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Training neural networks requires increasing amounts of memory.
Collective communication: theory, practice, and experience
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Earlier work this paper cites.
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Training deep nets with sublinear memory cost
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Gao Huang, Zhuang Liu, Laurens van der Maaten, and Kilian Q. Weinberger · 2017
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Pedro Savarese and Michael Maire · 2019
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Mingxing Tan and Quoc V. Le · 2019
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Prodsumnet: reducing model parameters in deep neural networks via product-of-sums matrix decompositions, 2019
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Resource constrained neural network architecture search: Will a submodularity assumption help?
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Checkmate: Breaking the memory wall with optimal tensor rematerialization
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