| Towards deep learning models resistant to adversarial attacks A Madry, A Makelov, L Schmidt, D Tsipras, A Vladu arXiv preprint arXiv:1706.06083, 2017 | 12112 | 2017 |
| Do ImageNet Classifiers Generalize to ImageNet? B Recht, R Roelofs, L Schmidt, V Shankar arXiv preprint arXiv:1902.10811, 2019 | 1905* | 2019 |
| Laion-5b: An open large-scale dataset for training next generation image-text models C Schuhmann, R Beaumont, R Vencu, C Gordon, R Wightman, M Cherti, ... Advances in Neural Information Processing Systems 35, 25278-25294, 2022 | 1833 | 2022 |
| Exploring the Landscape of Spatial Robustness L Engstrom, B Tran, D Tsipras, L Schmidt, A Madry International Conference on Machine Learning, 1802-1811, 2019 | 847* | 2019 |
| Adversarially robust generalization requires more data L Schmidt, S Santurkar, D Tsipras, K Talwar, A Madry Advances in Neural Information Processing Systems 31, 5014-5026, 2018 | 830 | 2018 |
| Beyond the imitation game: Quantifying and extrapolating the capabilities of language models A Srivastava, A Rastogi, A Rao, AAM Shoeb, A Abid, A Fisch, AR Brown, ... arXiv preprint arXiv:2206.04615, 2022 | 828 | 2022 |
| Unlabeled data improves adversarial robustness Y Carmon, A Raghunathan, L Schmidt, JC Duchi, PS Liang Advances in Neural Information Processing Systems, 11192-11203, 2019 | 726 | 2019 |
| Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time M Wortsman, G Ilharco, SY Gadre, R Roelofs, R Gontijo-Lopes, ... International Conference on Machine Learning, 23965-23998, 2022 | 579 | 2022 |
| Practical and optimal LSH for angular distance A Andoni, P Indyk, T Laarhoven, I Razenshteyn, L Schmidt Advances in Neural Information Processing Systems, 1225-1233, 2015 | 552 | 2015 |
| Measuring robustness to natural distribution shifts in image classification R Taori, A Dave, V Shankar, N Carlini, B Recht, L Schmidt | 507 | 2020 |
| Robust fine-tuning of zero-shot models M Wortsman, G Ilharco, JW Kim, M Li, S Kornblith, R Roelofs, RG Lopes, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 463 | 2022 |
| Objaverse: A universe of annotated 3d objects M Deitke, D Schwenk, J Salvador, L Weihs, O Michel, E VanderBilt, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 397 | 2023 |
| Openclip (2021) G Ilharco, M Wortsman, R Wightman, C Gordon, N Carlini, R Taori, ... DOI: https://doi. org/10.5281/zenodo 5143773, 0 | 394* | |
| Retiring Adult: New Datasets for Fair Machine Learning F Ding, M Hardt, J Miller, L Schmidt Advances in Neural Information Processing Systems 34, 2021 | 364 | 2021 |
| Reproducible scaling laws for contrastive language-image learning M Cherti, R Beaumont, R Wightman, M Wortsman, G Ilharco, C Gordon, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 334 | 2023 |
| Openflamingo: An open-source framework for training large autoregressive vision-language models A Awadalla, I Gao, J Gardner, J Hessel, Y Hanafy, W Zhu, K Marathe, ... arXiv preprint arXiv:2308.01390, 2023 | 247 | 2023 |
| Accuracy on the Line: on the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization JP Miller, R Taori, A Raghunathan, S Sagawa, PW Koh, V Shankar, ... International Conference on Machine Learning, 7721-7735, 2021 | 247 | 2021 |
| Measuring and Narrowing the Compositionality Gap in Language Models O Press, M Zhang, S Min, L Schmidt, NA Smith, M Lewis arXiv preprint arXiv:2210.03350, 2022 | 224 | 2022 |
| Recent developments in the sparse Fourier transform: A compressed Fourier transform for big data AC Gilbert, P Indyk, M Iwen, L Schmidt IEEE Signal Processing Magazine 31 (5), 91-100, 2014 | 202 | 2014 |
| A meta-analysis of overfitting in machine learning R Roelofs, S Fridovich-Keil, J Miller, V Shankar, M Hardt, B Recht, ... Proceedings of the 33rd International Conference on Neural Information …, 2019 | 192 | 2019 |
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