default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDLisa Jöckel
Person information
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
2020 – today
- 2023
[c15]Janek Groß, Michael Kläs, Lisa Jöckel, Pascal Gerber:
Timeseries-aware Uncertainty Wrappers for Uncertainty Quantification of Information-Fusion-Enhanced AI Models based on Machine Learning. DSN-W 2023: 231-238- [c14]João-Vitor Zacchi
, Francesco Carella, Priyank Upadhya, Shanza Ali Zafar, John Molloy, Lisa Jöckel, Janek Groß, Núria Mata, Nguyen Anh Vu Doan:
Reliability Estimation of ML for Image Perception: A Lightweight Nonlinear Transformation Approach Based on Full Reference Image Quality Metrics. MCSoC 2023: 186-193 - [c13]Lisa Jöckel, Michael Kläs, Janek Groß, Pascal Gerber, Markus Scholz, Jonathan Eberle, Marc Teschner, Daniel Seifert, Richard Hawkins, John Molloy, Jens Ottnad:
Operationalizing Assurance Cases for Data Scientists: A Showcase of Concepts and Tooling in the Context of Test Data Quality for Machine Learning. PROFES (1) 2023: 151-158 - [c12]Lisa Jöckel, Michael Kläs, Janek Groß, Pascal Gerber:
Conformal Prediction and Uncertainty Wrapper: What Statistical Guarantees Can You Get for Uncertainty Quantification in Machine Learning? SAFECOMP Workshops 2023: 314-327 - [i9]Janek Groß, Michael Kläs, Lisa Jöckel, Pascal Gerber:
Timeseries-aware Uncertainty Wrappers for Uncertainty Quantification of Information-Fusion-Enhanced AI Models based on Machine Learning. CoRR abs/2305.14872 (2023) - [i8]Lisa Jöckel, Michael Kläs, Georg Popp, Nadja Hilger, Stephan Fricke:
Uncertainty Wrapper in the medical domain: Establishing transparent uncertainty quantification for opaque machine learning models in practice. CoRR abs/2311.05245 (2023) - [i7]Lisa Jöckel, Michael Kläs, Janek Groß, Pascal Gerber, Markus Scholz, Jonathan Eberle, Marc Teschner, Daniel Seifert, Richard Hawkins, John Molloy, Jens Ottnad:
Operationalizing Assurance Cases for Data Scientists: A Showcase of Concepts and Tooling in the Context of Test Data Quality for Machine Learning. CoRR abs/2312.04917 (2023) - 2022
- [j2]Julien Siebert, Lisa Jöckel, Jens Heidrich, Adam Trendowicz, Koji Nakamichi, Kyoko Ohashi, Isao Namba, Rieko Yamamoto, Mikio Aoyama:
Construction of a quality model for machine learning systems. Softw. Qual. J. 30(2): 307-335 (2022) - [c11]Pascal Gerber, Lisa Jöckel, Michael Kläs:
A Study on Mitigating Hard Boundaries of Decision-Tree-based Uncertainty Estimates for AI Models. SafeAI@AAAI 2022 - [c10]Janek Groß, Rasmus Adler, Michael Kläs, Jan Reich, Lisa Jöckel, Roman Gansch:
Architectural Patterns for Handling Runtime Uncertainty of Data-Driven Models in Safety-Critical Perception. SAFECOMP 2022: 284-297 - [i6]Pascal Gerber, Lisa Jöckel, Michael Kläs:
A Study on Mitigating Hard Boundaries of Decision-Tree-based Uncertainty Estimates for AI Models. CoRR abs/2201.03263 (2022) - [i5]Michael Kläs, Lisa Jöckel, Rasmus Adler, Jan Reich:
Integrating Testing and Operation-related Quantitative Evidences in Assurance Cases to Argue Safety of Data-Driven AI/ML Components. CoRR abs/2202.05313 (2022) - [i4]Janek Groß, Rasmus Adler, Michael Kläs, Jan Reich, Lisa Jöckel, Roman Gansch:
Architectural patterns for handling runtime uncertainty of data-driven models in safety-critical perception. CoRR abs/2206.06838 (2022) - 2021
- [c9]Michael Kläs, Rasmus Adler, Ioannis Sorokos, Lisa Jöckel, Jan Reich:
Handling Uncertainties of Data-Driven Models in Compliance with Safety Constraints for Autonomous Behaviour. EDCC 2021: 95-102 - [c8]Michael Klaes, Rasmus Adler, Lisa Jöckel, Janek Groß, Jan Reich:
Using Complementary Risk Acceptance Criteria to Structure Assurance Cases for Safety-Critical AI Components. AISafety@IJCAI 2021 - [c7]Lisa Jöckel, Thomas Bauer, Michael Kläs, Marc P. Hauer, Janek Groß:
Towards a Common Testing Terminology for Software Engineering and Data Science Experts. PROFES 2021: 281-289 - [c6]Lisa Jöckel, Michael Kläs:
Could We Relieve AI/ML Models of the Responsibility of Providing Dependable Uncertainty Estimates? A Study on Outside-Model Uncertainty Estimates. SAFECOMP 2021: 18-33 - [p1]Torsten Bandyszak, Lisa Jöckel, Michael Kläs, Sebastian Törsleff, Thorsten Weyer, Boris Wirtz:
Handling Uncertainty in Collaborative Embedded Systems Engineering. Model-Based Engineering of Collaborative Embedded Systems 2021: 147-170 - [i3]Lisa Jöckel, Thomas Bauer, Michael Kläs, Marc P. Hauer, Janek Groß:
Towards a Common Testing Terminology for Software Engineering and Artificial Intelligence Experts. CoRR abs/2108.13837 (2021) - 2020
- [c5]Julien Siebert, Lisa Jöckel, Jens Heidrich, Koji Nakamichi, Kyoko Ohashi, Isao Namba, Rieko Yamamoto, Mikio Aoyama:
Towards Guidelines for Assessing Qualities of Machine Learning Systems. QUATIC 2020: 17-31 - [c4]Koji Nakamichi, Kyoko Ohashi, Isao Namba, Rieko Yamamoto, Mikio Aoyama, Lisa Jöckel, Julien Siebert, Jens Heidrich:
Requirements-Driven Method to Determine Quality Characteristics and Measurements for Machine Learning Software and Its Evaluation. RE 2020: 260-270 - [c3]Michael Kläs, Lisa Jöckel:
A Framework for Building Uncertainty Wrappers for AI/ML-Based Data-Driven Components. SAFECOMP Workshops 2020: 315-327 - [i2]Julien Siebert, Lisa Jöckel, Jens Heidrich, Koji Nakamichi, Kyoko Ohashi, Isao Namba, Rieko Yamamoto, Mikio Aoyama:
Towards Guidelines for Assessing Qualities of Machine Learning Systems. CoRR abs/2008.11007 (2020)
2010 – 2019
- 2019
- [c2]Lisa Jöckel, Michael Kläs, Silverio Martínez-Fernández:
Safe Traffic Sign Recognition through Data Augmentation for Autonomous Vehicles Software. QRS Companion 2019: 540-541 - [c1]Lisa Jöckel, Michael Kläs:
Increasing Trust in Data-Driven Model Validation - A Framework for Probabilistic Augmentation of Images and Meta-data Generation Using Application Scope Characteristics. SAFECOMP 2019: 155-164 - [i1]Rasmus Adler, Mohammed Naveed Akram, Pascal Bauer, Patrik Feth, Pascal Gerber, Andreas Jedlitschka, Lisa Jöckel, Michael Kläs, Daniel Schneider:
Hardening of Artificial Neural Networks for Use in Safety-Critical Applications - A Mapping Study. CoRR abs/1909.03036 (2019) - 2017
- [j1]Mathias Hummel, Lisa Jöckel, J. Schäfer, Mark W. Hlawitschka, Christoph Garth:
Visualizing Probabilistic Multi-Phase Fluid Simulation Data using a Sampling Approach. Comput. Graph. Forum 36(3): 469-477 (2017)
Coauthor Index
aka: Michael Klaes
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-10-07 22:19 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
