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I plan to attempt a slow motion cleanup of reference spamming. The typical case is an obscure non-notable paper where it does a poor job of supporting the items which cited it, particularly where the material is still cited without it. When all of the references looked equally good/applicable on
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I am considering adding information regarding power consumption. Currently I see two directions: low-power inference for embedded devices and low-power training for huge networks. Please let me know if you know of more credible and/or overview sources discussing the concerns. p.s. This may be a
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Now transformer base model out perform sequence model like lstm or gru and Google use
Transformer base model to translate language
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Complete through interpretation section. That section still has some over-cited items, but the remaining ones all look useful.
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related articles on
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Gauen, Kent; Rangan, Rohit; Mohan, Anup; Lu, Yung-Hsiang; Liu, Wei; Berg, Alexander C. (2017-01).
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on
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This article was the subject of a Wiki
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Planned slow motion cleanup of reference spamming and overciting
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Find pictures for the biographies of computer scientists (see
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