Joachim Sicking

According to our database, Joachim Sicking authored at least 14 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Wasserstein dropout.

Joachim Sicking, Maram Akila, Maximilian Pintz, Tim Wirtz, Stefan Wrobel, Asja Fischer

Mach. Learn., May, 2024

VL4AD: Vision-Language Models Improve Pixel-wise Anomaly Detection.

Liangyu Zhong, Joachim Sicking, Fabian Hüger, Hanno Gottschalk

CoRR, 2024

2023

On Modeling and Assessing Uncertainty Estimates in Neural Learning Systems

Joachim Sicking

PhD thesis, 2023

Guideline for Trustworthy Artificial Intelligence - AI Assessment Catalog.

Maximilian Poretschkin, Anna Schmitz, Maram Akila, Linara Adilova, Daniel Becker, Armin B. Cremers, Dirk Hecker, Sebastian Houben, Michael Mock, Julia Rosenzweig, Joachim Sicking, Elena Schulz, Angelika Voss, Stefan Wrobel

CoRR, 2023

2022

A Survey on Uncertainty Toolkits for Deep Learning.

Maximilian Pintz, Joachim Sicking, Maximilian Poretschkin, Maram Akila

CoRR, 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

DenseHMM: Learning Hidden Markov Models by Learning Dense Representations.

Joachim Sicking, Maximilian Pintz, Maram Akila, Tim Wirtz

Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods, 2022

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, Christian Heinzemann, Marco Hoffmann, Nikhil Kapoor, Falk Kappel, Marvin Klingner, Jan Kronenberger, Fabian Küppers, Jonas Löhdefink, Michael Mlynarski, Michael Mock, Firas Mualla, Svetlana Pavlitskaya, Maximilian Poretschkin, Alexander Pohl, Varun Ravi Kumar, Julia Rosenzweig, Matthias Rottmann, Stefan Rüping, Timo Sämann, Jan David Schneider, Elena Schulz, Gesina Schwalbe, Joachim Sicking, Toshika Srivastava, Serin Varghese, Michael Weber, Sebastian Wirkert, Tim Wirtz, Matthias Woehrle

CoRR, 2021

Approaching Neural Network Uncertainty Realism.

Joachim Sicking, Alexander Kister, Matthias Fahrland, Stefan Eickeler, Fabian Hüger, Stefan Rüping, Peter Schlicht, Tim Wirtz

CoRR, 2021

A Novel Regression Loss for Non-Parametric Uncertainty Optimization.

Joachim Sicking, Maram Akila, Maximilian Pintz, Tim Wirtz, Asja Fischer, Stefan Wrobel

CoRR, 2021

Patch Shortcuts: Interpretable Proxy Models Efficiently Find Black-Box Vulnerabilities.

Julia Rosenzweig, Joachim Sicking, Sebastian Houben, Michael Mock, Maram Akila

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2021

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

Characteristics of Monte Carlo Dropout in Wide Neural Networks.

Joachim Sicking, Maram Akila, Tim Wirtz, Sebastian Houben, Asja Fischer

CoRR, 2020

2018

Efficient Decentralized Deep Learning by Dynamic Model Averaging.

Michael Kamp, Linara Adilova, Joachim Sicking, Fabian Hüger, Peter Schlicht, Tim Wirtz, Stefan Wrobel

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2018