• Stars
    star
    74
  • Rank 426,553 (Top 9 %)
  • Language
    Python
  • License
    MIT License
  • Created over 3 years ago
  • Updated over 2 years ago

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

This repository contains the implementation of Dynamask, a method to identify the features that are salient for a model to issue its prediction when the data is represented in terms of time series. For more details on the theoretical side, please read our ICML 2021 paper: 'Explaining Time Series Predictions with Dynamic Masks'.

More Repositories

1

Simplex

This repository contains the implementation of SimplEx, a method to explain the latent representations of black-box models with the help of a corpus of examples. For more details, please read our NeurIPS 2021 paper: 'Explaining Latent Representations with a Corpus of Examples'.
Python
22
star
2

Label-Free-XAI

This repository contains the implementation of Label-Free XAI, a new framework to adapt explanation methods to unsupervised models. For more details, please read our ICML 2022 paper: 'Label-Free Explainability for Unsupervised Models'.
Python
22
star
3

FourierDiffusion

This repository implements time series diffusion in the frequency domain.
Jupyter Notebook
20
star
4

Symbolic-Pursuit

Github for the NIPS 2020 paper "Learning outside the black-box: at the pursuit of interpretable models"
Jupyter Notebook
15
star
5

CARs

This repository contains the implementation of Concept Activation Regions, a new framework to explain deep neural networks with human concepts. For more details, please read our NeurIPS 2022 paper: 'Concept Activation Regions: a Generalized Framework for Concept-Based Explanations.
Python
9
star
6

RobustXAI

This repository contains the implementation of the explanation invariance and equivariance metrics, a framework to evaluate the robustness of interpretability methods.
Jupyter Notebook
8
star
7

ITErpretability

This repository contains the implementation of ITErpretability, a new framework to benchmark treatment effect deep neural network estimators with interpretability. For more details, please read our NeurIPS 2022 paper: 'Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability'.
Jupyter Notebook
2
star
8

Projet-Ray-Tracing

MATLAB
1
star