🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

A foundational Haxe framework for cross-platform development

Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

Python implementation of two low-light image enhancement techniques via illumination map estimation

Lime client built using flutter

Qt-DAB, a general software DAB (DAB+) decoder with a (slight) focus on showing the signal

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

Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)

C# LIME protocol implementation

Application of the LIME algorithm by Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin to the domain of time series classification

Multicycles.org aggregates on one map, more than 300 share vehicles like bikes, scooters, mopeds and cars. Demo APP for the Data Flow API, see https://flow.fluctuo.com

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

ProjectFNF is a mostly quality-of-life engine for Friday Night Funkin. It is easy to understand and is super flexible.

Implementation of the paper, "LIME: Low-Light Image Enhancement via Illumination Map Estimation", which is for my graduation thesis.

Simple C# Keylogger (Keyboard Layout)

Short overview over the components used by Lime Scooters fleet

Overview of different model interpretability libraries.

A port of Friday Night Funkin' v0.2.8 made by rebuilding the code via reverse engineering.

This repository will have all my gnuradio exsamples

Local explanations with uncertainty 💐!