| Utilizing explainable AI for quantization and pruning of deep neural networks M Sabih, F Hannig, J Teich arXiv preprint arXiv:2008.09072, 2020 | 32 | 2020 |
| Fault-tolerant low-precision dnns using explainable ai M Sabih, F Hannig, J Teich 2021 51st Annual IEEE/IFIP International Conference on Dependable Systems …, 2021 | 14 | 2021 |
| DyFiP: explainable AI-based dynamic filter pruning of convolutional neural networks M Sabih, F Hannig, J Teich Proceedings of the 2nd European Workshop on Machine Learning and Systems …, 2022 | 11 | 2022 |
| MOSP: Multi-objective sensitivity pruning of deep neural networks M Sabih, A Mishra, F Hannig, J Teich 2022 IEEE 13th International Green and Sustainable Computing Conference …, 2022 | 9 | 2022 |
| Robust and Tiny Binary Neural Networks using Gradient-based Explainability Methods M Sabih, M Yayla, F Hannig, J Teich, JJ Chen Proceedings of the 3rd Workshop on Machine Learning and Systems, 87-93, 2023 | 4 | 2023 |
| Clustering-based scenario-aware lte grant prediction P Brand, M Sabih, J Falk, JA Sue, J Teich 2020 IEEE Wireless Communications and Networking Conference (WCNC), 1-7, 2020 | 3 | 2020 |
| Hardware-Aware Evolutionary Explainable Filter Pruning for Convolutional Neural Networks C Heidorn, M Sabih, N Meyerhöfer, C Schinabeck, J Teich, F Hannig International Journal of Parallel Programming 52 (1), 40-58, 2024 | 2 | 2024 |
| Accelerating DNNs Using Weight Clustering on RISC-V Custom Functional Units M Sabih, B Sesli, F Hannig, J Teich 2024 Design, Automation & Test in Europe Conference & Exhibition (DATE), 1-2, 2024 | | 2024 |
