to be a normed space within the formal axiomatic framework. Based on the theory, a unified coordinate system is proposed. It can coordinatize every state of a model and rank them by compression of the maximal value of information entropy. The theory offers a novel priori framework for mathematical fundamentals of deep learning.

Abstract (translated)

URL

https://arxiv.org/abs/2608.29279

PDF

https://arxiv.org/pdf/2608.29279.pdf

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