AI BriefingTianhua Chen publishes 195-page mathematical primer on generative AI foundations
·2026.11.04 06:40
Key point
The arXiv paper (2605.29713) offers a derivation-oriented guide connecting PCA, VAEs, diffusion models, and GANs for researchers and students.
Details
- A 195-page book titled "The Little Book of Generative AI Foundations" provides a compact, derivation-oriented introduction to the mathematical foundations of modern generative AI.
- The text develops a coherent route through major model families, including PCA, probabilistic PCA, variational autoencoders, diffusion models, normalising flows, autoregressive factorisations, GANs, Wasserstein GANs, and energy-based models.
- The primer aims to make the structure of generative modelling accessible while retaining the mathematical substance needed to understand how these models are derived and related.
- Intended for mathematically curious researchers, practitioners, and students, the revised preprint (v2) includes added visual explanations throughout.
- Published on arXiv under cs.LG and cs.AI, the paper was submitted on 28 May 2026 and last revised on 8 Sep 2026.
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