| Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms H Xiao, K Rasul, R Vollgraf arXiv preprint arXiv:1708.07747, 2017 | 9926 | 2017 |
| FLAIR: An easy-to-use framework for state-of-the-art NLP A Akbik, T Bergmann, D Blythe, K Rasul, S Schweter, R Vollgraf Proceedings of the 2019 conference of the North American chapter of the …, 2019 | 1131 | 2019 |
| Lasagne: first release S Dieleman, J Schlüter, C Raffel, E Olson, SK Sønderby, D Nouri, ... Zenodo: Geneva, Switzerland 3, 74, 2015 | 442* | 2015 |
| Diffusers: State-of-the-art diffusion models P Von Platen, S Patil, A Lozhkov, P Cuenca, N Lambert, K Rasul, ... | 403 | 2022 |
| 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 | 311 | 2021 |
| Multivariate probabilistic time series forecasting via conditioned normalizing flows K Rasul, AS Sheikh, I Schuster, U Bergmann, R Vollgraf arXiv preprint arXiv:2002.06103, 2020 | 206 | 2020 |
| Trl: Transformer reinforcement learning L von Werra, Y Belkada, L Tunstall, E Beeching, T Thrush, N Lambert, ... GitHub. Available online at: https://github. com/lvwerra/trl, 2020 | 177 | 2020 |
| Lag-llama: Towards foundation models for time series forecasting K Rasul, A Ashok, AR Williams, A Khorasani, G Adamopoulos, ... R0-FoMo: Robustness of Few-shot and Zero-shot Learning in Large Foundation …, 2023 | 117* | 2023 |
| Modeling temporal data as continuous functions with process diffusion M Biloš, K Rasul, A Schneider, Y Nevmyvaka, S Günnemann | 52* | 2022 |
| The alignment handbook L Tunstall, E Beeching, N Lambert, N Rajani, S Huang, K Rasul, AM Rush, ... | 43 | 2023 |
| Provably convergent Schrödinger bridge with applications to probabilistic time series imputation Y Chen, W Deng, S Fang, F Li, NT Yang, Y Zhang, K Rasul, S Zhe, ... International Conference on Machine Learning, 4485-4513, 2023 | 27 | 2023 |
| Deep Learning based Forecasting: a case study from the online fashion industry M Kunz, S Birr, M Raslan, L Ma, T Januschowski Forecasting with Artificial Intelligence: Theory and Applications, 279-311, 2023 | 23 | 2023 |
| Probabilistic time series forecasting with implicit quantile networks A Gouttes, K Rasul, M Koren, J Stephan, T Naghibi arXiv preprint arXiv:2107.03743, 2021 | 23 | 2021 |
| The gridlab grid application toolkit G Allen, K Davis, T Dramlitsch, T Goodale, I Kelley, G Lanfermann, ... Proceedings of 11th IEEE International Symposium on High Performance …, 2002 | 21* | 2002 |
| Stackllama: An rl fine-tuned llama model for stack exchange question and answering, 2023 E Beeching, Y Belkada, K Rasul, L Tunstall, L von Werra, N Rajani, ... URL https://huggingface. co/blog/stackllama 1 (4.1), 4.1, 2023 | 19 | 2023 |
| Stochastic maximum likelihood optimization via hypernetworks AS Sheikh, K Rasul, A Merentitis, U Bergmann arXiv preprint arXiv:1712.01141, 2017 | 14 | 2017 |
| The annotated diffusion model N Rogge, K Rasul Hugging Face Blog, 2022 | 12 | 2022 |
| Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms. CoRR (2017) H Xiao, K Rasul, R Vollgraf arXiv preprint arXiv:1708.07747, 0 | 10 | |
| Vq-ar: Vector quantized autoregressive probabilistic time series forecasting K Rasul, YJ Park, MN Ramström, KM Kim arXiv preprint arXiv:2205.15894, 2022 | 9 | 2022 |
| Numinamath J Li, E Beeching, L Tunstall, B Lipkin, R Soletskyi, SC Huang, K Rasul, ... | 7* | 2024 |
Roland VollgrafZalando ResearchVerified email at zalando.de
