Hopfield Networks
2023
J. Schimunek, P. Seidl, L. Friedrich, D. Kuhn, F. Rippmann, S. Hochreiter, and G. Klambauer (2023) Context-Enriched Molecule Representations Improve Few-Shot Drug Discovery. arXiv:2305.09481, 2023-04-24. (more) (download)
S. Chang, M. Kopp, and P. Ghamisi (2023) Dsfer-Net: A Deep Supervision and Feature Retrieval Network for Bitemporal Change Detection Using Modern Hopfield Networks. arXiv:2304.01101, 2023-04-03. (more) (download)
A. Auer, M. Gauch, D. Klotz, and S. Hochreiter (2023) Conformal Prediction for Time Series with Modern Hopfield Networks. arXiv:2303.12783, 2023-03-22. (more) (download)
2022
A. Fürst, E. Rumetshofer, J. Lehner, V. T. Tran, F. Tang, H. Ramsauer, D. Kreil, M. Kopp, G. Klambauer, A. Bitto, and S. Hochreiter (2022) CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP. Advances in Neural Information Processing Systems (NeurIPS 2022), 35, 20450-20468, 2022-12-06. (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)
S. Chang, M. Kopp, and P. Ghamisi (2022) A Deep Feature Retrieved Network for Bitemporal Remote Sensing Image Change Detection. CEUR Workshop Proceedings, 3207, 2022-07-25. (more) (download)
F. Paischer, T. Adler, V. Patil, A. Bitto-Nemling, M. Holzleitner, S. Lehner, H. Eghbal-zadeh, and S. Hochreiter (2022) History Compression via Language Models in Reinforcement Learning. Proceedings of the 39th International Conference on Machine Learning, PMLR, 162, 17156-17185, 2022-06-28. (more) (download)
B. Schäfl, L. Gruber, A. Bitto-Nemling, and S. Hochreiter (2022) Hopular: Modern Hopfield Networks for Tabular Data. arXiv:2206.00664, 2022-06-01. (more) (download)
S. Hochreiter (2022) Toward a broad AI. Communications of the ACM, 65, 4, 56-57, 2022-03-19. (more) (download)
P. Seidl, P. Renz, N. Dyubankova, P. Neves, J. Verhoeven, J. K. Wegner, M. Segler, S. Hochreiter, and a. G. Klambauer (2022) Improving Few- and Zero-Shot Reaction Template Prediction Using Modern Hopfield Networks. Journal of Chemical Information and Modeling, 62, 9, 2111, 2022-01-15. (more) (download)
2021
F. Tang & M. Kopp (2021) A Remark on a Paper of Krotov and Hopfield. arXiv:2105.15034, 2021-06-03. (more) (download)
P. Seidl, P. Renz, N. Dyubankova, P. Neves, J. Verhoeven, J. K. Wegner, S. Hochreiter, and G. Klambauer (2021) Modern Hopfield Networks for Few- and Zero-Shot Reaction Prediction. arXiv:2104.03279, 2021-04-07. (more) (download)
2020
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)
M. Widrich, B. Schäfl, H. Ramsauer, M. Pavlović, L. Gruber, M. Holzleitner, J. Brandstetter, G. K. Sandve, V. Greiff, S. Hochreiter, and G. Klambauer (2020) Modern Hopfield Networks and Attention for Immune Repertoire Classification. arXiv:2007.13505, 2020-07-16. (more) (download)