| Improving neural network training in low dimensional random bases F Gressmann, Z Eaton-Rosen, C Luschi Advances in Neural Information Processing Systems 33, 12140-12150, 2020 | 17 | 2020 |
| Probabilistic supervised learning F Gressmann, FJ Király, B Mateen, H Oberhauser arXiv preprint arXiv:1801.00753, 2018 | 16 | 2018 |
| Towards structured dynamic sparse pre-training of bert A Dietrich, F Gressmann, D Orr, I Chelombiev, D Justus, C Luschi arXiv preprint arXiv:2108.06277, 2021 | 9 | 2021 |
| „Wirtschaftswachstum aufgeben? Eine Analyse wachstumskritischer Argumente “ F Funke, F Gressmann, P Mathé, M Oberhaus, JJ Obst, M Roesti, ... MCC Working Paper, 1, 2016 | 7 | 2016 |
| Groupbert: Enhanced transformer architecture with efficient grouped structures I Chelombiev, D Justus, D Orr, A Dietrich, F Gressmann, A Koliousis, ... arXiv preprint arXiv:2106.05822, 2021 | 5 | 2021 |
| Part-driven visual perception of 3D objects F Gressmann, T Lüddecke, T Ivanovska, M Schoeler, F Wörgötter International Conference on Computer Vision Theory and Applications 6, 370-377, 2017 | 1 | 2017 |
| skpro: A domain-agnostic modelling framework for probabilistic supervised learning F Gressmann, F Kiraly | | 2018 |
| Dynamic Sparse Pre-Training of BERT ASD Dietrich, F Gressmann, D Orr, I Chelombiev, D Justus, C Luschi | | |
