Model interpretability and understanding for PyTorch

Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.

Collection of NLP model explanations and accompanying analysis tools

An Open-Source Library for the interpretability of time series classifiers

Explainable AI in Julia.

A set of notebooks as a guide to the process of fine-grained image classification of birds species, using PyTorch based deep neural networks.

Counterfactual SHAP: a framework for counterfactual feature importance

Materials for "Quantifying the Plausibility of Context Reliance in Neural Machine Translation" at ICLR'24 🐑 🐑

Materials for the Lab "Explaining Neural Language Models from Internal Representations to Model Predictions" at AILC LCL 2023 🔍

The official repo for the EACL 2023 paper "Quantifying Context Mixing in Transformers"

Code and data for the ACL 2023 NLReasoning Workshop paper "Saliency Map Verbalization: Comparing Feature Importance Representations from Model-free and Instruction-based Methods" (Feldhus et al., 2023)

Implementation of the Integrated Directional Gradients method for Deep Neural Network model explanations.

⛈️ Code for the paper "End-to-End Prediction of Lightning Events from Geostationary Satellite Images"

Reproducible code for our paper "Explainable Learning with Gaussian Processes"

Bachelor's thesis for degree in Economics at HSE University, Saint-Petersburg (2022)

Codes for the paper On marginal feature attributions of tree-based models

Robustness of Global Feature Effect Explanations (ECML PKDD 2024)

NO2 Prediction: Performance and Robustness Comparison between Random Forest and Graph Neural Network

Efficient and Accurate Explanation Estimation with Distribution Compression (ICML 2024 Workshops)

Understanding the UNet for Traffic Forecasting Task - A Visual Analytics Approach