2 papers
cs.HC2019
explAIner: A Visual Analytics Framework for Interactive and Explainable Machine Learning
Thilo Spinner, Udo Schlegel, Hanna Schäfer +1
We propose a framework for interactive and explainable machine learning that enables users to (1) understand machine learning models; (2) diagnose model limitations using different…
cs.LG2019
Uncertainty-Aware Principal Component Analysis
Jochen Görtler, Thilo Spinner, Dirk Streeb +2
We present a technique to perform dimensionality reduction on data that is subject to uncertainty. Our method is a generalization of traditional principal component analysis (PCA)…