Články s príkazom na verejný prístup - Fabian HinderĎalšie informácie
Nedostupné nikde: 4
Fast non-parametric conditional density estimation using moment trees
F Hinder, V Vaquet, J Brinkrolf, B Hammer
2021 IEEE Symposium Series on Computational Intelligence (SSCI), 1-7, 2021
Príkazy: Federal Ministry of Education and Research, Germany
A shape-based method for concept drift detection and signal denoising
F Hinder, J Brinkrolf, V Vaquet, B Hammer
2021 IEEE Symposium Series on Computational Intelligence (SSCI), 01-08, 2021
Príkazy: Federal Ministry of Education and Research, Germany
Online learning on non-stationary data streams for image recognition using deep embeddings
V Vaquet, F Hinder, J Vaquet, J Brinkrolf, B Hammer
2021 IEEE Symposium Series on Computational Intelligence (SSCI), 1-7, 2021
Príkazy: Federal Ministry of Education and Research, Germany
Explaining concept drift by mean of direction
F Hinder, J Kummert, B Hammer
Artificial Neural Networks and Machine Learning–ICANN 2020: 29th …, 2020
Príkazy: Federal Ministry of Education and Research, Germany
Dostupné niekde: 18
Evaluating robustness of counterfactual explanations
A Artelt, V Vaquet, R Velioglu, F Hinder, J Brinkrolf, M Schilling, ...
2021 IEEE symposium series on computational intelligence (SSCI), 01-09, 2021
Príkazy: Volkswagen Foundation, Federal Ministry of Education and Research, Germany
Towards non-parametric drift detection via dynamic adapting window independence drift detection (dawidd)
F Hinder, A Artelt, B Hammer
International Conference on Machine Learning, 4249-4259, 2020
Príkazy: Volkswagen Foundation, Federal Ministry of Education and Research, Germany
Model-based explanations of concept drift
F Hinder, V Vaquet, J Brinkrolf, B Hammer
Neurocomputing 555, 126640, 2023
Príkazy: European Commission
Suitability of different metric choices for concept drift detection
F Hinder, V Vaquet, B Hammer
International Symposium on Intelligent Data Analysis, 157-170, 2022
Príkazy: Federal Ministry of Education and Research, Germany
One or two things we know about concept drift—a survey on monitoring in evolving environments. Part A: detecting concept drift
F Hinder, V Vaquet, B Hammer
Frontiers in Artificial Intelligence 7, 1330257, 2024
Príkazy: European Commission
One or Two Things We know about Concept Drift--A Survey on Monitoring Evolving Environments
F Hinder, V Vaquet, B Hammer
arXiv preprint arXiv:2310.15826, 2023
Príkazy: German Research Foundation, European Commission
Feature relevance determination for ordinal regression in the context of feature redundancies and privileged information
L Pfannschmidt, J Jakob, F Hinder, M Biehl, P Tino, B Hammer
Neurocomputing 416, 266-279, 2020
Príkazy: German Research Foundation, Federal Ministry of Education and Research, Germany
On the Hardness and Necessity of Supervised Concept Drift Detection.
F Hinder, V Vaquet, J Brinkrolf, B Hammer
ICPRAM, 164-175, 2023
Príkazy: Federal Ministry of Education and Research, Germany
Contrastive explanations for explaining model adaptations
A Artelt, F Hinder, V Vaquet, R Feldhans, B Hammer
International Work-Conference on Artificial Neural Networks, 101-112, 2021
Príkazy: Volkswagen Foundation, Federal Ministry of Education and Research, Germany
Contrasting explanations for understanding and regularizing model adaptations
A Artelt, F Hinder, V Vaquet, R Feldhans, B Hammer
Neural Processing Letters 55 (5), 5273-5297, 2023
Príkazy: Volkswagen Foundation, Federal Ministry of Education and Research, Germany
Localizing of Anomalies in Critical Infrastructure using Model-Based Drift Explanations
V Vaquet, F Hinder, J Vaquet, K Lammers, L Quakernack, B Hammer
2024 International Joint Conference on Neural Networks (IJCNN), 1-8, 2024
Príkazy: European Commission
Feature-based analyses of concept drift
F Hinder, V Vaquet, B Hammer
Neurocomputing 600, 127968, 2024
Príkazy: European Commission
Challenges, Methods, Data–A Survey of Machine Learning in Water Distribution Networks
V Vaquet, F Hinder, A Artelt, I Ashraf, J Strotherm, J Vaquet, J Brinkrolf, ...
International Conference on Artificial Neural Networks, 155-170, 2024
Príkazy: European Commission
A Water Futures Approach on Water Demand Forecasting with Online Ensemble Learning
D Zanutto, C Michalopoulos, GA Chatzistefanou, ...
Engineering Proceedings 69 (1), 60, 2024
Príkazy: European Commission
On the fine-structure of drifting features
F Hinder, V Vaquet, B Hammer
32nd European Symposium on Artificial Neural Networks, Computational …, 2024
Príkazy: European Commission
FairGLVQ: Fairness in Partition-Based Classification
F Störck, F Hinder, J Brinkrolf, B Paassen, V Vaquet, B Hammer
International Workshop on Self-Organizing Maps, Learning Vector Quantization …, 2024
Príkazy: European Commission
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