| Issue Date | Title | Involved Person(s) |
| Jan-2024 | Artifact reduction in 3D and 4D cone-beam computed tomography images with deep learning - a review | Amirian, Mohammadreza; Barco, Daniel; Herzig, Ivo; Schilling, Frank-Peter |
| 14-Jun-2019 | Automated machine learning in practice : state of the art and recent results | Tuggener, Lukas; Amirian, Mohammadreza; Rombach, Katharina; Lörwald, Stefan; Varlet, Anastasia, et al |
| 27-Oct-2021 | Bias, awareness, and ignorance in deep-learning-based face recognition | Wehrli, Samuel; Hertweck, Corinna; Amirian, Mohammadreza; Glüge, Stefan; Stadelmann, Thilo |
| 2023 | Deep learning for robust and explainable models in computer vision | Schwenker, Friedhelm; Stadelmann, Thilo; Jaggi, Martin; Amirian, Mohammadreza |
| 2018 | Deep learning in the wild | Stadelmann, Thilo; Amirian, Mohammadreza; Arabaci, Ismail; Arnold, Marek; Duivesteijn, Gilbert François, et al |
| 9-Jun-2022 | Deep learning-based simultaneous multi-phase deformable image registration of sparse 4D-CBCT | Herzig, Ivo; Paysan, Pascal; Scheib, Stefan; Züst, Alexander; Schilling, Frank-Peter, et al |
| 6-Nov-2020 | Design patterns for resource-constrained automated deep-learning methods | Tuggener, Lukas; Amirian, Mohammadreza; Benites de Azevedo e Souza, Fernando; von Däniken, Pius; Gupta, Prakhar, et al |
| 2019 | Efficient deep CNNs for cross-modal automated computer vision under time and space constraints | Amirian, Mohammadreza; Rombach, Katharina; Tuggener, Lukas; Schilling, Frank-Peter; Stadelmann, Thilo |
| 2-Sep-2020 | How (not) to measure bias in face recognition networks | Glüge, Stefan; Amirian, Mohammadreza; Flumini, Dandolo; Stadelmann, Thilo |
| 2018 | Learning neural models for end-to-end clustering | Meier, Benjamin Bruno; Elezi, Ismail; Amirian, Mohammadreza; Dürr, Oliver; Stadelmann, Thilo |
| 30-Mar-2023 | Mitigation of motion-induced artifacts in cone beam computed tomography using deep convolutional neural networks | Amirian, Mohammadreza; Montoya-Zegarra, Javier A.; Herzig, Ivo; Eggenberger Hotz, Peter; Lichtensteiger, Lukas, et al |
| 2021 | Multi-modal pain intensity recognition based on the SenseEmotion database | Thiam, Patrick; Kessler, Viktor; Amirian, Mohammadreza; Bellmann, Peter; Layher, Georg, et al |
| Oct-2021 | PrepNet : a convolutional auto-encoder to homogenize CT scans for cross-dataset medical image analysis | Amirian, Mohammadreza; Montoya, Javier; Gruss, Jonathan; Stebler, Yves D.; Bozkir, Ahmet Selman, et al |
| 2020 | Radial basis function networks for convolutional neural networks to learn similarity distance metric and improve interpretability | Amirian, Mohammadreza; Schwenker, Friedhelm |
| 2018 | Trace and detect adversarial attacks on CNNs using feature response maps | Amirian, Mohammadreza; Schwenker, Friedhelm; Stadelmann, Thilo |
| Mar-2021 | Two to trust : AutoML for safe modelling and interpretable deep learning for robustness | Amirian, Mohammadreza; Tuggener, Lukas; Chavarriaga, Ricardo; Satyawan, Yvan Putra; Schilling, Frank-Peter, et al |
| 2019 | Unsupervised learning and simulation for complexity management in business operations | Hollenstein, Lukas; Lichtensteiger, Lukas; Stadelmann, Thilo; Amirian, Mohammadreza; Budde, Lukas, et al |
