Missing values in real-world data pose a significant and unique challenge to algorithmic fairness.

One challenging modality for real-time processing is multi-photon calcium imaging (CI).

This paper presents an innovative approach to classification tasks called Credal Deep Ensembles (CreDEs), ensembles of novel Credal-Set Neural Networks (CreNets), aiming to improve EU quantification in the framework of credal inference.

To address this challenge, we propose the framework Lambda for dangling detection and entity alignment.