| Evaluating machine accuracy on imagenet V Shankar, R Roelofs, H Mania, A Fang, B Recht, L Schmidt International Conference on Machine Learning, 8634-8644, 2020 | 146 | 2020 |
| DataComp: In search of the next generation of multimodal datasets SY Gadre, G Ilharco, A Fang, J Hayase, G Smyrnis, T Nguyen, R Marten, ... arXiv preprint arXiv:2304.14108, 2023 | 123 | 2023 |
| Neural kernels without tangents V Shankar, A Fang, W Guo, S Fridovich-Keil, J Ragan-Kelley, L Schmidt, ... International Conference on Machine Learning, 8614-8623, 2020 | 96 | 2020 |
| Data determines distributional robustness in contrastive language image pre-training (clip) A Fang, G Ilharco, M Wortsman, Y Wan, V Shankar, A Dave, L Schmidt International Conference on Machine Learning, 6216-6234, 2022 | 90 | 2022 |
| Multimodal c4: An open, billion-scale corpus of images interleaved with text W Zhu, J Hessel, A Awadalla, SY Gadre, J Dodge, A Fang, Y Yu, ... arXiv preprint arXiv:2304.06939, 2023 | 73 | 2023 |
| Data Filtering Networks A Fang, AM Jose, A Jain, L Schmidt, A Toshev, V Shankar arXiv preprint arXiv:2309.17425, 2023 | 16 | 2023 |
| Does progress on ImageNet transfer to real-world datasets? A Fang, S Kornblith, L Schmidt arXiv preprint arXiv:2301.04644, 2023 | 13 | 2023 |
| Neural Priming for Sample-Efficient Adaptation M Wallingford, V Ramanujan, A Fang, A Kusupati, R Mottaghi, ... arXiv preprint arXiv:2306.10191, 2023 | 4 | 2023 |
| Neural Radiance Field Codebooks M Wallingford, A Kusupati, A Fang, V Ramanujan, A Kembhavi, ... arXiv preprint arXiv:2301.04101, 2023 | 4 | 2023 |
