Report · Page 181
report.pdf
Page Content
On the Opportunities and Risks of Foundation Models 181 Dan Hendrycks, Kevin Zhao, Steven Basart, Jacob Steinhardt, and Dawn Song. 2021e. Natural Adversarial Examples. arXiv:1907.07174 [cs.LG] Tom Henighan, Jared Kaplan, Mor Katz, Mark Chen, Christopher Hesse, Jacob Jackson, Heewoo Jun, T. Brown, Prafulla Dhariwal, Scott Gray, Chris Hallacy, Benjamin Mann, Alec Radford, Aditya Ramesh, Nick Ryder, Daniel M. Ziegler, John Schulman, Dario Amodei, and Sam McCandlish. 2020. Scaling Laws for Autoregressive Generative Modeling. ArXiv abs/2010.14701 (2020). https://arxiv.org/abs/2010.14701 Sylvia L. Herbert, Jason J. Choi, Suvansh Qazi, Marsalis Gibson, K. Sreenath, and C. Tomlin. 2021. Scalable Learning of Safety Guarantees for Autonomous Systems using Hamilton-Jacobi Reachability. arXiv preprint arXiv:2101.05916 (2021). Maguire Herriman, Elana Meer, Roy Rosin, Vivian Lee, Vindell Washington, and Kevin G Volpp. 2020. Asked and answered: building a chatbot to address Covid-19-related concerns. Nejm Catalyst Innovations in Care Delivery (2020). J. Hestness, Sharan Narang, Newsha Ardalani, G. Diamos, Heewoo Jun, Hassan Kianinejad, Md. Mostofa Ali Patwary, Y. Yang, and Yanqi Zhou. 2017. Deep Learning Scaling is Predictable, Empirically. ArXiv abs/1712.00409 (2017). John Hewitt and Percy Liang. 2019. Designing and Interpreting Probes with Control Tasks. 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. https://www. aclweb.org/anthology/D19-1275 John Hewitt and Christopher D. Manning. 2019. A Structural Probe for Finding Syntax in Word Representations. In North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL) (Minneapolis, USA). Association for Computational Linguistics. Hidalgo. 2021. How Humans Judge Machines. The MIT Press, Cambridge, Massachusetts. Brian Hie, Ellen D Zhong, Bonnie Berger, and Bryan Bryson. 2021. Learning the language of viral evolution and escape. Science 371, 6526 (2021), 284–288. Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015. Distilling the Knowledge in a Neural Network. arXiv preprint arXiv:1503.02531 (2015). Geoffrey E Hinton, Simon Osindero, and Yee-Whye Teh. 2006. A fast learning algorithm for deep belief nets. Neural computation 18, 7 (2006), 1527–1554. Daniel E Ho and Alice Xiang. 2020. Affirmative Algorithms: The Legal Grounds for Fairness as Awareness. U. Chi. L. Rev. Online (2020), 134. Jonathan Ho, Ajay Jain, and P. Abbeel. 2020. Denoising Diffusion Probabilistic Models. ArXiv abs/2006.11239 (2020). Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735–1780. Bas Hofstra, Vivek V. Kulkarni, Sebastian Munoz-Najar Galvez, Bryan He, Dan Jurafsky, and Daniel A. McFarland. 2020. The Diversity–Innovation Paradox in Science. Proceedings of the National Academy of Sciences 117, 17 (April 2020), 9284–9291. https://doi.org/10.1073/pnas.1915378117 Fred Hohman, Minsuk Kahng, Robert Pienta, and Duen Horng Chau. 2018. Visual analytics in deep learning: An interrogative survey for the next frontiers. IEEE transactions on visualization and computer graphics 25, 8 (2018), 2674–2693. Fred Hohman, Kanit Wongsuphasawat, Mary Beth Kery, and Kayur Patel. 2020. Understanding and visualizing data iteration in machine learning. In Proceedings o
Source Document
→ report.pdf
page 181