Interpretability in ML: A Broad Overview
An overview of the sub-field of machine learning interpretability.
interpretability machine-learning code article

This blog post is my attempt to give an overview of the sub-field of machine learning interpretability. This post isn't comprehensive, but my goal is to review conceptual frameworks, existing research, and future directions.

Don't forget to tag @owenshen24 in your comment, otherwise they may not be notified.

Authors original post
I'm interested in decision-making, productivity, and how to stop all of the incoming apocalypses.
Share this project
Similar projects
InterpretML
Fit interpretable machine learning models. Explain blackbox machine learning.
Structured Self Attention
Implementation for the paper A Structured Self-Attentive Sentence Embedding (https://arxiv.org/abs/1703.03130 ). Model interpretability / explainability.
Neural Additive Models: Interpretable ML with Neural Nets
Neural Additive Models (NAMs) which combine some of the expressivity of DNNs with the inherent intelligibility of generalized additive models.
What does a CNN see?
First super clean notebook showcasing @TensorFlow 2.0. An example of end-to-end DL with interpretability.
Top collections