Awesome Papers
An open-access scholarly resource comprising curated, continuously updated collections of research papers across machine learning, quantum computing, and related fields.
Research Collections
Large Language Models LLM
A comprehensive index of scholarly research on large language models, covering GPT, transformers, prompting, fine-tuning, multimodal AI, and natural language processing.
Quantum Computing QC
Research on quantum algorithms, quantum machine learning, error correction, quantum supremacy, variational quantum eigensolvers, and foundational quantum theory.
Learning to Hash L2H
A living literature review of learning-based hashing methods, approximate nearest neighbour search, locality sensitive hashing, deep hashing, and binary embeddings.
AI for Code AI4Code
Research on AI-assisted software engineering, covering code generation, program synthesis, code completion, code search, and LLM-based programming tools.
About This Project
Awesome Papers is a collection of open-access scholarly resources maintained by Sean Moran. Each collection is continuously updated with new publications and features interactive tools for exploring research, including 2D paper maps, topic explorers, and author networks.
Contributions of new papers, tools, or topic suggestions are welcome via each collection's submission page.