#Awesome#A curated list of awesome responsible machine learning resources.
#计算机科学#Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
#计算机科学#H2O.ai Machine Learning Interpretability Resources
#计算机科学#Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code. We are looking for co-authors to take this project forward. Reach out @ ms...
Predicting the Likelihood to Purchase a Financial Product Following a Direct Marketing Campaign
#计算机科学#Sample use case for Xavier AI in Healthcare conference: https://www.xavierhealth.org/ai-summit-day2/
An interpretable machine learning pipeline over knowledge graphs
#计算机科学#The code of AAAI 2020 paper "Transparent Classification with Multilayer Logical Perceptrons and Random Binarization".
#计算机科学#Slides, videos and other potentially useful artifacts from various presentations on responsible machine learning.
#计算机科学#Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.
#计算机科学#Article for Special Edition of Information: Machine Learning with Python
#计算机科学#Paper for 2018 Joint Statistical Meetings: https://ww2.amstat.org/meetings/jsm/2018/onlineprogram/AbstractDetails.cfm?abstractid=329539
#计算机科学#TeleGam: Combining Visualization and Verbalization for Interpretable Machine Learning
#计算机科学#Rule Extraction from Bayesian Networks
#计算机科学#Overview of machine learning interpretation techniques and their implementations
#计算机科学#INVASE: Instance-wise Variable Selection . For more details, read the paper "INVASE: Instance-wise Variable Selection using Neural Networks," International Conference on Learning Representations (ICL...
Demonstration of InterpretME, an interpretable machine learning pipeline
#计算机科学#This project contains the data, code and results used in the paper title "On the relationship of novelty and value in digitalization patents: A machine learning approach".
XMLX GitHub configuration