Dr Michael Kopp​

I. Svogor, C. Eichenberger, M. Spanring, M. Neun, and M. Kopp (2022) Profiling and Improving the PyTorch Dataloader for High-Latency Storage: A Technical Report. arXiv:2211.04908, 2022-11-09. (more) (download)

S. Chang, M. Kopp, and P. Ghamisi (2022) Sketched Multi-view Subspace Learning for Hyperspectral Anomalous Change Detection. arXiv:2210.04271, 2022-10-09. (more) (download)

Y. Xu, W. Yu, P. Ghamisi, M. Kopp, and S. Hochreiter (2022) Txt2Img-MHN: Remote Sensing Image Generation from Text Using Modern Hopfield Networks. arXiv:2208.04441, 2022-08-08. (more) (download)

P. Ghamisi, O. Ghorbanzadeh, Y. Xu, P. Herruzo, D. Kreil, M. Kopp, and S. Hochreiter (2022) The Landslide4Sense Competition 2022. CEUR Workshop Proceedings, 3207, 2022-07-25. (more) (download)

C. Eichenberger, M. Neun, H. Martin, P. Herruzo, M. Spanring, Y. Lu, S. Choi, V. Konyakhin, N. Lukashina, A. Shpilman, N. Wiedemann, M. Raubal, B. Wang, H. L. Vu, R. Mohajerpoor, C. Cai, I. Kim, L. Hermes, A. Melnik, R. Velioglu, M. Vieth, M. Schilling, A. Bojesomo, H. Al Marzouqi, P. Liatsis, J. Santokhi, D. Hillier, Y. Yang, J. Sarwar, A. Jordan, E. Hewage, D. Jonietz, F. Tang, A. Gruca, M. Kopp, D. Kreil, and S. Hochreiter (2022) Traffic4cast at NeurIPS 2021 – Temporal and Spatial Few-Shot Transfer Learning in Gridded Geo-Spatial Processes. NeurIPS 2021 Competitions and Demonstrations Track, PMLR, 176, 97-112, 2022-07-20. (more) (download)

O. Ghorbanzadeh, Y. Xu, P. Ghamisi, M. Kopp, and D. Kreil (2022) Landslide4Sense: Reference Benchmark Data and Deep Learning Models for Landslide Detection. arXiv:2206.00515, 2022-06-01. (more) (download)

P. Herruzo, A. Gruca, L. Lliso, X. Calbet, P. Rípodas, S. Hochreiter, M. Kopp, and D. P. Kreil (2021) High-Resolution Multi-Channel Weather Forecasting – First Insights on Transfer Learning from the Weather4cast Competitions 2021. 2021 IEEE International Conference on Big Data (Big Data), 5750-5757, 2021-12-15. (more) (download)

A. Gruca, P. Herruzo, P. Rípodas, A. Kucik, C. Briese, M. K. Kopp, S. Hochreiter, P. Ghamisi, and D. P. Kreil (2021) CDCEO’21 – First Workshop on Complex Data Challenges in Earth Observation. Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 4878–4879, 2021-10-26. (more) (download)

A. Fürst, E. Rumetshofer, V. Tran, H. Ramsauer, F. Tang, J. Lehner, D. Kreil, M. Kopp, G. Klambauer, A. Bitto-Nemling, and S. Hochreiter (2021) CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP. arXiv:2110.11316, 2021-10-21. (more) (download)

M. Kopp, D. Kreil, M. Neun, D. Jonietz, H. Martin, P. Herruzo, A. Gruca, A. Soleymani, F. Wu, Y. Liu, J. Xu, J. Zhang, J. Santokhi, A. Bojesomo, H. Al Marzouqi, P. Liatsis, P. H. Kwok, Q. Qi, and S. Hochreiter (2021) Traffic4cast at NeurIPS 2020 – Yet More on the Unreasonable Effectiveness of Gridded Geo-Spatial Processes. Proceedings of the NeurIPS 2020 Competition and Demonstration Track, PMLR, 133, 325-343, 2021. (more) (download)

F. Tang & M. Kopp (2021) A Remark on a Paper of Krotov and Hopfield. arXiv:2105.15034, 2021-06-03. (more) (download)

T. Adler, J. Brandstetter, M. Widrich, A. Mayr, D. Kreil, M. Kopp, G. Klambauer, and S. Hochreiter (2020) Cross-Domain Few-Shot Learning by Representation Fusion. arXiv:2010.06498, 2020-10-13. (more) (download)

D. P. Kreil, M. K. Kopp, D. Jonietz, M. Neun, A. Gruca, P. Herruzo, H. Martin, A. Soleymani, and S. Hochreiter (2020) The surprising efficiency of framing geo-spatial time series forecasting as a video prediction task – Insights from the IARAI Traffic4cast Competition at NeurIPS 2019. Proceedings of the NeurIPS 2019 Competition and Demonstration Track, PMLR, 123, 232-241, 2020-08-19. (more) (download)

H. Ramsauer, B. Schäfl, J. Lehner, P. Seidl, M. Widrich, L. Gruber, M. Holzleitner, M. Pavlović, G. K. Sandve, V. Greiff, D. Kreil, M. Kopp, G. Klambauer, J. Brandstetter, and S. Hochreiter (2020) Hopfield Networks is All You Need. arXiv:2008.02217, 2020-08-06. (more) (download)

D. Jonietz & M. Kopp (2019) Towards Modeling Geographical Processes with Generative Adversarial Networks (GANs). 14th International Conference on Spatial Information Theory (COSIT 2019), Leibniz International Proceedings in Informatics (LIPIcs), 142, 27, 2019-09-03. (more) (download)