This repository serves as a curated, annotated collection of research papers, primarily in machine learning and computational pathology, maintained by the author since 2018. It aims to provide a structured and accessible log of the author's reading, offering categorized summaries and personal annotations for over 460 papers. The target audience includes researchers, engineers, and practitioners seeking to quickly grasp the essence and key takeaways of various academic publications.

How It Works

The repository organizes papers by year and provides detailed annotations for each, including a summary of the paper's core contribution, personal insights, and critical questions or points of confusion. Papers are categorized into numerous fields, such as Uncertainty Estimation, Diffusion Models, Graph Neural Networks, and Computational Pathology, allowing users to navigate specific research areas. The annotations often highlight specific sections, figures, or tables that the author found particularly insightful or confusing, offering a nuanced perspective beyond a simple abstract.

Quick Start & Requirements

This repository is a collection of metadata and annotations for research papers; it does not require installation or execution. All papers are linked via their publicly available PDFs.

Highlighted Details

• Extensive categorization covering over 40 distinct research areas.
• Detailed personal annotations offering critical insights and questions for each paper.
• Chronological organization of papers read since 2018.
• Links to official PDFs for all cited papers.

Maintenance & Community

This is a personal project, with updates reflecting the author's ongoing research and reading habits. There are no explicit community channels or roadmaps provided.

Licensing & Compatibility

The repository itself contains no code or proprietary data. It is a collection of links to publicly available research papers. The licensing of the papers themselves is determined by their respective publishers or archives (e.g., arXiv, conference proceedings).

Limitations & Caveats

The annotations are subjective and reflect the author's personal understanding and perspective at the time of reading. They may not represent a universally agreed-upon interpretation of the papers. The depth of annotation can vary significantly between papers.