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Graduate research project in computer vision and deep learning explainability

Binary classification, SHAP (Explainable Artificial Intelligence), and Grid Search (for tuning hyperparameters) using EfficientNetV2-B0 on Cat VS Dog dataset.

Evaluation framework for post hoc explanation methods | Explainable AI (XAI)

A curated list of awesome contrastive explanation in ML resources

Source code of NeurIPS'21 paper: Learning Interpretable Decision Rule Sets: A Submodular Optimization Approach

Experiments on machine learning explainability

Demo on performing Explainable AI using the SHAP Library

Exploration of ML Explainability Methods on the Statlog (Heart) Data Set

Code for the paper Tětková et al.: Knowledge Graphs for Empirical Concept Retrieval (accepted to The 2nd World Conference on eXplainable Artificial Intelligence).

XMLX GitHub configuration

Awesome papers on Interpretable Machine Learning

This repository provides a summarization of recent empirical studies/human studies that measure human understanding with machine explanations in human-AI interactions.

Chest X-Ray Images (Pneumonia) classification: https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia

Validate the model card document in a GitHub action

Repository for the Linkit Beginner Challenge on Explainable ML using SHAP values.

Binary Classification with Neural Networks and Bayesian Optimization and SHAP Model Explanations

A method for conditional shapley value estimation, built off the shapr package: https://github.com/NorskRegnesentral/shapr/tree/master

Research project on generation of counterfactuals for eXplainable AI, based on Bayesian Generation

Ths repo has the list of Interesting Literature in the domain of XAI