Awesome-Examples of how to write a good paper for an AI open-source project

A curated, but incomplete, list of excellent writing samples on open-source AI project.

If you want to contribute to this list, please feel free to send a pull request. Also you can contact sirui_ding@outlook.com.

To whom it may be useful?

This repository gathers some awesome writing examples for the researchers and engineers who want to write a demo or industry track paper for their open-source AI projects, including but not limited to, ML/DL framework, Explainable AI(XAI), AutoML, Reinforcement Learning(RL). The resources are categorized into ML/DL engine, XAI, AutoML and RL these four types and taged into two types: Algorithm & Framework, or Platform & System.

• Algorithm & Framework: 🚀
• Platform & System: 💻

Table of Contents

• Engine
• AutoML
• XAI
• RL
• Miscellaneous

Engine

Engine part consists of some main stream computational frameworks for machine learning and deep learning applications. We didn't include the framework without a paper e.g. Keras, PyTorch though they are very excellent libraries, because this repo mainly focuses on the writing and literacy of an open-source library and project.

Caffe 🚀

• Caffe: Convolutional Architecture for Fast Feature Embedding [paper] [code].

Tensorflow 🚀

• TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems [paper] [code].

MXNet 🚀

• MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems[paper] [code].

Theano 🚀

• Theano: A Python framework for fast computation of mathematical expressions[paper] [code].

Scikit-learn 🚀

• Scikit-learn: Machine Learning in Python[paper] [code].

XGBoost 🚀

• XGBoost: A Scalable Tree Boosting System[paper] [code].

LightGBM 🚀

• LightGBM: A Highly Efficient Gradient Boosting Decision Tree[paper] [code].

Ray 🚀

• Ray: A Distributed Framework for Emerging AI Applications[paper] [code].

mlpack 🚀

• mlpack3: A fast, flexible machine learning library[paper] [code].

AutoML

AutoML part consists of famous and active automated machine learning and neural architecture search open-source project.

Auto-Keras 💻

• Auto-Keras: An Efficient Neural Architecture Search System[paper] [code].

Auto-WEKA 💻

• Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms[paper] [code].

Auto-WEKA 2.0 💻

• Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA[paper] [code].

Auto-Sklearn 💻

• Efficient and Robust Automated Machine Learning[paper] [code].

Auto-PyTorch 💻

• Efficient and Robust Automated Machine Learning[paper] [code].

Tune 💻

• Tune: A Research Platform for Distributed Model Selection and Training[paper] [code].

BOHB 🚀

• BOHB: Robust and Efficient Hyperparameter Optimization at Scale[paper] [code].

XAI

XAI part consists of famous and active explainable AI tools, algorithm and platform.

secml 💻

• secml: A Python Library for Secure and Explainable Machine Learning[paper] [code].

RL

RL part consists of famous and active reinforcement learning(RL) algorithm tools, platform and system.

TorchBeast 💻

• TorchBeast: A PyTorch Platform for Distributed RL [paper] [code].

RLlib 💻

• RLlib: Abstractions for Distributed Reinforcement Learning [paper] [code].

RLcard 💻

• RLCard: A Toolkit for Reinforcement Learning in Card Games [paper] [code].

Tensorlayer 💻

• TensorLayer: A Versatile Library for Efficient Deep Learning Development [paper] [code].

Miscellaneous

Famous labs, teams and individuals

• Texas A&M DATA Lab
• Freiburg-Hannover AutoML Lab
• UC Berkeley RISE Lab
• Google Keras Team
• Tianqi Chen
• Yangqing Jia

Useful handbooks and tips

• API design for machine learning software: experiences from the scikit-learn project
• Should I write a paper on open-source software I've built?