CreINNs: Credal-Set Interval Neural Networks for Uncertainty Estimation in Classification Tasks

no code implementations • 10 Jan 2024 • Kaizheng Wang, Keivan Shariatmadar, Shireen Kudukkil Manchingal, Fabio Cuzzolin, David Moens, Hans Hallez

Uncertainty estimation is increasingly attractive for improving the reliability of neural networks.

Out-of-Distribution Detection

Random-Set Convolutional Neural Network (RS-CNN) for Epistemic Deep Learning

no code implementations • 11 Jul 2023 • Shireen Kudukkil Manchingal, Muhammad Mubashar, Kaizheng Wang, Keivan Shariatmadar, Fabio Cuzzolin

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.

Epistemic Deep Learning

no code implementations • 15 Jun 2022 • Shireen Kudukkil Manchingal, Fabio Cuzzolin

The belief function approach to uncertainty quantification as proposed in the Demspter-Shafer theory of evidence is established upon the general mathematical models for set-valued observations, called random sets.

Uncertainty Quantification