| CLeaR: An adaptive continual learning framework for regression tasks Y He, B Sick AI Perspectives 3 (1), 2, 2021 | 30 | 2021 |
| Forecasting power grid states for regional energy markets with deep neural networks Y He, J Henze, B Sick 2020 International Joint Conference on Neural Networks (IJCNN), 1-8, 2020 | 14 | 2020 |
| Continuous learning of deep neural networks to improve forecasts for regional energy markets Y He, J Henze, B Sick IFAC-PapersOnLine 53 (2), 12175-12182, 2020 | 12 | 2020 |
| Toward application of continuous power forecasts in a regional flexibility market Y He, Z Huang, B Sick 2021 international joint conference on neural networks (IJCNN), 1-8, 2021 | 7 | 2021 |
| TPM Framework: a Comprehensive Kit for Exploring Applications with Textile Pressure Mapping Matrix B Zhou, J Cheng, A Mawandia, Y He, Z Huang, M Sundholm, M Yildrim, ... UBICOMM, 2017 | 3 | 2017 |
| Design of Explainability Module with Experts in the Loop for Visualization and Dynamic Adjustment of Continual Learning Y He, Z Huang, B Sick arXiv preprint arXiv:2202.06781, 2022 | 2 | 2022 |
| Adaptive Explainable Continual Learning Framework for Regression Problems with Focus on Power Forecasts Y He arXiv preprint arXiv:2108.10781, 2021 | 1 | 2021 |
| Spatio-Temporal Attention Graph Neural Network for Remaining Useful Life Prediction Z Huang, Y He, B Sick arXiv preprint arXiv:2401.15964, 2024 | | 2024 |
| PrOuD: Probabilistic Outlier Detection Solution for Time-Series Analysis of Real-World Photovoltaic Inverters Y He, Z Huang, S Vogt, B Sick Energies 17 (1), 64, 2023 | | 2023 |
| Active Learning with Fast Model Updates and Class-Balanced Selection for Imbalanced Datasets Z Huang, Y He, M Herde, D Huseljic, B Sick | | 2023 |
| Uncertainty and Utility Sampling with Pre-Clustering. Z Huang, Y He, S Vogt, B Sick IAL@ PKDD/ECML, 21-34, 2021 | | 2021 |
Bernhard SickProfessor of Intelligent Embedded Systems, University of KasselVerified email at uni-kassel.de
