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On the Opportunities and Risks of Foundation Models 211 Shijie Wu and Mark Dredze. 2019. Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, 833–844. https://doi.org/10.18653/v1/D19-1077 Shijie Wu and Mark Dredze. 2020. Are All Languages Created Equal in Multilingual BERT?. In Proceedings of the 5th Workshop on Representation Learning for NLP. 120–130. https://aclanthology.org/2020.repl4nlp-1.16 Yuhuai Wu, Albert Jiang, Jimmy Ba, and Roger Grosse. 2021a. INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving. (2021). https://openreview.net/forum?id=O6LPudowNQm Yuhuai Wu, Markus N. Rabe, Wenda Li, Jimmy Ba, Roger B. Grosse, and Christian Szegedy. 2021f. LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning. (2021). Zachary Wu, Kadina E Johnston, Frances H Arnold, and Kevin K Yang. 2021b. Protein sequence design with deep generative models. Current Opinion in Chemical Biology 65 (2021), 18–27. Zhengxuan Wu, Nelson F Liu, and Christopher Potts. 2021c. Identifying the Limits of Cross-Domain Knowledge Transfer for Pretrained Models. arXiv preprint arXiv:2104.08410 (2021). Zhirong Wu, Yuanjun Xiong, Stella X. Yu, and Dahua Lin. 2018. Unsupervised Feature Learning via Non-parametric Instance Discrimination. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (2018), 3733–3742. Alice Xiang. 2021. Reconciling legal and technical approaches to algorithmic bias. Tennessee Law Review 88, 3 (2021). Kai Xiao, Logan Engstrom, Andrew Ilyas, and Aleksander Madry. 2020. Noise or Signal: The Role of Image Backgrounds in Object Recognition. arXiv preprint arXiv:2006.09994 (2020). Tete Xiao, Xiaolong Wang, Alexei A. Efros, and Trevor Darrell. 2021. What Should Not Be Contrastive in Contrastive Learning. arXiv:2008.05659 [cs.CV] Michael Xie, Neal Jean, Marshall Burke, David Lobell, and Stefano Ermon. 2016. Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping. In Association for the Advancement of Artificial Intelligence (AAAI). Qizhe Xie, Minh-Thang Luong, Eduard Hovy, and Quoc V. Le. 2020. Self-training with Noisy Student improves ImageNet classification. arXiv (2020). Sang Michael Xie, Ananya Kumar, Robert Jones, Fereshte Khani, Tengyu Ma, and Percy Liang. 2021a. In-N-Out: Pre-Training and Self-Training using Auxiliary Information for Out-of-Distribution Robustness. In International Conference on Learning Representations (ICLR). Sang Michael Xie, Tengyu Ma, and Percy Liang. 2021b. Composed Fine-Tuning: Freezing Pre-Trained Denoising Autoencoders for Improved Generalization. International Conference on Machine Learning (ICML) (2021). Sang Michael Xie, Aditi Raghunathan, Percy Liang, and Tengyu Ma. 2021c. An Explanation of In-context Learning as Implicit Bayesian Inference. arXiv preprint arXiv:2111.02080 (2021). Caiming Xiong, Stephen Merity, and Richard Socher. 2016. Dynamic memory networks for visual and textual question answering. In International conference on machine learning. 2397–2406. Albert Xu, Eshaan Pathak, Eric Wallace, Suchin Gururangan, Maarten Sap, and Dan Klein. 2021. Detoxifying Language Models Risks Marginalizing Minority Voices. In Proceedings of the 2021 Conference of the North American Chapter of the As
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