| 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 | 5032 | 2020 |
| Universal Transformers M Dehghani, S Gouws, O Vinyals, J Uszkoreit, Ł Kaiser International Conference on Learning Representations (ICLR), 2019 | 501 | 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 | 319 | 2017 |
| Efficient transformers: A survey Y Tay, M Dehghani, D Bahri, D Metzler ACM Computing Surveys (CSUR), 2020 | 312 | 2020 |
| Vivit: A video vision transformer A Arnab*, M Dehghani*, G Heigold, C Sun, M Lučić, C Schmid arXiv preprint arXiv:2103.15691, 2021 | 276 | 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 | 258 | |
| 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 | 164 | 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 | 118 | 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 | 108* | 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 | 88 | 2017 |
| Fidelity-Weighted Learning M Dehghani, A Mehrjou, S Gouws, J Kamps, B Schölkopf International Conference on Learning Representations (ICLR2018), https …, 2018 | 57 | 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 | 48 | 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 | 46 | 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 | 38 | 2017 |
| 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 | 35 | 2017 |
| 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 | 33 | 2015 |
| Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworks R Karimi Mahabadi, S Ruder, M Dehghani, J Henderson arXiv e-prints, arXiv: 2106.04489, 2021 | 31* | 2021 |
| 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 | 31 | 2021 |
| 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 | 29 | 2016 |
| 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
