8 papers
Generative Models: Principles, Architectures, and Applications
Jun Lu
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 phot…
Bayesian Matrix Decomposition and Applications
Jun Lu
The sole aim of this book is to give a self-contained introduction to concepts and mathematical tools in Bayesian matrix decomposition in order to seamlessly introduce matrix decom…
A First Course in Sparse Optimization
Jun Lu
This article aims to provide a comprehensive overview of sparse optimization, with a focus on both sparse signal recovery and sparse regularization techniques. We will begin by exp…
Matrix Decomposition and Applications
Jun Lu
In 1954, Alston S. Householder published Principles of Numerical Analysis, one of the first modern treatments on matrix decomposition that favored a (block) LU decomposition-the fa…
A rigorous introduction to linear models
Jun Lu
This book is meant to provide an introduction to linear models and the theories behind them. Our goal is to give a rigorous introduction to the readers with prior exposure to ordin…
Practical Topics in Optimization
Jun Lu
In an era where data-driven decision-making and computational efficiency are paramount, optimization plays a foundational role in advancing fields such as mathematics, computer sci…