Model interpretability and understanding for PyTorch
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Dec 2, 2025 - Python
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feature-attribution
Here are 29 public repositories matching this topic...
Shapley Interactions and Shapley Values for Machine Learning
python machine-learning game-theory interpretability feature-importance explainable-ai banzhaf-index shapley interpretable-machine-learning shap explainability feature-attribution feature-interactions shapley-interactions shapiq
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Nov 26, 2025 - Python
Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.
python machine-learning deep-learning attribution pytorch interpretability interpretable-ai interpretable-ml explainable-ai xai lrp explainability feature-attribution
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Jul 31, 2025 - Python
Collection of NLP model explanations and accompanying analysis tools
natural-language-processing transformers datasets heatmaps interpretability explainability saliency-maps feature-attribution captum explainable-nlp
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Jun 26, 2023 - Jsonnet
An Open-Source Library for the interpretability of time series classifiers
time-series interpretability explainable-artificial-intelligence interpretable-ml explainable-ai explainable-ml interpretable-machine-learning counterfactual-explanations feature-attribution
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Nov 19, 2025 - Python
Explainable AI in Julia.
julia interpretability interpretable-ai explainable-ai xai lrp feature-attribution attribution-methods
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Dec 1, 2025 - 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.
machine-learning deep-neural-networks deep-learning cnn pytorch image-classification manifold-learning pytorch-tutorial classification-model cub-200-2011 feature-attribution activation-maximization caltech-birds
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Feb 21, 2025 - Jupyter Notebook
Counterfactual SHAP: a framework for counterfactual feature importance
machine-learning xgboost feature-importance explainable-artificial-intelligence explanations explainable-ai explainable-ml shapley tree-based shap explainability counterfactual-explanations shapley-values feature-attribution counterfactuals explainable-machine-learning algorithmic-recourse facct2022
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Jul 6, 2023 - HTML
Materials for "Quantifying the Plausibility of Context Reliance in Neural Machine Translation" at ICLR'24 🐑 🐑
machine-translation language-model interpretability explainable-ai feature-attribution document-level-nmt large-language-models
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Apr 18, 2024 - Jupyter Notebook
Materials for the Lab "Explaining Neural Language Models from Internal Representations to Model Predictions" at AILC LCL 2023 🔍
tutorial lab transformers interpretability ferret xai diagnostic-classifiers feature-attribution probing-task inseq ailc
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May 31, 2023 - Jupyter Notebook
The official repo for the EACL 2023 paper "Quantifying Context Mixing in Transformers"
transformers attention-mechanism interpretability contextualized-representation pre-trained-language-models feature-attribution
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Mar 31, 2023 - Python
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)
text-classification transformers interpretability explainability integrated-gradients saliency-maps feature-attribution saliency-methods explainable-nlp chatgpt
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Jul 27, 2023 - Python
Efficient and accurate explanation estimation with distribution compression (ICLR 2025 Spotlight)
sage pdp explainable-ai interpretable-machine-learning shap dalex feature-attribution kernel-thinning goodpoints
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Feb 11, 2025 - Python
⛈️ Code for the paper "End-to-End Prediction of Lightning Events from Geostationary Satellite Images"
machine-learning meteorology neural-networks satellite-imagery class-imbalance nowcasting feature-attribution lightning-prediction short-term-forecasts
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Jul 28, 2024 - Python
Implementation of the Integrated Directional Gradients method for Deep Neural Network model explanations.
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Aug 25, 2021 - Python
Sum-of-Parts: Self-Attributing Neural Networks with End-to-End Learning of Feature Groups
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Oct 7, 2025 - Jupyter Notebook
Reproducible code for our paper "Explainable Learning with Gaussian Processes"
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Jun 1, 2024 - MATLAB
Robustness of Global Feature Effect Explanations (ECML PKDD 2024)
partial-dependence-plot explainable-ai interpretable-machine-learning iml dalex feature-attribution explanatory-model-analysis explainable-machine-learning accumulated-local-effects
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Aug 26, 2024 - Jupyter Notebook
This article explores the theory behind explainable car pricing using value decomposition, showing how machine learning models can break a predicted price into intuitive components such as brand premium, age depreciation, mileage influence, condition effects, and transmission or fuel-type adjustments.
random-forest predictive-modeling sensitivity-analysis machine-learning-theory interpretable-ml explainable-ai model-interpretability economic-modeling feature-attribution applied-ml consumer-analytics value-decomposition data-science-methods transparent-ai pricing-intelligence automotive-analytics car-price-modeling ml-explainability automotive-ml explainable-pricing
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Dec 10, 2025 - Python
Bachelor's thesis for degree in Economics at HSE University, Saint-Petersburg (2022)
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Oct 14, 2022 - Jupyter Notebook
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