| Parameter-efficient transfer learning for NLP N Houlsby, A Giurgiu, S Jastrzebski, B Morrone, Q De Laroussilhe, ... International conference on machine learning, 2790-2799, 2019 | 4480 | 2019 |
| Temporal coding in spiking neural networks with alpha synaptic function IM Comsa, K Potempa, L Versari, T Fischbacher, A Gesmundo, ... ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 269 | 2020 |
| Ask the right questions: Active question reformulation with reinforcement learning C Buck, J Bulian, M Ciaramita, W Gajewski, A Gesmundo, N Houlsby, ... arXiv preprint arXiv:1705.07830, 2017 | 190 | 2017 |
| Scaling up models and data with t5x and seqio A Roberts, HW Chung, G Mishra, A Levskaya, J Bradbury, D Andor, ... Journal of Machine Learning Research 24 (377), 1-8, 2023 | 162 | 2023 |
| Transfer Automatic Machine Learning C Wong, N Houlsby, Y Lu, A Gesmundo arXiv preprint arXiv:1803.02780, 2018 | 144* | 2018 |
| Lemmatisation as a Tagging Task A Gesmundo, T Samardzic ACL 2012: the 50th Annual Meeting of the Association for Computational …, 2012 | 69 | 2012 |
| A latent variable model of synchronous syntactic-semantic parsing for multiple languages A Gesmundo, J Henderson, P Merlo, I Titov CoNLL 2009: Thirteenth Conference on Computational Natural Language Learning …, 2009 | 64 | 2009 |
| Machine translation of labeled discourse connectives T Meyer, A Popescu-Belis, N Hajlaoui, A Gesmundo Proceedings of the 10th Conference of the Association for Machine …, 2012 | 56 | 2012 |
| Evolutionary-neural hybrid agents for architecture search K Maziarz, M Tan, A Khorlin, M Georgiev, A Gesmundo arXiv preprint arXiv:1811.09828, 2018 | 38 | 2018 |
| An evolutionary approach to dynamic introduction of tasks in large-scale multitask learning systems A Gesmundo, J Dean arXiv preprint arXiv:2205.12755, 2022 | 30 | 2022 |
| Temporal coding in spiking neural networks with alpha synaptic function: learning with backpropagation IM Comşa, K Potempa, L Versari, T Fischbacher, A Gesmundo, ... IEEE transactions on neural networks and learning systems 33 (10), 5939-5952, 2021 | 30 | 2021 |
| munet: Evolving pretrained deep neural networks into scalable auto-tuning multitask systems A Gesmundo, J Dean arXiv preprint arXiv:2205.10937, 2022 | 22 | 2022 |
| A continual development methodology for large-scale multitask dynamic ML systems A Gesmundo arXiv preprint arXiv:2209.07326, 2022 | 21 | 2022 |
| Lemmatising Serbian as Category Tagging with Bidirectional Sequence Classification A Gesmundo, T Samardzic LREC 2012, 2012 | 18 | 2012 |
| Generating query variants using a trained generative model J Alakuijala, C Buck, J Bulian, M Ciaramita, W Gajewski, A Gesmundo, ... US Patent 11,663,201, 2023 | 16 | 2023 |
| Routing networks with co-training for continual learning M Collier, E Kokiopoulou, A Gesmundo, J Berent arXiv preprint arXiv:2009.04381, 2020 | 16 | 2020 |
| Fast task-aware architecture inference E Kokiopoulou, A Hauth, L Sbaiz, A Gesmundo, G Bartok, J Berent arXiv preprint arXiv:1902.05781, 2019 | 15 | 2019 |
| Gumbel-matrix routing for flexible multi-task learning K Maziarz, E Kokiopoulou, A Gesmundo, L Sbaiz, G Bartok, J Berent | 13 | 2019 |
| Hybrid neural architecture search A Gesmundo US Patent 11,544,536, 2023 | 11 | 2023 |
| Faster Cube Pruning A Gesmundo, J Henderson IWSLT 2012: the 7th International Workshop on Spoken Language Translation, 2010 | 11 | 2010 |
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