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explanations
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Popular algorithms explained in simple language with examples and links to their implementation in various programming languages and other required resources.
Algorithms for explaining machine learning models
moDel Agnostic Language for Exploration and eXplanation
InterpretDL: Interpretation of Deep Learning Models，基于『飞桨』的模型可解释性算法库。
Everything you need to know about Shadow DOM
Repository for the Explainable Deep One-Class Classification paper
Some information about parameters and options available in COLMAP - SfM & MVS software. https://colmap.github.io
Awesome Explainable AI (XAI) and Interpretable ML Papers and Resources
A collection of common algorithms and data structures implemented in Java.
A PyTorch 1.6 implementation of Layer-Wise Relevance Propagation (LRP).
A repository dedicated to showcasing best practices in Java and Spring through concise code snippets.
Adversarial Attacks on Post Hoc Explanation Techniques (LIME/SHAP)
Meaningfully debugging model mistakes with conceptual counterfactual explanations. ICML 2022
Explaining dimensionality results using SHAP values
A utility for generating heatmaps of YOLOv8 using Layerwise Relevance Propagation (LRP/CRP).
A list of research papers of explainable machine learning.
PowerShell version of explainshell.com
General-purpose library for extracting interpretable models from Multi-Agent Reinforcement Learning systems
Counterfactual SHAP: a framework for counterfactual feature importance
Anupam Datta, Matt Fredrikson, Klas Leino, Kaiji Lu, Shayak Sen, Zifan Wang