| A new approach for supervised power disaggregation by using a deep recurrent LSTM network L Mauch, B Yang 2015 IEEE global conference on signal and information processing (GlobalSIP …, 2015 | 245 | 2015 |
| Mixed precision dnns: All you need is a good parametrization S Uhlich, L Mauch, F Cardinaux, K Yoshiyama, JA Garcia, S Tiedemann, ... arXiv preprint arXiv:1905.11452, 2019 | 172 | 2019 |
| Automated reference-free detection of motion artifacts in magnetic resonance images T Küstner, A Liebgott, L Mauch, P Martirosian, F Bamberg, K Nikolaou, ... Magnetic Resonance Materials in Physics, Biology and Medicine 31, 243-256, 2018 | 114 | 2018 |
| Automated detection of solar cell defects with deep learning A Bartler, L Mauch, B Yang, M Reuter, L Stoicescu 2018 26th European signal processing conference (EUSIPCO), 2035-2039, 2018 | 113 | 2018 |
| A novel DNN-HMM-based approach for extracting single loads from aggregate power signals L Mauch, B Yang 2016 IEEE International Conference on Acoustics, Speech and Signal …, 2016 | 85 | 2016 |
| A machine-learning framework for automatic reference-free quality assessment in MRI T Küstner, S Gatidis, A Liebgott, M Schwartz, L Mauch, P Martirosian, ... Magnetic resonance imaging 53, 134-147, 2018 | 74 | 2018 |
| Differentiable quantization of deep neural networks S Uhlich, L Mauch, K Yoshiyama, F Cardinaux, JA Garcia, S Tiedemann, ... arXiv preprint arXiv:1905.11452 2 (8), 2019 | 41 | 2019 |
| Neural network ensembles to real-time identification of plug-level appliance measurements KS Barsim, L Mauch, B Yang arXiv preprint arXiv:1802.06963, 2018 | 31 | 2018 |
| How well can HMM model load signals L Mauch, KS Barsim, B Yang Proceeding of the 3rd international workshop on non-intrusive load …, 2016 | 27 | 2016 |
| Iteratively training look-up tables for network quantization F Cardinaux, S Uhlich, K Yoshiyama, JA García, L Mauch, S Tiedemann, ... IEEE Journal of Selected Topics in Signal Processing 14 (4), 860-870, 2020 | 20 | 2020 |
| A novel layerwise pruning method for model reduction of fully connected deep neural networks L Mauch, B Yang 2017 IEEE International Conference on Acoustics, Speech and Signal …, 2017 | 16 | 2017 |
| On semantic image segmentation using deep convolutional neural network with shortcuts and easy class extension C Wang, L Mauch, Z Guo, B Yang 2016 Sixth international conference on image processing theory, tools and …, 2016 | 13 | 2016 |
| Neural Network Libraries: A Deep Learning Framework Designed from Engineers' Perspectives T Narihira, J Alonsogarcia, F Cardinaux, A Hayakawa, M Ishii, K Iwaki, ... arXiv preprint arXiv:2102.06725, 2021 | 12 | 2021 |
| Automatic motion artifact detection for whole-body magnetic resonance imaging T Küstner, M Jandt, A Liebgott, L Mauch, P Martirosian, F Bamberg, ... 2018 IEEE International Conference on Acoustics, Speech and Signal …, 2018 | 11 | 2018 |
| A novel benchmark for few-shot semantic segmentation in the era of foundation models R Bensaid, V Gripon, F Leduc-Primeau, L Mauch, GB Hacene, ... arXiv preprint arXiv:2401.11311, 2024 | 10 | 2024 |
| Subset selection for visualization of relevant image fractions for deep learning based semantic image segmentation L Mauch, C Wang, B Yang Journal of the Franklin Institute 355 (4), 1931-1944, 2018 | 7 | 2018 |
| On the contextual aspects of using deep convolutional neural network for semantic image segmentation C Wang, L Mauch, MM Saxena, B Yang Journal of Electronic Imaging 27 (5), 051223-051223, 2018 | 6 | 2018 |
| Motion artifact quantification and localization for whole-body MRI T Küstner, M Jandt, A Liebgott, L Mauch, P Martirosian, F Bamberg, ... Proceedings of the International Society for Magnetic Resonance in Medicine …, 2018 | 6 | 2018 |
| Selecting optimal layer reduction factors for model reduction of deep neural networks L Mauch, B Yang 2017 IEEE International Conference on Acoustics, Speech and Signal …, 2017 | 6 | 2017 |
| Order-preserving gflownets Y Chen, L Mauch arXiv preprint arXiv:2310.00386, 2023 | 5 | 2023 |
Bin YangUniversity of Stuttgart, GermanyVerified email at iss.uni-stuttgart.de