Gentle Introduction
"Gentle Introduction" papers encompass introductory materials across diverse areas of machine learning and artificial intelligence, aiming to provide accessible entry points for researchers and practitioners. Current research focuses on explaining complex models (e.g., transformers, diffusion models), developing new algorithms for specific tasks (e.g., reinforcement learning, bi-level optimization), and addressing challenges in data handling (e.g., non-IID data in federated learning, data augmentation for singing voice synthesis). These introductory works are crucial for broadening participation in the field and facilitating the application of advanced AI techniques to various domains, including healthcare, robotics, and natural language processing.
Papers
An introduction to optimization under uncertainty -- A short survey
Keivan Shariatmadar, Kaizheng Wang, Calvin R. Hubbard, Hans Hallez, David Moens
An Introduction to Kernel and Operator Learning Methods for Homogenization by Self-consistent Clustering Analysis
Owen Huang, Sourav Saha, Jiachen Guo, Wing Kam Liu