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Helping end users comprehend the abstract distribution shifts can greatly facilitate AI deployment.
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A simple statistical method to detect covariate shift
Feutry, C., Piantanida, P., Alberge, F., and Duhamel, P · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Hendrycks, D. and Dietterich, T · 2019
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Hudson, D. A. and Manning, C. D · 2019
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Oksuz, K., Cam, B., Kalkan, S., and Akbas, E · 2019
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Peng, X., Bai, Q., Xia, X., Huang, Z., Saenko, K., and Wang, B · 2019
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Gulrajani, I. and Lopez-Paz, D · 2020
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A broader study of cross-domain few-shot learning
Guo, Y., Codella, N. C., Karlinsky, L., Codella, J. V., Smith, J. R., Saenko, K., Rosing, T., and Feris, R · 2020
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Learning to prompt for vision-language models
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Meaningfully explaining model mistakes using conceptual counterfactuals
Abid, A., Yuksekgonul, M., and Zou, J · 2021
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Failing loudly: An empirical study of methods for detecting dataset shift
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Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al · 2019
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Sagawa, S., Koh, P. W., Hashimoto, T. B., and Liang, P · 2019
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Wikipedia word frequency
Semenov, I. and Arefin, S · 2019
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An image is worth 16x16 words: Transformers for image recognition at scale
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Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al · 2021
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Domino: Discovering systematic errors with cross-modal embeddings
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Summarizing differences between text distributions with natural language
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