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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.

dynamask

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'.

fourierdiffusion

This repository implements time series diffusion in the frequency domain.

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'.

jonathancrabbe.github.io

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'.

personal-website-content

projet-3-m-canique-rationnelle-ii-

Fichiers relatifs au projet de mécanique rationnelle 2

projet-ba2-show-laser

Github des fichiers utiles au projet

projet-infoh200

projet-ray-tracing

robustxai

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

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'.

symbolic-pursuit

Github for the NIPS 2020 paper "Learning outside the black-box: at the pursuit of interpretable models"

syntheses

Synthèses de l'École Polytechnique de Bruxelles

time_series_explainability

ts-interpretability-benchmark