Artificial Intelligence1 min read

The Little Book of Generative AI Foundations: An Intuitive Mathematical Primer

By · Published by Everything Blog

In short

The text traces the mathematical principles linking prominent generative modeling families, prioritizing core theory over exhaustive architectural lists. Among the featured techniques are principal component analysis, variational autoencoders, diffusion models, and energy-based formulations. It also thoroughly details normalizing flows, autoregressive factorizations, and generative adversarial networks, including Wasserstein GANs. Initially submitted on May 28, 2026, the foundational work was later revised on September 8, 2026.

Key points

  • The text prioritizes rigorous formal derivations over exhaustive technical implementation…: The text prioritizes rigorous formal derivations over exhaustive technical implementation details.
  • Readers can explore the shared theoretical foundations linking diffusion models, autoenco…: Readers can explore the shared theoretical foundations linking diffusion models, autoencoders, and normalizing flows.
  • The primer targets mathematically curious students, researchers, and practitioners seekin…: The primer targets mathematically curious students, researchers, and practitioners seeking a compact conceptual guide.

Sources checked by our analysis pipeline: 1

Computer Science > Machine Learning

[Submitted on 28 May 2026 (v1), last revised 8 Sep 2026 (this version, v2)]

Title:The Little Book of Generative AI Foundations: An Intuitive Mathematical Primer

View PDF HTML (experimental)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.

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)

References & Citations

Loading...

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)

Connected Papers (What is Connected Papers?)

Litmaps (What…

Artificial Intelligence