Report · Page 206

206
Center for Research on Foundation Models (CRFM)
arXiv preprint arXiv:2105.07949 (2021).
Annamalai Suresh, R Udendhran, and S Vimal. 2020. Deep neural networks for multimodal imaging and biomedical applications.
IGI Global.
Alexey Svyatkovskiy, Shao Kun Deng, Shengyu Fu, and Neel Sundaresan. 2020. Intellicode compose: Code generation using
transformer. In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium
on the Foundations of Software Engineering. 1433–1443.
Latanya Sweeney. 2013. Discrimination in Online Ad Delivery. Queue 11, 3, Article 10 (March 2013), 20 pages.
https:
//doi.org/10.1145/2460276.2460278
Stephen J Swensen, Gary S Kaplan, Gregg S Meyer, Eugene C Nelson, Gordon C Hunt, David B Pryor, Jed I Weissberg,
Jennifer Daley, Gary R Yates, and Mark R Chassin. 2011. Controlling healthcare costs by removing waste: what American
doctors can do now. BMJ quality & safety 20, 6 (2011), 534–537.
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent
Vanhoucke, and Andrew Rabinovich. 2015. Going deeper with convolutions. In Proceedings of the IEEE conference on
computer vision and pattern recognition. 1–9.
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus. 2014.
Intriguing properties of neural networks. In International Conference on Learning Representations (ICLR).
Andrew Szot, Alex Clegg, Eric Undersander, Erik Wijmans, Yili Zhao, John Turner, Noah Maestre, Mustafa Mukadam,
Devendra Chaplot, Oleksandr Maksymets, Aaron Gokaslan, Vladimir Vondrus, Sameer Dharur, Franziska Meier, Wojciech
Galuba, Angel Chang, Zsolt Kira, Vladlen Koltun, Jitendra Malik, Manolis Savva, and Dhruv Batra. 2021. Habitat 2.0:
Training Home Assistants to Rearrange their Habitat. arXiv:2106.14405 [cs.LG]
Alex Tamkin, Miles Brundage, Jack Clark, and Deep Ganguli. 2021a. Understanding the Capabilities, Limitations, and
Societal Impact of Large Language Models. arXiv:2102.02503 [cs.CL]
Alex Tamkin, Vincent Liu, Rongfei Lu, Daniel Fein, Colin Schultz, and Noah Goodman. 2021b. DABS: A Domain-Agnostic
Benchmark for Self-Supervised Learning. arXiv:2111.12062 [cs.LG]
A. Tamkin, Mike Wu, and Noah D. Goodman. 2021c. Viewmaker Networks: Learning Views for Unsupervised Representation
Learning. ArXiv abs/2010.07432 (2021).
Hao Tan and Mohit Bansal. 2020. Vokenization: Improving Language Understanding via Contextualized, Visually-Grounded
Supervision. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP).
2066–2080.
Hao Hao Tan and Mohit Bansal. 2019. LXMERT: Learning Cross-Modality Encoder Representations from Transformers. In
Empirical Methods in Natural Language Processing (EMNLP).
Mingxing Tan and Quoc V Le. 2021. Efficientnetv2: Smaller models and faster training. arXiv preprint arXiv:2104.00298
(2021).
Rohan Taori, Achal Dave, Vaishaal Shankar, Nicholas Carlini, Benjamin Recht, and Ludwig Schmidt. 2020. Measuring
Robustness to Natural Distribution Shifts in Image Classification. arXiv preprint arXiv:2007.00644 (2020).
Rachael Tatman. 2017. Gender and Dialect Bias in YouTube’s Automatic Captions. In Workshop on Ethics in Natural Langauge
Processing, Vol. 1. 53–59.
Nicholas P Tatonetti, P Ye Patrick, Roxana Daneshjou, and Russ B Altman. 2012. Data-driven prediction of drug effects and
interactions. Science translational medicine 4, 125 (2012), 125ra31–125ra31.
Yi