股票预测:滑动窗口+双向LSTM+Attention,关注关键时间点。 文本生成:堆叠LSTM+温度采样(Temperature Scaling)控制多样性。 传感器异常检测:LSTM-Autoencoder重构误差判定异常。 通过结合领域知识针对性设计模型结构和参数,配合实验验证,可显著提升LSTM性能。
注意力模型(解码器Decoder) 如上图,注意力机制下的Seq2Seq模型的输入与输出是等长的,和上面博主介绍的多输入多输出的RNN结构一样,只是输入变了,输入不是直接的序列输入,而是经过编码器encoder转换的中间语义C,而这些输入C也各不相同,每一个C都是由权重w和译码器的隐藏层输出h加权组成,如下图。 中间语义转换示意...
autoencoderdynamic attributed networkgraph attention networkRecently, anomaly detection in dynamic networks has received increased attention due to massive network-structured data arising in many fields, such as network security, intelligent transportation systems, and computational biology. However, many ...
attention mechanism --seq-len SEQ_LEN window length to use for forecasting --hidden-size-encoder HIDDEN_SIZE_ENCODER size of the encoder's hidden states --hidden-size-decoder HIDDEN_SIZE_DECODER size of the decoder's hidden states --reg-factor1 REG_FACTOR1 contribution factor of the L1 ...
【14】使用BERT和基于自我关注的嵌入技术进行论点挖掘Argument Mining using BERT and Self-Attention ...
加入attention机制的Seq2Seq结构 LSTM原理 RNN循环神经网络 原理 简单BP神经网络结构 简单RNN结构 首先我们来对比简单的BP和RNN的结构有啥异同,我们会发现RNN比BP多了参数h0,因此RNN的神经元公式会比BP神经元多一项(f为激励函数),如下图。至于训练过程,和BP神经网络并没有区别,都是是基于梯度下降的方法去不断缩小...
Attention 机制最早提出于 ICLR 2015 的文章Neural machine translation by jointly learning to align and translate,这篇文章基于 Encoder-Decoder 结构做机器翻译任务,它的思想很简单,Decoder 输出每个词时不要再只依赖一个隐状态特征向量sss,而是去看完整地看一遍要翻译的原句,从原句的所有样本中提取信息汇聚成上下...
A deep learning framework for financial time series using stacked autoencoders and long short term memory The application of deep learning approaches to finance has received a great deal of attention from both investors and researchers. This study presents a novel deep learning framework where wavelet...
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AddAG-AE: Anomaly Detection in Dynamic Attributed Graph Based on Graph Attention Network and LSTM Autoencoder 来自 科研支点 喜欢 0 阅读量: 5 作者:G Miao,G Wu,Z Zhang,Y Tong,B Lu 摘要: Recently, anomaly detection in dynamic networks has received increased attention due to massive network-...