论文解读:On The Alignment Problem In Multi-Head Attention-Based Neural Machine Translation 机器翻译是自然语言处理的任务之一。基于transformer和multi-head attention在机器翻译中的应用十分广泛。注意力机制在神经机器翻译(NMT)模型中通常扮演着统计机器翻译(SMT)中的对齐机制(Alignment Mechanism),通过注意力...
SAM: self-attention mechanism, proposed method. TrDP: Training dataset processing. TeDP: test dataset processing. PUR: purification. LI: linear interpolation. HI [12]: hierarchical. ME: mean error. MAE: mean absolute error. MSE: mean standard error. 3.3. Performance evaluation for ESA dose ...
To tackle this issue, we propose a novel convolutional attention mechanism Multi-head Self-attention mechanism based on Deformable convolution (DCMSA) achieving efficient fusion of diffusion models with convolutional attention. The implementation of DCMSA is as follows: First, we integrate DCMSA into ...
Self-attention mechanism The importances of words in a sentence are different for the ADR detection task. However, each input word shares the same weight in the input layer of neural net- works. It is necessary to assign the weight for each word according to its contribution to ADR ...
多头注意力机制(Multi-head-attention) 为了让注意力更好的发挥性能,作者提出了多头注意力的思想,其实就是将每个query、key、value分出来多个分支,有多少个分支就叫多少头,对Q, K, V求多次不同的注意力计算,得到多个不同的output,再把这些不同的output拼接起来得到最终的output。
After constructing network traffic graph structure, we combine the Graph Convolutional Neural network and a Multi-Head Self-Attention mechanism to form an effective malicious traffic detection method called GCN-MHSA, thereby further improving the stability of detection model and the detection efficiency ...
To address these challenges, this paper proposes a multi-head mixed attention mechanism-based method for real-time wear prediction of TBM disc cutter. First, a method of cutter wear normalization to eliminate measurement noise is explored. Then, considering the complex correlation of TBM operating ...
While Multi-Head Self-Attention (MH-SA) is added to the Bi-LSTM model to perform relation extraction, which can effectively avoid complex feature engineering in traditional tasks. In the process of image extraction, the channel attention module (CAM) and the spatial attention module (SAM) are ...
transformer layer仍然是常规的做法,由多个Self-attention Layer组成的multi-head self-attention。(论文的公式写错了,V是在softmax之后的) Self-attention Layer Q,K,V分别表示输入embedding的query、key和value d为K的维度的缩放因子 Q,K,V这三个矩阵是时间序列特征embedding的同等维度下的线性映射,如下式head_i ...
(1) Specially designed multi-head probsparse self-attention mechanism can effectively highlight the dominant attention, which makes the TFT have considerable performance in reducing the computational complexity of extremely long time-series; (2) The TFT trained by knowledge-induced distillation strategy ...