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An empirical analysis of compute-optimal large language model training., , , , , , , , , and 12 other author(s). NeurIPS, (2022)DSD: Dense-Sparse-Dense Training for Deep Neural Networks., , , , , , , , , and 2 other author(s). ICLR (Poster), OpenReview.net, (2017)Poster reception - N-Body simulation on GPUs., , , , , and . SC, page 188. ACM Press, (2006)Improving language models by retrieving from trillions of tokens, , , , , , , , , and 18 other author(s). (2021)cite arxiv:2112.04426Comment: Fix incorrect reported numbers in Table 14.The State of Sparse Training in Deep Reinforcement Learning., , , and . ICML, volume 162 of Proceedings of Machine Learning Research, page 7766-7792. PMLR, (2022)Improving Language Models by Retrieving from Trillions of Tokens., , , , , , , , , and 18 other author(s). ICML, volume 162 of Proceedings of Machine Learning Research, page 2206-2240. PMLR, (2022)Practical Real Time Recurrent Learning with a Sparse Approximation., , , , , and . ICLR, OpenReview.net, (2021)N-Body Simulations on GPUs, , , , , and . CoRR, (2007)Mixed Precision Training, , , , , , , , , and 1 other author(s). (2017)cite arxiv:1710.03740Comment: Published as a conference paper at ICLR 2018.Mixed Precision Training., , , , , , , , , and 1 other author(s). ICLR (Poster), OpenReview.net, (2018)