Machine Learning

Rider Rider is a search engine for research papers that I built to support my own research workflow. 🔗 Live Demo: http://rider.cs.niu.edu/ Motivation Existing academic search platforms such as Google Scholar and Semantic Scholar are extremely useful, but they often introduce friction in daily research workflows: Noisy or inconsistent metadata Limited control over conference-level filtering Difficulty focusing on high-quality venues Mixed archival vs. non-archival content Ranking not always aligned with research needs To address these issues, Rider focuses on clean, curated, and conference-aware search for research papers. ...

OpenPapers OpenPapers is a robust Python toolkit for systematically collecting research papers from top-tier AI and machine learning conferences. 🔗 GitHub Repository: https://github.com/zhangleiniu/OpenPapers Motivation Since the rise of deep learning (~2013 onward), the number of publications in AI has grown exponentially. This creates challenges for: Literature review Citation analysis Research trend detection Paper recommendation Research gap identification OpenPapers builds a curated and structured dataset that enables: Citation and reference recommendation Structured PDF parsing (e.g., via GROBID/Nougat) Research limitation analysis Intelligent paper reading recommendation systems Overview The rapid growth of AI and ML research has led to an overwhelming volume of publications each year. While platforms such as Google Scholar, Semantic Scholar, and OpenReview attempt to aggregate research output, they often suffer from: ...