Create Interpretable Machine Learning plots with an interactive Shiny based dashboard
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Updated
Sep 19, 2018 - R
Create Interpretable Machine Learning plots with an interactive Shiny based dashboard
Optimizing Mind static website v1
Interpretable AI with Safeguard AI (paper study, implement-code review)
Paper for 2018 Joint Statistical Meetings: https://ww2.amstat.org/meetings/jsm/2018/onlineprogram/AbstractDetails.cfm?abstractid=329539
PyTorch-based tools for visualizing and understanding the neurons of a GAN.
Slides, videos and other potentially useful artifacts from various presentations on responsible machine learning.
Tutorial on Representer Point Selection for Explaining Deep Neural Networks (CIFAR-10)
explainable and interpretable methods for AI and data science
Article for Special Edition of Information: Machine Learning with Python
Interpretability and Fairness in Machine Learning
H2O.ai Machine Learning Interpretability Resources
Overview of machine learning interpretation techniques and their implementations
Getting explanations for predictions made by black box models.
This repository contains an implementation of DISC, an algorithm for learning DFAs for multiclass sequence classification.
Pytorch-based tools for visualizing and understanding the neurons of a GAN. https://gandissect.csail.mit.edu/
Source code for PIP: Pictorial Interpretable Prototype Learning for Time Series Classification
A list of research papers of explainable machine learning.
Implementation of the Integrated Directional Gradients method for Deep Neural Network model explanations.
XMLX GitHub configuration
Pytorch-based tools for constructing a vocabulary of visual concepts in a GAN.
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