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https://doi.org/10.21256/zhaw-20217| Publikationstyp: | Konferenz: Paper |
| Art der Begutachtung: | Peer review (Abstract) |
| Neue Version verfügbar unter: | https://digitalcollection.zhaw.ch/handle/11475/22061 |
| Titel: | Two to trust : AutoML for safe modelling and interpretable deep learning for robustness |
| Autor/-in: | Amirian, Mohammadreza Tuggener, Lukas Chavarriaga, Ricardo Satyawan, Yvan Putra Schilling, Frank-Peter Schwenker, Friedhelm Stadelmann, Thilo |
| et. al: | No |
| DOI: | 10.21256/zhaw-20217 |
| Tagungsband: | Proceedings of the 1st TAILOR Workshop on Trustworthy AI at ECAI 2020 |
| Angaben zur Konferenz: | 1st TAILOR Workshop on Trustworthy AI at ECAI 2020, Santiago de Compostela, Spain, 29-30 August 2020 |
| Erscheinungsdatum: | Aug-2020 |
| Verlag / Hrsg. Institution: | Springer |
| Sprache: | Englisch |
| Schlagwörter: | Automated deep learning; AutoDL; Adversarial attacks |
| Fachgebiet (DDC): | 006: Spezielle Computerverfahren |
| Zusammenfassung: | With great power comes great responsibility. The success of machine learning, especially deep learning, in research and practice has attracted a great deal of interest, which in turn necessitates increased trust. Sources of mistrust include matters of model genesis ("Is this really the appropriate model?") and interpretability ("Why did the model come to this conclusion?", "Is the model safe from being easily fooled by adversaries?"). In this paper, two partners for the trustworthiness tango are presented: recent advances and ideas, as well as practical applications in industry in (a) Automated machine learning (AutoML), a powerful tool to optimize deep neural network architectures and netune hyperparameters, which promises to build models in a safer and more comprehensive way; (b) Interpretability of neural network outputs, which addresses the vital question regarding the reasoning behind model predictions and provides insights to improve robustness against adversarial attacks. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/20217 |
| Volltext Version: | Akzeptierte Version |
| Lizenz (gemäss Verlagsvertrag): | Lizenz gemäss Verlagsvertrag |
| Departement: | School of Engineering |
| Organisationseinheit: | |
| Publiziert im Rahmen des ZHAW-Projekts: | Ada – Advanced Algorithms for an Artificial Data Analyst QualitAI - Quality control of industrial products via deep learning on images |
| Enthalten in den Sammlungen: | Publikationen School of Engineering |
Dateien zu dieser Ressource:
| Datei | Beschreibung | Größe | Format | |
|---|---|---|---|---|
| 2020_Amirian_etal_AutoML-for-safe-modelling_TAILOR_ECAI.pdf | Accepted Version | 2.88 MB | Adobe PDF |  Öffnen/Anzeigen |
Zur Langanzeige
Amirian, M., Tuggener, L., Chavarriaga, R., Satyawan, Y. P., Schilling, F.-P., Schwenker, F., & Stadelmann, T. (2020, August). Two to trust : AutoML for safe modelling and interpretable deep learning for robustness. Proceedings of the 1st TAILOR Workshop on Trustworthy AI at ECAI 2020. https://doi.org/10.21256/zhaw-20217
Amirian, M. et al. (2020) ‘Two to trust : AutoML for safe modelling and interpretable deep learning for robustness’, in Proceedings of the 1st TAILOR Workshop on Trustworthy AI at ECAI 2020. Springer. Available at: https://doi.org/10.21256/zhaw-20217.
M. Amirian et al., “Two to trust : AutoML for safe modelling and interpretable deep learning for robustness,” in Proceedings of the 1st TAILOR Workshop on Trustworthy AI at ECAI 2020, Aug. 2020. doi: 10.21256/zhaw-20217.
AMIRIAN, Mohammadreza, Lukas TUGGENER, Ricardo CHAVARRIAGA, Yvan Putra SATYAWAN, Frank-Peter SCHILLING, Friedhelm SCHWENKER und Thilo STADELMANN, 2020. Two to trust : AutoML for safe modelling and interpretable deep learning for robustness. In: Proceedings of the 1st TAILOR Workshop on Trustworthy AI at ECAI 2020. Conference paper. Springer. August 2020
Amirian, Mohammadreza, Lukas Tuggener, Ricardo Chavarriaga, Yvan Putra Satyawan, Frank-Peter Schilling, Friedhelm Schwenker, and Thilo Stadelmann. 2020. “Two to Trust : AutoML for Safe Modelling and Interpretable Deep Learning for Robustness.” Conference paper. In Proceedings of the 1st TAILOR Workshop on Trustworthy AI at ECAI 2020. Springer. https://doi.org/10.21256/zhaw-20217.
Amirian, Mohammadreza, et al. “Two to Trust : AutoML for Safe Modelling and Interpretable Deep Learning for Robustness.” Proceedings of the 1st TAILOR Workshop on Trustworthy AI at ECAI 2020, Springer, 2020, https://doi.org/10.21256/zhaw-20217.
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