| A benchmark for interpretability methods in deep neural networks S Hooker, D Erhan, PJ Kindermans, B Kim Advances in neural information processing systems 32, 2019 | 1153* | 2019 |
| The state of sparsity in deep neural networks T Gale, E Elsen, S Hooker arXiv preprint arXiv:1902.09574, 2019 | 1086 | 2019 |
| The (un) reliability of saliency methods PJ Kindermans, S Hooker, J Adebayo, M Alber, KT Schütt, S Dähne, ... Explainable AI: Interpreting, explaining and visualizing deep learning, 267-280, 2019 | 989 | 2019 |
| Toward trustworthy AI development: mechanisms for supporting verifiable claims M Brundage, S Avin, J Wang, H Belfield, G Krueger, G Hadfield, H Khlaaf, ... arXiv preprint arXiv:2004.07213, 2020 | 767 | 2020 |
| Back to basics: Revisiting reinforce style optimization for learning from human feedback in llms A Ahmadian, C Cremer, M Gallé, M Fadaee, J Kreutzer, O Pietquin, ... arXiv preprint arXiv:2402.14740, 2024 | 542 | 2024 |
| The hardware lottery S Hooker Communications of the ACM 64 (12), 58-65, 2021 | 348 | 2021 |
| Aya model: An instruction finetuned open-access multilingual language model A Üstün, V Aryabumi, Z Yong, WY Ko, D D’souza, G Onilude, N Bhandari, ... Proceedings of the 62nd Annual Meeting of the Association for Computational …, 2024 | 329 | 2024 |
| What do compressed deep neural networks forget? S Hooker, A Courville, G Clark, Y Dauphin, A Frome WHI ICML 2019, 2019 | 284* | 2019 |
| Moving beyond “algorithmic bias is a data problem” S Hooker Patterns 2 (4), 2021 | 278 | 2021 |
| Frontier AI regulation: Managing emerging risks to public safety M Anderljung, J Barnhart, A Korinek, J Leung, C O'Keefe, J Whittlestone, ... arXiv preprint arXiv:2307.03718, 2023 | 250 | 2023 |
| Characterising bias in compressed models S Hooker, N Moorosi, G Clark, S Bengio, E Denton arXiv preprint arXiv:2010.03058, 2020 | 243 | 2020 |
| Evaluating the social impact of generative ai systems in systems and society I Solaiman, Z Talat, W Agnew, L Ahmad, D Baker, SL Blodgett, C Chen, ... arXiv preprint arXiv:2306.05949, 2023 | 208 | 2023 |
| Efficient methods for natural language processing: A survey M Treviso, JU Lee, T Ji, B Van Aken, Q Cao, MR Ciosici, M Hassid, ... Transactions of the Association for Computational Linguistics 11, 826-860, 2023 | 190 | 2023 |
| Aya dataset: An open-access collection for multilingual instruction tuning S Singh, F Vargus, D D’souza, BF Karlsson, A Mahendiran, WY Ko, ... Proceedings of the 62nd Annual Meeting of the Association for Computational …, 2024 | 181 | 2024 |
| Pushing mixture of experts to the limit: Extremely parameter efficient moe for instruction tuning T Zadouri, A Üstün, A Ahmadian, B Ermiş, A Locatelli, S Hooker arXiv preprint arXiv:2309.05444, 2023 | 175 | 2023 |
| When less is more: Investigating data pruning for pretraining llms at scale M Marion, A Üstün, L Pozzobon, A Wang, M Fadaee, S Hooker arXiv preprint arXiv:2309.04564, 2023 | 171 | 2023 |
| Estimating example difficulty using variance of gradients C Agarwal, D D'souza, S Hooker IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR) 2022, 2020 | 163 | 2020 |
| Aya 23: Open weight releases to further multilingual progress V Aryabumi, J Dang, D Talupuru, S Dash, D Cairuz, H Lin, B Venkitesh, ... arXiv preprint arXiv:2405.15032, 2024 | 153 | 2024 |
| Randomness in neural network training: Characterizing the impact of tooling D Zhuang, X Zhang, S Song, S Hooker Proceedings of Machine Learning and Systems 4, 316-336, 2022 | 133 | 2022 |
| The data provenance initiative: A large scale audit of dataset licensing & attribution in ai S Longpre, R Mahari, A Chen, N Obeng-Marnu, D Sileo, W Brannon, ... | 126* | 2023 |
Marzieh FadaeeHead of Cohere LabsVerified email at cohere.com
