| Bloom: A 176b-parameter open-access multilingual language model T Le Scao, A Fan, C Akiki, E Pavlick, S Ilić, D Hesslow, R Castagné, ... | 1146 | 2023 |
| Binder 2.0-Reproducible, interactive, sharable environments for science at scale B Ragan-Kelley, C Willing, F Akici, D Lippa, D Niederhut, M Pacer Proceedings of the 17th python in science conference, 113-120, 2018 | 181 | 2018 |
| Total property optimization system for energy efficiency and smart buildings RN Anderson, A Boulanger, V Bhandari, E Boniberger, A Gagneja, ... US Patent App. 14/341,718, 2015 | 92 | 2015 |
| Efficient methods for natural language processing: a survey M Treviso, T Ji, JU Lee, B van Aken, Q Cao, MR Ciosici, M Hassid, ... arXiv preprint arXiv:2209.00099, 2023 | 55 | 2023 |
| The Scientific Method in the Science of Machine Learning JZ Forde, M Paganini ICLR 2019 Debugging Machine Learning Models workshop, 2019 | 41 | 2019 |
| Reproducible research environments with repo2docker J Forde, T Head, C Holdgraf, Y Panda, G Nalvarete, B Ragan-Kelley, ... | 26 | 2018 |
| Hyperparameter optimization is deceiving us, and how to stop it AF Cooper, Y Lu, J Forde, CM De Sa Advances in Neural Information Processing Systems 34, 3081-3095, 2021 | 18 | 2021 |
| Model selection's disparate impact in real-world deep learning applications JZ Forde, AF Cooper, K Kwegyir-Aggrey, C De Sa, M Littman arXiv preprint arXiv:2104.00606, 2021 | 16 | 2021 |
| Evaluation Beyond Task Performance: Analyzing Concepts in AlphaZero in Hex C Lovering*, JZ Forde*, G Konidaris, E Pavlick, ML Littman arXiv preprint arXiv:2211.14673, 2022 | 15* | 2022 |
| On iterative neural network pruning, reinitialization, and the similarity of masks M Paganini, J Forde arXiv preprint arXiv:2001.05050, 2020 | 14 | 2020 |
| Prompting multilingual large language models to generate code-mixed texts: The case of south east asian languages ZX Yong, R Zhang, J Forde, S Wang, A Subramonian, H Lovenia, ... Proceedings of the 6th Workshop on Computational Approaches to Linguistic …, 2023 | 13* | 2023 |
| Streamlining tensor and network pruning in pytorch M Paganini, J Forde arXiv preprint arXiv:2004.13770, 2020 | 12 | 2020 |
| A tool for organizing key characteristics of virtual, augmented, and mixed reality for human–robot interaction systems: Synthesizing vam-hri trends and takeaways TR Groechel, ME Walker, CT Chang, E Rosen, JZ Forde IEEE Robotics & Automation Magazine 29 (1), 35-44, 2022 | 11* | 2022 |
| Neurips 2019 reproducibility challenge K Sinha, J Pineau, J Forde, RN Ke, H Larochelle ReScience C 6 (2), 11, 2020 | 11 | 2020 |
| Detecting insertion, substitution, and deletion errors in radiology reports using neural sequence-to-sequence models J Zech, J Forde, J Titano, D Kaji, A Costa, EK Oermann Annals of Translational Medicine, 2018 | 10 | 2018 |
| Towards reproducible machine learning research in natural language processing A Lucic, M Bleeker, S Bhargav, J Forde, K Sinha, J Dodge, S Luccioni, ... Proceedings of the 60th Annual Meeting of the Association for Computational …, 2022 | 8 | 2022 |
| Bespoke vs. Prêt-à-Porter Lottery Tickets: Exploiting Mask Similarity for Trainable Sub-Network Finding M Paganini, JZ Forde arXiv preprint arXiv:2007.04091, 2020 | 7 | 2020 |
| Reproducing machine learning research on binder J Forde, M Bussonnier, FA Fortin, B Granger, T Head, C Holdgraf, ... | 7 | 2018 |
| Reduce, reuse, recycle: Improving training efficiency with distillation C Blakeney, JZ Forde, J Frankle, Z Zong, ML Leavitt arXiv preprint arXiv:2211.00683, 2022 | 4 | 2022 |
| dagger: A python framework for reproducible machine learning experiment orchestration M Paganini, JZ Forde arXiv preprint arXiv:2006.07484, 2020 | 4 | 2020 |
Michela PaganiniDeepMindVerified email at google.com
