在这项工作中,我们解决了这些挑战,最终实现了条件计算的承诺,模型容量提高了 1000 倍以上,而现代 GPU 集群的计算效率仅略有损失。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...
· 《熬夜整理》保姆级系列教程-玩转Wireshark抓包神器教程(8)-Wireshark的TCP包详 · 一个有趣的插件,让写代码变成打怪升级的游戏 · 任务系统之任务流程可视化 Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer 笔记 2024-10-14 14:0228004970:59 ~ 1:39 MENU 博客...
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...
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer 来自 Semantic Scholar 喜欢 0 阅读量: 1273 作者:N Shazeer,A Mirhoseini,K Maziarz,A Davis,J Dean 摘要: The capacity of a neural network to absorb information is limited by its number of parameters. Conditional ...
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 ...
@misc{shazeer2017outrageously,title={Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer},author={Noam Shazeer and Azalia Mirhoseini and Krzysztof Maziarz and Andy Davis and Quoc Le and Geoffrey Hinton and Jeff Dean},year={2017},eprint={1701.06538},archivePrefix={arXiv...
OUTRAGEOUSLY LARGE NEURAL NETWORKS: THE SPARSELY-GATED MIXTURE-OF-EXPERTS LAYER [ICLR 2017], 2017-1-23 ↥ back to top Contributors This repository is actively maintained, and we welcome your contributions! If you have any questions about this list of resources, please feel free to contact...
Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. ICLR, 2017.概Mixture-of-Experts (MoE).MoE通过一 gating network 选择不同的 expert: y=n∑i=1G(x)iEi(x),y=∑i=1nG(x)iEi(x), 若G(x)i=0G(x)i=0, 则我们不需要计算 Ei(x)Ei(x). Ei(x)Ei(x) 可以...