| Gans trained by a two time-scale update rule converge to a local nash equilibrium M Heusel, H Ramsauer, T Unterthiner, B Nessler, S Hochreiter Advances in neural information processing systems 30, 2017 | 11488 | 2017 |
| Hopfield networks is all you need H Ramsauer, B Schäfl, J Lehner, P Seidl, M Widrich, T Adler, L Gruber, ... arXiv preprint arXiv:2008.02217, 2020 | 420 | 2020 |
| Modern hopfield networks and attention for immune repertoire classification M Widrich, B Schäfl, M Pavlović, H Ramsauer, L Gruber, M Holzleitner, ... Advances in neural information processing systems 33, 18832-18845, 2020 | 113 | 2020 |
| Cloob: Modern hopfield networks with infoloob outperform clip A Fürst, E Rumetshofer, J Lehner, VT Tran, F Tang, H Ramsauer, D Kreil, ... Advances in neural information processing systems 35, 20450-20468, 2022 | 85 | 2022 |
| Coulomb GANs: Provably optimal Nash equilibria via potential fields T Unterthiner, B Nessler, C Seward, G Klambauer, M Heusel, ... arXiv preprint arXiv:1708.08819, 2017 | 80 | 2017 |
| Gans trained by a two time-scale update rule converge to a local nash equilibrium. arXiv 2017 M Heusel, H Ramsauer, T Unterthiner, B Nessler, S Hochreiter arXiv preprint arXiv:1706.08500, 0 | 57 | |
| omas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. 2017. Gans trained by a two time-scale update rule converge to a local nash equilibrium M Heusel, H Ramsauer Advances in Neural Information Processing Systems, 6626-6637, 0 | 5 | |
| A GAN based solver of black-box inverse problems M Gillhofer, H Ramsauer, J Brandstetter, B Schäfl, S Hochreiter NeurIPS 2019 Workshop on Solving Inverse Problems with Deep Networks, 2019 | 3 | 2019 |
| Gans trained by a two time-scale update rule converge to a nash equilibrium. CoRR abs/1706.08500 M Heusel, H Ramsauer, T Unterthiner, B Nessler, G Klambauer, ... | 3 | 2017 |
| Generative adversarial networks M Heusel, H Ramsauer, T Unterthiner, B Nessler, S Hochreiter Curran Associates, Inc 30, 6626-6637, 2017 | 3 | 2017 |
| About gradient based importance weighting in feed-forward artificial neural networks/submitted by Hubert Ramsauer H Ramsauer | | 2017 |
