The classification of emotions are carried out using a novel Bi-directional Long Short-Term Memory with accuracy of 88.71%. Abstract With the increasing popularity of android gaming applications on smart phones, detection of emotional states of hard-core gamers become the interest of study among psy...
下面是相关论文介绍: 基于Bi-LSTM 和迁移学习的多元汇率预测研究 Bi-LSTM 模型 双向长短期记忆网络 (Bi-directional long short-termmemory, Bi-LSTM) 是 LSTM 模型的扩展, 其包括前向LSTM 和后向 LSTM, 前向和后向的 LSTM 应用可以改善模型学习的长期依赖性, 从而提高模型的准确性[。 其结构如图 1 所示。
We propose a bi-directional long short-term memory recurrent neural network with an attention mechanism (BILSTM-AT) model to predict the voltage degradation of the PEMFC stack. Random forest regression model is used to extract essential variables as inputs in the model. The prediction interval ...
提出了一种基于代码属性图的表征方式,利用从函 数的代码属性图中提取的抽象语法树序列和控制流图序列对函数进行表征,以减少代码表征过程中的语法和语义信息的损失,提高表征能力。 在特征提取阶段,基于Bi-GRU和Bi-LSTM(bi-directional long short-term memory)构建多个提取模型。通过实验发现,与基于Bi-LSTM构建的特征...
Aiming at the problem that proton exchange membrane fuel cell (PEMFC) remaining useful lifetime (RUL) is challenging to predict accurately with limited data, a multi-input single-output Bi-directional long short-term memory (MISO-BiLSTM) prediction method is proposed in this paper. A double-exp...
摘要:为了提高母线负荷预测精度,针对长短期记忆(long short term memory,LSTM)神经网络在母线负荷预测时存在对负荷规律提取不足导致精度下降、超参数设置依赖经验等问题,首先构建LSTM神经网络的变体网络——双向长短期记忆(Bi-directional ...
Robust action recognition in videos is a challenging task due to its complexity.To solve it,how to effectively capture the robust spatio-temporal features becomes very important.In this paper,we propose to exploit bi-directional long short-term memory (Bi-LSTM) model as main framework to capture...
针对此问题, 本文提出一种基于双向长短时记 忆神经网络 (bi-directional long short-term memory, Bi-LSTM) 的 CCFD 数字域自干扰抑制方法. 首先 根据多径信道的特征, 采用记忆多项式对自干扰信道进行建模; 然后采用 Wild Horse 优化算法 (Wild Horse optimizer, WHO), 通过迭代寻找到最优时延单位以确定训练数据...
论文《A Novel Bi-directional Interrelated Model for Joint Intent Detection and Slot Filling》,作者Haihong E(Beijing University of Posts and Telecommunications, Beijing, China),简称SF-ID Network,经典的NLU论文(Semantic Frame)。 2. 摘要 口语理解(SLU)系统包括两个主要任务,插槽填充(SF)和意图检测(ID)。
《论文阅读》Bi-directional Relationship Inferring Network for Referring Image Segmentation,程序员大本营,技术文章内容聚合第一站。