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  • On Layer Normalization in the Transformer Architecture
    The warm-up stage is practically helpful to avoid this problem Such an analysis motivates us to investigate a slightly modified Transformer architecture which locates the layer normalization inside the residual blocks We show that the gradients in this Transformer architecture are well-behaved at initialization
  • Peri-LN: Revisiting Normalization Layer in the Transformer. . .
    This paper provides an analysis of different layer normalization strategies and how they impact the training dynamics of large-scale transformers, finding that peripherally bracketing normalization layers around submodules (Peri-LN) can improve stability relative to the pre- and post-LN baselines
  • O LAYER NORMALIZATION IN THE TRANSFORMER ARCHITECTURE - OpenReview
    1 INTRODUCTION The Transformer is one of the most commonly used neural network architectures in natural language processing, and layer normalization is one of the key components in the Transformer The originally designed Transformer places the layer normalization between the residual blocks, which is usu-ally referred to as the Transformer with Post-Layer Normalization (Post-LN) This
  • 如何评价 Meta 新论文 Transformers without Normalization？
    再后来，transformer成为主流，nlp那边用layer norm居多，所以transformer继承了它，至于为什么不用BN而用LN，之前知乎一个问题大佬们都有很多讨论了： transformer 为什么使用 layer normalization，而不是其他的归一化方法？ 。
  • ResiDual: Transformer with Dual Residual Connections - OpenReview
    Transformer networks have become the preferred architecture for many tasks due to their state-of-the-art performance However, the optimal way to implement residual connections in Transformer, which are essential for effective training, is still debated Two widely used variants are the Post-Layer-Normalization (Post-LN) and Pre-Layer-Normalization (Pre-LN) Transformers, which apply layer
  • Full Stack Optimization of Transformer Inference - OpenReview
    In this work, we pursue a full-stack approach to optimizing Transformer inference We analyze the implications of the Transformer architecture on hardware, including the impact of nonlinear operations such as Layer Normalization, Softmax, and GELU, as well as linear operations, and we use this analysis to optimize a fixed Transformer architecture
  • ON RADEMACHER COMPLEXITY BASED GENERAL IZATION BOUNDS FOR THE . . .
    We derive the first end-to-end, data-dependent generalization bound for the Transformer architecture to explain its strong empirical performance Using Rademacher complexity and a novel Lipschitz analysis of self-attention, we con-struct a bound for deep, L-layer models The bound demonstrates that general-ization capacity is governed by depth, sequence length, and a polynomial of the model
  • 一文了解Transformer全貌（图解Transformer）
    Transformer整体结构（引自谷歌论文） 可以看到Encoder包含一个Muti-Head Attention模块，是由多个Self-Attention组成，而Decoder包含两个Muti-Head Attention。Muti-Head Attention上方还包括一个 Add Norm 层，Add 表示残差连接 (Residual Connection) 用于防止网络退化，Norm 表示 Layer Normalization，用于对每一层的激活值进行归一化
  • Peri-LN: Revisiting Normalization Layer in the Transformer Architecture
    We explore the placement of layer normalization within the Transformer architecture to better understand its role during training By systematically comparing Post-LN, Pre-LN, and newly termed Peri-LN, we highlight their distinct impacts on stability, final performance, and optimization dynamics
  • Equiformer: Equivariant Graph Attention Transformer for 3D. . .
    Layer Normalization is directly extending original Layer Normalization for scalars to support vectors of different types c Depth-wise Tensor Products can be viewed as an extension of fully connected tensor products used in SE (3)-Transformer We modify the dependence of output channels and restrict one output channel to depend on one input





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