Algorithms for explaining machine learning models
A game theoretic approach to explain the output of any machine learning model.
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
Fit interpretable models. Explain blackbox machine learning.
Standardized Serverless ML Inference Platform on Kubernetes
Code for the paper "Getting a CLUE: A Method for Explaining Uncertainty Estimates"
A curated list of awesome responsible machine learning resources.
StellarGraph - Machine Learning on Graphs
? Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM, IS-CAM, XGrad-CAM, Layer-CAM)
