Ada – Advanced Algorithms for an Artificial Data Analyst
Auf einen Blick
- Projektleiter/in : Prof. Dr. Thilo Stadelmann
- Projektteam : Mohammadreza Amirian, Dr. Fernando Benites de Azevedo e Souza, Katharina Rombach, Lukas Tuggener
- Projektvolumen : CHF 935'000
- Projektstatus : abgeschlossen
- Drittmittelgeber : KTI (KTI-Projekt / Projekt Nr. 25948.1 PFES-ES)
- Projektpartner : PricewaterhouseCoopers PwC AG (Zürich), Ecole polytechnique fédérale de Lausanne EPFL / Machine Learning and Optimization Laboratory
- Kontaktperson : Thilo Stadelmann
Beschreibung
Ada - the Artificial Data Analyst - raises the productivity of data science endeavours by applying data science to itself: we apply empirical optimization also to algorithm and feature selection. Recent developments, e.g. from the MIT, are thus made available as a data product for Swiss industry.
Publikationen
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Amirian, Mohammadreza; Tuggener, Lukas; Chavarriaga, Ricardo; Satyawan, Yvan Putra; Schilling, Frank-Peter; Schwenker, Friedhelm; Stadelmann, Thilo,
2021.
Two to trust : AutoML for safe modelling and interpretable deep learning for robustness [Paper].
In:
Postproceedings of the 1st TAILOR Workshop on Trustworthy AI at ECAI 2020.
1st TAILOR Workshop on Trustworthy AI at ECAI 2020, Santiago de Compostela, Spain, 29-30 August 2020.
Springer.
Verfügbar unter: https://doi.org/10.21256/zhaw-22061
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Tuggener, Lukas; Amirian, Mohammadreza; Benites de Azevedo e Souza, Fernando; von Däniken, Pius; Gupta, Prakhar; Schilling, Frank-Peter; Stadelmann, Thilo,
2020.
Design patterns for resource-constrained automated deep-learning methods.
AI.
1(4), S. 510-538.
Verfügbar unter: https://doi.org/10.3390/ai1040031
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Tuggener, Lukas; Amirian, Mohammadreza; Rombach, Katharina; Lörwald, Stefan; Varlet, Anastasia; Westermann, Christian; Stadelmann, Thilo,
2019.
Automated machine learning in practice : state of the art and recent results [Paper].
In:
2019 6th Swiss Conference on Data Science (SDS).
6th Swiss Conference on Data Science (SDS), Bern, 14. Juni 2019.
IEEE.
S. 31-36.
Verfügbar unter: https://doi.org/10.1109/SDS.2019.00-11
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Amirian, Mohammadreza; Rombach, Katharina; Tuggener, Lukas; Schilling, Frank-Peter; Stadelmann, Thilo,
2019.
Efficient deep CNNs for cross-modal automated computer vision under time and space constraints [Paper].
In:
ECML-PKDD 2019, Würzburg, Germany, 16-19 September 2019.
ZHAW Zürcher Hochschule für Angewandte Wissenschaften.
Verfügbar unter: https://doi.org/10.21256/zhaw-18357