#计算机科学#🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
翻译 - Shapash使机器学习模型透明且每个人都可以理解
#前端开发#An open source library for creative expression on the web, desktop, mobile and consoles. Inspired by the classic Flash and AIR APIs.
#前端开发#A foundational Haxe framework for cross-platform development
#计算机科学#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
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。
#计算机科学#Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)
#计算机科学#Application of the LIME algorithm by Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin to the domain of time series classification
Implementation of the paper, "LIME: Low-Light Image Enhancement via Illumination Map Estimation", which is for my graduation thesis.
#计算机科学#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.
Short overview over the components used by Lime Scooters fleet
Overview of different model interpretability libraries.
#计算机科学#Local explanations with uncertainty 💐!
#计算机科学#Local Interpretable (Model-agnostic) Visual Explanations - model visualization for regression problems and tabular data based on LIME method. Available on CRAN