Report · Page 194
report.pdf
Page Content
194 Center for Research on Foundation Models (CRFM) Hesham Mostafa and Xin Wang. 2019. Parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization. In International Conference on Machine Learning. PMLR, 4646–4655. http://proceedings.mlr. press/v97/mostafa19a.html ZSCC: 0000081 ISSN: 2640-3498. Hussein Mozannar and David Sontag. 2020. Consistent estimators for learning to defer to an expert. In International Conference on Machine Learning. PMLR, 7076–7087. Jesse Mu and Jacob Andreas. 2020. Compositional explanations of neurons. arXiv preprint arXiv:2006.14032 (2020). Dheevatsa Mudigere, Yuchen Hao, Jianyu Huang, Andrew Tulloch, Srinivas Sridharan, Xing Liu, Mustafa Ozdal, Jade Nie, Jongsoo Park, Liang Luo, et al. 2021. High-Performance, Distributed Training of Large-Scale Deep Learning Recommendation Models. arXiv preprint arXiv:2104.05158 (2021). Brad A. Myers, Scott E. Hudson, and Randy Pausch. 2000. Past, Present and Future of User Interface Software Tools. In ACM Transactions on Computer Human Interaction. ACM. Moin Nadeem, Anna Bethke, and Siva Reddy. 2021. StereoSet: Measuring stereotypical bias in pretrained language models. In Proceedings of ACL 2021. Vaishnavh Nagarajan, Anders Andreassen, and Behnam Neyshabur. 2020. Understanding the failure modes of out-of- distribution generalization. arXiv preprint arXiv:2010.15775 (2020). Arsha Nagrani, Shan Yang, Anurag Arnab, Aren Jansen, Cordelia Schmid, and Chen Sun. 2021. Attention Bottlenecks for Multimodal Fusion. arXiv preprint arXiv:2107.00135 (2021). Ashvin Nair, Vitchyr H. Pong, Murtaza Dalal, Shikhar Bahl, Steven Lin, and Sergey Levine. 2018. Visual Reinforcement Learning with Imagined Goals. In NeurIPS. V. Nair and G. E. Hinton. 2010. Rectified linear units improve restricted boltzmann machines. In International Conference on Machine Learning (ICML). 807–814. Preetum Nakkiran, Gal Kaplun, Yamini Bansal, Tristan Yang, Boaz Barak, and Ilya Sutskever. 2019. Deep double descent: Where bigger models and more data hurt. arXiv preprint arXiv:1912.02292 (2019). Nikita Nangia, Clara Vania, Rasika Bhalerao, and Samuel R. Bowman. 2020. CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, 1953–1967. https://doi.org/10.18653/v1/2020. emnlp-main.154 Arvind Narayanan, Arunesh Mathur, Marshini Chetty, and Mihir Kshirsagar. 2020. Dark Patterns: Past, Present, and Future. Commun. ACM 63, 9 (Aug. 2020), 42–47. https://doi.org/10.1145/3397884 Deepak Narayanan, Aaron Harlap, Amar Phanishayee, Vivek Seshadri, Nikhil R Devanur, Gregory R Ganger, Phillip B Gibbons, and Matei Zaharia. 2019. PipeDream: Generalized Pipeline Parallelism for DNN Training. In Proceedings of the 27th ACM Symposium on Operating Systems Principles. 1–15. Deepak Narayanan, Amar Phanishayee, Kaiyu Shi, Xie Chen, and Matei Zaharia. 2021a. Memory-Efficient Pipeline-Parallel DNN Training. In International Conference on Machine Learning. PMLR, 7937–7947. Deepak Narayanan, Keshav Santhanam, Amar Phanishayee, and Matei Zaharia. 2018. Accelerating Deep Learning Workloads through Efficient Multi-Model Execution. In NeurIPS Workshop on Systems for Machine Learning. 20. Deepak Narayanan, Mohammad Shoeybi, Jared Casper, Patrick LeGresley, Mostofa Patwary, Vijay Anand Korthikanti, Dmitri Vainbrand, Prethvi Kashinkun
Source Document
→ report.pdf
page 194