interpretable-ml
There are 52 repositories under interpretable-ml topic.
interpretml/interpret
Fit interpretable models. Explain blackbox machine learning.
pytorch/captum
Model interpretability and understanding for PyTorch
jphall663/awesome-machine-learning-interpretability
A curated list of awesome responsible machine learning resources.
MilesCranmer/PySR
High-Performance Symbolic Regression in Python and Julia
CSAILVision/gandissect
Pytorch-based tools for visualizing and understanding the neurons of a GAN. https://gandissect.csail.mit.edu/
jphall663/interpretable_machine_learning_with_python
Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
MilesCranmer/SymbolicRegression.jl
Distributed High-Performance Symbolic Regression in Julia
h2oai/mli-resources
H2O.ai Machine Learning Interpretability Resources
sergioburdisso/pyss3
A Python package implementing a new interpretable machine learning model for text classification (with visualization tools for Explainable AI :octocat:)
chr5tphr/zennit
Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.
fzi-forschungszentrum-informatik/TSInterpret
An Open-Source Library for the interpretability of time series classifiers
xiyanghu/OSDT
Optimal Sparse Decision Trees
12wang3/rrl
The code of NeurIPS 2021 paper "Scalable Rule-Based Representation Learning for Interpretable Classification" and TPAMI paper "Learning Interpretable Rules for Scalable Data Representation and Classification"
ModelOriented/survex
Explainable Machine Learning in Survival Analysis
M-Nauta/PIPNet
PIP-Net: Patch-based Intuitive Prototypes Network for Interpretable Image Classification (CVPR 2023)
deep-symbolic-mathematics/TPSR
[NeurIPS 2023] This is the official code for the paper "TPSR: Transformer-based Planning for Symbolic Regression"
jphall663/diabetes_use_case
Sample use case for Xavier AI in Healthcare conference: https://www.xavierhealth.org/ai-summit-day2/
willbakst/pytorch-lattice
A PyTorch implementation of constrained optimization and modeling techniques
jphall663/hc_ml
Slides, videos and other potentially useful artifacts from various presentations on responsible machine learning.
12wang3/mllp
The code of AAAI 2020 paper "Transparent Classification with Multilayer Logical Perceptrons and Random Binarization".
navdeep-G/interpretable-ml
Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.
benjaminpatrickevans/XAI
Genetic programming method for explaining complex black-box models
BirkhoffG/Explainable-ML-Papers
A list of research papers of explainable machine learning.
schwettmann/visual-vocab
Pytorch-based tools for constructing a vocabulary of visual concepts in a GAN.
slds-lmu/imlplots
Create Interpretable Machine Learning plots with an interactive Shiny based dashboard
h2oai/article-information-2019
Article for Special Edition of Information: Machine Learning with Python
pnxenopoulos/cav-keras
Concept activation vectors for Keras
jphall663/jsm_2018_paper
Paper for 2018 Joint Statistical Meetings: https://ww2.amstat.org/meetings/jsm/2018/onlineprogram/AbstractDetails.cfm?abstractid=329539
andrewli77/DISC
This repository contains an implementation of DISC, an algorithm for learning DFAs for multiclass sequence classification.
CeciPani/MARLENA
A python library to agnostically explain multi-label black-box classifiers (tabular data)
parantapa/integrated-directional-gradients
Implementation of the Integrated Directional Gradients method for Deep Neural Network model explanations.
akifcinar/Machine_Learning_Interpretability
Overview of machine learning interpretation techniques and their implementations
donlapark/XLabel
XLabel: An Explainable Data Labeling Assistant
marcovirgolin/CoGS
A baseline genetic algorithm for the discovery of counterfactuals, implemented in Python for ease of use and heavily leveraging NumPy for speed.
sumuzhao/Investigate-BERT-Non-linearity-Commutativity
Investigate BERT on Non-linearity and Layer Commutativity