| 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 | 78 | 2017 |
| Autoregressive denoising diffusion models for multivariate probabilistic time series forecasting K Rasul, C Seward, I Schuster, R Vollgraf International Conference on Machine Learning, 8857-8868, 2021 | 70 | 2021 |
| Disentangling multiple conditional inputs in GANs G Yildirim, C Seward, U Bergmann arXiv preprint arXiv:1806.07819, 2018 | 29 | 2018 |
| GANosaic: Mosaic Creation with Generative Texture Manifolds N Jetchev, U Bergmann, C Seward 31st Conference on Neural Information Processing Systems (NIPS 2017), Long …, 2017 | 7 | 2017 |
| Optimizing warehouse operations with machine learning on GPUs C Seward Edited by NVIDIA Developer Blog. NVIDIA. Available online at https://devel …, 2015 | 7 | 2015 |
| First order generative adversarial networks C Seward, T Unterthiner, U Bergmann, N Jetchev, S Hochreiter International Conference on Machine Learning, 4567-4576, 2018 | 6 | 2018 |
| Accelerating warehouse operations with neural networks C Seward Zalando AdTech Lab Hamburg, URL: https://tech. zalando. com/blog …, 2015 | 3 | 2015 |
| Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields Download PDF TU Thomas, B Nessler, CS Calvin, G Klambauer, M Heusel, H Ramsauer, ... | | |
| Posterior Sampling: Make Reinforcement Learning Sample Efficient Again C Seward, U Bergmann, R Vollgraf, S Hochreiter | | |
Sepp HochreiterInstitute for Machine Learning, Johannes Kepler University LinzVerified email at ml.jku.at
