| Dynamic particle swarm optimization of biomolecular simulation parameters with flexible objective functions M Weiel, M Götz, A Klein, D Coquelin, R Floca, A Schug Nature machine intelligence 3 (8), 727-734, 2021 | 38 | 2021 |
| HeAT–a distributed and GPU-accelerated tensor framework for data analytics M Götz, C Debus, D Coquelin, K Krajsek, C Comito, P Knechtges, ... 2020 IEEE International Conference on Big Data (Big Data), 276-287, 2020 | 17 | 2020 |
| Massively parallel genetic optimization through asynchronous propagation of populations O Taubert, M Weiel, D Coquelin, A Farshian, C Debus, A Schug, A Streit, ... International Conference on High Performance Computing, 106-124, 2023 | 12 | 2023 |
| Accelerating neural network training with distributed asynchronous and selective optimization (DASO) D Coquelin, C Debus, M Götz, F von der Lehr, J Kahn, M Siggel, A Streit Journal of Big Data 9 (1), 14, 2022 | 9 | 2022 |
| Hyde: The first open-source, python-based, gpu-accelerated hyperspectral denoising package D Coquelin, B Rasti, M Götz, P Ghamisi, R Gloaguen, A Streit 2022 12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution …, 2022 | 8 | 2022 |
| Feed-forward optimization with delayed feedback for neural networks K Flügel, D Coquelin, M Weiel, C Debus, A Streit, M Götz arXiv preprint arXiv:2304.13372, 2023 | 7 | 2023 |
| Evolutionary optimization of neural architectures in remote sensing classification problems D Coquelin, R Sedona, M Riedel, M Götz 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 1587 …, 2021 | 6 | 2021 |
| RNA contact prediction by data efficient deep learning O Taubert, F von der Lehr, A Bazarova, C Faber, P Knechtges, M Weiel, ... Communications biology 6 (1), 913, 2023 | 4 | 2023 |
| Harnessing Orthogonality to Train Low-Rank Neural Networks D Coquelin, K Flügel, M Weiel, N Kiefer, C Debus, A Streit, M Götz ECAI 2024, 2106-2113, 2024 | 1 | 2024 |
| helmholtz-analytics/heat: Scalable SVD, GSoC22 contributions, Docker image, PyTorch 2 support, AMD GPUs acceleration (v1. 3.0) C Comito, P Shah, SH Neo, M Siggel, D Coquelin, B Hagemeier, ... Jülich Supercomputing Center, 2023 | 1 | 2023 |
| Optimization of AI Methods on Distributed-Memory Computing Architectures D Coquelin Blekinge Institute of Technology, 2024 | | 2024 |
| Heat (release v1. 5.0) F Hoppe, F Osterfeld, JP Gutiérrez Hermosillo Muriedas, ... https://github. com/helmholtz-analytics/heat, 2024 | | 2024 |
| Beyond Backpropagation: Optimization with Multi-Tangent Forward Gradients K Flügel, D Coquelin, M Weiel, A Streit, M Götz arXiv preprint arXiv:2410.17764, 2024 | | 2024 |
| AB-Training: A Communication-Efficient Approach for Distributed Low-Rank Learning D Coquelin, K Flügel, M Weiel, N Kiefer, M Öz, C Debus, A Streit, M Götz arXiv preprint arXiv:2405.01067, 2024 | | 2024 |
| Heat (v1. 5.0) F Hoppe, A Vaithinathan Aravindan, P Knechtges, D Coquelin, A Rüttgers, ... Jülich Supercomputing Center, 2024 | | 2024 |
| Heat (v1. 4.0) C Comito, D Coquelin, A Rüttgers, P Knechtges, B Hagemeier, M Götz, ... Jülich Supercomputing Center, 2024 | | 2024 |
| Heat (v1. 5.0-rc1) C Comito, A Vaithinathan Aravindan, D Coquelin, A Rüttgers, ... Jülich Supercomputing Center, 2024 | | 2024 |
| Heat (release v1. 3.0) C Comito, JP Gutiérrez Hermosillo Muriedas, B Hagemeier, D Coquelin, ... https://github. com/helmholtz-analytics/heat, 2023 | | 2023 |
| Check for updates Massively Parallel Genetic Optimization Through Asynchronous Propagation of Populations O Taubert, M Weiel, D Coquelin, A Farshian, C Debus, A Schug, A Streit High Performance Computing: 38th International Conference, ISC High …, 2023 | | 2023 |
| helmholtz-analytics/heat: Heat 1.0: Data Parallel Neural Networks, and more D Coquelin, L Blind, B Bourgart, C Debus, P Glock, M Roehrig-Zoellner, ... Jülich Supercomputing Center, 2021 | | 2021 |
