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Joachim Sicking
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2020 – today
- 2024
- [j1]Joachim Sicking
, Maram Akila, Maximilian Pintz, Tim Wirtz, Stefan Wrobel, Asja Fischer:
Wasserstein dropout. Mach. Learn. 113(5): 3161-3204 (2024) - [i12]Liangyu Zhong, Joachim Sicking, Fabian Hüger, Hanno Gottschalk:
VL4AD: Vision-Language Models Improve Pixel-wise Anomaly Detection. CoRR abs/2409.17330 (2024) - 2023
- [b1]Joachim Sicking:
On Modeling and Assessing Uncertainty Estimates in Neural Learning Systems. University of Bonn, Germany, 2023 - [i11]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:
Guideline for Trustworthy Artificial Intelligence - AI Assessment Catalog. CoRR abs/2307.03681 (2023) - 2022
- [c3]Joachim Sicking, Maximilian Pintz, Maram Akila, Tim Wirtz:
DenseHMM: Learning Hidden Markov Models by Learning Dense Representations. ICPRAM 2022: 237-248 - [i10]Joachim Sicking, Maram Akila, Jan David Schneider, Fabian Hüger, Peter Schlicht, Tim Wirtz, Stefan Wrobel:
Tailored Uncertainty Estimation for Deep Learning Systems. CoRR abs/2204.13963 (2022) - [i9]Maximilian Pintz, Joachim Sicking, Maximilian Poretschkin, Maram Akila:
A Survey on Uncertainty Toolkits for Deep Learning. CoRR abs/2205.01040 (2022) - 2021
- [c2]Julia Rosenzweig, Joachim Sicking, Sebastian Houben, Michael Mock, Maram Akila:
Patch Shortcuts: Interpretable Proxy Models Efficiently Find Black-Box Vulnerabilities. CVPR Workshops 2021: 56-65 - [i8]Joachim Sicking, Maram Akila, Maximilian Pintz, Tim Wirtz, Asja Fischer, Stefan Wrobel:
A Novel Regression Loss for Non-Parametric Uncertainty Optimization. CoRR abs/2101.02726 (2021) - [i7]Joachim Sicking, Alexander Kister, Matthias Fahrland, Stefan Eickeler, Fabian Hüger, Stefan Rüping, Peter Schlicht, Tim Wirtz:
Approaching Neural Network Uncertainty Realism. CoRR abs/2101.02974 (2021) - [i6]Julia Rosenzweig, Joachim Sicking, Sebastian Houben, Michael Mock, Maram Akila:
Patch Shortcuts: Interpretable Proxy Models Efficiently Find Black-Box Vulnerabilities. CoRR abs/2104.11691 (2021) - [i5]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
Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety. CoRR abs/2104.14235 (2021) - 2020
- [i4]Joachim Sicking, Maram Akila, Tim Wirtz, Sebastian Houben, Asja Fischer:
Characteristics of Monte Carlo Dropout in Wide Neural Networks. CoRR abs/2007.05434 (2020) - [i3]Joachim Sicking, Maximilian Pintz, Maram Akila, Tim Wirtz:
DenseHMM: Learning Hidden Markov Models by Learning Dense Representations. CoRR abs/2012.09783 (2020) - [i2]Joachim Sicking, Maram Akila, Maximilian Pintz, Tim Wirtz, Asja Fischer, Stefan Wrobel:
Second-Moment Loss: A Novel Regression Objective for Improved Uncertainties. CoRR abs/2012.12687 (2020)
2010 – 2019
- 2018
- [c1]Michael Kamp, Linara Adilova, Joachim Sicking, Fabian Hüger, Peter Schlicht, Tim Wirtz, Stefan Wrobel:
Efficient Decentralized Deep Learning by Dynamic Model Averaging. ECML/PKDD (1) 2018: 393-409 - [i1]Michael Kamp, Linara Adilova, Joachim Sicking, Fabian Hüger, Peter Schlicht, Tim Wirtz, Stefan Wrobel:
Efficient Decentralized Deep Learning by Dynamic Model Averaging. CoRR abs/1807.03210 (2018)
Coauthor Index
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last updated on 2024-10-18 19:30 CEST by the dblp team
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