Neural Entropy
Abstract
We explore the connection between deep learning and information theory through the paradigm of diffusion models. A diffusion model converts noise into structured data by reinstating, imperfectly, information that is erased when data was diffused to noise. This information is stored in a neural network during training. We quantify this information by introducing a measure called \textit{neural entropy}, which is related to the total entropy produced by diffusion. Neural entropy is a function of not just the data distribution, but also the diffusive process itself. Measurements of neural entropy on a few simple image diffusion models reveal that they are extremely efficient at compressing large ensembles of structured data.
Cite
Text
Premkumar. "Neural Entropy." Advances in Neural Information Processing Systems, 2025.Markdown
[Premkumar. "Neural Entropy." Advances in Neural Information Processing Systems, 2025.](https://mlanthology.org/neurips/2025/premkumar2025neurips-neural/)BibTeX
@inproceedings{premkumar2025neurips-neural,
title = {{Neural Entropy}},
author = {Premkumar, Akhil},
booktitle = {Advances in Neural Information Processing Systems},
year = {2025},
url = {https://mlanthology.org/neurips/2025/premkumar2025neurips-neural/}
}