| 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 | 727* | 2019 |
| The state of sparsity in deep neural networks T Gale, E Elsen, S Hooker arXiv preprint arXiv:1902.09574, 2019 | 723 | 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 | 695 | 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 | 341 | 2020 |
| The hardware lottery S Hooker Communications of the ACM 64 (12), 58-65, 2021 | 188 | 2021 |
| What do compressed deep neural networks forget? S Hooker, A Courville, G Clark, Y Dauphin, A Frome WHI ICML 2019, 2019 | 186* | 2019 |
| Moving beyond “algorithmic bias is a data problem” S Hooker Patterns 2 (4), 2021 | 175 | 2021 |
| Characterising bias in compressed models S Hooker, N Moorosi, G Clark, S Bengio, E Denton arXiv preprint arXiv:2010.03058, 2020 | 162 | 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 | 80 | 2020 |
| 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 | 63 | 2022 |
| 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 | 55 | 2023 |
| Frontier AI regulation: Managing emerging risks to public safety M Anderljung, J Barnhart, J Leung, A Korinek, C O'Keefe, J Whittlestone, ... arXiv preprint arXiv:2307.03718, 2023 | 43 | 2023 |
| 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 | 42* | 2024 |
| 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 | 40 | 2021 |
| Evaluating the social impact of generative ai systems in systems and society I Solaiman, Z Talat, W Agnew, L Ahmad, D Baker, SL Blodgett, ... arXiv preprint arXiv:2306.05949, 2023 | 38 | 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 | 24 | 2023 |
| Keep the gradients flowing: Using gradient flow to study sparse network optimization K Tessera, S Hooker, B Rosman arXiv preprint arXiv:2102.01670, 2021 | 22 | 2021 |
| 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 | 21 | 2023 |
| Metadata Archaeology: Unearthing Data Subsets by Leveraging Training Dynamics SA Siddiqui, N Rajkumar, T Maharaj, D Krueger, S Hooker International Conference on Learning Representations, 2023, 2022 | 20 | 2022 |
| On the challenges of using black-box apis for toxicity evaluation in research L Pozzobon, B Ermis, P Lewis, S Hooker arXiv preprint arXiv:2304.12397, 2023 | 16 | 2023 |
Julius AdebayoGuide LabsVerified email at guidelabs.ai
