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On the Opportunities and Risks of Foundation Models 169 3D Vision (3DV) (2017). Hongyan Chang, Ta Duy Nguyen, Sasi Kumar Murakonda, Ehsan Kazemi, and Reza Shokri. 2020. On adversarial bias and the robustness of fair machine learning. arXiv preprint arXiv:2006.08669 (2020). Soravit Changpinyo, Piyush Sharma, Nan Ding, and Radu Soricut. 2021. Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts. In CVPR. Yatin Chaudhary, Pankaj Gupta, Khushbu Saxena, Vivek Kulkarni, Thomas Runkler, and Hinrich Schütze. 2020. TopicBERT for energy efficient document classification. arXiv preprint arXiv:2010.16407 (2020). Annie S. Chen, Suraj Nair, and Chelsea Finn. 2021c. Learning Generalizable Robotic Reward Functions from "In-The-Wild" Human Videos. In Robotics: Science and Systems (RSS). Annie S Chen, HyunJi Nam, Suraj Nair, and Chelsea Finn. 2021d. Batch exploration with examples for scalable robotic reinforcement learning. IEEE Robotics and Automation Letters 6, 3 (2021), 4401–4408. Chaofan Chen, Oscar Li, Chaofan Tao, Alina Jade Barnett, Jonathan Su, and Cynthia Rudin. 2018. This looks like that: deep learning for interpretable image recognition. arXiv preprint arXiv:1806.10574 (2018). Irene Y Chen, Shalmali Joshi, and Marzyeh Ghassemi. 2020b. Treating health disparities with artificial intelligence. Nature medicine 26, 1 (2020), 16–17. Irene Y Chen, Peter Szolovits, and Marzyeh Ghassemi. 2019. Can AI help reduce disparities in general medical and mental health care? AMA journal of ethics 21, 2 (2019), 167–179. Liang Chen, Peter Edwards, John D Nelson, and Timothy J Norman. 2015a. An access control model for protecting provenance graphs. In 2015 13th Annual Conference on Privacy, Security and Trust (PST). IEEE, 125–132. Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, M. Laskin, P. Abbeel, A. Srinivas, and Igor Mordatch. 2021b. Decision Transformer: Reinforcement Learning via Sequence Modeling. ArXiv abs/2106.01345 (2021). Mayee Chen, Karan Goel, Nimit S Sohoni, Fait Poms, Kayvon Fatahalian, and Christopher Ré. 2021a. Mandoline: Model Evaluation under Distribution Shift. In International Conference on Machine Learning. PMLR, 1617–1629. Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, and Ilya Sutskever. 2020d. Generative Pretraining From Pixels. In Proceedings of the 37th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 119), Hal Daumé III and Aarti Singh (Eds.). PMLR, 1691–1703. http://proceedings.mlr.press/v119/ chen20s.html Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian, Clemens Winter, Philippe Tillet, Felipe Petroski Such, Dave Cummings, Matthias Plappert, Fotios Chantzis, Elizabeth Barnes, Ariel Herbert-Voss, William Hebgen Guss, Alex Nichol, Alex Paino, Nikolas Tezak, Jie Tang, Igor Babuschkin, Suchir Balaji, Shantanu Jain, William Saunders, Christopher Hesse, Andrew N. Carr, Jan Leike, Josh Achiam, Vedant Misra, Evan Morikawa, Alec Radford, Matthew Knight, Miles Brundage, Mira Murati, Katie Mayer, Peter Welinder, Bob McGrew, Dario Amodei, Sam McCandlish, Ilya Sutskever, and Wojciech Zaremba. 2021f. Evaluating
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