In this paper, we exploit the attention mechanism and introduce a relational self-attention (RSA) module to simultaneously model the object and relation contexts. By stacking such RSA modules in depth, we obtain a deep relational self-attention network (RSAN), which is able to characterize ...
可以看出来,去掉relation之后,下降相对来说比去掉word-level更加明显。 总结 这篇文章虽然以Memory Network 为题,不过可以本质还是用Attention引入相关性。 word-level的动机来自Multi-Level 的那篇文章,计算word与target entity的相关性,并且可以多层,从而挖掘更深层次的关系。relation-level的动机则是考虑到数据中的关系...
CNN是利用到了临近单元具有某种特殊关系的假设; RNN用到了序列的时序性假设;attention利用的是信息量的信息表达形式的不平衡假设;DNN用到的是知识的多层级性假设. 这些就是一种对知识的结构性描述. 提出一种通用框架去描述基于实体和基于关系的推理, 也就是graph network. 统一并扩展了现在基于图的处理方法, 并描...
We leverage some of the advanced ConvNet architectures as a backbone-model of the proposed attention mapping network to build Cardio-XAttentionNet. The proposed model is trained on ChestX-Ray14, which is a publicly accessible chest X-ray dataset. The best single model achieves an overall ...
下一步我们要构建一个图注意力网络,(这里有个挺有意思的点就是图注意力网络Graph Attention Network的缩写是GAT,看来是为了和著名的生成对抗网络不撞名字。) 那么首先是要构建一个图(Graph Creation),利用的是维基公司关系数据,提取出S&P500指数下各个公司间的一阶与二阶关系。 一阶关系的提取 二阶关系的提取 ...
之上是一个attention层,计算候选新闻向量与用户点击历史向量之间的attentention权重,在顶层拼接两部分向量之后, 用DNN计算用户点击此新闻的概率。 框架整体包括三部分: 6.1知识提取(Knowledge Distillation) 过程分四步: 1.实体链接:识别出文本中的知识实体并利用实体链接技术消除歧义 ...
Attention-based deep residual learning network for entity relation extraction in Chinese EMRs. Deep residual learning networkEntity relation extractionElectronic medical records (EMRs) contain a variety of valuable medical concepts and relations. The ... Zhichang,Zhang,Tong,... - 《Bmc Medical Informat...
Liu et al. design a novel dehazing model20 based on attention and multi-scale network. However, all these dehazing methods are based on CNNs, which are limited by the local property of convolution. To capture the long dependency of hazy images, Guo et al. propose a transformer-based ...
上述“质疑”的核心问题之一是：用简单的pretrain network去学习feature embedding，然后再加上简单的分类...
首先使用一种融合了知识的卷积神经网络KCNN(knowledge-aware convolutional neural network),将新闻的语义表示与知识表示融合起来形成新的embedding表示,再建立从用户的新闻点击历史到候选新闻的attention机制,选出得分较高的新闻推荐给用户。并且在真实的线上新闻数据集上做了大量的实验,实验结果表示,DKN模型在F1-score,...