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Fit interpretable models. Explain blackbox machine learning.
Created
2019-05-03
3,458 commits to develop branch, last one 12 hours ago
Model interpretability and understanding for PyTorch
Created
2019-08-27
1,196 commits to master branch, last one 6 days ago
A curated list of awesome responsible machine learning resources.
r
xai
python
awesome
fairness
ai-safety
secure-ml
reliable-ai
awesome-list
data-science
transparency
explainable-ml
interpretability
interpretable-ai
interpretable-ml
machine-learning
interpretable-machine-learning
privacy-enhancing-technologies
machine-learning-interpretability
privacy-preserving-machine-learning
Created
2018-06-21
1,157 commits to master branch, last one 6 days ago
High-Performance Symbolic Regression in Python and Julia
Created
2020-09-14
2,411 commits to master branch, last one about a month ago
Pytorch-based tools for visualizing and understanding the neurons of a GAN. https://gandissect.csail.mit.edu/
Created
2018-11-19
35 commits to master branch, last one 4 years ago
This repository has no description...
Created
2017-12-27
86 commits to master branch, last one 4 years ago
Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
Created
2018-03-14
169 commits to master branch, last one about a year ago
Distributed High-Performance Symbolic Regression in Julia
Created
2021-01-14
2,982 commits to master branch, last one 7 days ago
A Python package implementing a new interpretable machine learning model for text classification (with visualization tools for Explainable AI :octocat:)
nlp
xai
data-mining
text-mining
text-labeling
ss3-classifier
interpretability
interpretable-ml
machine-learning
text-classification
early-classification
artificial-intelligence
document-categorization
document-classification
sentence-classification
multilabel-classification
machine-learning-algorithms
natural-language-processing
interpretable-machine-learning
explainable-artificial-intelligence
Created
2019-11-11
402 commits to master branch, last one about a year ago
Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.
Created
2020-11-10
232 commits to master branch, last one 7 months ago
An Open-Source Library for the interpretability of time series classifiers
Created
2022-08-15
451 commits to main branch, last one 2 months ago
Explainable Machine Learning in Survival Analysis
r
xai
shap
cox-model
r-package
brier-scores
biostatistics
censored-data
time-to-event
cox-regression
explainable-ai
explainable-ml
interpretable-ml
machine-learning
survival-analysis
variable-importance
explanatory-model-analysis
explainable-machine-learning
interpretable-machine-learning
probabilistic-machine-learning
Created
2022-08-31
480 commits to main branch, last one 4 months ago
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 Classif...
Created
2021-09-30
10 commits to main branch, last one 7 months ago
Optimal Sparse Decision Trees
Created
2019-06-05
28 commits to master branch, last one about a year ago
PIP-Net: Patch-based Intuitive Prototypes Network for Interpretable Image Classification (CVPR 2023)
Created
2023-03-24
15 commits to main branch, last one 8 months ago
[NeurIPS 2023] This is the official code for the paper "TPSR: Transformer-based Planning for Symbolic Regression"
Created
2023-10-09
86 commits to main branch, last one 2 days ago
A PyTorch implementation of constrained optimization and modeling techniques
Created
2023-11-08
76 commits to main branch, last one 6 months ago