Следене
Ludwig Schmidt
Ludwig Schmidt
University of Washington and Allen Institute for AI
Потвърден имейл адрес: cs.washington.edu - Начална страница
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Позовавания
Позовавания
Година
Towards deep learning models resistant to adversarial attacks
A Madry, A Makelov, L Schmidt, D Tsipras, A Vladu
arXiv preprint arXiv:1706.06083, 2017
86382017
Do ImageNet Classifiers Generalize to ImageNet?
B Recht, R Roelofs, L Schmidt, V Shankar
arXiv preprint arXiv:1902.10811, 2019
1227*2019
Exploring the Landscape of Spatial Robustness
L Engstrom, B Tran, D Tsipras, L Schmidt, A Madry
International Conference on Machine Learning, 1802-1811, 2019
700*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
6782018
Unlabeled data improves adversarial robustness
Y Carmon, A Raghunathan, L Schmidt, JC Duchi, PS Liang
Advances in Neural Information Processing Systems, 11192-11203, 2019
5602019
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
4662015
Measuring robustness to natural distribution shifts in image classification
R Taori, A Dave, V Shankar, N Carlini, B Recht, L Schmidt
3212020
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, ...
arXiv preprint arXiv:2210.08402, 2022
2612022
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
2062022
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
1832014
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
1772022
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
1662022
Retiring Adult: New Datasets for Fair Machine Learning
F Ding, M Hardt, J Miller, L Schmidt
Advances in Neural Information Processing Systems 34, 2021
1472021
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
1292019
Model reconstruction from model explanations
S Milli, L Schmidt, AD Dragan, M Hardt
Proceedings of the Conference on Fairness, Accountability, and Transparency, 1-9, 2019
1252019
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
1242021
Approximation algorithms for model-based compressive sensing
C Hegde, P Indyk, L Schmidt
IEEE Transactions on Information Theory 61 (9), 5129-5147, 2015
108*2015
A nearly-linear time framework for graph-structured sparsity
C Hegde, P Indyk, L Schmidt
International Conference on Machine Learning, 928-937, 2015
1072015
Evaluating Machine Accuracy on ImageNet
V Shankar, R Roelofs, H Mania, A Fang, B Recht, L Schmidt
International Conference on Machine Learning (ICML), 2020
1042020
The effect of natural distribution shift on question answering models
J Miller, K Krauth, B Recht, L Schmidt
International Conference on Machine Learning, 6905-6916, 2020
1012020
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Статии 1–20