Neural Entropy
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912684171067392 |
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| author | Premkumar, Akhil |
| author_facet | Premkumar, Akhil |
| contents | 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 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2409_03817 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Neural Entropy Premkumar, Akhil Machine Learning Statistical Mechanics Information Theory 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 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. |
| title | Neural Entropy |
| topic | Machine Learning Statistical Mechanics Information Theory |
| url | https://arxiv.org/abs/2409.03817 |