explanations 开源项目推荐（81 个）

TheAlgorithms /Algorithms-Explanation

Popular algorithms explained in simple language with examples and links to their implementation in various programming languages and other required resources.

SeldonIO /alibi

Algorithms for explaining machine learning models

ModelOriented /DALEX

moDel Agnostic Language for Exploration and eXplanation

PaddlePaddle /InterpretDL

InterpretDL: Interpretation of Deep Learning Models，基于『飞桨』的模型可解释性算法库。

mwtarnowski /colmap-parameters

Some information about parameters and options available in COLMAP - SfM & MVS software. https://colmap.github.io

guidelabs /steerling

Interpretable Causal Diffusion Language Models

praveenpuglia /shadow-dom-in-depth

Everything you need to know about Shadow DOM

liznerski /fcdd

Repository for the Explainable Deep One-Class Classification paper

altamiracorp /awesome-xai

Awesome Explainable AI (XAI) and Interpretable ML Papers and Resources

fhvilshoj /TorchLRP

A PyTorch 1.6 implementation of Layer-Wise Relevance Propagation (LRP).

agitrubard /java-spring-best-practices

A repository dedicated to showcasing best practices in Java and Spring through concise code snippets.

dylan-slack /Fooling-LIME-SHAP

Adversarial Attacks on Post Hoc Explanation Techniques (LIME/SHAP)

mertyg /debug-mistakes-cce

Meaningfully debugging model mistakes with conceptual counterfactual explanations. ICML 2022

wilsonjr /ClusterShapley

Explaining dimensionality results using SHAP values

BirkhoffG /Explainable-ML-Papers

A list of research papers of explainable machine learning.

akarasman /yolo-heatmaps

A utility for generating heatmaps of YOLO object detection models using Layerwise Relevance Propagation (LRP/CRP).

Jawz84 /explainpowershell

PowerShell version of explainshell.com

Villy-P /under-the-hood

All files included in my Youtube series: Under the Hood

jpmorganchase /cf-shap

Counterfactual SHAP: a framework for counterfactual feature importance

dmitrykazhdan /MARLeME

General-purpose library for extracting interpretable models from Multi-Agent Reinforcement Learning systems