| Combating mode collapse in gan training: An empirical analysis using hessian eigenvalues R Durall, A Chatzimichailidis, P Labus, J Keuper arXiv preprint arXiv:2012.09673, 2020 | 58 | 2020 |
| Proxsgd: Training structured neural networks under regularization and constraints Y Yang, Y Yuan, A Chatzimichailidis, RJG van Sloun, L Lei, S Chatzinotas International Conference on Learning Representations (ICLR) 2020, 2020 | 29 | 2020 |
| Gradvis: Visualization and second order analysis of optimization surfaces during the training of deep neural networks A Chatzimichailidis, J Keuper, FJ Pfreundt, NR Gauger 2019 IEEE/ACM Workshop on Machine Learning in High Performance Computing …, 2019 | 9 | 2019 |
| Msm: Multi-stage multicuts for scalable image clustering K Ho, A Chatzimichailidis, M Keuper, J Keuper High Performance Computing: ISC High Performance Digital 2021 International …, 2021 | 5 | 2021 |
| Alternative optimization methods for training of large deep neural networks A Chatzimichailidis Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau, 2023 | | 2023 |
| [AutoMLConf'22]: GSparsity: Unifying Network Pruning and Neural Architecture Search by Group Teaser A Chatzimichailidis, A Zela, J Keuper, Y Yang | | 2022 |
| GSparsity: Unifying Network Pruning and Neural Architecture Search by Group Sparsity A Chatzimichailidis, A Zela, J Keuper, Y Yang | | 2022 |
| Group Sparsity: A Unified Framework for Network Pruning and Neural Architecture Search A Chatzimichailidis, SS Arber Zela, P Labus, J Keuper, F Hutter, Y Yang CVPR NAS Workshop, 2021 | | 2021 |
| Combating Mode Collapse in GAN training: An Empirical Analysis using Hessian Eigenvalues R Durall Lopez, A Chatzimichailidis, P Labus, J Keuper | | 2020 |
| MLHPC 2019 G Heinrich, I Frosio, S Prudhomme, S Adrian, GE Moon, D Newman-Griffis, ... | | |
| Studies on time dependent activity distributions for the SAFIR project A Chatzimichailidis | | |
