| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ... arXiv preprint arXiv:2312.11805, 2023 | 939 | 2023 |
| Big self-supervised models advance medical image classification S Azizi, B Mustafa, F Ryan, Z Beaver, J Freyberg, J Deaton, A Loh, ... Proceedings of the IEEE/CVF international conference on computer vision …, 2021 | 517 | 2021 |
| Pali: A jointly-scaled multilingual language-image model X Chen, X Wang, S Changpinyo, AJ Piergiovanni, P Padlewski, D Salz, ... arXiv preprint arXiv:2209.06794, 2022 | 471 | 2022 |
| LiT: Zero-Shot Transfer with Locked-image Text Tuning X Zhai, X Wang, B Mustafa, A Steiner, D Keysers, A Kolesnikov, L Beyer Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 444 | 2021 |
| Scaling vision with sparse mixture of experts C Riquelme, J Puigcerver, B Mustafa, M Neumann, R Jenatton, ... Advances in Neural Information Processing Systems 34, 8583-8595, 2021 | 407 | 2021 |
| Scaling vision transformers to 22 billion parameters M Dehghani, J Djolonga, B Mustafa, P Padlewski, J Heek, J Gilmer, ... International Conference on Machine Learning, 7480-7512, 2023 | 330 | 2023 |
| Sigmoid loss for language image pre-training X Zhai, B Mustafa, A Kolesnikov, L Beyer Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 169 | 2023 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context M Reid, N Savinov, D Teplyashin, D Lepikhin, T Lillicrap, J Alayrac, ... arXiv preprint arXiv:2403.05530, 2024 | 163 | 2024 |
| Towards generalist biomedical AI T Tu, S Azizi, D Driess, M Schaekermann, M Amin, PC Chang, A Carroll, ... NEJM AI 1 (3), AIoa2300138, 2024 | 148 | 2024 |
| Multimodal contrastive learning with limoe: the language-image mixture of experts B Mustafa, C Riquelme, J Puigcerver, R Jenatton, N Houlsby Advances in Neural Information Processing Systems 35, 9564-9576, 2022 | 114 | 2022 |
| Does your dermatology classifier know what it doesn’t know? detecting the long-tail of unseen conditions AG Roy, J Ren, S Azizi, A Loh, V Natarajan, B Mustafa, N Pawlowski, ... Medical Image Analysis 75, 102274, 2022 | 108 | 2022 |
| Learning to segment medical images with scribble-supervision alone YB Can, K Chaitanya, B Mustafa, LM Koch, E Konukoglu, ... Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical …, 2018 | 100 | 2018 |
| Pali-x: On scaling up a multilingual vision and language model X Chen, J Djolonga, P Padlewski, B Mustafa, S Changpinyo, J Wu, ... arXiv preprint arXiv:2305.18565, 2023 | 99 | 2023 |
| Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging S Azizi, L Culp, J Freyberg, B Mustafa, S Baur, S Kornblith, T Chen, ... Nature Biomedical Engineering 7 (6), 756-779, 2023 | 87 | 2023 |
| Supervised transfer learning at scale for medical imaging B Mustafa, A Loh, J Freyberg, P MacWilliams, M Wilson, SM McKinney, ... arXiv preprint arXiv:2101.05913, 2021 | 71 | 2021 |
| Robust and efficient medical imaging with self-supervision S Azizi, L Culp, J Freyberg, B Mustafa, S Baur, S Kornblith, T Chen, ... arXiv preprint arXiv:2205.09723, 2022 | 59 | 2022 |
| Scalable transfer learning with expert models J Puigcerver, C Riquelme, B Mustafa, C Renggli, AS Pinto, S Gelly, ... 9th International Conference on Learning Representations, ICLR, 2021 | 58 | 2021 |
| Correlated input-dependent label noise in large-scale image classification M Collier, B Mustafa, E Kokiopoulou, R Jenatton, J Berent Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 57 | 2021 |
| From sparse to soft mixtures of experts J Puigcerver, C Riquelme, B Mustafa, N Houlsby arXiv preprint arXiv:2308.00951, 2023 | 51 | 2023 |
| Sparse upcycling: Training mixture-of-experts from dense checkpoints A Komatsuzaki, J Puigcerver, J Lee-Thorp, CR Ruiz, B Mustafa, J Ainslie, ... arXiv preprint arXiv:2212.05055, 2022 | 42 | 2022 |
