answer vector generation based on question上引入了 Attention Model。 QA representation independently [×] -> attention for ans generation 当biLSTM需要在长距离QA中传递依赖时, fixed width of hidden vectors 成为了瓶颈 Attention model针对这个问题,采用 使更多对回答问题 有信息价值的部分 动态排列起来。 3.4...
Then, the LSTM model was used to train the differences between the Tm values obtained by discrete integration of the ERA5 data and Tm values calculated by the ERATM model to enhance the accuracy of the ERATM model. We use the ERA5 and sounding data from 2021 to 2022 to analyze the ...
This study aims to apply deep learning algorithm, long short-term memory (LSTM), to predict the long-term settlement of land reclamation settlement and apply the LSTM-based model to land reclamations of Kansai International Airport (KIA) and then Chek Lap Kok Airport (CLKA). The LSTM-based...
Compared with random prediction method, our LSTM model improved the accuracy of stock returns prediction from 14.3% to 27.2%. The efforts demonstrated the power of LSTM in stock market prediction in China, which is mechanical yet much more unpredictable. 展开 ...
However, details on the attack vector generation process and the viability of the dataset utilized to train the model are most often insufficient. To bridge this problem, explicit instruction for building an attack vector using a formal model is provided in our study. From an existing work, we...
(=1) * n_hidden]59model = torch.mm(output, self.W) + self.b#model : [batch_size, n_class]60returnmodel61else:62X = X.transpose(0, 1)#X : [n_step, batch_size, n_class]63outputs, hidden =self.rnn(X, hidden)64#outputs : [n_step, batch_size, num_directions(=1) * n_...
We used the advanced models in trajectory prediction as the comparison models, such as LSTM, support vector machine (SVM), back propagation (BP) neural network, Hidden Markov Model (HMM), and convolutional long-term memory neural network (CNN-LSTM). The model we proposed is superior to the ...
【Model Architecture】 Overview A. Attentive Convolutional LSTM Long Short-Term Memory networks (LSTM) 适用于时间依赖性强的问题。在saliency model中用conventional 操作代替LSTM中的点积。工作方式:根据3个sigmoid gate顺序更新internal state。类似CNN,每次更新都关注图像不同的区域。且每次的输入Xt先和上一次的hi...
本次研究针对文本数据处理工作中的文本分类项目提出了一套基于Attention-Based LSTM算法的分类模型,根据Attention-Model的基本原理对Attention-Based LSTM算法数据处... 黄阿娜 - 《自动化技术与应用》 被引量: 0发表: 2022年 基于LSTM-Attention li CNN混合模型的文本分类方法 For the problem that traditional Long ...
The optimal polarization current forecasting model is established by relative forecasting assessment of the proposed residual LSTM model with attention-LSTM, LSTM, gated recurrent units (GRU), and convolutional neural network (CNN) models. 3. The Monte Carlo dropout prediction approach, is utilized ...