| Automated machine learning in practice: state of the art and recent results L Tuggener, M Amirian, K Rombach, S Lörwald, A Varlet, C Westermann, ... 2019 6th Swiss Conference on Data Science (SDS), 31-36, 2019 | 121 | 2019 |
| Methods for person-centered continuous pain intensity assessment from bio-physiological channels M Kächele, P Thiam, M Amirian, F Schwenker, G Palm IEEE Journal of Selected Topics in Signal Processing 10 (5), 854-864, 2016 | 94 | 2016 |
| Adaptive confidence learning for the personalization of pain intensity estimation systems M Kächele, M Amirian, P Thiam, P Werner, S Walter, G Palm, ... Evolving Systems 8, 71-83, 2017 | 91 | 2017 |
| Multi-Modal Pain Intensity Recognition Based on the SenseEmotion Database P Thiam, V Kessler, M Amirian, P Bellmann, G Layher, Y Zhang, M Velana, ... IEEE Transactions on Affective Computing 12 (3), 743-760, 2019 | 80 | 2019 |
| Multimodal data fusion for person-independent, continuous estimation of pain intensity M Kächele, P Thiam, M Amirian, P Werner, S Walter, F Schwenker, ... Engineering Applications of Neural Networks: 16th International Conference …, 2015 | 80 | 2015 |
| Classification of mammograms using texture and CNN based extracted features TG Debelee, A Gebreselasie, F Schwenker, M Amirian, D Yohannes Journal of Biomimetics, Biomaterials and Biomedical Engineering 42, 79-97, 2019 | 52 | 2019 |
| Deep learning in the wild T Stadelmann, M Amirian, I Arabaci, M Arnold, GF Duivesteijn, I Elezi, ... Artificial Neural Networks in Pattern Recognition: 8th IAPR TC3 Workshop …, 2018 | 50 | 2018 |
| Radial basis function networks for convolutional neural networks to learn similarity distance metric and improve interpretability M Amirian, F Schwenker IEEE Access 8, 123087-123097, 2020 | 47 | 2020 |
| Classification of mammograms using convolutional neural network based feature extraction TG Debelee, M Amirian, A Ibenthal, G Palm, F Schwenker International Conference on Information and Communication Technology for …, 2017 | 46 | 2017 |
| Bias, awareness, and ignorance in deep-learning-based face recognition S Wehrli, C Hertweck, M Amirian, S Glüge, T Stadelmann AI and Ethics 2 (3), 509-522, 2022 | 36 | 2022 |
| Pain recognition with camera photoplethysmography V Kessler, P Thiam, M Amirian, F Schwenker 2017 Seventh International Conference on Image Processing Theory, Tools and …, 2017 | 36 | 2017 |
| Learning neural models for end-to-end clustering BB Meier, I Elezi, M Amirian, O Dürr, T Stadelmann Artificial Neural Networks in Pattern Recognition: 8th IAPR TC3 Workshop …, 2018 | 31 | 2018 |
| Trace and detect adversarial attacks on CNNs using feature response maps M Amirian, F Schwenker, T Stadelmann Artificial Neural Networks in Pattern Recognition: 8th IAPR TC3 Workshop …, 2018 | 26 | 2018 |
| The S-transform using a new window to improve frequency and time resolutions K Kazemi, M Amirian, MJ Dehghani Signal, image and Video processing 8 (3), 533-541, 2014 | 26 | 2014 |
| Using radial basis function neural networks for continuous and discrete pain estimation from bio-physiological signals M Amirian, M Kächele, F Schwenker Artificial Neural Networks in Pattern Recognition: 7th IAPR TC3 Workshop …, 2016 | 23 | 2016 |
| How (not) to measure bias in face recognition networks S Glüge, M Amirian, D Flumini, T Stadelmann IAPR Workshop on Artificial Neural Networks in Pattern Recognition, 125-137, 2020 | 21 | 2020 |
| Support vector regression of sparse dictionary-based features for view-independent action unit intensity estimation M Amirian, M Kächele, G Palm, F Schwenker 2017 12th IEEE International Conference on Automatic Face & Gesture …, 2017 | 19 | 2017 |
| Continuous multimodal human affect estimation using echo state networks M Amirian, M Kächele, P Thiam, V Kessler, F Schwenker Proceedings of the 6th International workshop on audio/visual emotion …, 2016 | 18 | 2016 |
| Multimodal fusion including camera photoplethysmography for pain recognition V Kessler, P Thiam, M Amirian, F Schwenker 2017 International Conference on Companion Technology (ICCT), 1-4, 2017 | 17 | 2017 |
| Two to trust: Automl for safe modelling and interpretable deep learning for robustness M Amirian, L Tuggener, R Chavarriaga, YP Satyawan, FP Schilling, ... Trustworthy AI-Integrating Learning, Optimization and Reasoning: First …, 2021 | 10 | 2021 |
Friedhelm SchwenkerUlm University, Institute of Neural Information ProcessingDirección de correo verificada de uni-ulm.de
