Unverified50% confidenceFactExact time
Google Brain于2017年在《Outrageously Large Neural Networks》论文中将MoE规模化应用于Transformer框架
1
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50%
Confidence
Long-term
Relevance
7/17/2026
First Seen
Sources
Related Claims
Unverified2017年谷歌发表《Outrageously Large Neural Networks》，系统验证了稀疏MoE在Transformer结构中的有效性84% similarVerifiedThe MoE architecture was proposed by Shazeer et al. in 2017 in the paper "Outrageously Large Neural Networks" and subsequently validated at scale by Google in Switch Transformer (2021)83% similarVerifiedThe Mixture of Experts (MoE) architecture was scaled up within the Transformer framework by Google Brain in 2017; its core idea is to group model parameters into multiple expert sub-networks, with only a small subset activated during each inference.82% similarUnverifiedThe revival of modern MoE can be traced back to the 2017 paper published by the Google Brain team, 'Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer,' which was the first to integrate sparse MoE layers into large sequence models.79% similarUnverifiedSince the Transformer architecture was proposed by Google in 2017, its self-attention mechanism has a computational complexity that grows quadratically with sequence length77% similar
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