· Outrageously Large Neural Networks The Sparsely-Gated Mixture-of-Experts Layer · 探秘Transformer系列之(21)--- MoE · Google multitask模型SNR 阅读排行: · 【故障公告】博客主站遭遇很奇怪的疯狂攻击 · 我与博客园的20年 · 刚刚,Cu
我们引入了稀疏门控专家混合层 Sparsely-Gated Mixture-of-Experts layer(MoE),由多达数千个前馈子网络组成。可训练的门控网络确定用于每个示例的这些专家的稀疏组合。我们将 MoE 应用于语言建模和机器翻译的任务,其中模型容量对于吸收训练语料库中的大量可用知识至关重要。我们提出了模型架构,其中具有多达 1370 亿个...
主要提出了a Sparsely-Gated Mixture-of-Experts layer (MoE), 设计,提高模型容量,同时降低计算量,且获得了更好的效果(91年前就有MoE的研究了,不要误以为只有大模型后才有MoE,这对理解设计动机比较重要)。初学者,例如我,可能有几个误区: 1) 以为MoE是独立的网络结构,本文是设计在LSTM单元结合,它不用于改变时...
论文出自:Shazeer N, Mirhoseini A, Maziarz K, et al. Outrageously large neural networks: The sparsely-gated mixture-of-experts layer[J]. arXiv preprint arXiv:1701.06538, 2017. 摘要 神经网络的吸收信息的容量(capacity)受限于参数数目。 条件计算(conditional computation)针对于每个样本,激活网络的部分子...
1.2 Our Approach: The Sparsely-Gated Mixture-of-Experts Layer Our approach to conditional computation is to introduce a new type of general purpose neural network component: a Sparsely-Gated Mixture-of-Experts Layer (MoE). The MoE consists of a number of experts, each a simple feed-forward ne...
We in-troduce a Sparsely-Gated Mixture-of-Experts layer (MoE), consisting of up tothousands of feed-forward sub-networks. A trainable gating network determinesa sparse combination of these experts to use for each example. We apply the MoEto the tasks of language modeling and machine ...
A Pytorch implementation of Sparsely Gated Mixture of Experts, for massively increasing the capacity (parameter count) of a language model while keeping the computation constant. It will mostly be a line-by-line transcription of the tensorflow implementation here, with a few enhancements. Update: Yo...
Both countries have overwhelmingly urban populations (Australia 87%, Canada 82%) and vast tracts of sparsely populated territory, which means that strictly geographical election maps of both countries suffer from the “empty land doesn’t vote” problem. But that doesn’t seem to stop such maps ...
Awesome-Multitask-Learning Welcome to Awesome-Multitask-Learning, a curated repository of papers, code, and datasets in the field of Multi-Task Learning (MTL). This repo is designed to serve both newcomers and experienced researchers seeking a comprehensive understanding of the evolution, methods, ...
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