This repository serves as a curated collection of academic papers focused on Meta-Learning, also known as "Learning to Learn," encompassing related fields like One-Shot Learning, Few-Shot Learning, and Lifelong Learning. It is primarily intended for researchers and practitioners in machine learning and artificial intelligence seeking a comprehensive overview of foundational and recent advancements in this domain.

How It Works

The repository itself is a static collection of citations, organized into "Legacy Papers" and "Recent Papers." It does not contain code or implementations. The value lies in its compilation of seminal and contemporary research, providing a structured bibliography for understanding the evolution and key methodologies within meta-learning.

Quick Start & Requirements

No installation or specific software requirements are needed. Accessing the repository involves simply browsing the provided list of papers.

Highlighted Details

• Comprehensive bibliography covering foundational work from the 1990s to recent advancements.
• Categorization into "Legacy" and "Recent" papers aids in tracing the historical development of meta-learning.
• Includes papers from prominent researchers and institutions in the AI field.
• Covers a broad spectrum of meta-learning techniques, including gradient-based methods, memory-augmented networks, and reinforcement learning applications.

Maintenance & Community

The repository appears to be a static compilation, with no explicit mention of active maintenance, community forums, or ongoing development.

Licensing & Compatibility

The repository contains only citations to academic papers. The licensing of the individual papers would be governed by their respective publishers. This collection itself does not impose any licensing restrictions on its use as a reference list.

Limitations & Caveats

This repository is purely a bibliography and does not provide any code, implementations, or direct access to the papers themselves. Users will need to find and access the full papers through other academic resources.