文章:《Root Mean Square Layer Normalization》 链接: https://arxiv.org/pdf/1910.07467.pdfLayer Normalization计算效率低RMSNorm主要面向Layer Norm改进,归一化可以实现张量的聚集(re-centering)和缩放(re-…
层归一化(LayerNorm)在各种深度神经网络的应用比较成功,可以稳定模型训练、促进模型收敛。但LN的缺点是计算开销较大。 LN有两个特性:重新居中(re-centering)不变性和重新缩放(re-scaling)不变性。本篇论文假设 LayerNorm 中的重新居中不变性是可有可无的,并提出均方根层归一化(RMSNorm)。 【注】...
Root mean square layer normalization. NIPS, 2019.概RMSNorm 节省时间.RMSNorm假设输入为 x∈Rmx∈Rm, 然后 a=Wx∈Rn,y=f(Norm(a)+b)∈Rn.a=Wx∈Rn,y=f(Norm(a)+b)∈Rn. 其中f(⋅)f(⋅) 是element-wise 的激活函数. LayerNorm 采取的是如下的方式 (注意, 下面的 // 是element-wise 的...
Layer normalization (LayerNorm) has been successfully applied to various deep neural networks to help stabilize training and boost model convergence because of its capability in handling re-centering and re-scaling of both inputs and weight matrix. However, the computational overhead introduced by Lay...
《Root Mean Square Layer Normalization》B Zhang, R Sennrich [University of Edinburgh & University of Zurich] (2019) http://t.cn/Ai1SoR1L view:http://t.cn/Ai1SoR1w GitHub: http://t.cn/Ai3XDlsT
short forRoot Mean Square Layer Normalization RMSNormis a simplification of the original layer normalization (LayerNorm). LayerNorm is a regularization technique that might handle theinternal covariate shiftissue so as to stabilize the layer activations and improve model convergence. It has been proved...