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Joachim Sicking
Joachim Sicking
Geverifieerd e-mailadres voor iais.fraunhofer.de
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Efficient decentralized deep learning by dynamic model averaging
M Kamp, L Adilova, J Sicking, F Hüger, P Schlicht, T Wirtz, S Wrobel
Joint European conference on machine learning and knowledge discovery in …, 2018
982018
Pulse shape dependence in the dynamically assisted Sauter-Schwinger effect
MF Linder, C Schneider, J Sicking, N Szpak, R Schützhold
Physical Review D 92 (8), 085009, 2015
612015
Inspect, understand, overcome: a survey of practical methods for AI safety
S Houben, S Abrecht, M Akila, A Bär, F Brockherde, P Feifel, ...
Deep Neural Networks and Data for Automated Driving, 3-78, 2022
262022
Concurrent credit portfolio losses
J Sicking, T Guhr, R Schäfer
Plos one 13 (2), e0190263, 2018
82018
Vertrauenswürdiger Einsatz von Künstlicher Intelligenz. Handlungsfelder aus philosophischer, ethischer, rechtlicher und technologischer Sicht als Grundlage für eine …
AB Cremers, A Englander, M Gabriel, D Hecker, M Mock, M Poretschkin, ...
Fraunhofer-Institut für intelligente Analyse-und Informationssysteme (IAIS …, 2019
62019
Characteristics of monte carlo dropout in wide neural networks
J Sicking, M Akila, T Wirtz, S Houben, A Fischer
arXiv preprint arXiv:2007.05434, 2020
42020
A Novel Regression Loss for Non-Parametric Uncertainty Optimization
J Sicking, M Akila, M Pintz, T Wirtz, A Fischer, S Wrobel
arXiv preprint arXiv:2101.02726, 2021
32021
Vertrauenswürdiger Einsatz von Künstlicher Intelligenz
AB Cremers, A Englander, M Gabriel, D Hecker, M Mock, M Poretschkin, ...
Fraunhofer IAIS, 2019
32019
Leitfaden zur Gestaltung vertrauenswürdiger Künstlicher Intelligenz (KI-Prüfkatalog)
M Poretschkin, A Schmitz, M Akila, L Adilova, D Becker, AB Cremers, ...
Fraunhofer IAIS, 2021
22021
Approaching neural network uncertainty realism
J Sicking, A Kister, M Fahrland, S Eickeler, F Hüger, S Rüping, P Schlicht, ...
arXiv preprint arXiv:2101.02974, 2021
12021
Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety
S Rüping, E Schulz, J Sicking, T Wirtz, M Akila, SS Gannamaneni, M Mock, ...
Deep Neural Networks and Data for Automated Driving: Robustness, Uncertainty …, 2022
2022
A Survey on Uncertainty Toolkits for Deep Learning
M Pintz, J Sicking, M Poretschkin, M Akila
arXiv preprint arXiv:2205.01040, 2022
2022
Tailored Uncertainty Estimation for Deep Learning Systems
J Sicking, M Akila, JD Schneider, F Hüger, P Schlicht, T Wirtz, S Wrobel
arXiv preprint arXiv:2204.13963, 2022
2022
Patch Shortcuts: Interpretable Proxy Models Efficiently Find Black-Box Vulnerabilities
J Rosenzweig, J Sicking, S Houben, M Mock, M Akila
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021
2021
Second-Moment Loss: A Novel Regression Objective for Improved Uncertainties
J Sicking, M Akila, M Pintz, T Wirtz, A Fischer, S Wrobel
arXiv preprint arXiv:2012.12687, 2020
2020
DenseHMM: Learning Hidden Markov Models by Learning Dense Representations
J Sicking, M Pintz, M Akila, T Wirtz
arXiv preprint arXiv:2012.09783, 2020
2020
Wasserstein Dropout
J Sicking, M Akila, M Pintz, T Wirtz, A Fischer, S Wrobel
arXiv e-prints, arXiv: 2012.12687, 2020
2020
Towards More Realistic Neural Network Uncertainties
J Sicking, A Kister, M Fahrland, S Eickeler, F Hueger, S Rueping, ...
2019
Trustworthy Use of Artificial Intelligence
AB Cremers, A Englander, M Gabriel, D Hecker, M Mock, M Poretschkin, ...
2019
Big Data, IOT und Machine Learning-Neue Perspektiven für Lastdaten und Schadensvorhersagen
H Tiesler, M Messmer, N Paul, J Sicking
2019
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Artikelen 1–20