However, its computation is inefficient and does not preserve translation between entity and relation embedding. Therefore, dynamic convolution was designed to solve limited representation capability issues and
再利用GGNN的方法计算每个Header的隐藏状态。然后,在Seq2seq模型的编码阶段(Encoding),用每个输入的自然语言单词的词向量对表格所有Header的隐藏状态算一个Attention,利用Attention作为权重得到了每个自然语言单词的图感知的表示。在解码阶段(Decodi...
The organizational structure of this paper is presented as follows. The second section introduces the current research status of traffic flow prediction, attention mechanisms, and GCN. The third section introduces the proposed model in this paper. The fourth section analyses the experimental settings an...
compared to the ConvE model (which uses only 2D convolution), our proposed model achieves 13.7% and 14.7% improvement in MRR metrics, and compared to the SAttLE model (which uses only the self-attention mechanism) achieves 2.5% and 0.5% improvement in MRR ...
Convolutional Projection for Attention (CPA) 该模块可以视为原始 Transformer block 在卷积上的扩展,可以用于捕捉 local spatial context 上图中后两个子图中的每个 Convolutional Projection 都是下面的前向过程 \text{Depth-wise Conv2D → BN → Point-wise conv2D} ...
因此,在图1中给出的decoder的attention module结构:先对在target中添加timing信号 ,执行两次convolution step,然后再对source进行attending。整个结构如下公式所示:6.3 自回归结构(Autoregressive structure) 如图1所示,模型最终通过自回归(autoregressive)的方式预测output。在基于Depthwise separable convolution的seq2seq模型中...
The feature extraction backbone of this model is composed of an MDSC attention module that integrates multiple parallel depth-wise separable convolutions and an efficient attention mechanism, combined with a tansformer encoder (PPSformer) optimized using patch embedding and probesparse self attention ...
Self-attention mechanism in the transformer-based encoder captures local and global features after patch embedding. In the encoder, patch merging is applied for downsampling, while in the decoder, patch expanding is utilized to achieve upsampling. SwinMM [59] employs a self-supervised learning ...
The model uses self-supervised learning methods to obtain drug and target structure features. A Heterogeneous Interaction-enhanced Feature Fusion Module is designed for multi-graph construction, and the graph convolutional networks are used to extract node features. With the help of an attention ...
Forests are invaluable resources, and fire is a natural process that is considered an integral part of the forest ecosystem. Although fire offers several ecological benefits, its frequent occurrence in different parts of the world has raised concerns in