| 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 | 839* | 2019 |
| The state of sparsity in deep neural networks T Gale, E Elsen, S Hooker arXiv preprint arXiv:1902.09574, 2019 | 819 | 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 | 788 | 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 | 402 | 2020 |
| The hardware lottery S Hooker Communications of the ACM 64 (12), 58-65, 2021 | 230 | 2021 |
| What do compressed deep neural networks forget? S Hooker, A Courville, G Clark, Y Dauphin, A Frome WHI ICML 2019, 2019 | 211* | 2019 |
| Moving beyond “algorithmic bias is a data problem” S Hooker Patterns 2 (4), 2021 | 197 | 2021 |
| Characterising bias in compressed models S Hooker, N Moorosi, G Clark, S Bengio, E Denton arXiv preprint arXiv:2010.03058, 2020 | 186 | 2020 |
| 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 | 103 | 2020 |
| 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 | 95 | 2023 |
| Efficient methods for natural language processing: A survey M Treviso, JU Lee, T Ji, B Aken, Q Cao, MR Ciosici, M Hassid, K Heafield, ... Transactions of the Association for Computational Linguistics 11, 826-860, 2023 | 88 | 2023 |
| 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 | 81 | 2023 |
| 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 | 76 | 2022 |
| The goldilocks of pragmatic understanding: Fine-tuning strategy matters for implicature resolution by llms L Ruis, A Khan, S Biderman, S Hooker, T Rocktäschel, E Grefenstette Advances in Neural Information Processing Systems 36, 2024 | 59* | 2024 |
| Aya model: An instruction finetuned open-access multilingual language model A Üstün, V Aryabumi, ZX Yong, WY Ko, D D'souza, G Onilude, N Bhandari, ... arXiv preprint arXiv:2402.07827, 2024 | 59 | 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 | 58 | 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 | 50 | 2023 |
| The Low-Resource Double Bind: An Empirical Study of Pruning for Low-Resource Machine Translation O Ahia, J Kreutzer, S Hooker Findings of EMNLP 2021, 2021 | 47 | 2021 |
| 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, ... arXiv preprint arXiv:2310.16787, 2023 | 40* | 2023 |
| Aya dataset: An open-access collection for multilingual instruction tuning S Singh, F Vargus, D Dsouza, BF Karlsson, A Mahendiran, WY Ko, ... arXiv preprint arXiv:2402.06619, 2024 | 36 | 2024 |
Julius AdebayoGuide LabsVerified email at guidelabs.ai
