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200 Center for Research on Foundation Models (CRFM) Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo, Myle Ott, C. Lawrence Zitnick, Jerry Ma, and Rob Fergus. 2021. Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proceedings of the National Academy of Sciences 118, 15 (2021). https://doi.org/10.1073/pnas.2016239118 arXiv:https://www.pnas.org/content/118/15/e2016239118.full.pdf Adam Roberts, Colin Raffel, and Noam Shazeer. 2020. How Much Knowledge Can You Pack into the Parameters of a Language Model?. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 5418–5426. Phillip Rogaway. 2016. The Moral Character of Cryptographic Work. , 48 pages. Anna Rogers. 2020. Peer review in NLP: resource papers. https://hackingsemantics.xyz/2020/reviewing-data/ Anna Rogers. 2021. Changing the World by Changing the Data. ArXiv abs/2105.13947 (2021). https://arxiv.org/abs/2105.13947 Anna Rogers, Olga Kovaleva, and Anna Rumshisky. 2020. A primer in bertology: What we know about how bert works. Transactions of the Association for Computational Linguistics (TACL) 8 (2020), 842–866. David Rolnick, Priya L Donti, Lynn H Kaack, Kelly Kochanski, Alexandre Lacoste, Kris Sankaran, Andrew Slavin Ross, Nikola Milojevic-Dupont, Natasha Jaques, Anna Waldman-Brown, et al. 2019. Tackling climate change with machine learning. arXiv preprint arXiv:1906.05433 (2019). Paul M Romer. 1990. Endogenous technological change. Journal of political Economy 98, 5, Part 2 (1990), S71–S102. Frieda Rong. 2021. Extrapolating to Unnatural Language Processing with GPT-3’s In-context Learning: The Good, the Bad, and the Mysterious. http://ai.stanford.edu/blog/in-context-learning/ Stéphane Ross, Geoffrey Gordon, and Andrew Bagnell. 2011. A reduction of imitation learning and structured prediction to no-regret online learning. In Artificial Intelligence and Statistics (AISTATS). Edward Rosten and Tom Drummond. 2006. Machine learning for high-speed corner detection. In European conference on computer vision. Springer, 430–443. Daniel Rothchild, Alex Tamkin, Julie Yu, Ujval Misra, and Joseph Gonzalez. 2021. C5T5: Controllable Generation of Organic Molecules with Transformers. ArXiv abs/2108.10307 (2021). Baptiste Rozière, Marie-Anne Lachaux, Marc Szafraniec, and Guillaume Lample. 2021. DOBF: A Deobfuscation Pre-Training Objective for Programming Languages. CoRR abs/2102.07492 (2021). arXiv:2102.07492 https://arxiv.org/abs/2102.07492 Sebastian Ruder and Barbara Plank. 2018. Strong Baselines for Neural Semi-Supervised Learning under Domain Shift. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Melbourne, Australia). Association for Computational Linguistics, 1044–1054. http://aclweb.org/anthology/P18-1096 Cynthia Rudin. 2019. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence 1, 5 (2019), 206–215. Camilo Ruiz, Marinka Zitnik, and Jure Leskovec. 2020. Identification of disease treatment mechanisms through the multiscale interactome. Nature Communications (2020). Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al. 2015. Imagenet large scale visual recognition challenge. International journal of c
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