Papers with Code

Read the full post at Inside Science Resources.

Papers with Code is an excellent resource for anyone doing research related to or involving machine learning as well as researchers interested in open science and reproducibility. As the name indicates, every paper included in the Papers with Code database includes the associated code in a GitHub repository. These associated GitHub repositories allow users to examine the code, discover contributors, and make a copy (known as a “fork”). In addition to GitHub integration, papers that use Python can link to Google Colaboratory, a resource that allows users to execute Python code in their browser. The combination of GitHub repositories and Colaboratory make Papers with Code a powerful platform for those interested in reproducing machine learning research.