Report · Page 204

204
Center for Research on Foundation Models (CRFM)
K Simonyan and A. Zisserman. 2015. Very deep convolutional networks for large-scale image recognition. In International
Conference on Learning Representations (ICLR).
Audra Simpson. 2007. On Ethnographic Refusal: Indigeneity, ’Voice’ Colonial Citizenship. Junctures (Dec. 2007).
Avi Singh, Larry Yang, Kristian Hartikainen, Chelsea Finn, and Sergey Levine. 2019. End-to-End Robotic Reinforcement
Learning without Reward Engineering. In Robotics: Science and Systems (RSS).
Satinder Singh, Andrew G Barto, and Nuttapong Chentanez. 2005. Intrinsically motivated reinforcement learning. Technical
Report. MASSACHUSETTS UNIV AMHERST DEPT OF COMPUTER SCIENCE.
Anton Sinitsin, Vsevolod Plokhotnyuk, Dmitry Pyrkin, Sergei Popov, and Artem Babenko. 2020. Editable Neural Networks.
In International Conference on Learning Representations. https://openreview.net/forum?id=HJedXaEtvS
Vincent Sitzmann, Michael Zollhöfer, and Gordon Wetzstein. 2019. Scene representation networks: Continuous 3d-structure-
aware neural scene representations. arXiv preprint arXiv:1906.01618 (2019).
C Estelle Smith, Bowen Yu, Anjali Srivastava, Aaron Halfaker, Loren Terveen, and Haiyi Zhu. 2020. Keeping Community in
the Loop: Understanding Wikipedia Stakeholder Values for Machine Learning-Based Systems. In Proceedings of the 2020
CHI Conference on Human Factors in Computing Systems. 1–14.
Laura Smith, Nikita Dhawan, Marvin Zhang, P. Abbeel, and Sergey Levine. 2019. AVID: Learning Multi-Stage Tasks via
Pixel-Level Translation of Human Videos. ArXiv abs/1912.04443 (2019).
Jake Snell, Kevin Swersky, and Richard S Zemel. 2017. Prototypical networks for few-shot learning. arXiv preprint
arXiv:1703.05175 (2017).
David So, Quoc Le, and Chen Liang. 2019. The Evolved Transformer. In Proceedings of the 36th International Conference on
Machine Learning (Proceedings of Machine Learning Research, Vol. 97), Kamalika Chaudhuri and Ruslan Salakhutdinov
(Eds.). PMLR, 5877–5886. http://proceedings.mlr.press/v97/so19a.html
Nate Soares, Benja Fallenstein, Stuart Armstrong, and Eliezer Yudkowsky. 2015. Corrigibility. In Workshops at the Twenty-
Ninth AAAI Conference on Artificial Intelligence.
J. Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, and S. Ganguli. 2015.
Deep Unsupervised Learning using
Nonequilibrium Thermodynamics. ArXiv abs/1503.03585 (2015).
Irene Solaiman, Miles Brundage, Jack Clark, Amanda Askell, Ariel Herbert-Voss, Jeff Wu, Alec Radford, Gretchen Krueger,
Jong Wook Kim, Sarah Kreps, Miles McCain, Alex Newhouse, Jason Blazakis, Kris McGuffie, and Jasmine Wang. 2019.
Release Strategies and the Social Impacts of Language Models. Technical Report. OpenAI. http://arxiv.org/abs/1908.09203
Irene Solaiman and Christy Dennison. 2021. Process for Adapting Language Models to Society (PALMS) with Values-Targeted
Datasets. arXiv preprint arXiv:2106.10328 (2021).
Miriam Solomon. 2006. Norms of epistemic diversity. Episteme 3, 1 (2006), 23–36.
Hamid Soltanian-Zadeh. 2019. Multimodal Analysis in Biomedicine. In Big Data in Multimodal Medical Imaging. Chapman
and Hall/CRC, 193–203.
Congzheng Song, Thomas Ristenpart, and Vitaly Shmatikov. 2017. Machine Learning Models That Remember Too Much. In
Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (Dallas, Texas, USA) (CCS
’17). Association for Computing Machinery, New York, NY, USA, 587–601. https://doi.org/10.1145/3133956.3134077
Congzheng Song and Vitaly Shmatik