Inspecting adversarial examples using the fisher information. - Jörg Martin; Clemens Elster (Neurocomputing, 2020)

Detecting unusual input to neural networks. - Jörg Martin; Clemens Elster (Appl. Intell., 2021)

About the regularity of the discriminator in conditional WGANs. - Jörg Martin (CoRR, 2021)

Errors-in-Variables for deep learning: rethinking aleatoric uncertainty. - Jörg Martin; Clemens Elster (CoRR, 2021)

Explainability for deep learning in mammography image quality assessment. - Narbota Amanova; Jörg Martin; Clemens Elster (Mach. Learn. Sci. Technol., 2022)

Aleatoric Uncertainty for Errors-in-Variables Models in Deep Regression. - Jörg Martin; Clemens Elster (Neural Process. Lett., 2023)

A framework for benchmarking uncertainty in deep regression. - Franko Schmähling; Jörg Martin; Clemens Elster (Appl. Intell., 2023)

About the generalizability of deep learning based image quality assessment in mammography. - Josua Faller; Narbota Amanova; Ruben E. van Engen; Jörg Martin; Clemens Elster (Mach. Learn. Sci. Technol., 2023)

Finding the input features that reduce the entropy of a neural network's prediction. - Narbota Amanova; Jörg Martin; Clemens Elster (Appl. Intell., 2024)

Optimal Subspace Inference for the Laplace Approximation of Bayesian Neural Networks. - Josua Faller; Jörg Martin (CoRR, 2025)

Deep Errors-in-Variables using a diffusion model. - Josua Faller; Jörg Martin; Clemens Elster (Mach. Learn., 2025)