This repository curates a comprehensive list of landmark papers in machine learning, categorized by subfield. It serves as a valuable resource for researchers, students, and practitioners seeking to understand the foundational works and key advancements in various ML domains. The project aims to provide a structured overview of influential research, facilitating deeper learning and historical context.

How It Works

The project organizes papers by topic, including areas like Deep Learning, Ensemble Methods, NLP, and Recommender Systems. Each entry typically links to the original publication, often with annotations indicating freely available versions, associated code, or precursor/historical context. This curated approach highlights the evolution of techniques and provides direct access to seminal research.

Quick Start & Requirements

No installation or execution is required. This is a curated list of papers.

Highlighted Details

• Extensive categorization of ML subfields and key papers within each.
• Annotations for paper accessibility (paywalled, author-provided, code available, historical context).
• Links to original publications, associated code repositories, and related resources.
• Includes foundational papers for concepts like CNNs, LSTMs, Transformers, GANs, and optimization algorithms.

Maintenance & Community

The project encourages community contributions via issues and pull requests for suggestions and additions.

Licensing & Compatibility

The repository itself is likely under a permissive license (e.g., MIT, Apache) given its nature as a curated list. However, the licensing of the linked papers varies, with many being behind paywalls or available under specific academic licenses.

Limitations & Caveats

The selection of "landmark" papers is subjective and reflects the curator's opinion. While efforts are made to provide alternative links, many papers remain behind paywalls. The list focuses on foundational papers and may not cover the very latest advancements.