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Credal Wrapper of Model Averaging for Uncertainty Estimation on Out-Of-Distribution Detection
This paper presents an innovative approach, called credal wrapper, to formulating a credal set representation of model averaging for Bayesian neural networks (BNNs) and deep ensembles, capable of improving uncertainty estimation in classification tasks.
CreINNs: Credal-Set Interval Neural Networks for Uncertainty Estimation in Classification Tasks
Uncertainty estimation is increasingly attractive for improving the reliability of neural networks.
Random-Set Convolutional Neural Network (RS-CNN) for Epistemic Deep Learning
Machine learning is increasingly deployed in safety-critical domains where robustness against adversarial attacks is crucial and erroneous predictions could lead to potentially catastrophic consequences.
An introduction to optimization under uncertainty -- A short survey
The goal of this survey paper is to briefly touch upon the state of the art in a variety of different methods and refer the reader to other literature for more in-depth treatments of the topics discussed here.
