#计算机科学#A game theoretic approach to explain the output of any machine learning model.
翻译 - 一种解释任何机器学习模型输出的博弈论方法。
#计算机科学#A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
翻译 - 精选的优秀开源库列表,用于部署,监视,版本化和扩展您的机器学习
#计算机科学#Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
翻译 - 在Pytorch中为CNN和视觉变压器实现了许多类激活图方法。包括Grad-CAM,Grad-CAM ++,Score-CAM,Ablation-CAM和XGrad-CAM
#计算机科学#Fit interpretable models. Explain blackbox machine learning.
翻译 - 拟合可解释的模型。说明黑匣子机器学习。
Model interpretability and understanding for PyTorch
翻译 - PyTorch的模型可解释性和理解
#计算机科学#StellarGraph - Machine Learning on Graphs
翻译 - StellarGraph-图上的机器学习
#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
A JAX research toolkit for building, editing, and visualizing neural networks.
ReFT: Representation Finetuning for Language Models
#计算机科学#A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.
翻译 - 一组用于Keras中的决策林模型的训练,服务和解释的最新算法。
#大语言模型#The Truth Is In There: Improving Reasoning in Language Models with Layer-Selective Rank Reduction