Felix Neutatz
Felix Neutatz
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HALEF: An Open-Source Standard-Compliant Telephony-Based Modular Spoken Dialog System: A Review and An Outlook
D Suendermann-Oeft, V Ramanarayanan, M Teckenbrock, F Neutatz, ...
Natural Language Dialog Systems and Intelligent Assistants, 53-61, 2015
ED2: A Case for Active Learning in Error Detection
F Neutatz, M Mahdavi, Z Abedjan
CIKM, 2249-2252, 2019
Towards Automated Data Cleaning Workflows
M Mahdavi, F Neutatz, L Visengeriyeva, Z Abedjan
Proceedings of the Conference on Learning. Knowledge. Data. Analytics., 10-19, 2019
Automated Feature Engineering for Algorithmic Fairness
R Salazar Diaz, F Neutatz, Z Abedjan
Proceedings of the VLDB Endowment 14 (9), 1694-1702, 2021
From Cleaning before ML to Cleaning for ML
F Neutatz, B Chen, Z Abedjan, E Wu
IEEE Data Engineering Bulletin 44, 24-41, 2021
Data Science für alle: Grundlagen der Datenprogrammierung: Ein Data-Science-Kurs für alle Studierenden der TU Berlin
Z Abedjan, H Anuth, M Esmailoghli, M Mahdavi, F Neutatz, B Chen
Informatik Spektrum 43, 129-136, 2020
Data Cleaning and AutoML: Would an optimizer choose to clean?
F Neutatz, B Chen, Y Alkhatib, J Ye, Z Abedjan
Datenbank-Spektrum, 2022
Enforcing Constraints for Machine Learning Systems via Declarative Feature Selection: An Experimental Study
F Neutatz, F Biessmann, Z Abedjan
Proceedings of the 2021 International Conference on Management of Data, 2021
ED2: Two-stage Active Learning for Error Detection - Technical Report
F Neutatz, M Mahdavi, Z Abedjan
arXiv preprint arXiv:1908.06309, 2019
Evaluating Acoustic, Textual and Grammar Features for Alcohol Classification
F Neutatz, D Schmidt, M Teckenbrock, D Suendermann-Oeft
ESSV, 2016
What is “Good” Training Data? - Data Quality Dimensions that Matter for Machine Learning
F Neutatz, Z Abedjan
Künstliche Intelligenz - Wie gelingt eine vertrauenswürdige Verwendung in …, 2022
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