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Johannes Haug
Johannes Haug
Data Scientist at Bosch Center for Artificial Intelligence, previously at University of Tuebingen
Verified email at uni-tuebingen.de
Title
Cited by
Cited by
Year
Deep neural networks and tabular data: A survey
V Borisov, T Leemann, K Seßler, J Haug, M Pawelczyk, G Kasneci
IEEE Transactions on Neural Networks and Learning Systems, 2022
4632022
A wearable sensor system for lameness detection in dairy cattle
J Haladjian, J Haug, S Nüske, B Bruegge
Multimodal Technologies and Interaction 2 (2), 27, 2018
632018
CancelOut: A Layer for Feature Selection in Deep Neural Networks
V Borisov, J Haug, G Kasneci
Artificial Neural Networks and Machine Learning–ICANN 2019: Deep Learning …, 2019
482019
Learning parameter distributions to detect concept drift in data streams
J Haug, G Kasneci
2020 25th international conference on pattern recognition (ICPR), 9452-9459, 2021
302021
On baselines for local feature attributions
J Haug, S Zürn, P El-Jiz, G Kasneci
arXiv preprint arXiv:2101.00905, 2021
262021
Leveraging model inherent variable importance for stable online feature selection
J Haug, M Pawelczyk, K Broelemann, G Kasneci
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge …, 2020
132020
TüEyeQ, a rich IQ test performance data set with eye movement, educational and socio-demographic information
E Kasneci, G Kasneci, T Appel, J Haug, F Wortha, M Tibus, U Trautwein, ...
Scientific Data 8 (1), 154, 2021
112021
Change detection for local explainability in evolving data streams
J Haug, A Braun, S Zürn, G Kasneci
Proceedings of the 31st ACM International Conference on Information …, 2022
62022
Dynamic model tree for interpretable data stream learning
J Haug, K Broelemann, G Kasneci
2022 IEEE 38th International Conference on Data Engineering (ICDE), 2562-2574, 2022
62022
Towards user empowerment
M Pawelczyk, J Haug, K Broelemann, G Kasneci
arXiv preprint arXiv:1910.09398, 2019
52019
Standardized Evaluation of Machine Learning Methods for Evolving Data Streams
J Haug, E Tramountani, G Kasneci
arXiv preprint arXiv:2204.13625, 2022
32022
Towards Reliable Machine Learning in Evolving Data Streams
JC Haug
Universität Tübingen, 2022
12022
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