Awesome Heart Sound Analysis - A Survey

Trustworthy AI/ML course by Professor Birhanu Eshete, University of Michigan, Dearborn.

Classification and Object Detection XAI methods (CAM-based, backpropagation-based, perturbation-based, statistic-based) for thyroid cancer ultrasound images

moDel Agnostic Language for Exploration and eXplanation

This repo contains the code of my Master's Thesis. Specifically, it consists in exploring different techniques(Explanable AI, Physics Informed NN, ...) to perform State Estimation

Local Universal Rule-based Explanations

Deep Insight And Neural Network Analysis

Explainable Artificial Intelligence through Contextual Importance and Utility

Endocrine Disruption Explainer is a code to generate structural alerts of endocrine disruption of chemcial compounds using Local Interpretable Model-Agnostic Explanations (LIME) of machine learning models from TOX-21, EDC, and EDKB-FDA datasets.

This project provides GOLang implementation of Neuro-Evolution of Augmenting Topologies (NEAT) with Novelty Search optimization aimed to solve deceptive tasks with strong local optima

Official Implementation of TMLR's paper: "TabCBM: Concept-based Interpretable Neural Networks for Tabular Data"

An Open-Source Library for the interpretability of time series classifiers

This repository is associated with interpretable/explainable ML model for liquefaction potential assessment of gravelly soils. This model is developed using LightGBM and SHAP.

ICCV2021 paper: Interpretable Image Recognition by Constructing Transparent Embedding Space (TesNet)

This repository contains the expliratory and research work from the DIANNA project

Repo for the paper: "Hide-and-Seek: A Template for Explainable AI", by Thanos Tagaris and Andreas Stafylopatis

A curated list of explainability-related papers, articles, and resources focused on Large Language Models (LLMs). This repository aims to provide researchers, practitioners, and enthusiasts with insights into the explainability implications, challenges, and advancements surrounding these powerful models.

El proyecto se centra en la destilación de conocimiento y técnicas de explicabilidad para mejorar el rendimiento de redes neuronales en imágenes naturales.

A library that helps to explain AI models in a really quick & easy way

Explainable Feature Construction (EFC)