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On the Opportunities and Risks of Foundation Models 199 Bharath Ramsundar, Steven M. Kearnes, Patrick Riley, Dale Webster, David E. Konerding, and Vijay S. Pande. 2015. Massively Multitask Networks for Drug Discovery. CoRR abs/1502.02072 (2015). arXiv:1502.02072 http://arxiv.org/abs/1502.02072 Jeff Rasley, Samyam Rajbhandari, Olatunji Ruwase, and Yuxiong He. 2020. DeepSpeed: System Optimizations Enable Training Deep Learning Models with over 100 Billion Parameters. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 3505–3506. Laila Rasmy, Yang Xiang, Ziqian Xie, Cui Tao, and Degui Zhi. 2021. Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction. NPJ digital medicine 4, 1 (2021), 1–13. R. Ratcliff. 1990. Connectionist models of recognition memory: constraints imposed by learning and forgetting functions. Psychological review 97 2 (1990), 285–308. Alexander Ratner, Stephen H. Bach, Henry Ehrenberg, Jason Fries, Sen Wu, and Christopher Ré. 2017. Snorkel: Rapid Training Data Creation with Weak Supervision. Proceedings of the VLDB Endowment (PVLDB) (2017). Gerald K Ray and Jeffrey S Lubbers. 2014. A government success story: How data analysis by the Social Security Appeals Council (with a push from the Administrative Conference of the United States) is transforming social security disability adjudication. Geo. Wash. L. Rev. 83 (2014), 1575. Christopher Ré, Feng Niu, Pallavi Gudipati, and Charles Srisuwananukorn. 2019. Overton: A data system for monitoring and improving machine-learned products. arXiv preprint arXiv:1909.05372 (2019). Richard M Re and Alicia Solow-Niederman. 2019. Developing artificially intelligent justice. Stan. Tech. L. Rev. 22 (2019), 242. Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar. 2019. Do ImageNet Classifiers Generalize to ImageNet?. In International Conference on Machine Learning (ICML). Colorado J. Reed, Xiangyu Yue, Ani Nrusimha, Sayna Ebrahimi, Vivek Vijaykumar, Richard Mao, Bo Li, Shanghang Zhang, Devin Guillory, Sean Metzger, Kurt Keutzer, and Trevor Darrell. 2021. Self-Supervised Pretraining Improves Self-Supervised Pretraining. arXiv:2103.12718 [cs.CV] Rob Reich, Mehran Sahami, and Jeremy M. Weinstein. 2021. System Error: Where Big Tech Went Wrong and How We Can Reboot. Harper. https://books.google.com/books?id=mU0QEAAAQBAJ Theodoros Rekatsinas, Xu Chu, Ihab F. Ilyas, and Christopher Ré. 2017a. Holoclean: Holistic data repairs with probabilistic inference. Proceedings of the VLDB Endowment (PVLDB) (2017). Theodoros Rekatsinas, Manas Joglekar, Hector Garcia-Molina, Aditya Parameswaran, and Christopher Ré. 2017b. Slimfast: Guaranteed results for data fusion and source reliability. In Proceedings of the 2017 ACM International Conference on Management of Data. 1399–1414. Hongyu Ren, Hanjun Dai, Zihang Dai, Mengjiao Yang, Jure Leskovec, Dale Schuurmans, and Bo Dai. 2021. Combiner: Full Attention Transformer with Sparse Computation Cost. arXiv preprint arXiv:2107.05768 (2021). Hongyu Ren, Weihua Hu, and Jure Leskovec. 2020. Query2box: Reasoning over knowledge graphs in vector space using box embeddings. In International Conference on Learning Representations (ICLR). Hongyu Ren and Jure Leskovec. 2020. Beta embeddings for multi-hop logical reasoning in knowledge graphs. In NeurIPS. Adithya Renduchintala, Denise Diaz, Kenneth Heafield, Xian Li, and Mona Diab. 2021. Gender bias
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