This repository is a curated, up-to-date list of significant AI research papers, organized by publication date and categorized into computer vision, natural language processing, audio processing, multimodal learning, and reinforcement learning. It serves researchers and practitioners by highlighting influential, state-of-the-art, and trending papers, aiding in staying current with AI advancements.

How It Works

The list is curated by a maintainer and uses a subjective citation-based taxonomy to categorize papers: "🏆 Historical Paper" (10k+ citations, decisive impact), "⭐ Important Paper" (50+ citations, state-of-the-art), and "⏫ Trend" (1-50 citations, recent, innovative, growing adoption). Papers are further categorized by AI subfield and publication year, providing a structured overview of AI research progress.

Highlighted Details

• Comprehensive coverage across five major AI domains: Computer Vision, NLP, Audio Processing, Multimodal Learning, and Reinforcement Learning.
• Detailed categorization of papers by impact and recency (Historical, Important, Trend).
• Extensive historical section covering foundational AI concepts from the Perceptron (1958) to Transformers (2017) and BERT (2018).
• Includes significant recent papers from 2023, such as SAM, ControlNet, LLaMA, GPT-4, and SDXL.

Maintenance & Community

The repository is maintained by Aimerou Ndiaye. Further community engagement or roadmap details are not specified in the README.

Licensing & Compatibility

The repository itself is a list of links and does not contain code. The licensing of the individual papers referenced is not specified.

Limitations & Caveats

The selection criteria are subjective and based on citation counts and perceived impact, which may not capture all relevant or emerging research. The list is primarily a reference and does not provide code implementations or direct access to the papers.