This repository aggregates research papers on meta-learning topics including learning to learn, few-shot learning, and one-shot learning. It covers foundational work from the early 1990s through recent deep learning advances, with papers organized chronologically. The collection serves as a reference for researchers studying how neural networks can learn to acquire new knowledge more efficiently.