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Ludwig Schmidt
Ludwig Schmidt
Stanford University and Anthropic
Dirección de correo verificada de cs.washington.edu - Página principal
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Citado por
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Año
Towards deep learning models resistant to adversarial attacks
A Madry, A Makelov, L Schmidt, D Tsipras, A Vladu
arXiv preprint arXiv:1706.06083, 2017
130542017
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
22782022
Do ImageNet Classifiers Generalize to ImageNet?
B Recht, R Roelofs, L Schmidt, V Shankar
arXiv preprint arXiv:1902.10811, 2019
2081*2019
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
9992022
Exploring the Landscape of Spatial Robustness
L Engstrom, B Tran, D Tsipras, L Schmidt, A Madry
International Conference on Machine Learning, 1802-1811, 2019
882*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
8702018
Unlabeled data improves adversarial robustness
Y Carmon, A Raghunathan, L Schmidt, JC Duchi, PS Liang
Advances in Neural Information Processing Systems, 11192-11203, 2019
7882019
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
7252022
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
5762015
Measuring robustness to natural distribution shifts in image classification
R Taori, A Dave, V Shankar, N Carlini, B Recht, L Schmidt
5642020
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
5422022
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
5122023
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
4942023
Openclip (2021)
G Ilharco, M Wortsman, R Wightman, C Gordon, N Carlini, R Taori, ...
DOI: https://doi. org/10.5281/zenodo 5143773, 0
488*
Retiring Adult: New Datasets for Fair Machine Learning
F Ding, M Hardt, J Miller, L Schmidt
Advances in Neural Information Processing Systems 34, 2021
4102021
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
3362023
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
2712022
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
2712021
Editing Models with Task Arithmetic
G Ilharco, MT Ribeiro, M Wortsman, S Gururangan, L Schmidt, ...
arXiv preprint arXiv:2212.04089, 2022
2692022
DataComp: In search of the next generation of multimodal datasets
S Yitzhak Gadre, G Ilharco, A Fang, J Hayase, G Smyrnis, T Nguyen, ...
arXiv e-prints, arXiv: 2304.14108, 2023
234*2023
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Artículos 1–20