Awesome-SE-for-AI

Awesome material(papers, tools, etc.) from SE to improve the AI. Previously the repo is called awesome-ml-testing.

This repo will be updated continuously, don't hesitate to add new Pull Request or Issues if you find anything is missing! Please use the format here.

A seperate web page for paper list is here. The webpage has fancy searchbox. Thanks to @Troublor.

My personal webpage is here and here.

Table of Contents):

• Tools
• Datasets
• Papers
• Talks
• Free GPU Resources

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Format

This repo use a specific format. When you open a new issues, you will find the template.

For tools:

• Project Name: <URL_to_project>
  • A short description (please add the related paper if this tool is from academic paper)"

For paper:

• Paper Name: <URL_to_paper>
  • Author(s) Name(s)
  • Conference/Journal name
  • A short description

Tools

• EvalDNN: https://github.com/yqtianust/EvalDNN

  • EvalDNN: A Toolbox for Evaluating Deep Neural Network Models.
  • Benchmark: https://yqtianust.github.io/EvalDNN-benchmark/index.html

• MuDNN: https://github.com/microsoft/MMdnn

  • MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.

• Netron: https://github.com/lutzroeder/netron

  • a viewer for neural network, deep learning and machine learning models.

• AIF360: https://github.com/IBM/AIF360, http://aif360.mybluemix.net/

  • A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.

• sotabench: https://sotabench.com

  • A free benchmarking service for all open source ML repositories. You can submit your model and this web will run it against some benchmakrs.

• Paper with Code: https://paperswithcode.com/sota

  • A website contains evalution results reported in DL/ML papers, as well as their code (if any). Note the code could be implemented by 3rd party.

• Distiller: https://github.com/NervanaSystems/distiller

  • An open-source Python package for neural network compression research (Pytorch).

• NNCF: https://github.com/openvinotoolkit/nncf_pytorch

  • Neural Network Compression Framework (NNCF) on Pytorch.

• ML-Fairness: https://github.com/sumonbis/ML-Fairness

  • Do the Machine Learning Models on a Crowd Sourced Platform Exhibit Bias? An Empirical Study on Model Fairness.

Dataset

• APRICOT: A Dataset of Physical Adversarial Attacks on Object Detection: https://arxiv.org/abs/1912.08166

Papers

See Paper List. Thanks to @Troublor

Talks

• Safety and Robustness for Deep Learning with Provable Guarantees by Marta Kwiatkowska: slides video

Courses:

• CS 590 Program Analysis For Deep Learning at Purdue University by Xiangyu Zhang: http://xyz-wiki.cs.purdue.edu/cs590-19f/doku.php?id=home

Free GPU Resources

• Google Colab
• Paperspace gradient: https://gradient.paperspace.com/