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180 Center for Research on Foundation Models (CRFM) Peter Hase, Mona T. Diab, Asli Celikyilmaz, Xian Li, Zornitsa Kozareva, Veselin Stoyanov, Mohit Bansal, and Srinivasan Iyer. 2021. Do Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model Beliefs. CoRR abs/2111.13654 (2021). https://arxiv.org/abs/2111.13654 Tatsunori Hashimoto, Megha Srivastava, Hongseok Namkoong, and Percy Liang. 2018. Fairness without demographics in repeated loss minimization. In International Conference on Machine Learning. PMLR, 1929–1938. Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. 2021. Masked autoencoders are scalable vision learners. arXiv preprint arXiv:2111.06377 (2021). Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. 2019. Momentum Contrast for Unsupervised Visual Representation Learning. arXiv preprint arXiv:1911.05722 (2019). Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross B. Girshick. 2020. Momentum Contrast for Unsupervised Visual Representation Learning. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020), 9726–9735. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016a. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 770–778. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016b. Deep Residual Learning for Image Recognition. In Computer Vision and Pattern Recognition (CVPR). Allison Hegel, Marina Shah, Genevieve Peaslee, Brendan Roof, and Emad Elwany. 2021. The Law of Large Documents: Understanding the Structure of Legal Contracts Using Visual Cues. arXiv preprint arXiv:2107.08128 (2021). Fabian Caba Heilbron, Victor Escorcia, Bernard Ghanem, and Juan Carlos Niebles. 2015. ActivityNet: A Large-Scale Video Benchmark for Human Activity Understanding. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 961–970. Robert Heilmayr, Cristian Echeverría, and Eric F Lambin. 2020. Impacts of Chilean forest subsidies on forest cover, carbon and biodiversity. Nature Sustainability 3, 9 (2020), 701–709. Christina Heinze-Deml and Nicolai Meinshausen. 2017. Conditional variance penalties and domain shift robustness. arXiv preprint arXiv:1710.11469 (2017). Kyle Helfrich, Devin Willmott, and Qiang Ye. 2018. Orthogonal recurrent neural networks with scaled Cayley transform. In International Conference on Machine Learning. PMLR, 1969–1978. Joseph M Hellerstein and Michael Stonebraker. 2005. Readings in database systems. MIT press. Deborah Hellman. 2020. Measuring algorithmic fairness. Va. L. Rev. 106 (2020), 811. Deborah Hellman. 2021. Big Data and Compounding Injustice. Journal of Moral Philosophy, forthcoming, Virginia Public Law and Legal Theory Research Paper 2021-27 (2021). Mikael Henaff, Jason Weston, Arthur Szlam, Antoine Bordes, and Yann LeCun. 2016. Tracking the world state with recurrent entity networks. arXiv preprint arXiv:1612.03969 (2016). Olivier J Hénaff, Skanda Koppula, Jean-Baptiste Alayrac, Aaron van den Oord, Oriol Vinyals, and João Carreira. 2021. Efficient visual pretraining with contrastive detection. ICCV (2021). Peter Henderson, Jieru Hu, Joshua Romoff, Emma Brunskill, Dan Jurafsky, and Joelle Pineau. 2020. Towards the systematic reporting of the energy and carbon footprints of machine learning. Journal of Machine Learning Research 21, 248 (2020), 1–43. Peter Henderson, Koustuv Sinha, Nicolas Angelard-G
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