| 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 | 4022 | 2020 |
| Universal Transformers M Dehghani, S Gouws, O Vinyals, J Uszkoreit, £ Kaiser International Conference on Learning Representations (ICLR), 2019 | 470 | 2019 |
| Neural Ranking Models with Weak Supervision M Dehghani, H Zamani, A Severyn, J Kamps, WB Croft The 40th International ACM SIGIR Conference on Research and Development in …, 2017 | 307 | 2017 |
| Efficient transformers: A survey Y Tay, M Dehghani, D Bahri, D Metzler arXiv preprint arXiv:2009.06732, 2020 | 269 | 2020 |
| Vivit: A video vision transformer A Arnab*, M Dehghani*, G Heigold, C Sun, M Luèiæ, C Schmid arXiv preprint arXiv:2103.15691, 2021 | 218 | 2021 |
| An image is worth 16x16 words: Transformers for image recognition at scale. arXiv 2020 A Dosovitskiy, L Beyer, A Kolesnikov, D Weissenborn, X Zhai, ... arXiv preprint arXiv:2010.11929, 0 | 172 | |
| Long Range Arena: A Benchmark for Efficient Transformers Y Tay*, M Dehghani*, S Abnar, Y Shen, D Bahri, P Pham, J Rao, L Yang, ... arXiv preprint arXiv:2011.04006, 2020 | 137 | 2020 |
| From neural re-ranking to neural ranking: Learning a sparse representation for inverted indexing H Zamani, M Dehghani, WB Croft, E Learned-Miller, J Kamps Proceedings of the 27th ACM international conference on information and …, 2018 | 113 | 2018 |
| Metnet: A neural weather model for precipitation forecasting CK Sønderby, L Espeholt, J Heek, M Dehghani, A Oliver, T Salimans, ... arXiv preprint arXiv:2003.12140, 2020 | 93* | 2020 |
| Learning to Attend, Copy, and Generate for Session-Based Query Suggestion M Dehghani, S Rothe, E Alfonseca, P Fleury International Conference on Information and Knowledge Management (CIKM'17), 2017 | 86 | 2017 |
| Fidelity-Weighted Learning M Dehghani, A Mehrjou, S Gouws, J Kamps, B Schölkopf International Conference on Learning Representations (ICLR2018), https …, 2018 | 56 | 2018 |
| Learning to Learn from Weak Supervision by Full Supervision M Dehghani, A Severyn, S Rothe, J Kamps NIPS2017 workshop on Meta-Learning (MetaLearn 2017), 2017 | 46 | 2017 |
| Words are Malleable: Computing Semantic Shifts in Political and Media Discourse H Azarbonyad, M Dehghani, K Beelen, A Arkut, M Marx, K Jaap International Conference on Information and Knowledge Management (CIKM'17), 2017 | 45 | 2017 |
| Avoiding your teacher's mistakes: Training neural networks with controlled weak supervision M Dehghani, A Severyn, S Rothe, J Kamps arXiv preprint arXiv:1711.00313, 2017 | 37 | 2017 |
| Matthias M., G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby. 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 2 (3), 2020 | 32* | 2020 |
| Time-aware authorship attribution for short text streams H Azarbonyad, M Dehghani, M Marx, J Kamps Proceedings of the 38th International ACM SIGIR Conference on Research and …, 2015 | 32 | 2015 |
| Neural Networks for Information Retrieval T Kenter, A Borisov, C Van Gysel, M Dehghani, M de Rijke, B Mitra Tutorial at the 40th International ACM SIGIR Conference on Research and …, 2017 | 31 | 2017 |
| Luhn revisited: Significant words language models M Dehghani, H Azarbonyad, J Kamps, D Hiemstra, M Marx Proceedings of the 25th ACM International on Conference on Information and …, 2016 | 28 | 2016 |
| Are pre-trained convolutions better than pre-trained transformers? Y Tay, M Dehghani, J Gupta, D Bahri, V Aribandi, Z Qin, D Metzler arXiv preprint arXiv:2105.03322, 2021 | 27 | 2021 |
| A learning approach for email conversation thread reconstruction M Dehghani, A Shakery, M Asadpour, A Koushkestani Journal of Information Science 39 (6), 846-863, 2013 | 27 | 2013 |
Jaap KampsUniversity of AmsterdamVerified email at uva.nl
