MDAL: Multi-task Dual Attention LSTM Model for Semi-supervised Network EmbeddingDual attentionNetwork embeddingMulti-task learningIn recent years, both the academic and commercial communities have paid great attentions on embedding methods to analyze all kinds of network data. Despite of the great ...
Therefore, we propose a novel model called M ulti-task L earning based D ual B idirectional LSTM M odel (MLDBM) for ABSA of drug reviews. The MLDBM leverages BERT and incorporates a multi-head self-attention mechanism to produce aspect-specific representations which are further processed ...
整体的模型架构: 通过ResNet,LSTM和一个Embedding层处理图片,Question和Answer的输入。 并且通过一个Attention得到融合了QA信息的视觉特征Va和Vq。 通过一组dual Mutan模块进行特征融合。 并且通过LSTM和一个Linear的分类器得到最后的输出。 Mutan 来源于[1705.06676] MUTAN: Multimodal Tucker Fusion for Visual Question ...
respectively [1]. Predictive maintenance effectively reduces maintenance costs [2], and its attainment relies on the awareness of state changes. Since the state change in the abnormal stage is more significant than the healthy
Encoder是一个Bidirectional LSTM Decoder将Encoder LSTM两个方向的隐状态concat,和Decoder LSTM的隐状态做attention。 如下的三个公式对应: 用src_enc_att(W@src_enc)和当前时间步的ht计算attention score score经过softmax得到attention weight attention weight和src_enc(encoder的h)按位相乘 ...
adecoderto predict the answer with a linear transform or to generate the question sequence with an LSTM, GRU. animage modelwhich can be a pretrained VGG16 or ResNet-152, optionally, anattention schemewhich may have several "glimpses".