Neural Entropy

2024/09/05 by Akhil Premkumar, Premkumar, Akhil · 1 voice · 5 citations
Computer Science · Mathematics · Medicine · #Advanced Neuroimaging Techniques and Applications #Artificial intelligence #Artificial neural network #Computer science #Generative Adversarial Networks and Image Synthesis #Mathematics #Physics #Statistical physics #Stochastic Gradient Optimization Techniques

paper · pdf · doi:10.48550/arxiv.2409.03817

published in arXiv (Cornell University) (Cornell University)

openalex publication_date 2024/09/05 · openalex created_date 2024/10/21 · openalex updated_date 2026/07/28

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 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.

Cited by

• Stochastic Thermodynamics for Autoregressive Generative Models: A Non-Markovian Perspective
• The Information Dynamics of Generative Diffusion
• Entropic Time Schedulers for Generative Diffusion Models
• Mutual Information Estimation via Score-to-Fisher Bridge for Nonlinear Gaussian Noise Channels
• A Unified Kullback--Leibler Divergence Analysis of Generative Diffusion Models via Entropy Production Rate

Discussions

• Tomorrow (Monday) Akhil Premkumar will present his "Neural Entropy" arxiv.org/abs/2409.03817 Join us on zoom at 9am PT / 12pm ET / 6pm CET: portal.valencelabs.com/logg [bsky, 18 points, 0 comments]

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