brightjade

brightjade / paper-explorer

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Crawl accepted papers and citation data from ML/DL/NLP/CV/Robotics/Security/SE conferences

brightjade.github.iopaper-explorer
28
2
100% credibility
Found Apr 08, 2026 at 28 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A collection of tools to download and explore metadata for accepted papers from major conferences in AI/ML/NLP/CV/robotics/security/software engineering, including a web app for browsing, searching, and analyzing trends.

How It Works

1
📚 Discover Paper Explorer

You find this handy tool on GitHub that gathers lists of accepted papers from top AI, machine learning, language, vision, robotics, security, and software conferences.

2
💻 Set it up on your computer

Follow simple steps to download everything you need and prepare it to run on your own machine.

3
⬇️ Download ready-to-use paper data

With one quick action, you grab the latest collection of paper details from dozens of conferences.

4
🔍 Connect extra services if you want

Optionally link a free account to unlock full details from certain sites, making your data even richer.

5
✨ Refresh or enhance the paper lists

Easily update the info or add citation counts and summaries to see what's popular.

6
🌐 Launch your web explorer

Build and open a beautiful web page right on your computer to browse everything interactively.

🎉 Dive into research trends

Search papers, spot top authors, explore trends over years, and stay ahead of cutting-edge work across fields.

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Star Growth

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AI-Generated Review

What is paper-explorer?

Paper-explorer is a Python CLI tool that crawls accepted papers and citation data from top ML, NLP, CV, robotics, security, and SE conferences using OpenReview APIs, web scraping, and Semantic Scholar. Run `ppr crawl iclr_2025` to fetch metadata like titles, authors, abstracts, and tracks; `ppr enrich` adds citation counts and TL;DRs; `ppr validate` cross-checks against DBLP. It bundles data snapshots via bash setup scripts and builds a React web app for browsing, searching, and trends.

Why is it gaining traction?

Unlike manual PDF hunts or brittle scrapers, it handles 50+ venues (ICLR to ICSE) with YAML configs, optional auth, and resume-friendly enrichment—perfect for bash crawl github workflows on accepted papers. The web UI offers sortable tables, author graphs, n-gram trends, and citation box plots without hosting your own DB. Devs dig the zero-config uv install and JSONL exports for downstream tools.

Who should use this?

ML researchers surveying "efficient LLM inference" across NeurIPS/ICML/ACL; conference chairs validating paper counts pre-proceedings; data scientists building citation graphs or topic models from real accepted lists. Skip if you need full-text parsing or pre-2023 data.

Verdict

Solid for niche lit review automation—grab data in minutes, explore via web—but 28 stars and 1.0% credibility signal early maturity; docs shine, but add tests for production. Try for your next survey; fork if scaling crawls.

(198 words)

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