Generative Models: Principles, Architectures, and Applications
Jun Lu
Abstract
Generative AI has emerged as one of the most transformative forces in modern artificial intelligence, reshaping how we create, imagine, and interact with digital content. From photorealistic images to coherent text, from immersive videos to novel molecular structures, generative models now power applications that were once confined to science fiction. This book is designed to guide readers through the foundational principles, mathematical underpinnings, and practical architectures that underpin this revolution.
Create a lesson
Related papers
MUSE: Benchmarking Large Vision-Language Models on Multi-Modal Understanding in Situated Education
Luyao Zhu, Xun Wei Yee, Wei Li et al.
Infinite-Parameter LLMs: Generating and Adapting Weights from Live Data
Jinli Hu, Ross M. Clarke, Yichuan Zhang et al.
Compositional Policy Violations: When Step-Level Compliance Fails In Agentic AI Workflows
Ashwini Kurady, Sri Sai Charith Grandhi, Rajesh Gupta et al.
CERA-MoA: Co-Evolving Routing Mechanisms with Continually Learning LLM Agents
Jiaxuan Jiang, Liyuan He, Zhixuan Fang
Version- and Scope-Aware Question Answering over Normative Documents: A Deployed System and an End-to-End Evaluation at Production Scale
Liuyin Wang, Shuaipeng Jin, Jiwei Shi et al.
Clueing up LLMs with Tool-Augmented Deductive Reasoning
Rebecca Ansell, Autumn Toney-Wails