| 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 | 13821 | 2020 |
| Vivit: A video vision transformer A Arnab*, M Dehghani*, G Heigold, C Sun, M Lučić, C Schmid arXiv preprint arXiv:2103.15691, 2021 | 799 | 2021 |
| Universal Transformers M Dehghani, S Gouws, O Vinyals, J Uszkoreit, Ł Kaiser International Conference on Learning Representations (ICLR), 2019 | 656 | 2019 |
| Efficient transformers: A survey Y Tay, M Dehghani, D Bahri, D Metzler ACM Computing Surveys 55 (6), 1-28, 2022 | 568 | 2022 |
| 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 | 350 | 2017 |
| 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 | 253 | 2020 |
| 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 | 181* | 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 | 137 | 2018 |
| 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 | 100 | 2017 |
| Parameter-efficient multi-task fine-tuning for transformers via shared hypernetworks RK Mahabadi, S Ruder, M Dehghani, J Henderson arXiv preprint arXiv:2106.04489, 2021 | 89 | 2021 |
| Scaling instruction-finetuned language models HW Chung, L Hou, S Longpre, B Zoph, Y Tay, W Fedus, E Li, X Wang, ... arXiv preprint arXiv:2210.11416, 2022 | 62 | 2022 |
| Fidelity-Weighted Learning M Dehghani, A Mehrjou, S Gouws, J Kamps, B Schölkopf International Conference on Learning Representations (ICLR2018), https …, 2018 | 62 | 2018 |
| Exploring the limits of large scale pre-training S Abnar, M Dehghani, B Neyshabur, H Sedghi arXiv preprint arXiv:2110.02095, 2021 | 55 | 2021 |
| 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 | 55 | 2017 |
| Tokenlearner: What can 8 learned tokens do for images and videos? MS Ryoo, AJ Piergiovanni, A Arnab, M Dehghani, A Angelova arXiv preprint arXiv:2106.11297, 2021 | 54 | 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 | 53 | 2021 |
| 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 | 53 | 2017 |
| Tokenlearner: Adaptive space-time tokenization for videos M Ryoo, AJ Piergiovanni, A Arnab, M Dehghani, A Angelova Advances in Neural Information Processing Systems 34, 12786-12797, 2021 | 52 | 2021 |
| 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 | 44 | 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 | 43 | 2015 |
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