| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ... arXiv preprint arXiv:2312.11805, 2023 | 3572 | 2023 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context G Team, P Georgiev, VI Lei, R Burnell, L Bai, A Gulati, G Tanzer, ... arXiv preprint arXiv:2403.05530, 2024 | 1408 | 2024 |
| Are Sixteen Heads Really Better than One? P Michel, O Levy, G Neubig NeurIPS 2019, 2019 | 1194 | 2019 |
| Gemma: Open models based on gemini research and technology G Team, T Mesnard, C Hardin, R Dadashi, S Bhupatiraju, S Pathak, ... arXiv preprint arXiv:2403.08295, 2024 | 1144 | 2024 |
| Gemma 2: Improving open language models at a practical size G Team, M Riviere, S Pathak, PG Sessa, C Hardin, S Bhupatiraju, ... arXiv preprint arXiv:2408.00118, 2024 | 567 | 2024 |
| Weight Poisoning Attacks on Pre-trained Models K Kurita, P Michel, G Neubig ACL 2020, 2020 | 466 | 2020 |
| Dynet: The dynamic neural network toolkit G Neubig, C Dyer, Y Goldberg, A Matthews, W Ammar, A Anastasopoulos, ... arXiv preprint arXiv:1701.03980, 2017 | 449* | 2017 |
| On Evaluation of Adversarial Perturbations for Sequence-to-Sequence Models P Michel, X Li, G Neubig, JM Pino NAACL 2019, 2019 | 158 | 2019 |
| MTNT: A Testbed for Machine Translation of Noisy Text P Michel, G Neubig EMNLP 2018, 2018 | 151 | 2018 |
| compare-mt: A Tool for Holistic Comparison of Language Generation Systems G Neubig, ZY Dou, J Hu, P Michel, D Pruthi, X Wang NAACL 2019 Demo, 2019 | 136 | 2019 |
| Extreme Adaptation for Personalized Neural Machine Translation P Michel, G Neubig ACL 2018, 2018 | 118 | 2018 |
| Findings of the first shared task on machine translation robustness X Li, P Michel, A Anastasopoulos, Y Belinkov, N Durrani, O Firat, P Koehn, ... WMT 2019, 2019 | 94 | 2019 |
| Codegemma: Open code models based on gemma CG Team, H Zhao, J Hui, J Howland, N Nguyen, S Zuo, A Hu, ... arXiv preprint arXiv:2406.11409, 2024 | 75 | 2024 |
| Examining and Combating Spurious Features under Distribution Shift C Zhou, X Ma, P Michel, G Neubig ICML 2021, 2021 | 75 | 2021 |
| Optimizing data usage via differentiable rewards X Wang, H Pham, P Michel, A Anastasopoulos, J Carbonell, G Neubig International Conference on Machine Learning, 9983-9995, 2020 | 68 | 2020 |
| Modeling the Second Player in Distributionally Robust Optimization P Michel, T Hashimoto, G Neubig ICLR 2021, 2021 | 37 | 2021 |
| Blind phoneme segmentation with temporal prediction errors P Michel, O Räsänen, R Thiolliere, E Dupoux ACL SRW 2017, 2016 | 35 | 2016 |
| Should we be pre-training? an argument for end-task aware training as an alternative LM Dery, P Michel, A Talwalkar, G Neubig ICLR 2022, 2021 | 34 | 2021 |
| Emergent communication: Generalization and overfitting in lewis games M Rita, C Tallec, P Michel, JB Grill, O Pietquin, E Dupoux, F Strub Advances in neural information processing systems 35, 1389-1404, 2022 | 32 | 2022 |
| Distributionally Robust Models with Parametric Likelihood Ratios P Michel, T Hashimoto, G Neubig ICLR 2022, 2022 | 23 | 2022 |
Graham NeubigCarnegie Mellon University, All Hands AIDirección de correo verificada de cs.cmu.edu
