Efficient Decentralized Deep Learning by Dynamic Model Averaging. - Michael Kamp; Linara Adilova; Joachim Sicking; Fabian Hüger; Peter Schlicht; Tim Wirtz; Stefan Wrobel (ECML/PKDD (1), 2018)

Characteristics of Monte Carlo Dropout in Wide Neural Networks. - Joachim Sicking; Maram Akila; Tim Wirtz; Sebastian Houben; Asja Fischer (CoRR, 2020)

Second-Moment Loss: A Novel Regression Objective for Improved Uncertainties. - Joachim Sicking; Maram Akila; Maximilian Pintz; Tim Wirtz; Asja Fischer; Stefan Wrobel (CoRR, 2020)

Patch Shortcuts: Interpretable Proxy Models Efficiently Find Black-Box Vulnerabilities. - Julia Rosenzweig; Joachim Sicking; Sebastian Houben; Michael Mock; Maram Akila (CVPR Workshops, 2021)

A Novel Regression Loss for Non-Parametric Uncertainty Optimization. - Joachim Sicking; Maram Akila; Maximilian Pintz; Tim Wirtz; Asja Fischer; Stefan Wrobel (CoRR, 2021)

Approaching Neural Network Uncertainty Realism. - Joachim Sicking; Alexander Kister; Matthias Fahrland; Stefan Eickeler; Fabian Hüger; Stefan Rüping 001; Peter Schlicht; Tim Wirtz (CoRR, 2021)

Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety. - Sebastian Houben; Stephanie Abrecht; Maram Akila; Andreas Bär; Felix Brockherde; Patrick Feifel; Tim Fingscheidt; Sujan Sai Gannamaneni; Seyed Eghbal Ghobadi; Ahmed Hammam; Anselm Haselhoff; Felix Hauser; et al. (CoRR, 2021)

DenseHMM: Learning Hidden Markov Models by Learning Dense Representations. - Joachim Sicking; Maximilian Pintz; Maram Akila; Tim Wirtz (ICPRAM, 2022)

Tailored Uncertainty Estimation for Deep Learning Systems. - Joachim Sicking; Maram Akila; Jan David Schneider; Fabian Hüger; Peter Schlicht; Tim Wirtz; Stefan Wrobel (CoRR, 2022)

A Survey on Uncertainty Toolkits for Deep Learning. - Maximilian Pintz; Joachim Sicking; Maximilian Poretschkin; Maram Akila (CoRR, 2022)

Guideline for Trustworthy Artificial Intelligence - AI Assessment Catalog. - Maximilian Poretschkin; Anna Schmitz 001; Maram Akila; Linara Adilova; Daniel Becker 010; Armin B. Cremers; Dirk Hecker; Sebastian Houben; Michael Mock; Julia Rosenzweig; Joachim Sicking; Elena Schulz; et al. (CoRR, 2023)

On Modeling and Assessing Uncertainty Estimates in Neural Learning Systems - Joachim Sicking (2023)

VL4AD: Vision-Language Models Improve Pixel-wise Anomaly Detection. - Liangyu Zhong; Joachim Sicking; Fabian Hüger; Hanno Gottschalk (CoRR, 2024)

Wasserstein dropout. - Joachim Sicking; Maram Akila; Maximilian Pintz; Tim Wirtz; Stefan Wrobel; Asja Fischer (Mach. Learn., 2024)

FOCUS: Internal MLLM Representations for Efficient Fine-Grained Visual Question Answering. - Liangyu Zhong; Fabio Rosenthal; Joachim Sicking; Fabian Hüger; Thorsten Bagdonat; Hanno Gottschalk; Leo Schwinn (CoRR, 2025)