Tianhua Chen Traces the Math Behind Generative AI — SkimNews
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- Tianhua Chen submitted version 1 of The Little Book of Generative AI Foundations to arXiv on 28 May 2026 and version 2 on 8 September 2026.
- The Little Book of Generative AI Foundations is aimed at mathematically curious researchers, practitioners, and students, with the stated aim of making generative modelling more accessible without removing the mathematics needed to understand derivations and relationships.
- The Little Book of Generative AI Foundations traces a coherent route through PCA, probabilistic PCA, variational autoencoders, diffusion models, normalising flows, autoregressive factorisations, GANs, Wasserstein GANs and energy-based models.
- The Little Book of Generative AI Foundations explicitly favours explaining how model families are derived and related rather than surveying every recent architecture or implementation detail.
- arXiv lists the book in PDF and experimental HTML formats; its submission record shows a file size of 125 KB for v1 and 1,168 KB for v2.
Why it matters: For mathematically curious researchers, practitioners, and students, the 8 September 2026 revision offers a derivation-led route across nine listed approaches. Its unified treatment gives readers a concrete way to compare the structures behind PCA, diffusion, flows, GANs and energy-based models instead of encountering each as a separate technique.
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