Understanding Deep Learning via Entropy Space Theory
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 mathem...
| Authors | Li Li, Tong Zhang, Wentao Yu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 29 August 2026 |
| Last updated at source | 29 August 2026 |
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