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《生成式 AI 基础小书》：一本直观的数学入门指南
The Little Book of Generative AI Foundations: An Intuitive Mathematical Primer
AI 导读
Tianhua Chen 发布 195 页修订版预印本《The Little Book of Generative AI Foundations》，以推导为导向串联 PCA、变分自编码器、扩散模型、归一化流、自回归分解、GAN、Wasserstein GAN 与能量模型等生成模型家族的数学基础。新版补充了贯穿全书的可视化解释，面向研究者、从业者与学生，arXiv 编号 2605.29713。
正文 · 原文
Abstract:This book provides a compact, derivation-oriented introduction to the mathematical foundations of modern generative artificial intelligence. Rather than surveying every recent architecture or implementation detail, it develops a coherent route through the ideas connecting major families of generative models, from PCA, probabilistic PCA, variational autoencoders, and diffusion models to normalising flows, autoregressive factorisations, GANs, Wasserstein GANs, and energy-based models. The aim is to make the structure of generative modelling more accessible without removing the mathematical substance needed to understand how these models are derived and related. The book is intended as a foundation-building primer for mathematically curious researchers, practitioners, and students.
| Comments: | Revised preprint version, 195 pages. Added visual explanations throughout. Comments and corrections are welcome |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.29713 [cs.LG] |
| (or arXiv:2605.29713v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.29713 arXiv-issued DOI via DataCite |
Submission history
From: Tianhua Chen [view email]
[v1]
Thu, 28 May 2026 10:12:00 UTC (125 KB)
[v2]
Tue, 8 Sep 2026 07:17:36 UTC (1,168 KB)
来源：Hacker News · arxiv.org