https://github.com/wgierke/csbestpaperawards

Clone of jeffhuang.com/best_paper_awards.html for easier maintenance
https://github.com/wgierke/csbestpaperawards

ai computer-science cv data-mining databases hci knowledge-management machine-learning networking nlp operating-systems papers performance research security software-engineering theory www

Last synced: about 2 months ago
JSON representation

Clone of jeffhuang.com/best_paper_awards.html for easier maintenance

• Host: GitHub
• URL: https://github.com/wgierke/csbestpaperawards
• Owner: WGierke
• Created: 2020-01-07T22:13:59.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2020-02-09T22:59:22.000Z (almost 5 years ago)
• Last Synced: 2024-10-29T09:54:49.040Z (3 months ago)
• Topics: ai, computer-science, cv, data-mining, databases, hci, knowledge-management, machine-learning, networking, nlp, operating-systems, papers, performance, research, security, software-engineering, theory, www
• Language: HTML
• Homepage: https://wgierke.github.io/CSBestPaperAwards/
• Size: 458 KB
• Stars: 1
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Best Paper Awards in Computer Science (since 1996)

I rebuilt [Jeff Huang's website](https://jeffhuang.com/best_paper_awards.html) such that it'll be easier for contributors to add new papers or top conferences.

Ideally this will help to maintain the website and keep it up-to-date.

## Installation

```python

pip3 install yattag

```

## Usage

```bash

cat rawBpa | python3 generate_html.py > index.html

```

## Demo

https://wgierke.github.io/CSBestPaperAwards/

## Format

`rawBpa` contains the data about the best papers in the format

```

conference_lower_abbreviation, conference_name (conference_topic)

 paper_year, paper_url, paper_title

  author_name, author_institution

 ```

such as

```

icml, ICML (Machine Learning)

 2019, https://arxiv.org/abs/1811.12359, Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations

  Francesco Locatello ETH Zurich, Max-Planck Institute for Intelligent Systems

 ```