arXiv:2605.29713v1 Announce Type: new 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 ac
The proliferation of generative AI models across various applications necessitates a foundational understanding of their underlying mathematics to push advancements further.
This primer provides a canonical and accessible resource for researchers, developers, and policymakers to grasp the core principles of generative AI, facilitating innovation and informed decision-making.
A clearer, more consolidated pedagogical pathway for understanding generative AI foundations becomes available, potentially accelerating R&D and critical application development.
- · AI researchers
- · AI developers
- · Educational institutions
- · AI-driven industries
- · Those lacking foundational AI understanding
Increased accessibility to the mathematical underpinnings of generative AI.
Faster development and deployment of more robust and novel generative AI applications.
Enhanced AI literacy across technical fields, potentially driving broader societal adoption and innovative uses of generative models.
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Read at arXiv cs.LG