| An image is worth 16x16 words: Transformers for image recognition at scale A Dosovitskiy, L Beyer, A Kolesnikov, D Weissenborn, X Zhai, ... arXiv preprint arXiv:2010.11929, 2020 | 40119 | 2020 |
| Object-centric learning with slot attention F Locatello, D Weissenborn, T Unterthiner, A Mahendran, G Heigold, ... Advances in neural information processing systems 33, 11525-11538, 2020 | 701 | 2020 |
| An overview of the BIOASQ large-scale biomedical semantic indexing and question answering competition G Tsatsaronis, G Balikas, P Malakasiotis, I Partalas, M Zschunke, ... BMC bioinformatics 16, 1-28, 2015 | 613 | 2015 |
| Axial attention in multidimensional transformers J Ho, N Kalchbrenner, D Weissenborn, T Salimans arXiv preprint arXiv:1912.12180, 2019 | 534 | 2019 |
| Simple open-vocabulary object detection M Minderer, A Gritsenko, A Stone, M Neumann, D Weissenborn, ... European Conference on Computer Vision, 728-755, 2022 | 332 | 2022 |
| & Houlsby, N.(2020). An image is worth 16x16 words: Transformers for image recognition at scale A Dosovitskiy, L Beyer, A Kolesnikov, D Weissenborn, X Zhai, ... arXiv preprint arXiv:2010.11929, 2010 | 323 | 2010 |
| Making Neural QA as Simple as Possible but not Simpler D Weissenborn, G Wiese, L Seiffe CoNLL, 2017 | 319* | 2017 |
| Scaling autoregressive video models D Weissenborn, O Täckström, J Uszkoreit arXiv preprint arXiv:1906.02634, 2019 | 218 | 2019 |
| Colorization transformer M Kumar, D Weissenborn, N Kalchbrenner arXiv preprint arXiv:2102.04432, 2021 | 186 | 2021 |
| Neural Domain Adaptation for Biomedical Question Answering G Wiese, D Weissenborn, M Neves CoNLL, 2017 | 150* | 2017 |
| International Conference on Learning Representations A Dosovitskiy, L Beyer, A Kolesnikov, D Weissenborn, X Zhai, ... ICLR 2010, 11929, 2021 | 102 | 2021 |
| Differentiable patch selection for image recognition JB Cordonnier, A Mahendran, A Dosovitskiy, D Weissenborn, J Uszkoreit, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 92 | 2021 |
| An image is worth 16× 16 words: Transformers for image recognition at scale. arXiv preprint arXiv: 2010.11929 A Dosovitskiy, L Beyer, A Kolesnikov, D Weissenborn, X Zhai, ... Published online, 2020 | 79 | 2020 |
| Dynamic Integration of Background Knowledge in Neural NLU Systems D Weissenborn, T Kočiský, C Dyer arXiv preprint arXiv:1706.02596, 2017 | 76* | 2017 |
| Multi-objective optimization for the joint disambiguation of nouns and named entities D Weissenborn, L Hennig, F Xu, H Uszkoreit Proceedings of the 53rd Annual Meeting of the Association for Computational …, 2015 | 47 | 2015 |
| Event linking with sentential features from convolutional neural networks S Krause, F Xu, H Uszkoreit, D Weissenborn Proceedings of the 20th SIGNLL Conference on Computational Natural Language …, 2016 | 44 | 2016 |
| Answering factoid questions in the biomedical domain. D Weissenborn, G Tsatsaronis, M Schroeder BioASQ@ CLEF 1094, 2013 | 30 | 2013 |
| Sar-graphs: A language resource connecting linguistic knowledge with semantic relations from knowledge graphs S Krause, L Hennig, A Moro, D Weissenborn, F Xu, H Uszkoreit, R Navigli Journal of Web Semantics 37, 112-131, 2016 | 29 | 2016 |
| Discovering relations between indirectly connected biomedical concepts D Weissenborn, M Schroeder, G Tsatsaronis Journal of biomedical semantics 6, 1-19, 2015 | 25 | 2015 |
| Cross-lingual candidate search for biomedical concept normalization R Roller, M Kittner, D Weissenborn, U Leser arXiv preprint arXiv:1805.01646, 2018 | 24 | 2018 |
