Model interpretability and understanding for PyTorch
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Aug 30, 2026 - Python
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feature-attribution
Here are 45 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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Sep 1, 2026 - 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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May 13, 2026 - Python
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
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
Explainable AI in Julia.
julia interpretability interpretable-ai explainable-ai xai lrp feature-attribution attribution-methods
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Jul 13, 2026 - 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
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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Aug 27, 2026 - Python
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
Similarity-first interpretability studio for breast tumor samples: pick a case, find its closest “twins” (benign/malignant look-alikes), visualize neighborhood structure, compare feature fingerprints, and run minimal-change counterfactual edits toward a target class. Educational demo only, not for diagnosis.
python machine-learning exploratory-data-analysis plotly data-visualization classification nearest-neighbors knn kaggle-dataset interpretability breast-cancer similarity-search case-based-reasoning explainable-ai streamlit feature-attribution counterfactuals healthcare-analytics medical-ml educational-demo
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Dec 21, 2025 - Python
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
Explainability and saliency maps for PyTorch vision models — GradCAM, EigenCAM, Attention Rollout for CNNs, Vision Transformers (ViT), CLIP, YOLO, DETR, DINO. One-line API.
computer-vision deep-learning pytorch yolo image-classification object-detection cam clip saliency-map dino interpretability class-activation-maps explainable-ai xai gradcam attention-visualization explainability feature-attribution detr vision-transformer
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Jul 11, 2026 - 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
This repository contains the code and material to reproduce the results of the ICML'25 paper "Gradient-based Explanations for Deep Learning Survival Models".
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May 28, 2025 - HTML
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