In this paper, for resolving the above problems and further improve the model, we introduce ELMo representations and add a gated self-attention layer to the Bi-Directional Attention Flow network (BIDAF). In addition, we employ the feature reuse method and modify the linear function of answer ...
(c)gated axial attention layer,它是在门控轴向transformer层中的高度和宽度gated multi-head attention blocks的基本构件。 Self-Attention Overview 具有高度H、权重W和通道 C_{in} 的输入特征映射x∈ R^{C_{in} \times H \times W} 。借助投影输入,使用以下公式计算自注意力层的输出y∈ R^{C_{out}...
proposed a graph convolutional network model (i.e. AGGCN) which established an adaptive correlation matrix through the self-attention mechanism. • Two-Phase BERT29 Wang et al. employed the pre-trained BERT model for the task of document-level relation extraction, and the model employed a two...
As animals explore an environment, the hippocampus is thought to automatically form and maintain a place code by combining sensory and self-motion signals. Instead, we observed an extensive degradation of the place code when mice voluntarily disengaged from a virtual navigation task, remarkably even ...
computing such affinities are computationally very expensive and with increased feature map size it often becomes infeasible to use self-attention for vision model architectures. Moreover, unlike convolutional layer, self-attention layer does not utilize any positional information while computing the non...
【BI-DIRECTIONAL ATTENTION FLOW FOR MACHINE COMPREHENSION】论文笔记 前三层分别是char embedding、word embedding、context embedding,不再细说。 主要想记录一下对Attention Flow Layer的一些思考。 首先, 引入attention这一层的目的是为了将“问题的特征”融入到给定的context的embedding中去。 也就是说,在给出合理...
Checklist I have checked FAQs and existing issues for similar problems Please report this bug in English to ensure wider understanding and support Describe the Bug I believe this line should be dz = ... flash-linear-attention/fla/modules...
有效性(efficient):卷积实现避免了Self Attention的平方复杂度。 可扩展性(extendable):可以通过调整参数实现更高阶的空间交互,从而进一步提升模型的建模能力。而且结构中可以兼容不同的卷积核大小以及空间混合策略,像是更大卷积核的深度分离卷积或者是基于傅里叶变换的Global Filter。
Paper tables with annotated results for Deconstructing Recurrence, Attention, and Gating: Investigating the transferability of Transformers and Gated Recurrent Neural Networks in forecasting of dynamical systems
Refs. [26,27] proposes deep attention based on the low-level attentional information that can automatically determine the refinement of attentional weights in a layer-wire manner. Currently, refs. [28,29] proposes a self-attention deep NMT model with a text mining approach to identify the ...