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Ludwig Schmidt
Ludwig Schmidt
University of Washington and Allen Institute for AI
Bestätigte E-Mail-Adresse bei cs.washington.edu - Startseite
Titel
Zitiert von
Zitiert von
Jahr
Towards deep learning models resistant to adversarial attacks
A Madry, A Makelov, L Schmidt, D Tsipras, A Vladu
arXiv preprint arXiv:1706.06083, 2017
117332017
Do ImageNet Classifiers Generalize to ImageNet?
B Recht, R Roelofs, L Schmidt, V Shankar
arXiv preprint arXiv:1902.10811, 2019
1845*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
15842022
Exploring the Landscape of Spatial Robustness
L Engstrom, B Tran, D Tsipras, L Schmidt, A Madry
International Conference on Machine Learning, 1802-1811, 2019
839*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
8112018
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
7572022
Unlabeled data improves adversarial robustness
Y Carmon, A Raghunathan, L Schmidt, JC Duchi, PS Liang
Advances in Neural Information Processing Systems, 11192-11203, 2019
7152019
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
5392015
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
5272022
Measuring robustness to natural distribution shifts in image classification
R Taori, A Dave, V Shankar, N Carlini, B Recht, L Schmidt
4942020
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
4282022
Retiring Adult: New Datasets for Fair Machine Learning
F Ding, M Hardt, J Miller, L Schmidt
Advances in Neural Information Processing Systems 34, 2021
3372021
Openclip (2021)
G Ilharco, M Wortsman, R Wightman, C Gordon, N Carlini, R Taori, ...
DOI: https://doi. org/10.5281/zenodo 5143773, 0
337*
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
3232023
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
2872023
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
2372021
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
2052023
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
2042022
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
2012014
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
1892019
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