Artikkelit, joihin on yleisen käytön mandaatti - Ludwig SchmidtLisätietoja
Saatavilla jossain: 48
Towards deep learning models resistant to adversarial attacks
A Madry, A Makelov, L Schmidt, D Tsipras, A Vladu
arXiv preprint arXiv:1706.06083, 2017
Mandaatit: US National Science Foundation
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
Mandaatit: Federal Ministry of Education and Research, Germany
Do ImageNet Classifiers Generalize to ImageNet?
B Recht, R Roelofs, L Schmidt, V Shankar
arXiv preprint arXiv:1902.10811, 2019
Mandaatit: US National Science Foundation, US Department of Energy, US Department of …
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
Mandaatit: US National Science Foundation, US Department of Defense
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
Mandaatit: US National Science Foundation
Exploring the Landscape of Spatial Robustness
L Engstrom, B Tran, D Tsipras, L Schmidt, A Madry
International Conference on Machine Learning, 1802-1811, 2019
Mandaatit: US National Science Foundation
Unlabeled data improves adversarial robustness
Y Carmon, A Raghunathan, L Schmidt, JC Duchi, PS Liang
Advances in Neural Information Processing Systems, 11192-11203, 2019
Mandaatit: US National Science Foundation, US Department of Defense
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
Mandaatit: US National Science Foundation, US Department of Defense
Measuring robustness to natural distribution shifts in image classification
R Taori, A Dave, V Shankar, N Carlini, B Recht, L Schmidt
Mandaatit: US Department of Defense
Retiring Adult: New Datasets for Fair Machine Learning
F Ding, M Hardt, J Miller, L Schmidt
Advances in Neural Information Processing Systems 34, 2021
Mandaatit: US National Science Foundation
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
Mandaatit: US National Science Foundation, Helmholtz Association
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
Mandaatit: US National Science Foundation
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
Mandaatit: US Department of Defense
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
Mandaatit: US National Science Foundation
Evaluating Machine Accuracy on ImageNet
V Shankar, R Roelofs, H Mania, A Fang, B Recht, L Schmidt
International Conference on Machine Learning (ICML), 2020
Mandaatit: US Department of Defense
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
Mandaatit: US National Science Foundation
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, ...
Advances in Neural Information Processing Systems 36, 2024
Mandaatit: US National Science Foundation, US Department of Defense
Predicting with confidence on unseen distributions
D Guillory, V Shankar, S Ebrahimi, T Darrell, L Schmidt
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021
Mandaatit: US Department of Defense
Patching open-vocabulary models by interpolating weights
G Ilharco, M Wortsman, SY Gadre, S Song, H Hajishirzi, S Kornblith, ...
Advances in Neural Information Processing Systems 35, 29262-29277, 2022
Mandaatit: US National Science Foundation, US Department of Defense
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
Mandaatit: US National Science Foundation
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