Explain a black-box module in natural language.
-
Updated
Jun 1, 2024 - HTML
Explain a black-box module in natural language.
A project focusing on binary classification using Explainable Artificial Intelligence (XAI) methods, specifically SHAP (SHapley Additive exPlanations), and Grid Search for hyperparameter tuning. The project utilizes EfficientNetV2-B0 architecture on the Cat VS Dog dataset.
SHAP Interaction Quantification (short SHAP-IQ) is an XAI framework extending on the well-known shap explanations by introducing interactions i.e. synergy scores.
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
Fit interpretable models. Explain blackbox machine learning.
Explainable Reinforcement Learning (XRL) Resources
A curated list of awesome academic research, books, code of ethics, data sets, institutes, newsletters, principles, podcasts, reports, tools, regulations and standards related to Responsible AI, Trustworthy AI, and Human-Centered AI.
Efficient R implementation of SHAP
Explain LLMs for Entity Resolution
GNN Explainability in a regression setting - semester project for Applied Mathematics MSc @ EPFL
Collecting fish image data, after training classifiers grad-cam is applied for the prediction interpretation
A curated list of awesome responsible machine learning resources.
Papers about explainability of GNNs
👋 Xplique is a Neural Networks Explainability Toolbox
An easier approach to using and understanding ML models
Prototypical Concept-based Explanations, accepted at SAIAD workshop at CVPR 2024.
NeuroXAI: Adaptive, Robust, Explainable Surrogate Framework for Determination of Channel Importance in EEG Application
An open platform for accelerating the development of eXplainable AI systems
Scikit-learn friendly library to interpret, and prompt-engineer text datasets using large language models.
Add a description, image, and links to the xai topic page so that developers can more easily learn about it.
To associate your repository with the xai topic, visit your repo's landing page and select "manage topics."