Implementation of the Integrated Directional Gradients method for Deep Neural Network model explanations.
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Updated
Aug 25, 2021 - Python
Implementation of the Integrated Directional Gradients method for Deep Neural Network model explanations.
Bachelor's thesis for degree in Economics at HSE University, Saint-Petersburg (2022)
Reproducible code for our paper "Explainable Learning with Gaussian Processes"
Codes for the paper On marginal feature attributions of tree-based models
Feature Attribution methods for neurons and Evolution experiments
Materials for "Quantifying the Plausibility of Context Reliance in Neural Machine Translation" at ICLR'24 🐑 🐑
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)
⛈️ Code for the paper "End-to-End Prediction of Lightning Events from Geostationary Satellite Images"
Materials for the Lab "Explaining Neural Language Models from Internal Representations to Model Predictions" at AILC LCL 2023 🔍
Explainable AI in Julia.
The official repo for the EACL 2023 paper "Quantifying Context Mixing in Transformers"
Counterfactual SHAP: a framework for counterfactual feature importance
A set of notebooks as a guide to the process of fine-grained image classification of birds species, using PyTorch based deep neural networks.
Collection of NLP model explanations and accompanying analysis tools
An Open-Source Library for the interpretability of time series classifiers
Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.
Model interpretability and understanding for PyTorch
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