#计算机科学#🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
#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
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Overview of different model interpretability libraries.
#计算机科学#Local explanations with uncertainty 💐!